Architecture and settings optimization procedure of a TES frequency domain multiplexed readout firmware

NASA Astrophysics Data System (ADS)

Clenet, A.; Ravera, L.; Bertrand, B.; den Hartog, R.; Jackson, B.; van Leeuwen, B.-J.; van Loon, D.; Parot, Y.; Pointecouteau, E.; Sournac, A.

2014-11-01

IRAP is developing the readout electronics of the SPICA-SAFARI's TES bolometer arrays. Based on the frequency domain multiplexing technique the readout electronics provides the AC-signals to voltage-bias the detectors; it demodulates the data; and it computes a feedback to linearize the detection chain. The feedback is computed with a specific technique, so called baseband feedback (BBFB) which ensures that the loop is stable even with long propagation and processing delays (i.e. several μ s) and with fast signals (i.e. frequency carriers of the order of 5 MHz). To optimize the power consumption we took advantage of the reduced science signal bandwidth to decouple the signal sampling frequency and the data processing rate. This technique allowed a reduction of the power consumption of the circuit by a factor of 10. Beyond the firmware architecture the optimization of the instrument concerns the characterization routines and the definition of the optimal parameters. Indeed, to operate an array TES one has to properly define about 21000 parameters. We defined a set of procedures to automatically characterize these parameters and find out the optimal settings.

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm

PubMed Central

Tamjidy, Mehran; Baharudin, B. T. Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-01-01

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon’s entropy. PMID:28772893

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm.

PubMed

Tamjidy, Mehran; Baharudin, B T Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-05-15

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon's entropy.

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

NASA Astrophysics Data System (ADS)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Application of multivariable search techniques to the optimization of airfoils in a low speed nonlinear inviscid flow field

NASA Technical Reports Server (NTRS)

Hague, D. S.; Merz, A. W.

1975-01-01

Multivariable search techniques are applied to a particular class of airfoil optimization problems. These are the maximization of lift and the minimization of disturbance pressure magnitude in an inviscid nonlinear flow field. A variety of multivariable search techniques contained in an existing nonlinear optimization code, AESOP, are applied to this design problem. These techniques include elementary single parameter perturbation methods, organized search such as steepest-descent, quadratic, and Davidon methods, randomized procedures, and a generalized search acceleration technique. Airfoil design variables are seven in number and define perturbations to the profile of an existing NACA airfoil. The relative efficiency of the techniques are compared. It is shown that elementary one parameter at a time and random techniques compare favorably with organized searches in the class of problems considered. It is also shown that significant reductions in disturbance pressure magnitude can be made while retaining reasonable lift coefficient values at low free stream Mach numbers.

Practical input optimization for aircraft parameter estimation experiments. Ph.D. Thesis, 1990

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1993-01-01

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.

Development of a parameter optimization technique for the design of automatic control systems

NASA Technical Reports Server (NTRS)

Whitaker, P. H.

1977-01-01

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

PubMed

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open source, available under an MIT license, and can be installed using the Julia package manager from the JuPOETs GitHub repository.

Automated parameterization of intermolecular pair potentials using global optimization techniques

NASA Astrophysics Data System (ADS)

Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk

2014-12-01

In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.

Application of nonlinear least-squares regression to ground-water flow modeling, west-central Florida

USGS Publications Warehouse

Yobbi, D.K.

2000-01-01

A nonlinear least-squares regression technique for estimation of ground-water flow model parameters was applied to an existing model of the regional aquifer system underlying west-central Florida. The regression technique minimizes the differences between measured and simulated water levels. Regression statistics, including parameter sensitivities and correlations, were calculated for reported parameter values in the existing model. Optimal parameter values for selected hydrologic variables of interest are estimated by nonlinear regression. Optimal estimates of parameter values are about 140 times greater than and about 0.01 times less than reported values. Independently estimating all parameters by nonlinear regression was impossible, given the existing zonation structure and number of observations, because of parameter insensitivity and correlation. Although the model yields parameter values similar to those estimated by other methods and reproduces the measured water levels reasonably accurately, a simpler parameter structure should be considered. Some possible ways of improving model calibration are to: (1) modify the defined parameter-zonation structure by omitting and/or combining parameters to be estimated; (2) carefully eliminate observation data based on evidence that they are likely to be biased; (3) collect additional water-level data; (4) assign values to insensitive parameters, and (5) estimate the most sensitive parameters first, then, using the optimized values for these parameters, estimate the entire data set.

Solving fuel-optimal low-thrust orbital transfers with bang-bang control using a novel continuation technique

NASA Astrophysics Data System (ADS)

Zhu, Zhengfan; Gan, Qingbo; Yang, Xin; Gao, Yang

2017-08-01

We have developed a novel continuation technique to solve optimal bang-bang control for low-thrust orbital transfers considering the first-order necessary optimality conditions derived from Lawden's primer vector theory. Continuation on the thrust amplitude is mainly described in this paper. Firstly, a finite-thrust transfer with an ;On-Off-On; thrusting sequence is modeled using a two-impulse transfer as initial solution, and then the thrust amplitude is decreased gradually to find an optimal solution with minimum thrust. Secondly, the thrust amplitude is continued from its minimum value to positive infinity to find the optimal bang-bang control, and a thrust switching principle is employed to determine the control structure by monitoring the variation of the switching function. In the continuation process, a bifurcation of bang-bang control is revealed and the concept of critical thrust is proposed to illustrate this phenomenon. The same thrust switching principle is also applicable to the continuation on other parameters, such as transfer time, orbital phase angle, etc. By this continuation technique, fuel-optimal orbital transfers with variable mission parameters can be found via an automated algorithm, and there is no need to provide an initial guess for the costate variables. Moreover, continuation is implemented in the solution space of bang-bang control that is either optimal or non-optimal, which shows that a desired solution of bang-bang control is obtained via continuation on a single parameter starting from an existing solution of bang-bang control. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed continuation technique. Specifically, this continuation technique provides an approach to find multiple solutions satisfying the first-order necessary optimality conditions to the same orbital transfer problem, and a continuation strategy is presented as a preliminary approach for solving the bang-bang control of many-revolution orbital transfers.

Comparison of dose volume parameters evaluated using three forward planning – optimization techniques in cervical cancer brachytherapy involving two applicators

PubMed Central

Basu-Roy, Somapriya; Kar, Sanjay Kumar; Das, Sounik; Lahiri, Annesha

2017-01-01

Purpose This study is intended to compare dose-volume parameters evaluated using different forward planning- optimization techniques, involving two applicator systems in intracavitary brachytherapy for cervical cancer. It looks for the best applicator-optimization combination to fulfill recommended dose-volume objectives in different high-dose-rate (HDR) fractionation schedules. Material and methods We used tandem-ring and Fletcher-style tandem-ovoid applicator in same patients in two fractions of brachytherapy. Six plans were generated for each patient utilizing 3 forward optimization techniques for each applicator used: equal dwell weight/times (‘no optimization’), ‘manual dwell weight/times’, and ‘graphical’. Plans were normalized to left point A and dose of 8 Gy was prescribed. Dose volume and dose point parameters were compared. Results Without graphical optimization, maximum width and thickness of volume enclosed by 100% isodose line, dose to 90%, and 100% of clinical target volume (CTV); minimum, maximum, median, and average dose to both rectum and bladder are significantly higher with Fletcher applicator. Even if it is done, dose to both points B, minimum dose to CTV, and treatment time; dose to 2 cc (D2cc) rectum and rectal point etc.; D2cc, minimum, maximum, median, and average dose to sigmoid colon; D2cc of bladder remain significantly higher with this applicator. Dose to bladder point is similar (p > 0.05) between two applicators, after all optimization techniques. Conclusions Fletcher applicator generates higher dose to both CTV and organs at risk (2 cc volumes) after all optimization techniques. Dose restriction to rectum is possible using graphical optimization only during selected HDR fractionation schedules. Bladder always receives dose higher than recommended, and 2 cc sigmoid colon always gets permissible dose. Contrarily, graphical optimization with ring applicators fulfills all dose volume objectives in all HDR fractionations practiced. PMID:29204164

Genetic algorithm optimization of transcutaneous energy transmission systems for implantable ventricular assist devices.

PubMed

Byron, Kelly; Bluvshtein, Vlad; Lucke, Lori

2013-01-01

Transcutaneous energy transmission systems (TETS) wirelessly transmit power through the skin. TETS is particularly desirable for ventricular assist devices (VAD), which currently require cables through the skin to power the implanted pump. Optimizing the inductive link of the TET system is a multi-parameter problem. Most current techniques to optimize the design simplify the problem by combining parameters leading to sub-optimal solutions. In this paper we present an optimization method using a genetic algorithm to handle a larger set of parameters, which leads to a more optimal design. Using this approach, we were able to increase efficiency while also reducing power variability in a prototype, compared to a traditional manual design method.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

NASA Astrophysics Data System (ADS)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range

USDA-ARS?s Scientific Manuscript database

The primary advantage of Dynamically Dimensioned Search algorithm (DDS) is that it outperforms many other optimization techniques in both convergence speed and the ability in searching for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation f...

Primary gamma ray selection in a hybrid timing/imaging Cherenkov array

NASA Astrophysics Data System (ADS)

Postnikov, E. B.; Grinyuk, A. A.; Kuzmichev, L. A.; Sveshnikova, L. G.

2017-06-01

This work is a methodical study on hybrid reconstruction techniques for hybrid imaging/timing Cherenkov observations. This type of hybrid array is to be realized at the gamma-observatory TAIGA intended for very high energy gamma-ray astronomy (> 30 TeV). It aims at combining the cost-effective timing-array technique with imaging telescopes. Hybrid operation of both of these techniques can lead to a relatively cheap way of development of a large area array. The joint approach of gamma event selection was investigated on both types of simulated data: the image parameters from the telescopes, and the shower parameters reconstructed from the timing array. The optimal set of imaging parameters and shower parameters to be combined is revealed. The cosmic ray background suppression factor depending on distance and energy is calculated. The optimal selection technique leads to cosmic ray background suppression of about 2 orders of magnitude on distances up to 450 m for energies greater than 50 TeV.

Multi-parameter optimization of piezoelectric actuators for multi-mode active vibration control of cylindrical shells

NASA Astrophysics Data System (ADS)

Hu, K. M.; Li, Hua

2018-07-01

A novel technique for the multi-parameter optimization of distributed piezoelectric actuators is presented in this paper. The proposed method is designed to improve the performance of multi-mode vibration control in cylindrical shells. The optimization parameters of actuator patch configuration include position, size, and tilt angle. The modal control force of tilted orthotropic piezoelectric actuators is derived and the multi-parameter cylindrical shell optimization model is established. The linear quadratic energy index is employed as the optimization criterion. A geometric constraint is proposed to prevent overlap between tilted actuators, which is plugged into a genetic algorithm to search the optimal configuration parameters. A simply-supported closed cylindrical shell with two actuators serves as a case study. The vibration control efficiencies of various parameter sets are evaluated via frequency response and transient response simulations. The results show that the linear quadratic energy indexes of position and size optimization decreased by 14.0% compared to position optimization; those of position and tilt angle optimization decreased by 16.8%; and those of position, size, and tilt angle optimization decreased by 25.9%. It indicates that, adding configuration optimization parameters is an efficient approach to improving the vibration control performance of piezoelectric actuators on shells.

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

NASA Astrophysics Data System (ADS)

Belwanshi, Vinod; Topkar, Anita

2016-05-01

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

Method of optimization onboard communication network

NASA Astrophysics Data System (ADS)

Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.

2018-02-01

In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

NASA Astrophysics Data System (ADS)

Dhingra, Sunil; Bhushan, Gian; Dubey, Kashyap Kumar

2014-03-01

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NO x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NO x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

An effective parameter optimization technique for vibration flow field characterization of PP melts via LS-SVM combined with SALS in an electromagnetism dynamic extruder

NASA Astrophysics Data System (ADS)

Xian, Guangming

2018-03-01

A method for predicting the optimal vibration field parameters by least square support vector machine (LS-SVM) is presented in this paper. One convenient and commonly used technique for characterizing the the vibration flow field of polymer melts films is small angle light scattering (SALS) in a visualized slit die of the electromagnetism dynamic extruder. The optimal value of vibration vibration frequency, vibration amplitude, and the maximum light intensity projection area can be obtained by using LS-SVM for prediction. For illustrating this method and show its validity, the flowing material is used with polypropylene (PP) and fifteen samples are tested at the rotation speed of screw at 36rpm. This paper first describes the apparatus of SALS to perform the experiments, then gives the theoretical basis of this new method, and detail the experimental results for parameter prediction of vibration flow field. It is demonstrated that it is possible to use the method of SALS and obtain detailed information on optimal parameter of vibration flow field of PP melts by LS-SVM.

Shape optimization techniques for musical instrument design

NASA Astrophysics Data System (ADS)

Henrique, Luis; Antunes, Jose; Carvalho, Joao S.

2002-11-01

The design of musical instruments is still mostly based on empirical knowledge and costly experimentation. One interesting improvement is the shape optimization of resonating components, given a number of constraints (allowed parameter ranges, shape smoothness, etc.), so that vibrations occur at specified modal frequencies. Each admissible geometrical configuration generates an error between computed eigenfrequencies and the target set. Typically, error surfaces present many local minima, corresponding to suboptimal designs. This difficulty can be overcome using global optimization techniques, such as simulated annealing. However these methods are greedy, concerning the number of function evaluations required. Thus, the computational effort can be unacceptable if complex problems, such as bell optimization, are tackled. Those issues are addressed in this paper, and a method for improving optimization procedures is proposed. Instead of using the local geometric parameters as searched variables, the system geometry is modeled in terms of truncated series of orthogonal space-funcitons, and optimization is performed on their amplitude coefficients. Fourier series and orthogonal polynomials are typical such functions. This technique reduces considerably the number of searched variables, and has a potential for significant computational savings in complex problems. It is illustrated by optimizing the shapes of both current and uncommon marimba bars.

The Inverse Optimal Control Problem for a Three-Loop Missile Autopilot

NASA Astrophysics Data System (ADS)

Hwang, Donghyeok; Tahk, Min-Jea

2018-04-01

The performance characteristics of the autopilot must have a fast response to intercept a maneuvering target and reasonable robustness for system stability under the effect of un-modeled dynamics and noise. By the conventional approach, the three-loop autopilot design is handled by time constant, damping factor and open-loop crossover frequency to achieve the desired performance requirements. Note that the general optimal theory can be also used to obtain the same gain as obtained from the conventional approach. The key idea of using optimal control technique for feedback gain design revolves around appropriate selection and interpretation of the performance index for which the control is optimal. This paper derives an explicit expression, which relates the weight parameters appearing in the quadratic performance index to the design parameters such as open-loop crossover frequency, phase margin, damping factor, or time constant, etc. Since all set of selection of design parameters do not guarantee existence of optimal control law, explicit inequalities, which are named the optimality criteria for the three-loop autopilot (OC3L), are derived to find out all set of design parameters for which the control law is optimal. Finally, based on OC3L, an efficient gain selection procedure is developed, where time constant is set to design objective and open-loop crossover frequency and phase margin as design constraints. The effectiveness of the proposed technique is illustrated through numerical simulations.

Technique of optimization of minimum temperature driving forces in the heaters of regeneration system of a steam turbine unit

NASA Astrophysics Data System (ADS)

Shamarokov, A. S.; Zorin, V. M.; Dai, Fam Kuang

2016-03-01

At the current stage of development of nuclear power engineering, high demands on nuclear power plants (NPP), including on their economy, are made. In these conditions, improving the quality of NPP means, in particular, the need to reasonably choose the values of numerous managed parameters of technological (heat) scheme. Furthermore, the chosen values should correspond to the economic conditions of NPP operation, which are postponed usually a considerable time interval from the point of time of parameters' choice. The article presents the technique of optimization of controlled parameters of the heat circuit of a steam turbine plant for the future. Its particularity is to obtain the results depending on a complex parameter combining the external economic and operating parameters that are relatively stable under the changing economic environment. The article presents the results of optimization according to this technique of the minimum temperature driving forces in the surface heaters of the heat regeneration system of the steam turbine plant of a K-1200-6.8/50 type. For optimization, the collector-screen heaters of high and low pressure developed at the OAO All-Russia Research and Design Institute of Nuclear Power Machine Building, which, in the authors' opinion, have the certain advantages over other types of heaters, were chosen. The optimality criterion in the task was the change in annual reduced costs for NPP compared to the version accepted as the baseline one. The influence on the decision of the task of independent variables that are not included in the complex parameter was analyzed. An optimization task was decided using the alternating-variable descent method. The obtained values of minimum temperature driving forces can guide the design of new nuclear plants with a heat circuit, similar to that accepted in the considered task.

Optimizing Photosynthetic and Respiratory Parameters Based on the Seasonal Variation Pattern in Regional Net Ecosystem Productivity Obtained from Atmospheric Inversion

NASA Astrophysics Data System (ADS)

Chen, Z.; Chen, J.; Zheng, X.; Jiang, F.; Zhang, S.; Ju, W.; Yuan, W.; Mo, G.

2014-12-01

In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation pattern of the net carbon flux. An optimization scheme is proposed to estimate two key parameters (Vcmax and Q10) by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate Vcmax and Q10 of the Boreal Ecosystem Productivity Simulator (BEPS) to improve its NEP simulation in the Boreal North America (BNA) region. Simultaneously, in-situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results have the implication on using atmospheric CO2 data for optimizing ecosystem parameters through atmospheric inversion or data assimilation techniques.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

A hybrid optimization approach to the estimation of distributed parameters in two-dimensional confined aquifers

USGS Publications Warehouse

Heidari, M.; Ranjithan, S.R.

1998-01-01

In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.

Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

NASA Astrophysics Data System (ADS)

Bouzaida, Sana; Sakly, Anis; M'Sahli, Faouzi

2014-01-01

This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.

Trajectory Optimization for Missions to Small Bodies with a Focus on Scientific Merit.

PubMed

Englander, Jacob A; Vavrina, Matthew A; Lim, Lucy F; McFadden, Lucy A; Rhoden, Alyssa R; Noll, Keith S

2017-01-01

Trajectory design for missions to small bodies is tightly coupled both with the selection of targets for a mission and with the choice of spacecraft power, propulsion, and other hardware. Traditional methods of trajectory optimization have focused on finding the optimal trajectory for an a priori selection of destinations and spacecraft parameters. Recent research has expanded the field of trajectory optimization to multidisciplinary systems optimization that includes spacecraft parameters. The logical next step is to extend the optimization process to include target selection based not only on engineering figures of merit but also scientific value. This paper presents a new technique to solve the multidisciplinary mission optimization problem for small-bodies missions, including classical trajectory design, the choice of spacecraft power and propulsion systems, and also the scientific value of the targets. This technique, when combined with modern parallel computers, enables a holistic view of the small body mission design process that previously required iteration among several different design processes.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Microspoiler Actuation for Guided Projectiles

DTIC Science & Technology

2016-01-06

and be hardened to gun -launch. Several alternative designs will be explored using various actuation techniques, and downselection to an optimal design...aerodynamic optimization of the microspoiler mechanism, mechanical design/ gun hardening, and parameter estimation from experimental data. These...performed using the aerodynamic parameters in Table 2. Projectile trajectories were simulated without gravity at zero gun elevation. The standard 30mm

Optimizing dual-energy x-ray parameters for the ExacTrac clinical stereoscopic imaging system to enhance soft-tissue imaging.

PubMed

Bowman, Wesley A; Robar, James L; Sattarivand, Mike

2017-03-01

Stereoscopic x-ray image guided radiotherapy for lung tumors is often hindered by bone overlap and limited soft-tissue contrast. This study aims to evaluate the feasibility of dual-energy imaging techniques and to optimize parameters of the ExacTrac stereoscopic imaging system to enhance soft-tissue imaging for application to lung stereotactic body radiation therapy. Simulated spectra and a physical lung phantom were used to optimize filter material, thickness, tube potentials, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number range (3-83) based on a metric defined to separate spectra of high and low-energies. Both energies used the same filter due to time constraints of imaging in the presence of respiratory motion. The lung phantom contained bone, soft tissue, and tumor mimicking materials, and it was imaged with a filter thickness in the range of (0-0.7) mm and a kVp range of (60-80) for low energy and (120,140) for high energy. Optimal dual-energy weighting factors were obtained when the bone to soft-tissue contrast-to-noise ratio (CNR) was minimized. Optimal filter thickness and tube potential were achieved by maximizing tumor-to-background CNR. Using the optimized parameters, dual-energy images of an anthropomorphic Rando phantom with a spherical tumor mimicking material inserted in his lung were acquired and evaluated for bone subtraction and tumor contrast. Imaging dose was measured using the dual-energy technique with and without beam filtration and matched to that of a clinical conventional single energy technique. Tin was the material of choice for beam filtering providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-weighted image in the lung phantom was obtained using 0.2 mm tin and (140, 60) kVp pair. Dual-energy images of the Rando phantom with the tin filter had noticeable improvement in bone elimination, tumor contrast, and noise content when compared to dual-energy imaging with no filtration. The surface dose was 0.52 mGy per each stereoscopic view for both clinical single energy technique and the dual-energy technique in both cases of with and without the tin filter. Dual-energy soft-tissue imaging is feasible without additional imaging dose using the ExacTrac stereoscopic imaging system with optimized acquisition parameters and no beam filtration. Addition of a single tin filter for both the high and low energies has noticeable improvements on dual-energy imaging with optimized parameters. Clinical implementation of a dual-energy technique on ExacTrac stereoscopic imaging could improve lung tumor visibility. © 2017 American Association of Physicists in Medicine.

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

PubMed Central

Sánchez, Daniela; Melin, Patricia

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition. PMID:28894461

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

PubMed

Sánchez, Daniela; Melin, Patricia; Castillo, Oscar

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

Aerodynamic configuration design using response surface methodology analysis

NASA Technical Reports Server (NTRS)

Engelund, Walter C.; Stanley, Douglas O.; Lepsch, Roger A.; Mcmillin, Mark M.; Unal, Resit

1993-01-01

An investigation has been conducted to determine a set of optimal design parameters for a single-stage-to-orbit reentry vehicle. Several configuration geometry parameters which had a large impact on the entry vehicle flying characteristics were selected as design variables: the fuselage fineness ratio, the nose to body length ratio, the nose camber value, the wing planform area scale factor, and the wing location. The optimal geometry parameter values were chosen using a response surface methodology (RSM) technique which allowed for a minimum dry weight configuration design that met a set of aerodynamic performance constraints on the landing speed, and on the subsonic, supersonic, and hypersonic trim and stability levels. The RSM technique utilized, specifically the central composite design method, is presented, along with the general vehicle conceptual design process. Results are presented for an optimized configuration along with several design trade cases.

An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.

2007-01-01

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs, such as thrust. The engine's performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends on knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined that accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.

2007-01-01

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs, such as thrust. The engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends on knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined that accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least-squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.

2005-01-01

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs such as thrust. The engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends upon knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined which accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

Simultaneous beam sampling and aperture shape optimization for SPORT.

PubMed

Zarepisheh, Masoud; Li, Ruijiang; Ye, Yinyu; Xing, Lei

2015-02-01

Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

Simultaneous beam sampling and aperture shape optimization for SPORT

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

Zarepisheh, Masoud; Li, Ruijiang; Xing, Lei, E-mail: Lei@stanford.edu

Purpose: Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: The authors build a mathematical model with the fundamental station point parameters as the decisionmore » variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. Results: A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. Conclusions: The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.« less

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

2015-06-01

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

Optimization of structures undergoing harmonic or stochastic excitation. Ph.D. Thesis; [atmospheric turbulence and white noise

NASA Technical Reports Server (NTRS)

Johnson, E. H.

1975-01-01

The optimal design was investigated of simple structures subjected to dynamic loads, with constraints on the structures' responses. Optimal designs were examined for one dimensional structures excited by harmonically oscillating loads, similar structures excited by white noise, and a wing in the presence of continuous atmospheric turbulence. The first has constraints on the maximum allowable stress while the last two place bounds on the probability of failure of the structure. Approximations were made to replace the time parameter with a frequency parameter. For the first problem, this involved the steady state response, and in the remaining cases, power spectral techniques were employed to find the root mean square values of the responses. Optimal solutions were found by using computer algorithms which combined finite elements methods with optimization techniques based on mathematical programming. It was found that the inertial loads for these dynamic problems result in optimal structures that are radically different from those obtained for structures loaded statically by forces of comparable magnitude.

A knowledge-based approach to improving optimization techniques in system planning

NASA Technical Reports Server (NTRS)

Momoh, J. A.; Zhang, Z. Z.

1990-01-01

A knowledge-based (KB) approach to improve mathematical programming techniques used in the system planning environment is presented. The KB system assists in selecting appropriate optimization algorithms, objective functions, constraints and parameters. The scheme is implemented by integrating symbolic computation of rules derived from operator and planner's experience and is used for generalized optimization packages. The KB optimization software package is capable of improving the overall planning process which includes correction of given violations. The method was demonstrated on a large scale power system discussed in the paper.

Optimal startup control of a jacketed tubular reactor.

NASA Technical Reports Server (NTRS)

Hahn, D. R.; Fan, L. T.; Hwang, C. L.

1971-01-01

The optimal startup policy of a jacketed tubular reactor, in which a first-order, reversible, exothermic reaction takes place, is presented. A distributed maximum principle is presented for determining weak necessary conditions for optimality of a diffusional distributed parameter system. A numerical technique is developed for practical implementation of the distributed maximum principle. This involves the sequential solution of the state and adjoint equations, in conjunction with a functional gradient technique for iteratively improving the control function.

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.

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

Searches for millisecond pulsations in low-mass X-ray binaries

NASA Technical Reports Server (NTRS)

Wood, K. S.; Hertz, P.; Norris, J. P.; Vaughan, B. A.; Michelson, P. F.; Mitsuda, K.; Lewin, W. H. G.; Van Paradijs, J.; Penninx, W.; Van Der Klis, M.

1991-01-01

High-sensitivity search techniques for millisecond periods are presented and applied to data from the Japanese satellite Ginga and HEAO 1. The search is optimized for pulsed signals whose period, drift rate, and amplitude conform with what is expected for low-class X-ray binary (LMXB) sources. Consideration is given to how the current understanding of LMXBs guides the search strategy and sets these parameter limits. An optimized one-parameter coherence recovery technique (CRT) developed for recovery of phase coherence is presented. This technique provides a large increase in sensitivity over the method of incoherent summation of Fourier power spectra. The range of spin periods expected from LMXB phenomenology is discussed, the necessary constraints on the application of CRT are described in terms of integration time and orbital parameters, and the residual power unrecovered by the quadratic approximation for realistic cases is estimated.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

Optimization of process parameters in welding of dissimilar steels using robot TIG welding

NASA Astrophysics Data System (ADS)

Navaneeswar Reddy, G.; VenkataRamana, M.

2018-03-01

Robot TIG welding is a modern technique used for joining two work pieces with high precision. Design of Experiments is used to conduct experiments by varying weld parameters like current, wire feed and travelling speed. The welding parameters play important role in joining of dissimilar stainless steel SS 304L and SS430. In this work, influences of welding parameter on Robot TIG Welded specimens are investigated using Response Surface Methodology. The Micro Vickers hardness tests of the weldments are measured. The process parameters are optimized to maximize the hardness of the weldments.

Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.

PubMed

Hüffner, Falk; Komusiewicz, Christian; Niedermeier, Rolf; Wernicke, Sebastian

2017-01-01

Fixed-parameter algorithms are designed to efficiently find optimal solutions to some computationally hard (NP-hard) problems by identifying and exploiting "small" problem-specific parameters. We survey practical techniques to develop such algorithms. Each technique is introduced and supported by case studies of applications to biological problems, with additional pointers to experimental results.

Optimization of freeform surfaces using intelligent deformation techniques for LED applications

NASA Astrophysics Data System (ADS)

Isaac, Annie Shalom; Neumann, Cornelius

2018-04-01

For many years, optical designers have great interests in designing efficient optimization algorithms to bring significant improvement to their initial design. However, the optimization is limited due to a large number of parameters present in the Non-uniform Rationaly b-Spline Surfaces. This limitation was overcome by an indirect technique known as optimization using freeform deformation (FFD). In this approach, the optical surface is placed inside a cubical grid. The vertices of this grid are modified, which deforms the underlying optical surface during the optimization. One of the challenges in this technique is the selection of appropriate vertices of the cubical grid. This is because these vertices share no relationship with the optical performance. When irrelevant vertices are selected, the computational complexity increases. Moreover, the surfaces created by them are not always feasible to manufacture, which is the same problem faced in any optimization technique while creating freeform surfaces. Therefore, this research addresses these two important issues and provides feasible design techniques to solve them. Finally, the proposed techniques are validated using two different illumination examples: street lighting lens and stop lamp for automobiles.

Application of Layered Perforation Profile Control Technique to Low Permeable Reservoir

NASA Astrophysics Data System (ADS)

Wei, Sun

2018-01-01

it is difficult to satisfy the demand of profile control of complex well section and multi-layer reservoir by adopting the conventional profile control technology, therefore, a research is conducted on adjusting the injection production profile with layered perforating parameters optimization. i.e. in the case of coproduction for multi-layer, water absorption of each layer is adjusted by adjusting the perforating parameters, thus to balance the injection production profile of the whole well section, and ultimately enhance the oil displacement efficiency of water flooding. By applying the relationship between oil-water phase percolation theory/perforating damage and capacity, a mathematic model of adjusting the injection production profile with layered perforating parameters optimization, besides, perforating parameters optimization software is programmed. Different types of optimization design work are carried out according to different geological conditions and construction purposes by using the perforating optimization design software; furthermore, an application test is done for low permeable reservoir, and the water injection profile tends to be balanced significantly after perforation with optimized parameters, thereby getting a good application effect on site.

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Optimization of Coolant Technique Conditions for Machining A319 Aluminium Alloy Using Response Surface Method (RSM)

NASA Astrophysics Data System (ADS)

Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.

2018-03-01

Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.

Optimization of a chemical identification algorithm

NASA Astrophysics Data System (ADS)

Chyba, Thomas H.; Fisk, Brian; Gunning, Christin; Farley, Kevin; Polizzi, Amber; Baughman, David; Simpson, Steven; Slamani, Mohamed-Adel; Almassy, Robert; Da Re, Ryan; Li, Eunice; MacDonald, Steve; Slamani, Ahmed; Mitchell, Scott A.; Pendell-Jones, Jay; Reed, Timothy L.; Emge, Darren

2010-04-01

A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be applied. The limitations of applying this framework to chemical detection problems are discussed along with means to mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared. Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC techniques are employed to optimize chemical-specific parameters to further improve performance.

Warpage minimization on wheel caster by optimizing process parameters using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, it is important to keep the productivity increase constantly with least of waste produced such as warpage defect. Thus, this study is concerning on minimizing warpage defect on wheel caster part. Apart from eliminating product wastes, this project also giving out best optimization techniques using response surface methodology. This research studied on five parameters A-packing pressure, B-packing time, C-mold temperature, D-melting temperature and E-cooling time. The optimization showed that packing pressure is the most significant parameter. Warpage have been improved 42.64% from 0.6524 mm to 0.3742mm.

Error reduction and parameter optimization of the TAPIR method for fast T1 mapping.

PubMed

Zaitsev, M; Steinhoff, S; Shah, N J

2003-06-01

A methodology is presented for the reduction of both systematic and random errors in T(1) determination using TAPIR, a Look-Locker-based fast T(1) mapping technique. The relations between various sequence parameters were carefully investigated in order to develop recipes for choosing optimal sequence parameters. Theoretical predictions for the optimal flip angle were verified experimentally. Inversion pulse imperfections were identified as the main source of systematic errors in T(1) determination with TAPIR. An effective remedy is demonstrated which includes extension of the measurement protocol to include a special sequence for mapping the inversion efficiency itself. Copyright 2003 Wiley-Liss, Inc.

Least squares QR-based decomposition provides an efficient way of computing optimal regularization parameter in photoacoustic tomography.

PubMed

Shaw, Calvin B; Prakash, Jaya; Pramanik, Manojit; Yalavarthy, Phaneendra K

2013-08-01

A computationally efficient approach that computes the optimal regularization parameter for the Tikhonov-minimization scheme is developed for photoacoustic imaging. This approach is based on the least squares-QR decomposition which is a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution enabled via finding an optimal regularization parameter. The computational efficiency and performance of the proposed method are shown using a test case of numerical blood vessel phantom, where the initial pressure is exactly known for quantitative comparison.

Optimization of injection molding process parameters for a plastic cell phone housing component

NASA Astrophysics Data System (ADS)

Rajalingam, Sokkalingam; Vasant, Pandian; Khe, Cheng Seong; Merican, Zulkifli; Oo, Zeya

2016-11-01

To produce thin-walled plastic items, injection molding process is one of the most widely used application tools. However, to set optimal process parameters is difficult as it may cause to produce faulty items on injected mold like shrinkage. This study aims at to determine such an optimum injection molding process parameters which can reduce the fault of shrinkage on a plastic cell phone cover items. Currently used setting of machines process produced shrinkage and mis-specified length and with dimensions below the limit. Thus, for identification of optimum process parameters, maintaining closer targeted length and width setting magnitudes with minimal variations, more experiments are needed. The mold temperature, injection pressure and screw rotation speed are used as process parameters in this research. For optimal molding process parameters the Response Surface Methods (RSM) is applied. The major contributing factors influencing the responses were identified from analysis of variance (ANOVA) technique. Through verification runs it was found that the shrinkage defect can be minimized with the optimal setting found by RSM.

Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

PubMed Central

Parida, Arun Kumar; Routara, Bharat Chandra

2014-01-01

Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (R a). The machining is conducted with Taguchi L9 orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v 3-f 2-d 3. Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v 1-f 1-d 3. Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach. PMID:27437503

Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods

NASA Astrophysics Data System (ADS)

Rogers, Adam; Safi-Harb, Samar; Fiege, Jason

2015-08-01

The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.

Efficient Parameter Searches for Colloidal Materials Design with Digital Alchemy

NASA Astrophysics Data System (ADS)

Dodd, Paul, M.; Geng, Yina; van Anders, Greg; Glotzer, Sharon C.

Optimal colloidal materials design is challenging, even for high-throughput or genomic approaches, because the design space provided by modern colloid synthesis techniques can easily have dozens of dimensions. In this talk we present the methodology of an inverse approach we term ''digital alchemy'' to perform rapid searches of design-paramenter spaces with up to 188 dimensions that yield thermodynamically optimal colloid parameters for target crystal structures with up to 20 particles in a unit cell. The method relies only on fundamental principles of statistical mechanics and Metropolis Monte Carlo techniques, and yields particle attribute tolerances via analogues of familiar stress-strain relationships.

Color separation in forensic image processing using interactive differential evolution.

PubMed

Mushtaq, Harris; Rahnamayan, Shahryar; Siddiqi, Areeb

2015-01-01

Color separation is an image processing technique that has often been used in forensic applications to differentiate among variant colors and to remove unwanted image interference. This process can reveal important information such as covered text or fingerprints in forensic investigation procedures. However, several limitations prevent users from selecting the appropriate parameters pertaining to the desired and undesired colors. This study proposes the hybridization of an interactive differential evolution (IDE) and a color separation technique that no longer requires users to guess required control parameters. The IDE algorithm optimizes these parameters in an interactive manner by utilizing human visual judgment to uncover desired objects. A comprehensive experimental verification has been conducted on various sample test images, including heavily obscured texts, texts with subtle color variations, and fingerprint smudges. The advantage of IDE is apparent as it effectively optimizes the color separation parameters at a level indiscernible to the naked eyes. © 2014 American Academy of Forensic Sciences.

Optimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Mariajayaprakash, Arokiasamy; Senthilvelan, Thiyagarajan; Vivekananthan, Krishnapillai Ponnambal

2013-07-01

The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and fixture height), washing process parameters (total alkalinity, temperature, pH value of rinsing water, and timing), and painting process parameters (flowability, coating thickness, pointage, and temperature). In this paper, the process parameters, namely, painting and washing process parameters, are optimized by Taguchi method. Though the defects are reasonably minimized by Taguchi method, in order to achieve zero defects during the processes, genetic algorithm technique is applied on the optimized parameters obtained by Taguchi method.

An optimal system design process for a Mars roving vehicle

NASA Technical Reports Server (NTRS)

Pavarini, C.; Baker, J.; Goldberg, A.

1971-01-01

The problem of determining the optimal design for a Mars roving vehicle is considered. A system model is generated by consideration of the physical constraints on the design parameters and the requirement that the system be deliverable to the Mars surface. An expression which evaluates system performance relative to mission goals as a function of the design parameters only is developed. The use of nonlinear programming techniques to optimize the design is proposed and an example considering only two of the vehicle subsystems is formulated and solved.

Cost minimizing of cutting process for CNC thermal and water-jet machines

NASA Astrophysics Data System (ADS)

Tavaeva, Anastasia; Kurennov, Dmitry

2015-11-01

This paper deals with optimization problem of cutting process for CNC thermal and water-jet machines. The accuracy of objective function parameters calculation for optimization problem is investigated. This paper shows that working tool path speed is not constant value. One depends on some parameters that are described in this paper. The relations of working tool path speed depending on the numbers of NC programs frames, length of straight cut, configuration part are presented. Based on received results the correction coefficients for working tool speed are defined. Additionally the optimization problem may be solved by using mathematical model. Model takes into account the additional restrictions of thermal cutting (choice of piercing and output tool point, precedence condition, thermal deformations). At the second part of paper the non-standard cutting techniques are considered. Ones may lead to minimizing of cutting cost and time compared with standard cutting techniques. This paper considers the effectiveness of non-standard cutting techniques application. At the end of the paper the future research works are indicated.

Concerning the Development of the Wide-Field Optics for WFXT Including Methods of Optimizing X-Ray Optical Prescriptions for Wide-Field Applications

NASA Technical Reports Server (NTRS)

Weisskopf, M. C.; Elsner, R. F.; O'Dell, S. L.; Ramsey, B. D.

2010-01-01

We present a progress report on the various endeavors we are undertaking at MSFC in support of the Wide Field X-Ray Telescope development. In particular we discuss assembly and alignment techniques, in-situ polishing corrections, and the results of our efforts to optimize mirror prescriptions including polynomial coefficients, relative shell displacements, detector placements and tilts. This optimization does not require a blind search through the multi-dimensional parameter space. Under the assumption that the parameters are small enough so that second order expansions are valid, we show that the performance at the detector can be expressed as a quadratic function with numerical coefficients derived from a ray trace through the underlying Wolter I optic. The optimal values for the parameters are found by solving the linear system of equations creating by setting derivatives of this function with respect to each parameter to zero.

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

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

Zarepisheh, M; Li, R; Xing, L

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) andmore » aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves quality of resultant treatment plans as compared with conventional VMAT or IMRT treatments.« less

Electro-optic Mach-Zehnder Interferometer based Optical Digital Magnitude Comparator and 1's Complement Calculator

NASA Astrophysics Data System (ADS)

Kumar, Ajay; Raghuwanshi, Sanjeev Kumar

2016-06-01

The optical switching activity is one of the most essential phenomena in the optical domain. The electro-optic effect-based switching phenomena are applicable to generate some effective combinational and sequential logic circuits. The processing of digital computational technique in the optical domain includes some considerable advantages of optical communication technology, e.g. immunity to electro-magnetic interferences, compact size, signal security, parallel computing and larger bandwidth. The paper describes some efficient technique to implement single bit magnitude comparator and 1's complement calculator using the concepts of electro-optic effect. The proposed techniques are simulated on the MATLAB software. However, the suitability of the techniques is verified using the highly reliable Opti-BPM software. It is interesting to analyze the circuits in order to specify some optimized device parameter in order to optimize some performance affecting parameters, e.g. crosstalk, extinction ratio, signal losses through the curved and straight waveguide sections.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Launch Vehicle Propulsion Design with Multiple Selection Criteria

NASA Technical Reports Server (NTRS)

Shelton, Joey D.; Frederick, Robert A.; Wilhite, Alan W.

2005-01-01

The approach and techniques described herein define an optimization and evaluation approach for a liquid hydrogen/liquid oxygen single-stage-to-orbit system. The method uses Monte Carlo simulations, genetic algorithm solvers, a propulsion thermo-chemical code, power series regression curves for historical data, and statistical models in order to optimize a vehicle system. The system, including parameters for engine chamber pressure, area ratio, and oxidizer/fuel ratio, was modeled and optimized to determine the best design for seven separate design weight and cost cases by varying design and technology parameters. Significant model results show that a 53% increase in Design, Development, Test and Evaluation cost results in a 67% reduction in Gross Liftoff Weight. Other key findings show the sensitivity of propulsion parameters, technology factors, and cost factors and how these parameters differ when cost and weight are optimized separately. Each of the three key propulsion parameters; chamber pressure, area ratio, and oxidizer/fuel ratio, are optimized in the seven design cases and results are plotted to show impacts to engine mass and overall vehicle mass.

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography.

PubMed

Prakash, Jaya; Yalavarthy, Phaneendra K

2013-03-01

Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

Error analysis and system optimization of non-null aspheric testing system

NASA Astrophysics Data System (ADS)

Luo, Yongjie; Yang, Yongying; Liu, Dong; Tian, Chao; Zhuo, Yongmo

2010-10-01

A non-null aspheric testing system, which employs partial null lens (PNL for short) and reverse iterative optimization reconstruction (ROR for short) technique, is proposed in this paper. Based on system modeling in ray tracing software, the parameter of each optical element is optimized and this makes system modeling more precise. Systematic error of non-null aspheric testing system is analyzed and can be categorized into two types, the error due to surface parameters of PNL in the system modeling and the rest from non-null interferometer by the approach of error storage subtraction. Experimental results show that, after systematic error is removed from testing result of non-null aspheric testing system, the aspheric surface is precisely reconstructed by ROR technique and the consideration of systematic error greatly increase the test accuracy of non-null aspheric testing system.

Building Energy Modeling and Control Methods for Optimization and Renewables Integration

NASA Astrophysics Data System (ADS)

Burger, Eric M.

This dissertation presents techniques for the numerical modeling and control of building systems, with an emphasis on thermostatically controlled loads. The primary objective of this work is to address technical challenges related to the management of energy use in commercial and residential buildings. This work is motivated by the need to enhance the performance of building systems and by the potential for aggregated loads to perform load following and regulation ancillary services, thereby enabling the further adoption of intermittent renewable energy generation technologies. To increase the generalizability of the techniques, an emphasis is placed on recursive and adaptive methods which minimize the need for customization to specific buildings and applications. The techniques presented in this dissertation can be divided into two general categories: modeling and control. Modeling techniques encompass the processing of data streams from sensors and the training of numerical models. These models enable us to predict the energy use of a building and of sub-systems, such as a heating, ventilation, and air conditioning (HVAC) unit. Specifically, we first present an ensemble learning method for the short-term forecasting of total electricity demand in buildings. As the deployment of intermittent renewable energy resources continues to rise, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. Second, we present a recursive parameter estimation technique for identifying a thermostatically controlled load (TCL) model that is non-linear in the parameters. For TCLs to perform demand response services in real-time markets, online methods for parameter estimation are needed. Third, we develop a piecewise linear thermal model of a residential building and train the model using data collected from a custom-built thermostat. This model is capable of approximating unmodeled dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

Batch technique to evaluate the efficiency of different natural adsorbents for defluoridation from groundwater

NASA Astrophysics Data System (ADS)

Kumar, Pankaj; Saraswat, Chitresh; Mishra, Binaya Kumar; Avtar, Ram; Patel, Hiral; Patel, Asha; Sharma, Tejal; Patel, Roshni

2017-09-01

Fluoride pollution (with concentration >1.0 mg/L) in groundwater has become a global threat in the recent past due to the lesser availability of potable groundwater resource. In between several defluoridation techniques discovered so far, the adsorption process proved to be most economic and efficient. This study is an effort to evaluate defluoridation efficiency of powdered rice husk, fine chopped rice husk and sawdust by the batch adsorption process. Optimum defluoridation capacity is achieved by optimizing various parameters, viz. dose of adsorbent, pH, contact time and initial concentration. It was found that all three materials can be employed for the defluoridation technique, but powdered rice husk is the best adsorbent in the midst of all three. Powdered rice husk showed fluoride removal efficiency ranging between 85 and 90 % in the contact period of 7 h only in conditions of all optimized parameter. Following this parameter optimization, adsorption efficiency was also evaluated at natural pH of groundwater to minimize the cost of defluoridation. No significant difference was found between fluoride adsorption at optimized pH (pH = 4) and natural one (pH = 7), which concludes that powdered rice husk can be efficiently used for the defluoridation technique at field scale. The adsorption isotherm using this adsorbent perfectly followed Langmuir isotherms. The value of calculated separation factor also suggests the favourable adsorption of fluoride onto this adsorbent under the conditions used for the experiments. The field application for defluoridation of groundwater using this adsorbent (based on pH of natural groundwater there and seasonal variation of temperature) showed the high success rate.

Optimization of spatial frequency domain imaging technique for estimating optical properties of food and biological materials

USDA-ARS?s Scientific Manuscript database

Spatial frequency domain imaging technique has recently been developed for determination of the optical properties of food and biological materials. However, accurate estimation of the optical property parameters by the technique is challenging due to measurement errors associated with signal acquis...

Techniques for shuttle trajectory optimization

NASA Technical Reports Server (NTRS)

Edge, E. R.; Shieh, C. J.; Powers, W. F.

1973-01-01

The application of recently developed function-space Davidon-type techniques to the shuttle ascent trajectory optimization problem is discussed along with an investigation of the recently developed PRAXIS algorithm for parameter optimization. At the outset of this analysis, the major deficiency of the function-space algorithms was their potential storage problems. Since most previous analyses of the methods were with relatively low-dimension problems, no storage problems were encountered. However, in shuttle trajectory optimization, storage is a problem, and this problem was handled efficiently. Topics discussed include: the shuttle ascent model and the development of the particular optimization equations; the function-space algorithms; the operation of the algorithm and typical simulations; variable final-time problem considerations; and a modification of Powell's algorithm.

Inverse design of bulk morphologies in block copolymers using particle swarm optimization

NASA Astrophysics Data System (ADS)

Khadilkar, Mihir; Delaney, Kris; Fredrickson, Glenn

Multiblock polymers are a versatile platform for creating a large range of nanostructured materials with novel morphologies and properties. However, achieving desired structures or property combinations is difficult due to a vast design space comprised of parameters including monomer species, block sequence, block molecular weights and dispersity, copolymer architecture, and binary interaction parameters. Navigating through such vast design spaces to achieve an optimal formulation for a target structure or property set requires an efficient global optimization tool wrapped around a forward simulation technique such as self-consistent field theory (SCFT). We report on such an inverse design strategy utilizing particle swarm optimization (PSO) as the global optimizer and SCFT as the forward prediction engine. To avoid metastable states in forward prediction, we utilize pseudo-spectral variable cell SCFT initiated from a library of defect free seeds of known block copolymer morphologies. We demonstrate that our approach allows for robust identification of block copolymers and copolymer alloys that self-assemble into a targeted structure, optimizing parameters such as block fractions, blend fractions, and Flory chi parameters.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models.

PubMed

Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Abdullah, Sharifah Mastura Syed; El-Shafie, Ahmed

2018-05-01

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Extended Analytic Device Optimization Employing Asymptotic Expansion

NASA Technical Reports Server (NTRS)

Mackey, Jonathan; Sehirlioglu, Alp; Dynsys, Fred

2013-01-01

Analytic optimization of a thermoelectric junction often introduces several simplifying assumptionsincluding constant material properties, fixed known hot and cold shoe temperatures, and thermallyinsulated leg sides. In fact all of these simplifications will have an effect on device performance,ranging from negligible to significant depending on conditions. Numerical methods, such as FiniteElement Analysis or iterative techniques, are often used to perform more detailed analysis andaccount for these simplifications. While numerical methods may stand as a suitable solution scheme,they are weak in gaining physical understanding and only serve to optimize through iterativesearching techniques. Analytic and asymptotic expansion techniques can be used to solve thegoverning system of thermoelectric differential equations with fewer or less severe assumptionsthan the classic case. Analytic methods can provide meaningful closed form solutions and generatebetter physical understanding of the conditions for when simplifying assumptions may be valid.In obtaining the analytic solutions a set of dimensionless parameters, which characterize allthermoelectric couples, is formulated and provide the limiting cases for validating assumptions.Presentation includes optimization of both classic rectangular couples as well as practically andtheoretically interesting cylindrical couples using optimization parameters physically meaningful toa cylindrical couple. Solutions incorporate the physical behavior for i) thermal resistance of hot andcold shoes, ii) variable material properties with temperature, and iii) lateral heat transfer through legsides.

Optimization of cladding parameters for resisting corrosion on low carbon steels using simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Balan, A. V.; Shivasankaran, N.; Magibalan, S.

2018-04-01

Low carbon steels used in chemical industries are frequently affected by corrosion. Cladding is a surfacing process used for depositing a thick layer of filler metal in a highly corrosive materials to achieve corrosion resistance. Flux cored arc welding (FCAW) is preferred in cladding process due to its augmented efficiency and higher deposition rate. In this cladding process, the effect of corrosion can be minimized by controlling the output responses such as minimizing dilution, penetration and maximizing bead width, reinforcement and ferrite number. This paper deals with the multi-objective optimization of flux cored arc welding responses by controlling the process parameters such as wire feed rate, welding speed, Nozzle to plate distance, welding gun angle for super duplex stainless steel material using simulated annealing technique. Regression equation has been developed and validated using ANOVA technique. The multi-objective optimization of weld bead parameters was carried out using simulated annealing to obtain optimum bead geometry for reducing corrosion. The potentiodynamic polarization test reveals the balanced formation of fine particles of ferrite and autenite content with desensitized nature of the microstructure in the optimized clad bead.

Optimization of GATE and PHITS Monte Carlo code parameters for spot scanning proton beam based on simulation with FLUKA general-purpose code

NASA Astrophysics Data System (ADS)

Kurosu, Keita; Das, Indra J.; Moskvin, Vadim P.

2016-01-01

Spot scanning, owing to its superior dose-shaping capability, provides unsurpassed dose conformity, in particular for complex targets. However, the robustness of the delivered dose distribution and prescription has to be verified. Monte Carlo (MC) simulation has the potential to generate significant advantages for high-precise particle therapy, especially for medium containing inhomogeneities. However, the inherent choice of computational parameters in MC simulation codes of GATE, PHITS and FLUKA that is observed for uniform scanning proton beam needs to be evaluated. This means that the relationship between the effect of input parameters and the calculation results should be carefully scrutinized. The objective of this study was, therefore, to determine the optimal parameters for the spot scanning proton beam for both GATE and PHITS codes by using data from FLUKA simulation as a reference. The proton beam scanning system of the Indiana University Health Proton Therapy Center was modeled in FLUKA, and the geometry was subsequently and identically transferred to GATE and PHITS. Although the beam transport is managed by spot scanning system, the spot location is always set at the center of a water phantom of 600 × 600 × 300 mm3, which is placed after the treatment nozzle. The percentage depth dose (PDD) is computed along the central axis using 0.5 × 0.5 × 0.5 mm3 voxels in the water phantom. The PDDs and the proton ranges obtained with several computational parameters are then compared to those of FLUKA, and optimal parameters are determined from the accuracy of the proton range, suppressed dose deviation, and computational time minimization. Our results indicate that the optimized parameters are different from those for uniform scanning, suggesting that the gold standard for setting computational parameters for any proton therapy application cannot be determined consistently since the impact of setting parameters depends on the proton irradiation technique. We therefore conclude that customization parameters must be set with reference to the optimized parameters of the corresponding irradiation technique in order to render them useful for achieving artifact-free MC simulation for use in computational experiments and clinical treatments.

Guiding automated left ventricular chamber segmentation in cardiac imaging using the concept of conserved myocardial volume.

PubMed

Garson, Christopher D; Li, Bing; Acton, Scott T; Hossack, John A

2008-06-01

The active surface technique using gradient vector flow allows semi-automated segmentation of ventricular borders. The accuracy of the algorithm depends on the optimal selection of several key parameters. We investigated the use of conservation of myocardial volume for quantitative assessment of each of these parameters using synthetic and in vivo data. We predicted that for a given set of model parameters, strong conservation of volume would correlate with accurate segmentation. The metric was most useful when applied to the gradient vector field weighting and temporal step-size parameters, but less effective in guiding an optimal choice of the active surface tension and rigidity parameters.

A novel method for the production of core-shell microparticles by inverse gelation optimized with artificial intelligent tools.

PubMed

Rodríguez-Dorado, Rosalia; Landín, Mariana; Altai, Ayça; Russo, Paola; Aquino, Rita P; Del Gaudio, Pasquale

2018-03-01

Numerous studies have been focused on hydrophobic compounds encapsulation as oils. In fact, oils can provide numerous health benefits as synergic ingredient combined with other hydrophobic active ingredients. However, stable microparticles for pharmaceutical purposes are difficult to achieve when commonly techniques are used. In this work, sunflower oil was encapsulated in calcium-alginate capsules by prilling technique in co-axial configuration. Core-shell beads were produced by inverse gelation directly at the nozzle using a w/o emulsion containing aqueous calcium chloride solution in sunflower oil pumped through the inner nozzle while an aqueous alginate solution, coming out from the annular nozzle, produced the beads shell. To optimize process parameters artificial intelligence tools were proposed to optimize the numerous prilling process variables. Homogeneous and spherical microcapsules with narrow size distribution and a thin alginate shell were obtained when the parameters as w/o constituents, polymer concentrations, flow rates and frequency of vibration were optimized by two commercial software, FormRules® and INForm®, which implement neurofuzzy logic and Artificial Neural Networks together with genetic algorithms, respectively. This technique constitutes an innovative approach for hydrophobic compounds microencapsulation. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Freitez, Juan A.; Sanchez, Morella; Ruette, Fernando

Application of simulated annealing (SA) and simplified GSA (SGSA) techniques for parameter optimization of parametric quantum chemistry method (CATIVIC) was performed. A set of organic molecules were selected for test these techniques. Comparison of the algorithms was carried out for error function minimization with respect to experimental values. Results show that SGSA is more efficient than SA with respect to computer time. Accuracy is similar in both methods; however, there are important differences in the final set of parameters.

Optimal estimation of parameters and states in stochastic time-varying systems with time delay

NASA Astrophysics Data System (ADS)

Torkamani, Shahab; Butcher, Eric A.

2013-08-01

In this study estimation of parameters and states in stochastic linear and nonlinear delay differential systems with time-varying coefficients and constant delay is explored. The approach consists of first employing a continuous time approximation to approximate the stochastic delay differential equation with a set of stochastic ordinary differential equations. Then the problem of parameter estimation in the resulting stochastic differential system is represented as an optimal filtering problem using a state augmentation technique. By adapting the extended Kalman-Bucy filter to the resulting system, the unknown parameters of the time-delayed system are estimated from noise-corrupted, possibly incomplete measurements of the states.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model.

PubMed

An, Yan; Zou, Zhihong; Zhao, Yanfei

2015-03-01

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. Copyright © 2015. Published by Elsevier B.V.

Using optimal control methods with constraints to generate singlet states in NMR

NASA Astrophysics Data System (ADS)

Rodin, Bogdan A.; Kiryutin, Alexey S.; Yurkovskaya, Alexandra V.; Ivanov, Konstantin L.; Yamamoto, Satoru; Sato, Kazunobu; Takui, Takeji

2018-06-01

A method is proposed for optimizing the performance of the APSOC (Adiabatic-Passage Spin Order Conversion) technique, which can be exploited in NMR experiments with singlet spin states. In this technique magnetization-to-singlet conversion (and singlet-to-magnetization conversion) is performed by using adiabatically ramped RF-fields. Optimization utilizes the GRAPE (Gradient Ascent Pulse Engineering) approach, in which for a fixed search area we assume monotonicity to the envelope of the RF-field. Such an approach allows one to achieve much better performance for APSOC; consequently, the efficiency of magnetization-to-singlet conversion is greatly improved as compared to simple model RF-ramps, e.g., linear ramps. We also demonstrate that the optimization method is reasonably robust to possible inaccuracies in determining NMR parameters of the spin system under study and also in setting the RF-field parameters. The present approach can be exploited in other NMR and EPR applications using adiabatic switching of spin Hamiltonians.

Flat-plate photovoltaic array design optimization

NASA Technical Reports Server (NTRS)

Ross, R. G., Jr.

1980-01-01

An analysis is presented which integrates the results of specific studies in the areas of photovoltaic structural design optimization, optimization of array series/parallel circuit design, thermal design optimization, and optimization of environmental protection features. The analysis is based on minimizing the total photovoltaic system life-cycle energy cost including repair and replacement of failed cells and modules. This approach is shown to be a useful technique for array optimization, particularly when time-dependent parameters such as array degradation and maintenance are involved.

Optimization of Interior Permanent Magnet Motor by Quality Engineering and Multivariate Analysis

NASA Astrophysics Data System (ADS)

Okada, Yukihiro; Kawase, Yoshihiro

This paper has described the method of optimization based on the finite element method. The quality engineering and the multivariable analysis are used as the optimization technique. This optimizing method consists of two steps. At Step.1, the influence of parameters for output is obtained quantitatively, at Step.2, the number of calculation by the FEM can be cut down. That is, the optimal combination of the design parameters, which satisfies the required characteristic, can be searched for efficiently. In addition, this method is applied to a design of IPM motor to reduce the torque ripple. The final shape can maintain average torque and cut down the torque ripple 65%. Furthermore, the amount of permanent magnets can be reduced.

Constrained optimization of image restoration filters

NASA Technical Reports Server (NTRS)

Riemer, T. E.; Mcgillem, C. D.

1973-01-01

A linear shift-invariant preprocessing technique is described which requires no specific knowledge of the image parameters and which is sufficiently general to allow the effective radius of the composite imaging system to be minimized while constraining other system parameters to remain within specified limits.

Optimal control of information epidemics modeled as Maki Thompson rumors

NASA Astrophysics Data System (ADS)

Kandhway, Kundan; Kuri, Joy

2014-12-01

We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Digital robust active control law synthesis for large order flexible structure using parameter optimization

NASA Technical Reports Server (NTRS)

Mukhopadhyay, V.

1988-01-01

A generic procedure for the parameter optimization of a digital control law for a large-order flexible flight vehicle or large space structure modeled as a sampled data system is presented. A linear quadratic Guassian type cost function was minimized, while satisfying a set of constraints on the steady-state rms values of selected design responses, using a constrained optimization technique to meet multiple design requirements. Analytical expressions for the gradients of the cost function and the design constraints on mean square responses with respect to the control law design variables are presented.

Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

OPTIMAL EXPERIMENT DESIGN FOR MAGNETIC RESONANCE FINGERPRINTING

PubMed Central

Zhao, Bo; Haldar, Justin P.; Setsompop, Kawin; Wald, Lawrence L.

2017-01-01

Magnetic resonance (MR) fingerprinting is an emerging quantitative MR imaging technique that simultaneously acquires multiple tissue parameters in an efficient experiment. In this work, we present an estimation-theoretic framework to evaluate and design MR fingerprinting experiments. More specifically, we derive the Cramér-Rao bound (CRB), a lower bound on the covariance of any unbiased estimator, to characterize parameter estimation for MR fingerprinting. We then formulate an optimal experiment design problem based on the CRB to choose a set of acquisition parameters (e.g., flip angles and/or repetition times) that maximizes the signal-to-noise ratio efficiency of the resulting experiment. The utility of the proposed approach is validated by numerical studies. Representative results demonstrate that the optimized experiments allow for substantial reduction in the length of an MR fingerprinting acquisition, and substantial improvement in parameter estimation performance. PMID:28268369

Optimal experiment design for magnetic resonance fingerprinting.

PubMed

Bo Zhao; Haldar, Justin P; Setsompop, Kawin; Wald, Lawrence L

2016-08-01

Magnetic resonance (MR) fingerprinting is an emerging quantitative MR imaging technique that simultaneously acquires multiple tissue parameters in an efficient experiment. In this work, we present an estimation-theoretic framework to evaluate and design MR fingerprinting experiments. More specifically, we derive the Cramér-Rao bound (CRB), a lower bound on the covariance of any unbiased estimator, to characterize parameter estimation for MR fingerprinting. We then formulate an optimal experiment design problem based on the CRB to choose a set of acquisition parameters (e.g., flip angles and/or repetition times) that maximizes the signal-to-noise ratio efficiency of the resulting experiment. The utility of the proposed approach is validated by numerical studies. Representative results demonstrate that the optimized experiments allow for substantial reduction in the length of an MR fingerprinting acquisition, and substantial improvement in parameter estimation performance.

Finite-dimensional approximation for optimal fixed-order compensation of distributed parameter systems

NASA Technical Reports Server (NTRS)

Bernstein, Dennis S.; Rosen, I. G.

1988-01-01

In controlling distributed parameter systems it is often desirable to obtain low-order, finite-dimensional controllers in order to minimize real-time computational requirements. Standard approaches to this problem employ model/controller reduction techniques in conjunction with LQG theory. In this paper we consider the finite-dimensional approximation of the infinite-dimensional Bernstein/Hyland optimal projection theory. This approach yields fixed-finite-order controllers which are optimal with respect to high-order, approximating, finite-dimensional plant models. The technique is illustrated by computing a sequence of first-order controllers for one-dimensional, single-input/single-output, parabolic (heat/diffusion) and hereditary systems using spline-based, Ritz-Galerkin, finite element approximation. Numerical studies indicate convergence of the feedback gains with less than 2 percent performance degradation over full-order LQG controllers for the parabolic system and 10 percent degradation for the hereditary system.

Efficient Round-Trip Time Optimization for Replica-Exchange Enveloping Distribution Sampling (RE-EDS).

PubMed

Sidler, Dominik; Cristòfol-Clough, Michael; Riniker, Sereina

2017-06-13

Replica-exchange enveloping distribution sampling (RE-EDS) allows the efficient estimation of free-energy differences between multiple end-states from a single molecular dynamics (MD) simulation. In EDS, a reference state is sampled, which can be tuned by two types of parameters, i.e., smoothness parameters(s) and energy offsets, such that all end-states are sufficiently sampled. However, the choice of these parameters is not trivial. Replica exchange (RE) or parallel tempering is a widely applied technique to enhance sampling. By combining EDS with the RE technique, the parameter choice problem could be simplified and the challenge shifted toward an optimal distribution of the replicas in the smoothness-parameter space. The choice of a certain replica distribution can alter the sampling efficiency significantly. In this work, global round-trip time optimization (GRTO) algorithms are tested for the use in RE-EDS simulations. In addition, a local round-trip time optimization (LRTO) algorithm is proposed for systems with slowly adapting environments, where a reliable estimate for the round-trip time is challenging to obtain. The optimization algorithms were applied to RE-EDS simulations of a system of nine small-molecule inhibitors of phenylethanolamine N-methyltransferase (PNMT). The energy offsets were determined using our recently proposed parallel energy-offset (PEOE) estimation scheme. While the multistate GRTO algorithm yielded the best replica distribution for the ligands in water, the multistate LRTO algorithm was found to be the method of choice for the ligands in complex with PNMT. With this, the 36 alchemical free-energy differences between the nine ligands were calculated successfully from a single RE-EDS simulation 10 ns in length. Thus, RE-EDS presents an efficient method for the estimation of relative binding free energies.

Experimental Investigation and Optimization of Response Variables in WEDM of Inconel - 718

NASA Astrophysics Data System (ADS)

Karidkar, S. S.; Dabade, U. A.

2016-02-01

Effective utilisation of Wire Electrical Discharge Machining (WEDM) technology is challenge for modern manufacturing industries. Day by day new materials with high strengths and capabilities are being developed to fulfil the customers need. Inconel - 718 is similar kind of material which is extensively used in aerospace applications, such as gas turbine, rocket motors, and spacecraft as well as in nuclear reactors and pumps etc. This paper deals with the experimental investigation of optimal machining parameters in WEDM for Surface Roughness, Kerf Width and Dimensional Deviation using DoE such as Taguchi methodology, L9 orthogonal array. By keeping peak current constant at 70 A, the effect of other process parameters on above response variables were analysed. Obtained experimental results were statistically analysed using Minitab-16 software. Analysis of Variance (ANOVA) shows pulse on time as the most influential parameter followed by wire tension whereas spark gap set voltage is observed to be non-influencing parameter. Multi-objective optimization technique, Grey Relational Analysis (GRA), shows optimal machining parameters such as pulse on time 108 Machine unit, spark gap set voltage 50 V and wire tension 12 gm for optimal response variables considered for the experimental analysis.

Adaptive parametric model order reduction technique for optimization of vibro-acoustic models: Application to hearing aid design

NASA Astrophysics Data System (ADS)

Creixell-Mediante, Ester; Jensen, Jakob S.; Naets, Frank; Brunskog, Jonas; Larsen, Martin

2018-06-01

Finite Element (FE) models of complex structural-acoustic coupled systems can require a large number of degrees of freedom in order to capture their physical behaviour. This is the case in the hearing aid field, where acoustic-mechanical feedback paths are a key factor in the overall system performance and modelling them accurately requires a precise description of the strong interaction between the light-weight parts and the internal and surrounding air over a wide frequency range. Parametric optimization of the FE model can be used to reduce the vibroacoustic feedback in a device during the design phase; however, it requires solving the model iteratively for multiple frequencies at different parameter values, which becomes highly time consuming when the system is large. Parametric Model Order Reduction (pMOR) techniques aim at reducing the computational cost associated with each analysis by projecting the full system into a reduced space. A drawback of most of the existing techniques is that the vector basis of the reduced space is built at an offline phase where the full system must be solved for a large sample of parameter values, which can also become highly time consuming. In this work, we present an adaptive pMOR technique where the construction of the projection basis is embedded in the optimization process and requires fewer full system analyses, while the accuracy of the reduced system is monitored by a cheap error indicator. The performance of the proposed method is evaluated for a 4-parameter optimization of a frequency response for a hearing aid model, evaluated at 300 frequencies, where the objective function evaluations become more than one order of magnitude faster than for the full system.

Mathematical simulation and optimization of cutting mode in turning of workpieces made of nickel-based heat-resistant alloy

NASA Astrophysics Data System (ADS)

Bogoljubova, M. N.; Afonasov, A. I.; Kozlov, B. N.; Shavdurov, D. E.

2018-05-01

A predictive simulation technique of optimal cutting modes in the turning of workpieces made of nickel-based heat-resistant alloys, different from the well-known ones, is proposed. The impact of various factors on the cutting process with the purpose of determining optimal parameters of machining in concordance with certain effectiveness criteria is analyzed in the paper. A mathematical model of optimization, algorithms and computer programmes, visual graphical forms reflecting dependences of the effectiveness criteria – productivity, net cost, and tool life on parameters of the technological process - have been worked out. A nonlinear model for multidimensional functions, “solution of the equation with multiple unknowns”, “a coordinate descent method” and heuristic algorithms are accepted to solve the problem of optimization of cutting mode parameters. Research shows that in machining of workpieces made from heat-resistant alloy AISI N07263, the highest possible productivity will be achieved with the following parameters: cutting speed v = 22.1 m/min., feed rate s=0.26 mm/rev; tool life T = 18 min.; net cost – 2.45 per hour.

Optimization of laser butt welding parameters with multiple performance characteristics

NASA Astrophysics Data System (ADS)

Sathiya, P.; Abdul Jaleel, M. Y.; Katherasan, D.; Shanmugarajan, B.

2011-04-01

This paper presents a study carried out on 3.5 kW cooled slab laser welding of 904 L super austenitic stainless steel. The joints have butts welded with different shielding gases, namely argon, helium and nitrogen, at a constant flow rate. Super austenitic stainless steel (SASS) normally contains high amount of Mo, Cr, Ni, N and Mn. The mechanical properties are controlled to obtain good welded joints. The quality of the joint is evaluated by studying the features of weld bead geometry, such as bead width (BW) and depth of penetration (DOP). In this paper, the tensile strength and bead profiles (BW and DOP) of laser welded butt joints made of AISI 904 L SASS are investigated. The Taguchi approach is used as a statistical design of experiment (DOE) technique for optimizing the selected welding parameters. Grey relational analysis and the desirability approach are applied to optimize the input parameters by considering multiple output variables simultaneously. Confirmation experiments have also been conducted for both of the analyses to validate the optimized parameters.

Taguchi's technique: an effective method for improving X-ray medical radiographic screen performance.

PubMed

Vlachogiannis, J G

2003-01-01

Taguchi's technique is a helpful tool to achieve experimental optimization of a large number of decision variables with a small number of off-line experiments. The technique appears to be an ideal tool for improving the performance of X-ray medical radiographic screens under a noise source. Currently there are very many guides available for improving the efficiency of X-ray medical radiographic screens. These guides can be refined using a second-stage parameter optimization. based on Taguchi's technique, selecting the optimum levels of controllable X-ray radiographic screen factors. A real example of the proposed technique is presented giving certain performance criteria. The present research proposes the reinforcement of X-ray radiography by Taguchi's technique as a novel hardware mechanism.

Multiobjective optimization techniques for structural design

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The multiobjective programming techniques are important in the design of complex structural systems whose quality depends generally on a number of different and often conflicting objective functions which cannot be combined into a single design objective. The applicability of multiobjective optimization techniques is studied with reference to simple design problems. Specifically, the parameter optimization of a cantilever beam with a tip mass and a three-degree-of-freedom vabration isolation system and the trajectory optimization of a cantilever beam are considered. The solutions of these multicriteria design problems are attempted by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It has been observed that the game theory approach required the maximum computational effort, but it yielded better optimum solutions with proper balance of the various objective functions in all the cases.

Tuning of PID controller using optimization techniques for a MIMO process

NASA Astrophysics Data System (ADS)

Thulasi dharan, S.; Kavyarasan, K.; Bagyaveereswaran, V.

2017-11-01

In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.

Evaluation of ultrasonics and optimized radiography for 2219-T87 aluminum weldments

NASA Technical Reports Server (NTRS)

Clotfelter, W. N.; Hoop, J. M.; Duren, P. C.

1975-01-01

Ultrasonic studies are described which are specifically directed toward the quantitative measurement of randomly located defects previously found in aluminum welds with radiography or with dye penetrants. Experimental radiographic studies were also made to optimize techniques for welds of the thickness range to be used in fabricating the External Tank of the Space Shuttle. Conventional and innovative ultrasonic techniques were applied to the flaw size measurement problem. Advantages and disadvantages of each method are discussed. Flaw size data obtained ultrasonically were compared to radiographic data and to real flaw sizes determined by destructive measurements. Considerable success was achieved with pulse echo techniques and with 'pitch and catch' techniques. The radiographic work described demonstrates that careful selection of film exposure parameters for a particular application must be made to obtain optimized flaw detectability. Thus, film exposure techniques can be improved even though radiography is an old weld inspection method.

A Taguchi approach on optimal process control parameters for HDPE pipe extrusion process

NASA Astrophysics Data System (ADS)

Sharma, G. V. S. S.; Rao, R. Umamaheswara; Rao, P. Srinivasa

2017-06-01

High-density polyethylene (HDPE) pipes find versatile applicability for transportation of water, sewage and slurry from one place to another. Hence, these pipes undergo tremendous pressure by the fluid carried. The present work entails the optimization of the withstanding pressure of the HDPE pipes using Taguchi technique. The traditional heuristic methodology stresses on a trial and error approach and relies heavily upon the accumulated experience of the process engineers for determining the optimal process control parameters. This results in setting up of less-than-optimal values. Hence, there arouse a necessity to determine optimal process control parameters for the pipe extrusion process, which can ensure robust pipe quality and process reliability. In the proposed optimization strategy, the design of experiments (DoE) are conducted wherein different control parameter combinations are analyzed by considering multiple setting levels of each control parameter. The concept of signal-to-noise ratio ( S/ N ratio) is applied and ultimately optimum values of process control parameters are obtained as: pushing zone temperature of 166 °C, Dimmer speed at 08 rpm, and Die head temperature to be 192 °C. Confirmation experimental run is also conducted to verify the analysis and research result and values proved to be in synchronization with the main experimental findings and the withstanding pressure showed a significant improvement from 0.60 to 1.004 Mpa.

Optimization of porthole die geometrical variables by Taguchi method

NASA Astrophysics Data System (ADS)

Gagliardi, F.; Ciancio, C.; Ambrogio, G.; Filice, L.

2017-10-01

Porthole die extrusion is commonly used to manufacture hollow profiles made of lightweight alloys for numerous industrial applications. The reliability of extruded parts is affected strongly by the quality of the longitudinal and transversal seam welds. According to that, the die geometry must be designed correctly and the process parameters must be selected properly to achieve the desired product quality. In this study, numerical 3D simulations have been created and run to investigate the role of various geometrical variables on punch load and maximum pressure inside the welding chamber. These are important outputs to take into account affecting, respectively, the necessary capacity of the extrusion press and the quality of the welding lines. The Taguchi technique has been used to reduce the number of the required numerical simulations necessary for considering the influence of twelve different geometric variables. Moreover, the Analysis of variance (ANOVA) has been implemented to individually analyze the effect of each input parameter on the two responses. Then, the methodology has been utilized to determine the optimal process configuration individually optimizing the two investigated process outputs. Finally, the responses of the optimized parameters have been verified through finite element simulations approximating the predicted value closely. This study shows the feasibility of the Taguchi technique for predicting performance, optimization and therefore for improving the design of a porthole extrusion process.

Identification of Optimum Magnetic Behavior of NanoCrystalline CmFeAl Type Heusler Alloy Powders Using Response Surface Methodology

NASA Astrophysics Data System (ADS)

Srivastava, Y.; Srivastava, S.; Boriwal, L.

2016-09-01

Mechanical alloying is a novelistic solid state process that has received considerable attention due to many advantages over other conventional processes. In the present work, Co2FeAl healer alloy powder, prepared successfully from premix basic powders of Cobalt (Co), Iron (Fe) and Aluminum (Al) in stoichiometric of 60Co-26Fe-14Al (weight %) by novelistic mechano-chemical route. Magnetic properties of mechanically alloyed powders were characterized by vibrating sample magnetometer (VSM). 2 factor 5 level design matrix was applied to experiment process. Experimental results were used for response surface methodology. Interaction between the input process parameters and the response has been established with the help of regression analysis. Further analysis of variance technique was applied to check the adequacy of developed model and significance of process parameters. Test case study was performed with those parameters, which was not selected for main experimentation but range was same. Response surface methodology, the process parameters must be optimized to obtain improved magnetic properties. Further optimum process parameters were identified using numerical and graphical optimization techniques.

Development of mathematical models and optimization of the process parameters of laser surface hardened EN25 steel using elitist non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Vignesh, S.; Dinesh Babu, P.; Surya, G.; Dinesh, S.; Marimuthu, P.

2018-02-01

The ultimate goal of all production entities is to select the process parameters that would be of maximum strength, minimum wear and friction. The friction and wear are serious problems in most of the industries which are influenced by the working set of parameters, oxidation characteristics and mechanism involved in formation of wear. The experimental input parameters such as sliding distance, applied load, and temperature are utilized in finding out the optimized solution for achieving the desired output responses such as coefficient of friction, wear rate, and volume loss. The optimization is performed with the help of a novel method, Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based on an evolutionary algorithm. The regression equations obtained using Response Surface Methodology (RSM) are used in determining the optimum process parameters. Further, the results achieved through desirability approach in RSM are compared with that of the optimized solution obtained through NSGA-II. The results conclude that proposed evolutionary technique is much effective and faster than the desirability approach.

Optimal Trajectories Generation in Robotic Fiber Placement Systems

NASA Astrophysics Data System (ADS)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

Piezoresistive Cantilever Performance—Part II: Optimization

PubMed Central

Park, Sung-Jin; Doll, Joseph C.; Rastegar, Ali J.; Pruitt, Beth L.

2010-01-01

Piezoresistive silicon cantilevers fabricated by ion implantation are frequently used for force, displacement, and chemical sensors due to their low cost and electronic readout. However, the design of piezoresistive cantilevers is not a straightforward problem due to coupling between the design parameters, constraints, process conditions, and performance. We systematically analyzed the effect of design and process parameters on force resolution and then developed an optimization approach to improve force resolution while satisfying various design constraints using simulation results. The combined simulation and optimization approach is extensible to other doping methods beyond ion implantation in principle. The optimization results were validated by fabricating cantilevers with the optimized conditions and characterizing their performance. The measurement results demonstrate that the analytical model accurately predicts force and displacement resolution, and sensitivity and noise tradeoff in optimal cantilever performance. We also performed a comparison between our optimization technique and existing models and demonstrated eight times improvement in force resolution over simplified models. PMID:20333323

Weak-value amplification and optimal parameter estimation in the presence of correlated noise

NASA Astrophysics Data System (ADS)

Sinclair, Josiah; Hallaji, Matin; Steinberg, Aephraim M.; Tollaksen, Jeff; Jordan, Andrew N.

2017-11-01

We analytically and numerically investigate the performance of weak-value amplification (WVA) and related parameter estimation methods in the presence of temporally correlated noise. WVA is a special instance of a general measurement strategy that involves sorting data into separate subsets based on the outcome of a second "partitioning" measurement. Using a simplified correlated noise model that can be analyzed exactly together with optimal statistical estimators, we compare WVA to a conventional measurement method. We find that WVA indeed yields a much lower variance of the parameter of interest than the conventional technique does, optimized in the absence of any partitioning measurements. In contrast, a statistically optimal analysis that employs partitioning measurements, incorporating all partitioned results and their known correlations, is found to yield an improvement—typically slight—over the noise reduction achieved by WVA. This result occurs because the simple WVA technique is not tailored to any specific noise environment and therefore does not make use of correlations between the different partitions. We also compare WVA to traditional background subtraction, a familiar technique where measurement outcomes are partitioned to eliminate unknown offsets or errors in calibration. Surprisingly, for the cases we consider, background subtraction turns out to be a special case of the optimal partitioning approach, possessing a similar typically slight advantage over WVA. These results give deeper insight into the role of partitioning measurements (with or without postselection) in enhancing measurement precision, which some have found puzzling. They also resolve previously made conflicting claims about the usefulness of weak-value amplification to precision measurement in the presence of correlated noise. We finish by presenting numerical results to model a more realistic laboratory situation of time-decaying correlations, showing that our conclusions hold for a wide range of statistical models.

Optimization of parameter values for complex pulse sequences by simulated annealing: application to 3D MP-RAGE imaging of the brain.

PubMed

Epstein, F H; Mugler, J P; Brookeman, J R

1994-02-01

A number of pulse sequence techniques, including magnetization-prepared gradient echo (MP-GRE), segmented GRE, and hybrid RARE, employ a relatively large number of variable pulse sequence parameters and acquire the image data during a transient signal evolution. These sequences have recently been proposed and/or used for clinical applications in the brain, spine, liver, and coronary arteries. Thus, the need for a method of deriving optimal pulse sequence parameter values for this class of sequences now exists. Due to the complexity of these sequences, conventional optimization approaches, such as applying differential calculus to signal difference equations, are inadequate. We have developed a general framework for adapting the simulated annealing algorithm to pulse sequence parameter value optimization, and applied this framework to the specific case of optimizing the white matter-gray matter signal difference for a T1-weighted variable flip angle 3D MP-RAGE sequence. Using our algorithm, the values of 35 sequence parameters, including the magnetization-preparation RF pulse flip angle and delay time, 32 flip angles in the variable flip angle gradient-echo acquisition sequence, and the magnetization recovery time, were derived. Optimized 3D MP-RAGE achieved up to a 130% increase in white matter-gray matter signal difference compared with optimized 3D RF-spoiled FLASH with the same total acquisition time. The simulated annealing approach was effective at deriving optimal parameter values for a specific 3D MP-RAGE imaging objective, and may be useful for other imaging objectives and sequences in this general class.

Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.; Boyle, Richard D.

2014-01-01

Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.

Nonlinear programming extensions to rational function approximation methods for unsteady aerodynamic forces

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1988-01-01

The approximation of unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft are discussed. Two methods of formulating these approximations are extended to include the same flexibility in constraining the approximations and the same methodology in optimizing nonlinear parameters as another currently used extended least-squares method. Optimal selection of nonlinear parameters is made in each of the three methods by use of the same nonlinear, nongradient optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is lower order than that required when no optimization of the nonlinear terms is performed. The free linear parameters are determined using the least-squares matrix techniques of a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from different approaches are described and results are presented that show comparative evaluations from application of each of the extended methods to a numerical example.

Optimization of the volume reconstruction for classical Tomo-PIV algorithms (MART, BIMART and SMART): synthetic and experimental studies

NASA Astrophysics Data System (ADS)

Thomas, L.; Tremblais, B.; David, L.

2014-03-01

Optimization of multiplicative algebraic reconstruction technique (MART), simultaneous MART and block iterative MART reconstruction techniques was carried out on synthetic and experimental data. Different criteria were defined to improve the preprocessing of the initial images. Knowledge of how each reconstruction parameter influences the quality of particle volume reconstruction and computing time is the key in Tomo-PIV. These criteria were applied to a real case, a jet in cross flow, and were validated.

Parameter estimation techniques based on optimizing goodness-of-fit statistics for structural reliability

NASA Technical Reports Server (NTRS)

Starlinger, Alois; Duffy, Stephen F.; Palko, Joseph L.

1993-01-01

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell's numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper's nonlinear regression algorithm.

Financial model calibration using consistency hints.

PubMed

Abu-Mostafa, Y S

2001-01-01

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.

Simultaneous structural and control optimization via linear quadratic regulator eigenstructure assignment

NASA Technical Reports Server (NTRS)

Becus, G. A.; Lui, C. Y.; Venkayya, V. B.; Tischler, V. A.

1987-01-01

A method for simultaneous structural and control design of large flexible space structures (LFSS) to reduce vibration generated by disturbances is presented. Desired natural frequencies and damping ratios for the closed loop system are achieved by using a combination of linear quadratic regulator (LQR) synthesis and numerical optimization techniques. The state and control weighing matrices (Q and R) are expressed in terms of structural parameters such as mass and stiffness. The design parameters are selected by numerical optimization so as to minimize the weight of the structure and to achieve the desired closed-loop eigenvalues. An illustrative example of the design of a two bar truss is presented.

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

PubMed

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

Vibroacoustic optimization using a statistical energy analysis model

NASA Astrophysics Data System (ADS)

Culla, Antonio; D`Ambrogio, Walter; Fregolent, Annalisa; Milana, Silvia

2016-08-01

In this paper, an optimization technique for medium-high frequency dynamic problems based on Statistical Energy Analysis (SEA) method is presented. Using a SEA model, the subsystem energies are controlled by internal loss factors (ILF) and coupling loss factors (CLF), which in turn depend on the physical parameters of the subsystems. A preliminary sensitivity analysis of subsystem energy to CLF's is performed to select CLF's that are most effective on subsystem energies. Since the injected power depends not only on the external loads but on the physical parameters of the subsystems as well, it must be taken into account under certain conditions. This is accomplished in the optimization procedure, where approximate relationships between CLF's, injected power and physical parameters are derived. The approach is applied on a typical aeronautical structure: the cabin of a helicopter.

Simultaneous analysis and design

NASA Technical Reports Server (NTRS)

Haftka, R. T.

1984-01-01

Optimization techniques are increasingly being used for performing nonlinear structural analysis. The development of element by element (EBE) preconditioned conjugate gradient (CG) techniques is expected to extend this trend to linear analysis. Under these circumstances the structural design problem can be viewed as a nested optimization problem. There are computational benefits to treating this nested problem as a large single optimization problem. The response variables (such as displacements) and the structural parameters are all treated as design variables in a unified formulation which performs simultaneously the design and analysis. Two examples are used for demonstration. A seventy-two bar truss is optimized subject to linear stress constraints and a wing box structure is optimized subject to nonlinear collapse constraints. Both examples show substantial computational savings with the unified approach as compared to the traditional nested approach.

Recent experience in simultaneous control-structure optimization

NASA Technical Reports Server (NTRS)

Salama, M.; Ramaker, R.; Milman, M.

1989-01-01

To show the feasibility of simultaneous optimization as design procedure, low order problems were used in conjunction with simple control formulations. The numerical results indicate that simultaneous optimization is not only feasible, but also advantageous. Such advantages come at the expense of introducing complexities beyond those encountered in structure optimization alone, or control optimization alone. Examples include: larger design parameter space, optimization may combine continuous and combinatoric variables, and the combined objective function may be nonconvex. Future extensions to include large order problems, more complex objective functions and constraints, and more sophisticated control formulations will require further research to ensure that the additional complexities do not outweigh the advantages of simultaneous optimization. Some areas requiring more efficient tools than currently available include: multiobjective criteria and nonconvex optimization. Efficient techniques to deal with optimization over combinatoric and continuous variables, and with truncation issues for structure and control parameters of both the model space as well as the design space need to be developed.

Optimization of segmented thermoelectric generator using Taguchi and ANOVA techniques.

PubMed

Kishore, Ravi Anant; Sanghadasa, Mohan; Priya, Shashank

2017-12-01

Recent studies have demonstrated that segmented thermoelectric generators (TEGs) can operate over large thermal gradient and thus provide better performance (reported efficiency up to 11%) as compared to traditional TEGs, comprising of single thermoelectric (TE) material. However, segmented TEGs are still in early stages of development due to the inherent complexity in their design optimization and manufacturability. In this study, we demonstrate physics based numerical techniques along with Analysis of variance (ANOVA) and Taguchi optimization method for optimizing the performance of segmented TEGs. We have considered comprehensive set of design parameters, such as geometrical dimensions of p-n legs, height of segmentation, hot-side temperature, and load resistance, in order to optimize output power and efficiency of segmented TEGs. Using the state-of-the-art TE material properties and appropriate statistical tools, we provide near-optimum TEG configuration with only 25 experiments as compared to 3125 experiments needed by the conventional optimization methods. The effect of environmental factors on the optimization of segmented TEGs is also studied. Taguchi results are validated against the results obtained using traditional full factorial optimization technique and a TEG configuration for simultaneous optimization of power and efficiency is obtained.

Parameters optimization for the fabrication of phosphate glass/hydroxyapatite nanocomposite scaffold

NASA Astrophysics Data System (ADS)

Govindan, R.; Girija, E. K.

2015-06-01

Three-dimensional, highly porous, bioactive and biodegradable phosphate glass and nanohydroxyapatite (n-HA) composite scaffolds was fabricated by the polymer foam replication technique. Polyurethane foam (PU) and polyvinyl alcohol (PVA) were used as template and binder, respectively. Optimization of composition and sintering temperature is carried out for tissue engineering scaffold fabrication.

Optimization of commercial scale photonuclear production of radioisotopes

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

Bindu, K. C.; Harmon, Frank; Starovoitova, Valeriia N.

2013-04-19

Photonuclear production of radioisotopes driven by bremsstrahlung photons using a linear electron accelerator in the suitable energy range is a promising method for producing radioisotopes. The photonuclear production method is capable of making radioisotopes more conveniently, cheaply and with much less radioactive waste compared to existing methods. Historically, photo-nuclear reactions have not been exploited for isotope production because of the low specific activity that is generally associated with this production process, although the technique is well-known to be capable of producing large quantities of certain radioisotopes. We describe an optimization technique for a set of parameters to maximize specific activitymore » of the final product. This set includes the electron beam energy and current, the end station design (an integrated converter and target as well as cooling system), the purity of materials used, and the activation time. These parameters are mutually dependent and thus their optimization is not trivial. {sup 67}Cu photonuclear production via {sup 68}Zn({gamma}p){sup 67}Cu reaction was used as an example of such an optimization process.« less

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2016-10-01

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

Active vibration mitigation of distributed parameter, smart-type structures using Pseudo-Feedback Optimal Control (PFOC)

NASA Technical Reports Server (NTRS)

Patten, W. N.; Robertshaw, H. H.; Pierpont, D.; Wynn, R. H.

1989-01-01

A new, near-optimal feedback control technique is introduced that is shown to provide excellent vibration attenuation for those distributed parameter systems that are often encountered in the areas of aeroservoelasticity and large space systems. The technique relies on a novel solution methodology for the classical optimal control problem. Specifically, the quadratic regulator control problem for a flexible vibrating structure is first cast in a weak functional form that admits an approximate solution. The necessary conditions (first-order) are then solved via a time finite-element method. The procedure produces a low dimensional, algebraic parameterization of the optimal control problem that provides a rigorous basis for a discrete controller with a first-order like hold output. Simulation has shown that the algorithm can successfully control a wide variety of plant forms including multi-input/multi-output systems and systems exhibiting significant nonlinearities. In order to firmly establish the efficacy of the algorithm, a laboratory control experiment was implemented to provide planar (bending) vibration attenuation of a highly flexible beam (with a first clamped-free mode of approximately 0.5 Hz).

Quantitative CT: technique dependence of volume estimation on pulmonary nodules

NASA Astrophysics Data System (ADS)

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Colsher, James; Amurao, Maxwell; Samei, Ehsan

2012-03-01

Current estimation of lung nodule size typically relies on uni- or bi-dimensional techniques. While new three-dimensional volume estimation techniques using MDCT have improved size estimation of nodules with irregular shapes, the effect of acquisition and reconstruction parameters on accuracy (bias) and precision (variance) of the new techniques has not been fully investigated. To characterize the volume estimation performance dependence on these parameters, an anthropomorphic chest phantom containing synthetic nodules was scanned and reconstructed with protocols across various acquisition and reconstruction parameters. Nodule volumes were estimated by a clinical lung analysis software package, LungVCAR. Precision and accuracy of the volume assessment were calculated across the nodules and compared between protocols via a generalized estimating equation analysis. Results showed that the precision and accuracy of nodule volume quantifications were dependent on slice thickness, with different dependences for different nodule characteristics. Other parameters including kVp, pitch, and reconstruction kernel had lower impact. Determining these technique dependences enables better volume quantification via protocol optimization and highlights the importance of consistent imaging parameters in sequential examinations.

Parameter Optimization for Turbulent Reacting Flows Using Adjoints

NASA Astrophysics Data System (ADS)

Lapointe, Caelan; Hamlington, Peter E.

2017-11-01

The formulation of a new adjoint solver for topology optimization of turbulent reacting flows is presented. This solver provides novel configurations (e.g., geometries and operating conditions) based on desired system outcomes (i.e., objective functions) for complex reacting flow problems of practical interest. For many such problems, it would be desirable to know optimal values of design parameters (e.g., physical dimensions, fuel-oxidizer ratios, and inflow-outflow conditions) prior to real-world manufacture and testing, which can be expensive, time-consuming, and dangerous. However, computational optimization of these problems is made difficult by the complexity of most reacting flows, necessitating the use of gradient-based optimization techniques in order to explore a wide design space at manageable computational cost. The adjoint method is an attractive way to obtain the required gradients, because the cost of the method is determined by the dimension of the objective function rather than the size of the design space. Here, the formulation of a novel solver is outlined that enables gradient-based parameter optimization of turbulent reacting flows using the discrete adjoint method. Initial results and an outlook for future research directions are provided.

Low order H∞ optimal control for ACFA blended wing body aircraft

NASA Astrophysics Data System (ADS)

Haniš, T.; Kucera, V.; Hromčík, M.

2013-12-01

Advanced nonconvex nonsmooth optimization techniques for fixed-order H∞ robust control are proposed in this paper for design of flight control systems (FCS) with prescribed structure. Compared to classical techniques - tuning of and successive closures of particular single-input single-output (SISO) loops like dampers, attitude stabilizers, etc. - all loops are designed simultaneously by means of quite intuitive weighting filters selection. In contrast to standard optimization techniques, though (H2, H∞ optimization), the resulting controller respects the prescribed structure in terms of engaged channels and orders (e. g., proportional (P), proportional-integral (PI), and proportional-integralderivative (PID) controllers). In addition, robustness with regard to multimodel uncertainty is also addressed which is of most importance for aerospace applications as well. Such a way, robust controllers for various Mach numbers, altitudes, or mass cases can be obtained directly, based only on particular mathematical models for respective combinations of the §ight parameters.

A Case Study on the Application of a Structured Experimental Method for Optimal Parameter Design of a Complex Control System

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2015-01-01

This report documents a case study on the application of Reliability Engineering techniques to achieve an optimal balance between performance and robustness by tuning the functional parameters of a complex non-linear control system. For complex systems with intricate and non-linear patterns of interaction between system components, analytical derivation of a mathematical model of system performance and robustness in terms of functional parameters may not be feasible or cost-effective. The demonstrated approach is simple, structured, effective, repeatable, and cost and time efficient. This general approach is suitable for a wide range of systems.

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems.

PubMed

Ihme, Matthias; Marsden, Alison L; Pitsch, Heinz

2008-02-01

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.

An image morphing technique based on optimal mass preserving mapping.

PubMed

Zhu, Lei; Yang, Yan; Haker, Steven; Tannenbaum, Allen

2007-06-01

Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The L(2) mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods.

An Image Morphing Technique Based on Optimal Mass Preserving Mapping

PubMed Central

Zhu, Lei; Yang, Yan; Haker, Steven; Tannenbaum, Allen

2013-01-01

Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The L2 mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods. PMID:17547128

A Search for WIMP Dark Matter Using an Optimized Chi-square Technique on the Final Data from the Cryogenic Dark Matter Search Experiment (CDMS II)

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

Manungu Kiveni, Joseph

2012-12-01

This dissertation describes the results of a WIMP search using CDMS II data sets accumulated at the Soudan Underground Laboratory in Minnesota. Results from the original analysis of these data were published in 2009; two events were observed in the signal region with an expected leakage of 0.9 events. Further investigation revealed an issue with the ionization-pulse reconstruction algorithm leading to a software upgrade and a subsequent reanalysis of the data. As part of the reanalysis, I performed an advanced discrimination technique to better distinguish (potential) signal events from backgrounds using a 5-dimensional chi-square method. This dataanalysis technique combines themore » event information recorded for each WIMP-search event to derive a backgrounddiscrimination parameter capable of reducing the expected background to less than one event, while maintaining high efficiency for signal events. Furthermore, optimizing the cut positions of this 5-dimensional chi-square parameter for the 14 viable germanium detectors yields an improved expected sensitivity to WIMP interactions relative to previous CDMS results. This dissertation describes my improved (and optimized) discrimination technique and the results obtained from a blind application to the reanalyzed CDMS II WIMP-search data.« less

Parameter Design in Fusion Welding of AA 6061 Aluminium Alloy using Desirability Grey Relational Analysis (DGRA) Method

NASA Astrophysics Data System (ADS)

Adalarasan, R.; Santhanakumar, M.

2015-01-01

In the present work, yield strength, ultimate strength and micro-hardness of the lap joints formed with Al 6061 alloy sheets by using the processes of Tungsten Inert Gas (TIG) welding and Metal Inert Gas (MIG) welding were studied for various combinations of the welding parameters. The parameters taken for study include welding current, voltage, welding speed and inert gas flow rate. Taguchi's L9 orthogonal array was used to conduct the experiments and an integrated technique of desirability grey relational analysis was disclosed for optimizing the welding parameters. The ignored robustness in desirability approach is compensated by the grey relational approach to predict the optimal setting of input parameters for the TIG and MIG welding processes which were validated through the confirmation experiments.

Application of optimal control theory to the design of the NASA/JPL 70-meter antenna servos

NASA Technical Reports Server (NTRS)

Alvarez, L. S.; Nickerson, J.

1989-01-01

The application of Linear Quadratic Gaussian (LQG) techniques to the design of the 70-m axis servos is described. Linear quadratic optimal control and Kalman filter theory are reviewed, and model development and verification are discussed. Families of optimal controller and Kalman filter gain vectors were generated by varying weight parameters. Performance specifications were used to select final gain vectors.

Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 2: Analytic manual

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Space Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows subproblems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Autocollimation system for measuring angular deformations with reflector designed by quaternionic method

NASA Astrophysics Data System (ADS)

Hoang, Phong V.; Konyakhin, Igor A.

2017-06-01

Autocollimators are widely used for angular measurements in instrument-making and the manufacture of elements of optical systems (wedges, prisms, plane-parallel plates) to check their shape parameters (rectilinearity, parallelism and planarity) and retrieve their optical parameters (curvature radii, measure and test their flange focusing). Autocollimator efficiency is due to the high sensitivity of the autocollimation method to minor rotations of the reflecting control element or the controlled surface itself. We consider using quaternions to optimize reflector parameters during autocollimation measurements as compared to the matrix technique. Mathematical model studies have demonstrated that the orthogonal positioning of the two basic unchanged directions of the tetrahedral reflector of the autocollimator is optimal by the criterion of reducing measurement errors where the axis of actual rotation is in a bisecting position towards them. Computer results are presented of running quaternion models that yielded conditions for diminishing measurement errors provided apriori information is available on the position of rotation axis. A practical technique is considered for synthesizing the parameters of the tetrahedral reflector that employs the newly-retrieved relationships. Following the relationships found between the angles of the tetrahedral reflector and the angles of the parameters of its initial orientation, an applied technique was developed to synthesize the control element for autocollimation measurements in case apriori information is available on the axis of actual rotation during monitoring measurements of shaft or pipeline deformation.

Predictive Modeling and Optimization of Vibration-assisted AFM Tip-based Nanomachining

NASA Astrophysics Data System (ADS)

Kong, Xiangcheng

The tip-based vibration-assisted nanomachining process offers a low-cost, low-effort technique in fabricating nanometer scale 2D/3D structures in sub-100 nm regime. To understand its mechanism, as well as provide the guidelines for process planning and optimization, we have systematically studied this nanomachining technique in this work. To understand the mechanism of this nanomachining technique, we firstly analyzed the interaction between the AFM tip and the workpiece surface during the machining process. A 3D voxel-based numerical algorithm has been developed to calculate the material removal rate as well as the contact area between the AFM tip and the workpiece surface. As a critical factor to understand the mechanism of this nanomachining process, the cutting force has been analyzed and modeled. A semi-empirical model has been proposed by correlating the cutting force with the material removal rate, which was validated using experimental data from different machining conditions. With the understanding of its mechanism, we have developed guidelines for process planning of this nanomachining technique. To provide the guideline for parameter selection, the effect of machining parameters on the feature dimensions (depth and width) has been analyzed. Based on ANOVA test results, the feature width is only controlled by the XY vibration amplitude, while the feature depth is affected by several machining parameters such as setpoint force and feed rate. A semi-empirical model was first proposed to predict the machined feature depth under given machining condition. Then, to reduce the computation intensity, linear and nonlinear regression models were also proposed and validated using experimental data. Given the desired feature dimensions, feasible machining parameters could be provided using these predictive feature dimension models. As the tip wear is unavoidable during the machining process, the machining precision will gradually decrease. To maintain the machining quality, the guideline for when to change the tip should be provided. In this study, we have developed several metrics to detect tip wear, such as tip radius and the pull-off force. The effect of machining parameters on the tip wear rate has been studied using these metrics, and the machining distance before a tip must be changed has been modeled using these machining parameters. Finally, the optimization functions have been built for unit production time and unit production cost subject to realistic constraints, and the optimal machining parameters can be found by solving these functions.

From nonlinear optimization to convex optimization through firefly algorithm and indirect approach with applications to CAD/CAM.

PubMed

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently.

From Nonlinear Optimization to Convex Optimization through Firefly Algorithm and Indirect Approach with Applications to CAD/CAM

PubMed Central

Gálvez, Akemi; Iglesias, Andrés

2013-01-01

Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently. PMID:24376380

Robust approximate optimal guidance strategies for aeroassisted orbital transfer missions

NASA Astrophysics Data System (ADS)

Ilgen, Marc R.

This thesis presents the application of game theoretic and regular perturbation methods to the problem of determining robust approximate optimal guidance laws for aeroassisted orbital transfer missions with atmospheric density and navigated state uncertainties. The optimal guidance problem is reformulated as a differential game problem with the guidance law designer and Nature as opposing players. The resulting equations comprise the necessary conditions for the optimal closed loop guidance strategy in the presence of worst case parameter variations. While these equations are nonlinear and cannot be solved analytically, the presence of a small parameter in the equations of motion allows the method of regular perturbations to be used to solve the equations approximately. This thesis is divided into five parts. The first part introduces the class of problems to be considered and presents results of previous research. The second part then presents explicit semianalytical guidance law techniques for the aerodynamically dominated region of flight. These guidance techniques are applied to unconstrained and control constrained aeroassisted plane change missions and Mars aerocapture missions, all subject to significant atmospheric density variations. The third part presents a guidance technique for aeroassisted orbital transfer problems in the gravitationally dominated region of flight. Regular perturbations are used to design an implicit guidance technique similar to the second variation technique but that removes the need for numerically computing an optimal trajectory prior to flight. This methodology is then applied to a set of aeroassisted inclination change missions. In the fourth part, the explicit regular perturbation solution technique is extended to include the class of guidance laws with partial state information. This methodology is then applied to an aeroassisted plane change mission using inertial measurements and subject to uncertainties in the initial value of the flight path angle. A summary of performance results for all these guidance laws is presented in the fifth part of this thesis along with recommendations for further research.

Techniques for optimal crop selection in a controlled ecological life support system

NASA Technical Reports Server (NTRS)

Mccormack, Ann; Finn, Cory; Dunsky, Betsy

1993-01-01

A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods.

Techniques for optimal crop selection in a controlled ecological life support system

NASA Technical Reports Server (NTRS)

Mccormack, Ann; Finn, Cory; Dunsky, Betsy

1992-01-01

A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods.

Joint optimization of a partially coherent Gaussian beam for free-space optical communication over turbulent channels with pointing errors.

PubMed

Lee, It Ee; Ghassemlooy, Zabih; Ng, Wai Pang; Khalighi, Mohammad-Ali

2013-02-01

Joint beam width and spatial coherence length optimization is proposed to maximize the average capacity in partially coherent free-space optical links, under the combined effects of atmospheric turbulence and pointing errors. An optimization metric is introduced to enable feasible translation of the joint optimal transmitter beam parameters into an analogous level of divergence of the received optical beam. Results show that near-ideal average capacity is best achieved through the introduction of a larger receiver aperture and the joint optimization technique.

Evaluation of laser cutting process with auxiliary gas pressure by soft computing approach

NASA Astrophysics Data System (ADS)

Lazov, Lyubomir; Nikolić, Vlastimir; Jovic, Srdjan; Milovančević, Miloš; Deneva, Heristina; Teirumenieka, Erika; Arsic, Nebojsa

2018-06-01

Evaluation of the optimal laser cutting parameters is very important for the high cut quality. This is highly nonlinear process with different parameters which is the main challenge in the optimization process. Data mining methodology is one of most versatile method which can be used laser cutting process optimization. Support vector regression (SVR) procedure is implemented since it is a versatile and robust technique for very nonlinear data regression. The goal in this study was to determine the optimal laser cutting parameters to ensure robust condition for minimization of average surface roughness. Three cutting parameters, the cutting speed, the laser power, and the assist gas pressure, were used in the investigation. As a laser type TruLaser 1030 technological system was used. Nitrogen as an assisted gas was used in the laser cutting process. As the data mining method, support vector regression procedure was used. Data mining prediction accuracy was very high according the coefficient (R2) of determination and root mean square error (RMSE): R2 = 0.9975 and RMSE = 0.0337. Therefore the data mining approach could be used effectively for determination of the optimal conditions of the laser cutting process.

Optimization of dual energy contrast enhanced breast tomosynthesis for improved mammographic lesion detection and diagnosis

NASA Astrophysics Data System (ADS)

Saunders, R.; Samei, E.; Badea, C.; Yuan, H.; Ghaghada, K.; Qi, Y.; Hedlund, L. W.; Mukundan, S.

2008-03-01

Dual-energy contrast-enhanced breast tomosynthesis has been proposed as a technique to improve the detection of early-stage cancer in young, high-risk women. This study focused on optimizing this technique using computer simulations. The computer simulation used analytical calculations to optimize the signal difference to noise ratio (SdNR) of resulting images from such a technique at constant dose. The optimization included the optimal radiographic technique, optimal distribution of dose between the two single-energy projection images, and the optimal weighting factor for the dual energy subtraction. Importantly, the SdNR included both anatomical and quantum noise sources, as dual energy imaging reduces anatomical noise at the expense of increases in quantum noise. Assuming a tungsten anode, the maximum SdNR at constant dose was achieved for a high energy beam at 49 kVp with 92.5 μm copper filtration and a low energy beam at 49 kVp with 95 μm tin filtration. These analytical calculations were followed by Monte Carlo simulations that included the effects of scattered radiation and detector properties. Finally, the feasibility of this technique was tested in a small animal imaging experiment using a novel iodinated liposomal contrast agent. The results illustrated the utility of dual energy imaging and determined the optimal acquisition parameters for this technique. This work was supported in part by grants from the Komen Foundation (PDF55806), the Cancer Research and Prevention Foundation, and the NIH (NCI R21 CA124584-01). CIVM is a NCRR/NCI National Resource under P41-05959/U24-CA092656.

Optimal lunar soft landing trajectories using taboo evolutionary programming

NASA Astrophysics Data System (ADS)

Mutyalarao, M.; Raj, M. Xavier James

A safe lunar landing is a key factor to undertake an effective lunar exploration. Lunar lander consists of four phases such as launch phase, the earth-moon transfer phase, circumlunar phase and landing phase. The landing phase can be either hard landing or soft landing. Hard landing means the vehicle lands under the influence of gravity without any deceleration measures. However, soft landing reduces the vertical velocity of the vehicle before landing. Therefore, for the safety of the astronauts as well as the vehicle lunar soft landing with an acceptable velocity is very much essential. So it is important to design the optimal lunar soft landing trajectory with minimum fuel consumption. Optimization of Lunar Soft landing is a complex optimal control problem. In this paper, an analysis related to lunar soft landing from a parking orbit around Moon has been carried out. A two-dimensional trajectory optimization problem is attempted. The problem is complex due to the presence of system constraints. To solve the time-history of control parameters, the problem is converted into two point boundary value problem by using the maximum principle of Pontrygen. Taboo Evolutionary Programming (TEP) technique is a stochastic method developed in recent years and successfully implemented in several fields of research. It combines the features of taboo search and single-point mutation evolutionary programming. Identifying the best unknown parameters of the problem under consideration is the central idea for many space trajectory optimization problems. The TEP technique is used in the present methodology for the best estimation of initial unknown parameters by minimizing objective function interms of fuel requirements. The optimal estimation subsequently results into an optimal trajectory design of a module for soft landing on the Moon from a lunar parking orbit. Numerical simulations demonstrate that the proposed approach is highly efficient and it reduces the minimum fuel consumption. The results are compared with the available results in literature shows that the solution of present algorithm is better than some of the existing algorithms. Keywords: soft landing, trajectory optimization, evolutionary programming, control parameters, Pontrygen principle.

3He Lung Morphometry Technique: Accuracy Analysis and Pulse Sequence Optimization

PubMed Central

Sukstanskii, A.L.; Conradi, M.S.; Yablonskiy, D.A.

2010-01-01

The 3He lung morphometry technique (Yablonskiy et al, JAP, 2009), based on MRI measurements of hyperpolarized gas diffusion in lung airspaces, provides unique information on the lung microstructure at the alveolar level. 3D tomographic images of standard morphological parameters (mean airspace chord length, lung parenchyma surface-to-volume ratio, and the number of alveoli per unit lung volume) can be created from a rather short (several seconds) MRI scan. These parameters are most commonly used to characterize lung morphometry but were not previously available from in vivo studies. A background of the 3He lung morphometry technique is based on a previously proposed model of lung acinar airways, treated as cylindrical passages of external radius R covered by alveolar sleeves of depth h, and on a theory of gas diffusion in these airways. The initial works approximated the acinar airways as very long cylinders, all with the same R and h. The present work aims at analyzing effects of realistic acinar airway structures, incorporating airway branching, physiological airway lengths, a physiological ratio of airway ducts and sacs, and distributions of R and h. By means of Monte Carlo computer simulations, we demonstrate that our technique allows rather accurate measurements of geometrical and morphological parameters of acinar airways. In particular, the accuracy of determining one of the most important physiological parameter of acinar airways – surface-to-volume ratio – does not exceed several percent. Second, we analyze the effect of the susceptibility induced inhomogeneous magnetic field on the parameter estimate and demonstrate that this effect is rather negligible at B0 ≤ 3T and becomes substantial only at higher B0 Third, we theoretically derive an optimal choice of MR pulse sequence parameters, which should be used to acquire a series of diffusion attenuated MR signals, allowing a substantial decrease in the acquisition time and improvement in accuracy of the results. It is demonstrated that the optimal choice represents three not equidistant b-values: b1 = 0, b2 ~ 2 s/cm2, b3 ~ 8 s/cm2. PMID:20937564

Nuclear Electric Vehicle Optimization Toolset (NEVOT)

NASA Technical Reports Server (NTRS)

Tinker, Michael L.; Steincamp, James W.; Stewart, Eric T.; Patton, Bruce W.; Pannell, William P.; Newby, Ronald L.; Coffman, Mark E.; Kos, Larry D.; Qualls, A. Lou; Greene, Sherrell

2004-01-01

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a genetic algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be considered through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Knowledge-based system for detailed blade design of turbines

NASA Astrophysics Data System (ADS)

Goel, Sanjay; Lamson, Scott

1994-03-01

A design optimization methodology that couples optimization techniques to CFD analysis for design of airfoils is presented. This technique optimizes 2D airfoil sections of a blade by minimizing the deviation of the actual Mach number distribution on the blade surface from a smooth fit of the distribution. The airfoil is not reverse engineered by specification of a precise distribution of the desired Mach number plot, only general desired characteristics of the distribution are specified for the design. Since the Mach number distribution is very complex, and cannot be conveniently represented by a single polynomial, it is partitioned into segments, each of which is characterized by a different order polynomial. The sum of the deviation of all the segments is minimized during optimization. To make intelligent changes to the airfoil geometry, it needs to be associated with features observed in the Mach number distribution. Associating the geometry parameters with independent features of the distribution is a fairly complex task. Also, for different optimization techniques to work efficiently the airfoil geometry needs to be parameterized into independent parameters, with enough degrees of freedom for adequate geometry manipulation. A high-pressure, low reaction steam turbine blade section was optimized using this methodology. The Mach number distribution was partitioned into pressure and suction surfaces and the suction surface distribution was further subdivided into leading edge, mid section and trailing edge sections. Two different airfoil representation schemes were used for defining the design variables of the optimization problem. The optimization was performed by using a combination of heuristic search and numerical optimization. The optimization results for the two schemes are discussed in the paper. The results are also compared to a manual design improvement study conducted independently by an experienced airfoil designer. The turbine blade optimization system (TBOS) is developed using the described methodology of coupling knowledge engineering with multiple search techniques for blade shape optimization. TBOS removes a major bottleneck in the design cycle by performing multiple design optimizations in parallel, and improves design quality at the same time. TBOS not only improves the design but also the designers' quality of work by taking the mundane repetitive task of design iterations away and leaving them more time for innovative design.

A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

NASA Astrophysics Data System (ADS)

Amjad, M.; Salam, Z.; Ishaque, K.

2014-04-01

In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.

Optimal time points sampling in pathway modelling.

PubMed

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

Estimation of Gravity Parameters Related to Simple Geometrical Structures by Developing an Approach Based on Deconvolution and Linear Optimization Techniques

NASA Astrophysics Data System (ADS)

Asfahani, J.; Tlas, M.

2015-10-01

An easy and practical method for interpreting residual gravity anomalies due to simple geometrically shaped models such as cylinders and spheres has been proposed in this paper. This proposed method is based on both the deconvolution technique and the simplex algorithm for linear optimization to most effectively estimate the model parameters, e.g., the depth from the surface to the center of a buried structure (sphere or horizontal cylinder) or the depth from the surface to the top of a buried object (vertical cylinder), and the amplitude coefficient from the residual gravity anomaly profile. The method was tested on synthetic data sets corrupted by different white Gaussian random noise levels to demonstrate the capability and reliability of the method. The results acquired show that the estimated parameter values derived by this proposed method are close to the assumed true parameter values. The validity of this method is also demonstrated using real field residual gravity anomalies from Cuba and Sweden. Comparable and acceptable agreement is shown between the results derived by this method and those derived from real field data.

Multivariable PID controller design tuning using bat algorithm for activated sludge process

NASA Astrophysics Data System (ADS)

Atikah Nor’Azlan, Nur; Asmiza Selamat, Nur; Mat Yahya, Nafrizuan

2018-04-01

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

Optimization techniques applied to spectrum management for communications satellites

NASA Astrophysics Data System (ADS)

Ottey, H. R.; Sullivan, T. M.; Zusman, F. S.

This paper describes user requirements, algorithms and software design features for the application of optimization techniques to the management of the geostationary orbit/spectrum resource. Relevant problems include parameter sensitivity analyses, frequency and orbit position assignment coordination, and orbit position allotment planning. It is shown how integer and nonlinear programming as well as heuristic search techniques can be used to solve these problems. Formalized mathematical objective functions that define the problems are presented. Constraint functions that impart the necessary solution bounds are described. A versatile program structure is outlined, which would allow problems to be solved in stages while varying the problem space, solution resolution, objective function and constraints.

Weak value amplification considered harmful

NASA Astrophysics Data System (ADS)

Ferrie, Christopher; Combes, Joshua

2014-03-01

We show using statistically rigorous arguments that the technique of weak value amplification does not perform better than standard statistical techniques for the tasks of parameter estimation and signal detection. We show that using all data and considering the joint distribution of all measurement outcomes yields the optimal estimator. Moreover, we show estimation using the maximum likelihood technique with weak values as small as possible produces better performance for quantum metrology. In doing so, we identify the optimal experimental arrangement to be the one which reveals the maximal eigenvalue of the square of system observables. We also show these conclusions do not change in the presence of technical noise.

Hyoid bone development: An assessment of optimal CT scanner parameters and 3D volume rendering techniques

PubMed Central

Cotter, Meghan M.; Whyms, Brian J.; Kelly, Michael P.; Doherty, Benjamin M.; Gentry, Lindell R.; Bersu, Edward T.; Vorperian, Houri K.

2015-01-01

The hyoid bone anchors and supports the vocal tract. Its complex shape is best studied in three dimensions, but it is difficult to capture on computed tomography (CT) images and three-dimensional volume renderings. The goal of this study was to determine the optimal CT scanning and rendering parameters to accurately measure the growth and developmental anatomy of the hyoid and to determine whether it is feasible and necessary to use these parameters in the measurement of hyoids from in vivo CT scans. Direct linear and volumetric measurements of skeletonized hyoid bone specimens were compared to corresponding CT images to determine the most accurate scanning parameters and three-dimensional rendering techniques. A pilot study was undertaken using in vivo scans from a retrospective CT database to determine feasibility of quantifying hyoid growth. Scanning parameters and rendering technique affected accuracy of measurements. Most linear CT measurements were within 10% of direct measurements; however, volume was overestimated when CT scans were acquired with a slice thickness greater than 1.25 mm. Slice-by-slice thresholding of hyoid images decreased volume overestimation. The pilot study revealed that the linear measurements tested correlate with age. A fine-tuned rendering approach applied to small slice thickness CT scans produces the most accurate measurements of hyoid bones. However, linear measurements can be accurately assessed from in vivo CT scans at a larger slice thickness. Such findings imply that investigation into the growth and development of the hyoid bone, and the vocal tract as a whole, can now be performed using these techniques. PMID:25810349

Hyoid Bone Development: An Assessment Of Optimal CT Scanner Parameters and Three-Dimensional Volume Rendering Techniques.

PubMed

Cotter, Meghan M; Whyms, Brian J; Kelly, Michael P; Doherty, Benjamin M; Gentry, Lindell R; Bersu, Edward T; Vorperian, Houri K

2015-08-01

The hyoid bone anchors and supports the vocal tract. Its complex shape is best studied in three dimensions, but it is difficult to capture on computed tomography (CT) images and three-dimensional volume renderings. The goal of this study was to determine the optimal CT scanning and rendering parameters to accurately measure the growth and developmental anatomy of the hyoid and to determine whether it is feasible and necessary to use these parameters in the measurement of hyoids from in vivo CT scans. Direct linear and volumetric measurements of skeletonized hyoid bone specimens were compared with corresponding CT images to determine the most accurate scanning parameters and three-dimensional rendering techniques. A pilot study was undertaken using in vivo scans from a retrospective CT database to determine feasibility of quantifying hyoid growth. Scanning parameters and rendering technique affected accuracy of measurements. Most linear CT measurements were within 10% of direct measurements; however, volume was overestimated when CT scans were acquired with a slice thickness greater than 1.25 mm. Slice-by-slice thresholding of hyoid images decreased volume overestimation. The pilot study revealed that the linear measurements tested correlate with age. A fine-tuned rendering approach applied to small slice thickness CT scans produces the most accurate measurements of hyoid bones. However, linear measurements can be accurately assessed from in vivo CT scans at a larger slice thickness. Such findings imply that investigation into the growth and development of the hyoid bone, and the vocal tract as a whole, can now be performed using these techniques. © 2015 Wiley Periodicals, Inc.

Optimization and scale up of microfluidic nanolipomer production method for preclinical and potential clinical trials.

PubMed

Gdowski, Andrew; Johnson, Kaitlyn; Shah, Sunil; Gryczynski, Ignacy; Vishwanatha, Jamboor; Ranjan, Amalendu

2018-02-12

The process of optimization and fabrication of nanoparticle synthesis for preclinical studies can be challenging and time consuming. Traditional small scale laboratory synthesis techniques suffer from batch to batch variability. Additionally, the parameters used in the original formulation must be re-optimized due to differences in fabrication techniques for clinical production. Several low flow microfluidic synthesis processes have been reported in recent years for developing nanoparticles that are a hybrid between polymeric nanoparticles and liposomes. However, use of high flow microfluidic synthetic techniques has not been described for this type of nanoparticle system, which we will term as nanolipomer. In this manuscript, we describe the successful optimization and functional assessment of nanolipomers fabricated using a microfluidic synthesis method under high flow parameters. The optimal total flow rate for synthesis of these nanolipomers was found to be 12 ml/min and flow rate ratio 1:1 (organic phase: aqueous phase). The PLGA polymer concentration of 10 mg/ml and a DSPE-PEG lipid concentration of 10% w/v provided optimal size, PDI and stability. Drug loading and encapsulation of a representative hydrophobic small molecule drug, curcumin, was optimized and found that high encapsulation efficiency of 58.8% and drug loading of 4.4% was achieved at 7.5% w/w initial concentration of curcumin/PLGA polymer. The final size and polydispersity index of the optimized nanolipomer was 102.11 nm and 0.126, respectively. Functional assessment of uptake of the nanolipomers in C4-2B prostate cancer cells showed uptake at 1 h and increased uptake at 24 h. The nanolipomer was more effective in the cell viability assay compared to free drug. Finally, assessment of in vivo retention in mice of these nanolipomers revealed retention for up to 2 h and were completely cleared at 24 h. In this study, we have demonstrated that a nanolipomer formulation can be successfully synthesized and easily scaled up through a high flow microfluidic system with optimal characteristics. The process of developing nanolipomers using this methodology is significant as the same optimized parameters used for small batches could be translated into manufacturing large scale batches for clinical trials through parallel flow systems.

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.

Optimizing cosmological surveys in a crowded market

NASA Astrophysics Data System (ADS)

Bassett, Bruce A.

2005-04-01

Optimizing the major next-generation cosmological surveys (such as SNAP, KAOS, etc.) is a key problem given our ignorance of the physics underlying cosmic acceleration and the plethora of surveys planned. We propose a Bayesian design framework which (1) maximizes the discrimination power of a survey without assuming any underlying dark-energy model, (2) finds the best niche survey geometry given current data and future competing experiments, (3) maximizes the cross section for serendipitous discoveries and (4) can be adapted to answer specific questions (such as “is dark energy dynamical?”). Integrated parameter-space optimization (IPSO) is a design framework that integrates projected parameter errors over an entire dark energy parameter space and then extremizes a figure of merit (such as Shannon entropy gain which we show is stable to off-diagonal covariance matrix perturbations) as a function of survey parameters using analytical, grid or MCMC techniques. We discuss examples where the optimization can be performed analytically. IPSO is thus a general, model-independent and scalable framework that allows us to appropriately use prior information to design the best possible surveys.

Optimizing spectral CT parameters for material classification tasks

NASA Astrophysics Data System (ADS)

Rigie, D. S.; La Rivière, P. J.

2016-06-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies.

Optimizing Spectral CT Parameters for Material Classification Tasks

PubMed Central

Rigie, D. S.; La Rivière, P. J.

2017-01-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies. PMID:27227430

Petermann I and II spot size: Accurate semi analytical description involving Nelder-Mead method of nonlinear unconstrained optimization and three parameter fundamental modal field

NASA Astrophysics Data System (ADS)

Roy Choudhury, Raja; Roy Choudhury, Arundhati; Kanti Ghose, Mrinal

2013-01-01

A semi-analytical model with three optimizing parameters and a novel non-Gaussian function as the fundamental modal field solution has been proposed to arrive at an accurate solution to predict various propagation parameters of graded-index fibers with less computational burden than numerical methods. In our semi analytical formulation the optimization of core parameter U which is usually uncertain, noisy or even discontinuous, is being calculated by Nelder-Mead method of nonlinear unconstrained minimizations as it is an efficient and compact direct search method and does not need any derivative information. Three optimizing parameters are included in the formulation of fundamental modal field of an optical fiber to make it more flexible and accurate than other available approximations. Employing variational technique, Petermann I and II spot sizes have been evaluated for triangular and trapezoidal-index fibers with the proposed fundamental modal field. It has been demonstrated that, the results of the proposed solution identically match with the numerical results over a wide range of normalized frequencies. This approximation can also be used in the study of doped and nonlinear fiber amplifier.

A two-step parameter optimization algorithm for improving estimation of optical properties using spatial frequency domain imaging

NASA Astrophysics Data System (ADS)

Hu, Dong; Lu, Renfu; Ying, Yibin

2018-03-01

This research was aimed at optimizing the inverse algorithm for estimating the optical absorption (μa) and reduced scattering (μs‧) coefficients from spatial frequency domain diffuse reflectance. Studies were first conducted to determine the optimal frequency resolution and start and end frequencies in terms of the reciprocal of mean free path (1/mfp‧). The results showed that the optimal frequency resolution increased with μs‧ and remained stable when μs‧ was larger than 2 mm-1. The optimal end frequency decreased from 0.3/mfp‧ to 0.16/mfp‧ with μs‧ ranging from 0.4 mm-1 to 3 mm-1, while the optimal start frequency remained at 0 mm-1. A two-step parameter estimation method was proposed based on the optimized frequency parameters, which improved estimation accuracies by 37.5% and 9.8% for μa and μs‧, respectively, compared with the conventional one-step method. Experimental validations with seven liquid optical phantoms showed that the optimized algorithm resulted in the mean absolute errors of 15.4%, 7.6%, 5.0% for μa and 16.4%, 18.0%, 18.3% for μs‧ at the wavelengths of 675 nm, 700 nm, and 715 nm, respectively. Hence, implementation of the optimized parameter estimation method should be considered in order to improve the measurement of optical properties of biological materials when using spatial frequency domain imaging technique.

Approximate optimal guidance for the advanced launch system

NASA Technical Reports Server (NTRS)

Feeley, T. S.; Speyer, J. L.

1993-01-01

A real-time guidance scheme for the problem of maximizing the payload into orbit subject to the equations of motion for a rocket over a spherical, non-rotating earth is presented. An approximate optimal launch guidance law is developed based upon an asymptotic expansion of the Hamilton - Jacobi - Bellman or dynamic programming equation. The expansion is performed in terms of a small parameter, which is used to separate the dynamics of the problem into primary and perturbation dynamics. For the zeroth-order problem the small parameter is set to zero and a closed-form solution to the zeroth-order expansion term of Hamilton - Jacobi - Bellman equation is obtained. Higher-order terms of the expansion include the effects of the neglected perturbation dynamics. These higher-order terms are determined from the solution of first-order linear partial differential equations requiring only the evaluation of quadratures. This technique is preferred as a real-time, on-line guidance scheme to alternative numerical iterative optimization schemes because of the unreliable convergence properties of these iterative guidance schemes and because the quadratures needed for the approximate optimal guidance law can be performed rapidly and by parallel processing. Even if the approximate solution is not nearly optimal, when using this technique the zeroth-order solution always provides a path which satisfies the terminal constraints. Results for two-degree-of-freedom simulations are presented for the simplified problem of flight in the equatorial plane and compared to the guidance scheme generated by the shooting method which is an iterative second-order technique.

Mammalian cell culture process for monoclonal antibody production: nonlinear modelling and parameter estimation.

PubMed

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad; Roman, Monica

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

PubMed Central

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797

Optimum data weighting and error calibration for estimation of gravitational parameters

NASA Technical Reports Server (NTRS)

Lerch, Francis J.

1989-01-01

A new technique was developed for the weighting of data from satellite tracking systems in order to obtain an optimum least-squares solution and an error calibration for the solution parameters. Data sets from optical, electronic, and laser systems on 17 satellites in GEM-T1 Goddard Earth Model-T1 (GEM-T1) were employed toward application of this technique for gravity field parameters. Also GEM-T2 (31 satellites) was recently computed as a direct application of the method and is summarized. The method employs subset solutions of the data associated with the complete solution to agree with their error estimates. With the adjusted weights the process provides for an automatic calibration of the error estimates for the solution parameters. The data weights derived are generally much smaller than corresponding weights obtained from nominal values of observation accuracy or residuals. Independent tests show significant improvement for solutions with optimal weighting. The technique is general and may be applied to orbit parameters, station coordinates, or other parameters than the gravity model.

A pilot modeling technique for handling-qualities research

NASA Technical Reports Server (NTRS)

Hess, R. A.

1980-01-01

A brief survey of the more dominant analysis techniques used in closed-loop handling-qualities research is presented. These techniques are shown to rely on so-called classical and modern analytical models of the human pilot which have their foundation in the analysis and design principles of feedback control. The optimal control model of the human pilot is discussed in some detail and a novel approach to the a priori selection of pertinent model parameters is discussed. Frequency domain and tracking performance data from 10 pilot-in-the-loop simulation experiments involving 3 different tasks are used to demonstrate the parameter selection technique. Finally, the utility of this modeling approach in handling-qualities research is discussed.

Information fusion based techniques for HEVC

NASA Astrophysics Data System (ADS)

Fernández, D. G.; Del Barrio, A. A.; Botella, Guillermo; Meyer-Baese, Uwe; Meyer-Baese, Anke; Grecos, Christos

2017-05-01

Aiming at the conflict circumstances of multi-parameter H.265/HEVC encoder system, the present paper introduces the analysis of many optimizations' set in order to improve the trade-off between quality, performance and power consumption for different reliable and accurate applications. This method is based on the Pareto optimization and has been tested with different resolutions on real-time encoders.

Direct Digital Control Study.

DTIC Science & Technology

1985-02-01

Deck - Cold Deck Reset Reheat Coil Reset Steam Boiler Optimization [lot Water Outside Air Reset Chiller Optimization Chiller Water Temperature Reset...with programming techniques for each type of installed DDC in order to effect changes in operating setpoints and application programs. *Communication...can be changed without recailbration of instrumentation devices. Changes to the application software, operating setpoints and parameters require the

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Determination of optimal parameters for dual-layer cathode of polymer electrolyte fuel cell using computational intelligence-aided design.

PubMed

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference η and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs.

Computational Difficulties in the Identification and Optimization of Control Systems.

DTIC Science & Technology

1980-01-01

necessary and Identify by block number) - -. 3. iABSTRACT (Continue on revers, side It necessary and Identify by block number) As more realistic models ...Island 02912 ABSTRACT As more realistic models for resource management are developed, the need for efficient computational techniques for parameter...optimization (optimal control) in "state" models which This research was supported in part by ttfe National Science Foundation under grant NSF-MCS 79-05774

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

2012-03-01

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Design and optimization of the micro-engine turbine rotor manufacturing using the rapid prototyping technology

NASA Astrophysics Data System (ADS)

Vdovin, R. A.; Smelov, V. G.

2017-02-01

This work describes the experience in manufacturing the turbine rotor for the micro-engine. It demonstrates the design principles for the complex investment casting process combining the use of the ProCast software and the rapid prototyping techniques. At the virtual modelling stage, in addition to optimized process parameters, the casting structure was improved to obtain the defect-free section. The real production stage allowed demonstrating the performance and fitness of rapid prototyping techniques for the manufacture of geometrically-complex engine-building parts.

Development of optimized techniques and requirements for computer enhancement of structural weld radiographs. Volume 1: Technical report

NASA Technical Reports Server (NTRS)

Adams, J. R.; Hawley, S. W.; Peterson, G. R.; Salinger, S. S.; Workman, R. A.

1971-01-01

A hardware and software specification covering requirements for the computer enhancement of structural weld radiographs was considered. Three scanning systems were used to digitize more than 15 weld radiographs. The performance of these systems was evaluated by determining modulation transfer functions and noise characteristics. Enhancement techniques were developed and applied to the digitized radiographs. The scanning parameters of spot size and spacing and film density were studied to optimize the information content of the digital representation of the image.

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems.

PubMed

Jácome, Gabriel; Valarezo, Carla; Yoo, Changkyoo

2018-03-30

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

SU-F-T-201: Acceleration of Dose Optimization Process Using Dual-Loop Optimization Technique for Spot Scanning Proton Therapy

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

Hirayama, S; Fujimoto, R

Purpose: The purpose was to demonstrate a developed acceleration technique of dose optimization and to investigate its applicability to the optimization process in a treatment planning system (TPS) for proton therapy. Methods: In the developed technique, the dose matrix is divided into two parts, main and halo, based on beam sizes. The boundary of the two parts is varied depending on the beam energy and water equivalent depth by utilizing the beam size as a singular threshold parameter. The optimization is executed with two levels of iterations. In the inner loop, doses from the main part are updated, whereas dosesmore » from the halo part remain constant. In the outer loop, the doses from the halo part are recalculated. We implemented this technique to the optimization process in the TPS and investigated the dependence on the target volume of the speedup effect and applicability to the worst-case optimization (WCO) in benchmarks. Results: We created irradiation plans for various cubic targets and measured the optimization time varying the target volume. The speedup effect was improved as the target volume increased, and the calculation speed increased by a factor of six for a 1000 cm3 target. An IMPT plan for the RTOG benchmark phantom was created in consideration of ±3.5% range uncertainties using the WCO. Beams were irradiated at 0, 45, and 315 degrees. The target’s prescribed dose and OAR’s Dmax were set to 3 Gy and 1.5 Gy, respectively. Using the developed technique, the calculation speed increased by a factor of 1.5. Meanwhile, no significant difference in the calculated DVHs was found before and after incorporating the technique into the WCO. Conclusion: The developed technique could be adapted to the TPS’s optimization. The technique was effective particularly for large target cases.« less

Estimating stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization

NASA Astrophysics Data System (ADS)

Zhang, Chuan-Xin; Yuan, Yuan; Zhang, Hao-Wei; Shuai, Yong; Tan, He-Ping

2016-09-01

Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization (SPSO). We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment (MSX) catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.

Fast machine-learning online optimization of ultra-cold-atom experiments.

PubMed

Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

2016-05-16

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

Fast machine-learning online optimization of ultra-cold-atom experiments

PubMed Central

Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

2016-01-01

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. PMID:27180805

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.

Effects of dynamic diffraction conditions on magnetic parameter determination in a double perovskite Sr2FeMoO6 using electron energy-loss magnetic chiral dichroism.

PubMed

Wang, Z C; Zhong, X Y; Jin, L; Chen, X F; Moritomo, Y; Mayer, J

2017-05-01

Electron energy-loss magnetic chiral dichroism (EMCD) spectroscopy, which is similar to the well-established X-ray magnetic circular dichroism spectroscopy (XMCD), can determine the quantitative magnetic parameters of materials with high spatial resolution. One of the major obstacles in quantitative analysis using the EMCD technique is the relatively poor signal-to-noise ratio (SNR), compared to XMCD. Here, in the example of a double perovskite Sr 2 FeMoO 6 , we predicted the optimal dynamical diffraction conditions such as sample thickness, crystallographic orientation and detection aperture position by theoretical simulations. By using the optimized conditions, we showed that the SNR of experimental EMCD spectra can be significantly improved and the error of quantitative magnetic parameter determined by EMCD technique can be remarkably lowered. Our results demonstrate that, with enhanced SNR, the EMCD technique can be a unique tool to understand the structure-property relationship of magnetic materials particularly in the high-density magnetic recording and spintronic devices by quantitatively determining magnetic structure and properties at the nanometer scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimal structure and parameter learning of Ising models

DOE PAGES

Lokhov, Andrey; Vuffray, Marc Denis; Misra, Sidhant; ...

2018-03-16

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, whichmore » is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.« less

Optimal structure and parameter learning of Ising models

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

Lokhov, Andrey; Vuffray, Marc Denis; Misra, Sidhant

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, whichmore » is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.« less

CLFs-based optimization control for a class of constrained visual servoing systems.

PubMed

Song, Xiulan; Miaomiao, Fu

2017-03-01

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Automated Prescription of Oblique Brain 3D MRSI

PubMed Central

Ozhinsky, Eugene; Vigneron, Daniel B.; Chang, Susan M.; Nelson, Sarah J.

2012-01-01

Two major difficulties encountered in implementing Magnetic Resonance Spectroscopic Imaging (MRSI) in a clinical setting are limited coverage and difficulty in prescription. The goal of this project was to completely automate the process of 3D PRESS MRSI prescription, including placement of the selection box, saturation bands and shim volume, while maximizing the coverage of the brain. The automated prescription technique included acquisition of an anatomical MRI image, optimization of the oblique selection box parameters, optimization of the placement of OVS saturation bands, and loading of the calculated parameters into a customized 3D MRSI pulse sequence. To validate the technique and compare its performance with existing protocols, 3D MRSI data were acquired from 6 exams from 3 healthy volunteers. To assess the performance of the automated 3D MRSI prescription for patients with brain tumors, the data were collected from 16 exams from 8 subjects with gliomas. This technique demonstrated robust coverage of the tumor, high consistency of prescription and very good data quality within the T2 lesion. PMID:22692829

Optimization and Characterization of the Friction Stir Welded Sheets of AA 5754-H111: Monitoring of the Quality of Joints with Thermographic Techniques.

PubMed

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Palumbo, Davide; De Finis, Rosa; Galietti, Umberto

2017-10-11

Friction Stir Welding (FSW) is a solid-state welding process, based on frictional and stirring phenomena, that offers many advantages with respect to the traditional welding methods. However, several parameters can affect the quality of the produced joints. In this work, an experimental approach has been used for studying and optimizing the FSW process, applied on 5754-H111 aluminum plates. In particular, the thermal behavior of the material during the process has been investigated and two thermal indexes, the maximum temperature and the heating rate of the material, correlated to the frictional power input, were investigated for different process parameters (the travel and rotation tool speeds) configurations. Moreover, other techniques (micrographs, macrographs and destructive tensile tests) were carried out for supporting in a quantitative way the analysis of the quality of welded joints. The potential of thermographic technique has been demonstrated both for monitoring the FSW process and for predicting the quality of joints in terms of tensile strength.

Determining optimal parameters in magnetic spacecraft stabilization via attitude feedback

NASA Astrophysics Data System (ADS)

Bruni, Renato; Celani, Fabio

2016-10-01

The attitude control of a spacecraft using magnetorquers can be achieved by a feedback control law which has four design parameters. However, the practical determination of appropriate values for these parameters is a critical open issue. We propose here an innovative systematic approach for finding these values: they should be those that minimize the convergence time to the desired attitude. This a particularly diffcult optimization problem, for several reasons: 1) such time cannot be expressed in analytical form as a function of parameters and initial conditions; 2) design parameters may range over very wide intervals; 3) convergence time depends also on the initial conditions of the spacecraft, which are not known in advance. To overcome these diffculties, we present a solution approach based on derivative-free optimization. These algorithms do not need to write analytically the objective function: they only need to compute it in a number of points. We also propose a fast probing technique to identify which regions of the search space have to be explored densely. Finally, we formulate a min-max model to find robust parameters, namely design parameters that minimize convergence time under the worst initial conditions. Results are very promising.

Sensitivity analysis of pars-tensa young's modulus estimation using inverse finite-element modeling

NASA Astrophysics Data System (ADS)

Rohani, S. Alireza; Elfarnawany, Mai; Agrawal, Sumit K.; Ladak, Hanif M.

2018-05-01

Accurate estimates of the pars-tensa (PT) Young's modulus (EPT) are required in finite-element (FE) modeling studies of the middle ear. Previously, we introduced an in-situ EPT estimation technique by optimizing a sample-specific FE model to match experimental eardrum pressurization data. This optimization process requires choosing some modeling assumptions such as PT thickness and boundary conditions. These assumptions are reported with a wide range of variation in the literature, hence affecting the reliability of the models. In addition, the sensitivity of the estimated EPT to FE modeling assumptions has not been studied. Therefore, the objective of this study is to identify the most influential modeling assumption on EPT estimates. The middle-ear cavity extracted from a cadaveric temporal bone was pressurized to 500 Pa. The deformed shape of the eardrum after pressurization was measured using a Fourier transform profilometer (FTP). A base-line FE model of the unpressurized middle ear was created. The EPT was estimated using golden section optimization method, which minimizes the cost function comparing the deformed FE model shape to the measured shape after pressurization. The effect of varying the modeling assumptions on EPT estimates were investigated. This included the change in PT thickness, pars flaccida Young's modulus and possible FTP measurement error. The most influential parameter on EPT estimation was PT thickness and the least influential parameter was pars flaccida Young's modulus. The results of this study provide insight into how different parameters affect the results of EPT optimization and which parameters' uncertainties require further investigation to develop robust estimation techniques.

Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

IPOST is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence fo trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the coat function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Optimal Sensor Allocation for Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

2004-01-01

Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

Multi-response parametric optimization in drilling of bamboo/Kevlar fiber reinforced sandwich composite

NASA Astrophysics Data System (ADS)

Singh, Thingujam Jackson; Samanta, Sutanu

2016-09-01

In the present work an attempt was made towards parametric optimization of drilling bamboo/Kevlar K29 fiber reinforced sandwich composite to minimize the delamination occurred during the drilling process and also to maximize the tensile strength of the drilled composite. The spindle speed and the feed rate of the drilling operation are taken as the input parameters. The influence of these parameters on delamination and tensile strength of the drilled composite studied and analysed using Taguchi GRA and ANOVA technique. The results show that both the response parameters i.e. delamination and tensile strength are more influenced by feed rate than spindle speed. The percentage contribution of feed rate and spindle speed on response parameters are 13.88% and 81.74% respectively.

A review of distributed parameter groundwater management modeling methods

USGS Publications Warehouse

Gorelick, Steven M.

1983-01-01

Models which solve the governing groundwater flow or solute transport equations in conjunction with optimization techniques, such as linear and quadratic programing, are powerful aquifer management tools. Groundwater management models fall in two general categories: hydraulics or policy evaluation and water allocation. Groundwater hydraulic management models enable the determination of optimal locations and pumping rates of numerous wells under a variety of restrictions placed upon local drawdown, hydraulic gradients, and water production targets. Groundwater policy evaluation and allocation models can be used to study the influence upon regional groundwater use of institutional policies such as taxes and quotas. Furthermore, fairly complex groundwater-surface water allocation problems can be handled using system decomposition and multilevel optimization. Experience from the few real world applications of groundwater optimization-management techniques is summarized. Classified separately are methods for groundwater quality management aimed at optimal waste disposal in the subsurface. This classification is composed of steady state and transient management models that determine disposal patterns in such a way that water quality is protected at supply locations. Classes of research missing from the literature are groundwater quality management models involving nonlinear constraints, models which join groundwater hydraulic and quality simulations with political-economic management considerations, and management models that include parameter uncertainty.

Multiple response optimization of processing and formulation parameters of Eudragit RL/RS-based matrix tablets for sustained delivery of diclofenac.

PubMed

Elzayat, Ehab M; Abdel-Rahman, Ali A; Ahmed, Sayed M; Alanazi, Fars K; Habib, Walid A; Sakr, Adel

2017-11-01

Multiple response optimization is an efficient technique to develop sustained release formulation while decreasing the number of experiments based on trial and error approach. Diclofenac matrix tablets were optimized to achieve a release profile conforming to USP monograph, matching Voltaren ® SR and withstand formulation variables. The percent of drug released at predetermined multiple time points were the response variables in the design. Statistical models were obtained with relative contour diagrams being overlaid to predict process and formulation parameters expected to produce the target release profile. Tablets were prepared by wet granulation using mixture of equivalent quantities of Eudragit RL/RS at overall polymer concentration of 10-30%w/w and compressed at 5-15KN. Drug release from the optimized formulation E4 (15%w/w, 15KN) was similar to Voltaren, conformed to USP monograph and found to be stable. Substituting lactose with mannitol, reversing the ratio between lactose and microcrystalline cellulose or increasing drug load showed no significant difference in drug release. Using dextromethorphan hydrobromide as a model soluble drug showed burst release due to higher solubility and formation of micro cavities. A numerical optimization technique was employed to develop a stable consistent promising formulation for sustained delivery of diclofenac.

A Review of Distributed Parameter Groundwater Management Modeling Methods

NASA Astrophysics Data System (ADS)

Gorelick, Steven M.

1983-04-01

Models which solve the governing groundwater flow or solute transport equations in conjunction with optimization techniques, such as linear and quadratic programing, are powerful aquifer management tools. Groundwater management models fall in two general categories: hydraulics or policy evaluation and water allocation. Groundwater hydraulic management models enable the determination of optimal locations and pumping rates of numerous wells under a variety of restrictions placed upon local drawdown, hydraulic gradients, and water production targets. Groundwater policy evaluation and allocation models can be used to study the influence upon regional groundwater use of institutional policies such as taxes and quotas. Furthermore, fairly complex groundwater-surface water allocation problems can be handled using system decomposition and multilevel optimization. Experience from the few real world applications of groundwater optimization-management techniques is summarized. Classified separately are methods for groundwater quality management aimed at optimal waste disposal in the subsurface. This classification is composed of steady state and transient management models that determine disposal patterns in such a way that water quality is protected at supply locations. Classes of research missing from the literature are groundwater quality management models involving nonlinear constraints, models which join groundwater hydraulic and quality simulations with political-economic management considerations, and management models that include parameter uncertainty.

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

PubMed

Semenov, Mikhail A; Terkel, Dmitri A

2003-01-01

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Stepped MS(All) Relied Transition (SMART): An approach to rapidly determine optimal multiple reaction monitoring mass spectrometry parameters for small molecules.

PubMed

Ye, Hui; Zhu, Lin; Wang, Lin; Liu, Huiying; Zhang, Jun; Wu, Mengqiu; Wang, Guangji; Hao, Haiping

2016-02-11

Multiple reaction monitoring (MRM) is a universal approach for quantitative analysis because of its high specificity and sensitivity. Nevertheless, optimization of MRM parameters remains as a time and labor-intensive task particularly in multiplexed quantitative analysis of small molecules in complex mixtures. In this study, we have developed an approach named Stepped MS(All) Relied Transition (SMART) to predict the optimal MRM parameters of small molecules. SMART requires firstly a rapid and high-throughput analysis of samples using a Stepped MS(All) technique (sMS(All)) on a Q-TOF, which consists of serial MS(All) events acquired from low CE to gradually stepped-up CE values in a cycle. The optimal CE values can then be determined by comparing the extracted ion chromatograms for the ion pairs of interest among serial scans. The SMART-predicted parameters were found to agree well with the parameters optimized on a triple quadrupole from the same vendor using a mixture of standards. The parameters optimized on a triple quadrupole from a different vendor was also employed for comparison, and found to be linearly correlated with the SMART-predicted parameters, suggesting the potential applications of the SMART approach among different instrumental platforms. This approach was further validated by applying to simultaneous quantification of 31 herbal components in the plasma of rats treated with a herbal prescription. Because the sMS(All) acquisition can be accomplished in a single run for multiple components independent of standards, the SMART approach are expected to find its wide application in the multiplexed quantitative analysis of complex mixtures. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimization of process parameters for a quasi-continuous tablet coating system using design of experiments.

PubMed

Cahyadi, Christine; Heng, Paul Wan Sia; Chan, Lai Wah

2011-03-01

The aim of this study was to identify and optimize the critical process parameters of the newly developed Supercell quasi-continuous coater for optimal tablet coat quality. Design of experiments, aided by multivariate analysis techniques, was used to quantify the effects of various coating process conditions and their interactions on the quality of film-coated tablets. The process parameters varied included batch size, inlet temperature, atomizing pressure, plenum pressure, spray rate and coating level. An initial screening stage was carried out using a 2(6-1(IV)) fractional factorial design. Following these preliminary experiments, optimization study was carried out using the Box-Behnken design. Main response variables measured included drug-loading efficiency, coat thickness variation, and the extent of tablet damage. Apparent optimum conditions were determined by using response surface plots. The process parameters exerted various effects on the different response variables. Hence, trade-offs between individual optima were necessary to obtain the best compromised set of conditions. The adequacy of the optimized process conditions in meeting the combined goals for all responses was indicated by the composite desirability value. By using response surface methodology and optimization, coating conditions which produced coated tablets of high drug-loading efficiency, low incidences of tablet damage and low coat thickness variation were defined. Optimal conditions were found to vary over a large spectrum when different responses were considered. Changes in processing parameters across the design space did not result in drastic changes to coat quality, thereby demonstrating robustness in the Supercell coating process. © 2010 American Association of Pharmaceutical Scientists

The Taguchi Method Application to Improve the Quality of a Sustainable Process

NASA Astrophysics Data System (ADS)

Titu, A. M.; Sandu, A. V.; Pop, A. B.; Titu, S.; Ciungu, T. C.

2018-06-01

Taguchi’s method has always been a method used to improve the quality of the analyzed processes and products. This research shows an unusual situation, namely the modeling of some parameters, considered technical parameters, in a process that is wanted to be durable by improving the quality process and by ensuring quality using an experimental research method. Modern experimental techniques can be applied in any field and this study reflects the benefits of interacting between the agriculture sustainability principles and the Taguchi’s Method application. The experimental method used in this practical study consists of combining engineering techniques with experimental statistical modeling to achieve rapid improvement of quality costs, in fact seeking optimization at the level of existing processes and the main technical parameters. The paper is actually a purely technical research that promotes a technical experiment using the Taguchi method, considered to be an effective method since it allows for rapid achievement of 70 to 90% of the desired optimization of the technical parameters. The missing 10 to 30 percent can be obtained with one or two complementary experiments, limited to 2 to 4 technical parameters that are considered to be the most influential. Applying the Taguchi’s Method in the technique and not only, allowed the simultaneous study in the same experiment of the influence factors considered to be the most important in different combinations and, at the same time, determining each factor contribution.

RTDS implementation of an improved sliding mode based inverter controller for PV system.

PubMed

Islam, Gazi; Muyeen, S M; Al-Durra, Ahmed; Hasanien, Hany M

2016-05-01

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (Rt), maximum percentage overshoot (MPOS), settling time (Ts), and steady state error (Ess) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Some optimal considerations in attitude control systems. [evaluation of value of relative weighting between time and fuel for relay control law

NASA Technical Reports Server (NTRS)

Boland, J. S., III

1973-01-01

The conventional six-engine reaction control jet relay attitude control law with deadband is shown to be a good linear approximation to a weighted time-fuel optimal control law. Techniques for evaluating the value of the relative weighting between time and fuel for a particular relay control law is studied along with techniques to interrelate other parameters for the two control laws. Vehicle attitude control laws employing control moment gyros are then investigated. Steering laws obtained from the expression for the reaction torque of the gyro configuration are compared to a total optimal attitude control law that is derived from optimal linear regulator theory. This total optimal attitude control law has computational disadvantages in the solving of the matrix Riccati equation. Several computational algorithms for solving the matrix Riccati equation are investigated with respect to accuracy, computational storage requirements, and computational speed.

Performance optimization of an MHD generator with physical constraints

NASA Technical Reports Server (NTRS)

Pian, C. C. P.; Seikel, G. R.; Smith, J. M.

1979-01-01

A technique has been described which optimizes the power out of a Faraday MHD generator operating under a prescribed set of electrical and magnetic constraints. The method does not rely on complicated numerical optimization techniques. Instead the magnetic field and the electrical loading are adjusted at each streamwise location such that the resultant generator design operates at the most limiting of the cited stress levels. The simplicity of the procedure makes it ideal for optimizing generator designs for system analysis studies of power plants. The resultant locally optimum channel designs are, however, not necessarily the global optimum designs. The results of generator performance calculations are presented for an approximately 2000 MWe size plant. The difference between the maximum power generator design and the optimal design which maximizes net MHD power are described. The sensitivity of the generator performance to the various operational parameters are also presented.

Research on design method of the full form ship with minimum thrust deduction factor

NASA Astrophysics Data System (ADS)

Zhang, Bao-ji; Miao, Ai-qin; Zhang, Zhu-xin

2015-04-01

In the preliminary design stage of the full form ships, in order to obtain a hull form with low resistance and maximum propulsion efficiency, an optimization design program for a full form ship with the minimum thrust deduction factor has been developed, which combined the potential flow theory and boundary layer theory with the optimization technique. In the optimization process, the Sequential Unconstrained Minimization Technique (SUMT) interior point method of Nonlinear Programming (NLP) was proposed with the minimum thrust deduction factor as the objective function. An appropriate displacement is a basic constraint condition, and the boundary layer separation is an additional one. The parameters of the hull form modification function are used as design variables. At last, the numerical optimization example for lines of after-body of 50000 DWT product oil tanker was provided, which indicated that the propulsion efficiency was improved distinctly by this optimal design method.

Quantified Event Automata: Towards Expressive and Efficient Runtime Monitors

NASA Technical Reports Server (NTRS)

Barringer, Howard; Falcone, Ylies; Havelund, Klaus; Reger, Giles; Rydeheard, David

2012-01-01

Runtime verification is the process of checking a property on a trace of events produced by the execution of a computational system. Runtime verification techniques have recently focused on parametric specifications where events take data values as parameters. These techniques exist on a spectrum inhabited by both efficient and expressive techniques. These characteristics are usually shown to be conflicting - in state-of-the-art solutions, efficiency is obtained at the cost of loss of expressiveness and vice-versa. To seek a solution to this conflict we explore a new point on the spectrum by defining an alternative runtime verification approach.We introduce a new formalism for concisely capturing expressive specifications with parameters. Our technique is more expressive than the currently most efficient techniques while at the same time allowing for optimizations.

Modeling and design optimization of adhesion between surfaces at the microscale.

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

Sylves, Kevin T.

2008-08-01

This research applies design optimization techniques to structures in adhesive contact where the dominant adhesive mechanism is the van der Waals force. Interface finite elements are developed for domains discretized by beam elements, quadrilateral elements or triangular shell elements. Example analysis problems comparing finite element results to analytical solutions are presented. These examples are then optimized, where the objective is matching a force-displacement relationship and the optimization variables are the interface element energy of adhesion or the width of beam elements in the structure. Several parameter studies are conducted and discussed.

Automated optimization techniques for aircraft synthesis

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.

1976-01-01

Application of numerical optimization techniques to automated conceptual aircraft design is examined. These methods are shown to be a general and efficient way to obtain quantitative information for evaluating alternative new vehicle projects. Fully automated design is compared with traditional point design methods and time and resource requirements for automated design are given. The NASA Ames Research Center aircraft synthesis program (ACSYNT) is described with special attention to calculation of the weight of a vehicle to fly a specified mission. The ACSYNT procedures for automatically obtaining sensitivity of the design (aircraft weight, performance and cost) to various vehicle, mission, and material technology parameters are presented. Examples are used to demonstrate the efficient application of these techniques.

Research on subsurface defects of potassium dihydrogen phosphate crystals fabricated by single point diamond turning technique

NASA Astrophysics Data System (ADS)

Tie, Guipeng; Dai, Yifan; Guan, Chaoliang; Chen, Shaoshan; Song, Bing

2013-03-01

Potassium dihydrogen phosphate (KDP) crystals, which are widely used in high-power laser systems, are required to be free of defects on fabricated subsurfaces. The depth of subsurface defects (SSD) of KDP crystals is significantly influenced by the parameters used in the single point diamond turning technique. In this paper, based on the deliquescent magnetorheological finishing technique, the SSD of KDP crystals is observed and the depths under various cutting parameters are detected and discussed. The results indicate that no SSD is generated under small parameters and with the increase of cutting parameters, SSD appears and the depth rises almost linearly. Although the ascending trends of SSD depths caused by cutting depth and feed rate are much alike, the two parameters make different contributions. Taking the same material removal efficiency as a criterion, a large cutting depth generates shallower SSD depth than a large feed rate. Based on the experiment results, an optimized cutting procedure is obtained to generate defect-free surfaces.

Extraction optimization and UHPLC method development for determination of the 20-hydroxyecdysone in Sida tuberculata leaves.

PubMed

da Rosa, Hemerson S; Koetz, Mariana; Santos, Marí Castro; Jandrey, Elisa Helena Farias; Folmer, Vanderlei; Henriques, Amélia Teresinha; Mendez, Andreas Sebastian Loureiro

2018-04-01

Sida tuberculata (ST) is a Malvaceae species widely distributed in Southern Brazil. In traditional medicine, ST has been employed as hypoglycemic, hypocholesterolemic, anti-inflammatory and antimicrobial. Additionally, this species is chemically characterized by flavonoids, alkaloids and phytoecdysteroids mainly. The present work aimed to optimize the extractive technique and to validate an UHPLC method for the determination of 20-hydroxyecdsone (20HE) in the ST leaves. Box-Behnken Design (BBD) was used in method optimization. The extractive methods tested were: static and dynamic maceration, ultrasound, ultra-turrax and reflux. In the Box-Behnken three parameters were evaluated in three levels (-1, 0, +1), particle size, time and plant:solvent ratio. In validation method, the parameters of selectivity, specificity, linearity, limits of detection and quantification (LOD, LOQ), precision, accuracy and robustness were evaluated. The results indicate static maceration as better technique to obtain 20HE peak area in ST extract. The optimal extraction from surface response methodology was achieved with the parameters granulometry of 710 nm, 9 days of maceration and plant:solvent ratio 1:54 (w/v). The UHPLC-PDA analytical developed method showed full viability of performance, proving to be selective, linear, precise, accurate and robust for 20HE detection in ST leaves. The average content of 20HE was 0.56% per dry extract. Thus, the optimization of extractive method in ST leaves increased the concentration of 20HE in crude extract, and a reliable method was successfully developed according to validation requirements and in agreement with current legislation. Copyright © 2018 Elsevier Inc. All rights reserved.

Machine Learning Force Field Parameters from Ab Initio Data

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

Li, Ying; Li, Hui; Pickard, Frank C.

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor duringmore » the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.« less

SU-E-I-43: Pediatric CT Dose and Image Quality Optimization

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

Stevens, G; Singh, R

2014-06-01

Purpose: To design an approach to optimize radiation dose and image quality for pediatric CT imaging, and to evaluate expected performance. Methods: A methodology was designed to quantify relative image quality as a function of CT image acquisition parameters. Image contrast and image noise were used to indicate expected conspicuity of objects, and a wide-cone system was used to minimize scan time for motion avoidance. A decision framework was designed to select acquisition parameters as a weighted combination of image quality and dose. Phantom tests were used to acquire images at multiple techniques to demonstrate expected contrast, noise and dose.more » Anthropomorphic phantoms with contrast inserts were imaged on a 160mm CT system with tube voltage capabilities as low as 70kVp. Previously acquired clinical images were used in conjunction with simulation tools to emulate images at different tube voltages and currents to assess human observer preferences. Results: Examination of image contrast, noise, dose and tube/generator capabilities indicates a clinical task and object-size dependent optimization. Phantom experiments confirm that system modeling can be used to achieve the desired image quality and noise performance. Observer studies indicate that clinical utilization of this optimization requires a modified approach to achieve the desired performance. Conclusion: This work indicates the potential to optimize radiation dose and image quality for pediatric CT imaging. In addition, the methodology can be used in an automated parameter selection feature that can suggest techniques given a limited number of user inputs. G Stevens and R Singh are employees of GE Healthcare.« less

Optimal pricing and marketing planning for deteriorating items.

PubMed

Moosavi Tabatabaei, Seyed Reza; Sadjadi, Seyed Jafar; Makui, Ahmad

2017-01-01

Optimal pricing and marketing planning plays an essential role in production decisions on deteriorating items. This paper presents a mathematical model for a three-level supply chain, which includes one producer, one distributor and one retailer. The proposed study considers the production of a deteriorating item where demand is influenced by price, marketing expenditure, quality of product and after-sales service expenditures. The proposed model is formulated as a geometric programming with 5 degrees of difficulty and the problem is solved using the recent advances in optimization techniques. The study is supported by several numerical examples and sensitivity analysis is performed to analyze the effects of the changes in different parameters on the optimal solution. The preliminary results indicate that with the change in parameters influencing on demand, inventory holding, inventory deteriorating and set-up costs change and also significantly affect total revenue.

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2011-01-01

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Low Cost High Performance Phased Array Antennas with Beam Steering Capabilities

DTIC Science & Technology

2009-12-01

characteristics of BSTO, the RF vacuum sputtering technique has been used and we investigated effects of sputtering parameters such as substrate...sputtering parameters , various sets of BSTO films have been deposited on different substrates and various size of CPW phase shifters have been fabricated...measurement of phase shifter 18 4. Optimization of the sputtering parameters for BSTO deposition 19 4.1 The first BSTO film sample 20 4.2 The second BSTO

Space shuttle propulsion parameter estimation using optimal estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

The first twelve system state variables are presented with the necessary mathematical developments for incorporating them into the filter/smoother algorithm. Other state variables, i.e., aerodynamic coefficients can be easily incorporated into the estimation algorithm, representing uncertain parameters, but for initial checkout purposes are treated as known quantities. An approach for incorporating the NASA propulsion predictive model results into the optimal estimation algorithm was identified. This approach utilizes numerical derivatives and nominal predictions within the algorithm with global iterations of the algorithm. The iterative process is terminated when the quality of the estimates provided no longer significantly improves.

Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric

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

Song, Jung Heon; Wu, Kesheng; Simon, Horst D.

2014-03-01

VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010). The computation of VPIN requires the user to set up a handful of free parameters. The values of these parameters significantly affect the effectiveness of VPIN as measured by themore » false positive rate (FPR). An earlier publication reported that a brute-force search of simple parameter combinations yielded a number of parameter combinations with FPR of 7%. This work is a systematic attempt to find an optimal parameter set using an optimization package, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) by Audet, le digabel, and tribes (2009) and le digabel (2011). We have implemented a number of techniques to reduce the computation time with NOMAD. Tests show that we can reduce the FPR to only 2%. To better understand the parameter choices, we have conducted a series of sensitivity analysis via uncertainty quantification on the parameter spaces using UQTK (Uncertainty Quantification Toolkit). Results have shown dominance of 2 parameters in the computation of FPR. Using the outputs from NOMAD optimization and sensitivity analysis, We recommend A range of values for each of the free parameters that perform well on a large set of futures trading records.« less

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.

Optimal parameter estimation with a fixed rate of abstention

NASA Astrophysics Data System (ADS)

Gendra, B.; Ronco-Bonvehi, E.; Calsamiglia, J.; Muñoz-Tapia, R.; Bagan, E.

2013-07-01

The problems of optimally estimating a phase, a direction, and the orientation of a Cartesian frame (or trihedron) with general pure states are addressed. Special emphasis is put on estimation schemes that allow for inconclusive answers or abstention. It is shown that such schemes enable drastic improvements, up to the extent of attaining the Heisenberg limit in some cases, and the required amount of abstention is quantified. A general mathematical framework to deal with the asymptotic limit of many qubits or large angular momentum is introduced and used to obtain analytical results for all the relevant cases under consideration. Parameter estimation with abstention is also formulated as a semidefinite programming problem, for which very efficient numerical optimization techniques exist.

Determination of Optimal Parameters for Dual-Layer Cathode of Polymer Electrolyte Fuel Cell Using Computational Intelligence-Aided Design

PubMed Central

Chen, Yi; Huang, Weina; Peng, Bei

2014-01-01

Because of the demands for sustainable and renewable energy, fuel cells have become increasingly popular, particularly the polymer electrolyte fuel cell (PEFC). Among the various components, the cathode plays a key role in the operation of a PEFC. In this study, a quantitative dual-layer cathode model was proposed for determining the optimal parameters that minimize the over-potential difference and improve the efficiency using a newly developed bat swarm algorithm with a variable population embedded in the computational intelligence-aided design. The simulation results were in agreement with previously reported results, suggesting that the proposed technique has potential applications for automating and optimizing the design of PEFCs. PMID:25490761

Interplanetary program to optimize simulated trajectories (IPOST). Volume 4: Sample cases

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Simulated Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization are performed using the Standard NPSOL algorithm. The IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

PubMed

Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

2016-01-01

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

The trade-off between morphology and control in the co-optimized design of robots.

PubMed

Rosendo, Andre; von Atzigen, Marco; Iida, Fumiya

2017-01-01

Conventionally, robot morphologies are developed through simulations and calculations, and different control methods are applied afterwards. Assuming that simulations and predictions are simplified representations of our reality, how sure can roboticists be that the chosen morphology is the most adequate for the possible control choices in the real-world? Here we study the influence of the design parameters in the creation of a robot with a Bayesian morphology-control (MC) co-optimization process. A robot autonomously creates child robots from a set of possible design parameters and uses Bayesian Optimization (BO) to infer the best locomotion behavior from real world experiments. Then, we systematically change from an MC co-optimization to a control-only (C) optimization, which better represents the traditional way that robots are developed, to explore the trade-off between these two methods. We show that although C processes can greatly improve the behavior of poor morphologies, such agents are still outperformed by MC co-optimization results with as few as 25 iterations. Our findings, on one hand, suggest that BO should be used in the design process of robots for both morphological and control parameters to reach optimal performance, and on the other hand, point to the downfall of current design methods in face of new search techniques.

The trade-off between morphology and control in the co-optimized design of robots

PubMed Central

Iida, Fumiya

2017-01-01

Conventionally, robot morphologies are developed through simulations and calculations, and different control methods are applied afterwards. Assuming that simulations and predictions are simplified representations of our reality, how sure can roboticists be that the chosen morphology is the most adequate for the possible control choices in the real-world? Here we study the influence of the design parameters in the creation of a robot with a Bayesian morphology-control (MC) co-optimization process. A robot autonomously creates child robots from a set of possible design parameters and uses Bayesian Optimization (BO) to infer the best locomotion behavior from real world experiments. Then, we systematically change from an MC co-optimization to a control-only (C) optimization, which better represents the traditional way that robots are developed, to explore the trade-off between these two methods. We show that although C processes can greatly improve the behavior of poor morphologies, such agents are still outperformed by MC co-optimization results with as few as 25 iterations. Our findings, on one hand, suggest that BO should be used in the design process of robots for both morphological and control parameters to reach optimal performance, and on the other hand, point to the downfall of current design methods in face of new search techniques. PMID:29023482

Estimating soil hydraulic parameters from transient flow experiments in a centrifuge using parameter optimization technique

USGS Publications Warehouse

Šimůnek, Jirka; Nimmo, John R.

2005-01-01

A modified version of the Hydrus software package that can directly or inversely simulate water flow in a transient centrifugal field is presented. The inverse solver for parameter estimation of the soil hydraulic parameters is then applied to multirotation transient flow experiments in a centrifuge. Using time‐variable water contents measured at a sequence of several rotation speeds, soil hydraulic properties were successfully estimated by numerical inversion of transient experiments. The inverse method was then evaluated by comparing estimated soil hydraulic properties with those determined independently using an equilibrium analysis. The optimized soil hydraulic properties compared well with those determined using equilibrium analysis and steady state experiment. Multirotation experiments in a centrifuge not only offer significant time savings by accelerating time but also provide significantly more information for the parameter estimation procedure compared to multistep outflow experiments in a gravitational field.

CALIBRATION OF SEMI-ANALYTIC MODELS OF GALAXY FORMATION USING PARTICLE SWARM OPTIMIZATION

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

Ruiz, Andrés N.; Domínguez, Mariano J.; Yaryura, Yamila

2015-03-10

We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observedmore » galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.« less

TOPSIS based parametric optimization of laser micro-drilling of TBC coated nickel based superalloy

NASA Astrophysics Data System (ADS)

Parthiban, K.; Duraiselvam, Muthukannan; Manivannan, R.

2018-06-01

The technique for order of preference by similarity ideal solution (TOPSIS) approach was used for optimizing the process parameters of laser micro-drilling of nickel superalloy C263 with Thermal Barrier Coating (TBC). Plasma spraying was used to deposit the TBC and a pico-second Nd:YAG pulsed laser was used to drill the specimens. Drilling angle, laser scan speed and number of passes were considered as input parameters. Based on the machining conditions, Taguchi L8 orthogonal array was used for conducting the experimental runs. The surface roughness and surface crack density (SCD) were considered as the output measures. The surface roughness was measured using 3D White Light Interferometer (WLI) and the crack density was measured using Scanning Electron Microscope (SEM). The optimized result achieved from this approach suggests reduced surface roughness and surface crack density. The holes drilled at an inclination angle of 45°, laser scan speed of 3 mm/s and 400 number of passes found to be optimum. From the Analysis of variance (ANOVA), inclination angle and number of passes were identified as the major influencing parameter. The optimized parameter combination exhibited a 19% improvement in surface finish and 12% reduction in SCD.

Feasibility of the partial-single arc technique in RapidArc planning for prostate cancer treatment

PubMed Central

Rana, Suresh; Cheng, ChihYao

2013-01-01

The volumetric modulated arc therapy (VMAT) technique, in the form of RapidArc, is widely used to treat prostate cancer. The full-single arc (f-SA) technique in RapidArc planning for prostate cancer treatment provides efficient treatment, but it also delivers a higher radiation dose to the rectum. This study aimed to compare the dosimetric results from the new partial-single arc (p-SA) technique with those from the f-SA technique in RapidArc planning for prostate cancer treatment. In this study, 10 patients with low-risk prostate cancer were selected. For each patient, two sets of RapidArc plans (f-SA and p-SA) were created in the Eclipse treatment planning system. The f-SA plan was created using one full arc, and the p-SA plan was created using planning parameters identical to those of the f-SA plan but with anterior and posterior avoidance sectors. Various dosimetric parameters of the f-SA and p-SA plans were evaluated and compared for the same target coverage and identical plan optimization parameters. The f-SA and p-SA plans showed an average difference of ±1% for the doses to the planning target volume (PTV), and there were no clear differences in dose homogeneity or plan conformity. In comparison to the f-SA technique, the p-SA technique reduced the doses to the rectum by approximately 6.1% to 21.2%, to the bladder by approximately 10.3% to 29.5%, and to the penile bulb by approximately 2.2%. In contrast, the dose to the femoral heads, the integral dose, and the number of monitor units were higher in the p-SA plans by approximately 34.4%, 7.7%, and 9.2%, respectively. In conclusion, it is feasible to use the p-SA technique for RapidArc planning for prostate cancer treatment. For the same PTV coverage and identical plan optimization parameters, the p-SA technique is better in sparing the rectum and bladder without compromising plan conformity or target homogeneity when compared to the f-SA technique. PMID:23845140

Respiratory gating and multifield technique radiotherapy for esophageal cancer.

PubMed

Ohta, Atsushi; Kaidu, Motoki; Tanabe, Satoshi; Utsunomiya, Satoru; Sasamoto, Ryuta; Maruyama, Katsuya; Tanaka, Kensuke; Saito, Hirotake; Nakano, Toshimichi; Shioi, Miki; Takahashi, Haruna; Kushima, Naotaka; Abe, Eisuke; Aoyama, Hidefumi

2017-03-01

To investigate the effects of a respiratory gating and multifield technique on the dose-volume histogram (DVH) in radiotherapy for esophageal cancer. Twenty patients who underwent four-dimensional computed tomography for esophageal cancer were included. We retrospectively created the four treatment plans for each patient, with or without the respiratory gating and multifield technique: No gating-2-field, No gating-4-field, Gating-2-field, and Gating-4-field plans. We compared the DVH parameters of the lung and heart in the No gating-2-field plan with the other three plans. In the comparison of the parameters in the No gating-2-field plan, there are significant differences in the Lung V 5Gy , V 20Gy , mean dose with all three plans and the Heart V 25Gy -V 40Gy with Gating-2-field plan, V 35Gy , V 40Gy , mean dose with No Gating-4-field plan and V 30Gy -V 40Gy , and mean dose with Gating-4-field plan. The lung parameters were smaller in the Gating-2-field plan and larger in the No gating-4-field and Gating-4-field plans. The heart parameters were all larger in the No gating-2-field plan. The lung parameters were reduced by the respiratory gating technique and increased by the multifield technique. The heart parameters were reduced by both techniques. It is important to select the optimal technique according to the risk of complications.

Optimization of Process Parameters of Pulsed Electro Deposition Technique for Nanocrystalline Nickel Coating Using Gray Relational Analysis (GRA)

NASA Astrophysics Data System (ADS)

Venkatesh, C.; Sundara Moorthy, N.; Venkatesan, R.; Aswinprasad, V.

The moving parts of any mechanism and machine parts are always subjected to a significant wear due to the development of friction. It is an utmost important aspect to address the wear problems in present environment. But the complexity goes on increasing to replace the worn out parts if they are very precise. Technology advancement in surface engineering ensures the minimum surface wear with the introduction of polycrystalline nano nickel coating. The enhanced tribological property of the nano nickel coating was achieved by the development of grain size and hardness of the surface. In this study, it has been decided to focus on the optimized parameters of the pulsed electro deposition to develop such a coating. Taguchi’s method coupled gray relational analysis was employed by considering the pulse frequency, average current density and duty cycle as the chief process parameters. The grain size and hardness were considered as responses. Totally, nine experiments were conducted as per L9 design of experiment. Additionally, response graph method has been applied to determine the most significant parameter to influence both the responses. In order to improve the degree of validation, confirmation test and predicted gray grade were carried out with the optimized parameters. It has been observed that there was significant improvement in gray grade for the optimal parameters.

MHD performance calculations with oxygen enrichment

NASA Technical Reports Server (NTRS)

Pian, C. C. P.; Staiger, P. J.; Seikel, G. R.

1979-01-01

The impact of oxygen enrichment of the combustion air on the generator and overall plant performance was studied for the ECAS-scale MHD/steam plants. A channel optimization technique is described and the results of generator performance calculations using this technique are presented. Performance maps were generated to assess the impact of various generator parameters. Directly and separately preheated plant performance with varying O2 enrichment was calculated. The optimal level of enrichment was a function of plant type and preheat temperature. The sensitivity of overall plant performance to critical channel assumptions and oxygen plant performance characteristics was also examined.

Optimal properties for coated titanium implants with the hydroxyapatite layer formed by the pulsed laser deposition technique

NASA Astrophysics Data System (ADS)

Himmlova, Lucia; Dostalova, Tatjana; Jelinek, Miroslav; Bartova, Jirina; Pesakova, V.; Adam, M.

1999-02-01

Pulsed laser deposition technique allow to 'tailor' bioceramic coat for metal implants by the change of deposition conditions. Each attribute is influenced by the several deposition parameters and each parameter change several various properties. Problem caused that many parameters has an opposite function and improvement of one property is followed by deterioration of other attribute. This study monitor influence of each single deposition parameter and evaluate its importance form the point of view of coat properties. For deposition KrF excimer laser in stainless-steel deposition chamber was used. Deposition conditions (ambient composition and pressures, metallic substrate temperature, energy density and target-substrate distance) were changed according to the film properties. A non-coated titanium implant was used as a control. Films with promising mechanical quality underwent an in vitro biological tests -- measurement of proliferation activity, observing cell interactions with macrophages, fibroblasts, testing toxicity of percolates, observing a solubility of hydroxyapatite (HA) coat. Deposition conditions corresponding with the optimal mechanical and biochemical properties are: metal temperature 490 degrees Celsius, ambient-mixture of argon and water vapor, energy density 3 Jcm-2, target-substrate distance 7.5 cm.

Methodological optimization of tinnitus assessment using prepulse inhibition of the acoustic startle reflex.

PubMed

Longenecker, R J; Galazyuk, A V

2012-11-16

Recently prepulse inhibition of the acoustic startle reflex (ASR) became a popular technique for tinnitus assessment in laboratory animals. This method confers a significant advantage over the previously used time-consuming behavioral approaches utilizing basic mechanisms of conditioning. Although this technique has been successfully used to assess tinnitus in different laboratory animals, many of the finer details of this methodology have not been described enough to be replicated, but are critical for tinnitus assessment. Here we provide detail description of key procedures and methodological issues that provide guidance for newcomers with the process of learning to correctly apply gap detection techniques for tinnitus assessment in laboratory animals. The major categories of these issues include: refinement of hardware for best performance, optimization of stimulus parameters, behavioral considerations, and identification of optimal strategies for data analysis. This article is part of a Special Issue entitled: Tinnitus Neuroscience. Copyright © 2012. Published by Elsevier B.V.

Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Birge, B.

2013-01-01

A high fidelity simulation using a PC based Trick framework has been developed for Johnson Space Center's Morpheus test bed flight vehicle. There is an iterative development loop of refining and testing the hardware, refining the software, comparing the software simulation to hardware performance and adjusting either or both the hardware and the simulation to extract the best performance from the hardware as well as the most realistic representation of the hardware from the software. A Particle Swarm Optimization (PSO) based technique has been developed that increases speed and accuracy of the iterative development cycle. Parameters in software can be automatically tuned to make the simulation match real world subsystem data from test flights. Special considerations for scale, linearity, discontinuities, can be all but ignored with this technique, allowing fast turnaround both for simulation tune up to match hardware changes as well as during the test and validation phase to help identify hardware issues. Software models with insufficient control authority to match hardware test data can be immediately identified and using this technique requires very little to no specialized knowledge of optimization, freeing model developers to concentrate on spacecraft engineering. Integration of the PSO into the Morpheus development cycle will be discussed as well as a case study highlighting the tool's effectiveness.

Hybrid, experimental and computational, investigation of mechanical components

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Pryputniewicz, Ryszard J.

1996-07-01

Computational and experimental methodologies have unique features for the analysis and solution of a wide variety of engineering problems. Computations provide results that depend on selection of input parameters such as geometry, material constants, and boundary conditions which, for correct modeling purposes, have to be appropriately chosen. In addition, it is relatively easy to modify the input parameters in order to computationally investigate different conditions. Experiments provide solutions which characterize the actual behavior of the object of interest subjected to specific operating conditions. However, it is impractical to experimentally perform parametric investigations. This paper discusses the use of a hybrid, computational and experimental, approach for study and optimization of mechanical components. Computational techniques are used for modeling the behavior of the object of interest while it is experimentally tested using noninvasive optical techniques. Comparisons are performed through a fringe predictor program used to facilitate the correlation between both techniques. In addition, experimentally obtained quantitative information, such as displacements and shape, can be applied in the computational model in order to improve this correlation. The result is a validated computational model that can be used for performing quantitative analyses and structural optimization. Practical application of the hybrid approach is illustrated with a representative example which demonstrates the viability of the approach as an engineering tool for structural analysis and optimization.

Optimization of printing techniques for electrochemical biosensors

NASA Astrophysics Data System (ADS)

Zainuddin, Ahmad Anwar; Mansor, Ahmad Fairuzabadi Mohd; Rahim, Rosminazuin Ab; Nordin, Anis Nurashikin

2017-03-01

Electrochemical biosensors show great promise for point-of-care applications due to their low cost, portability and compatibility with microfluidics. The miniature size of these sensors provides advantages in terms of sensitivity, specificity and allows them to be mass produced in arrays. The most reliable fabrication technique for these sensors is lithography followed by metal deposition using sputtering or chemical vapor deposition techniques. This technique which is usually done in the cleanroom requires expensive masking followed by deposition. Recently, cheaper printing techniques such as screen-printing and ink-jet printing have become popular due to its low cost, ease of fabrication and mask-less method. In this paper, two different printing techniques namely inkjet and screen printing are demonstrated for an electrochemical biosensor. For ink-jet printing technique, optimization of key printing parameters, such as pulse voltages, drop spacing and waveform setting, in-house temperature and cure annealing for obtaining the high quality droplets, are discussed. These factors are compared with screen-printing parameters such as mesh size, emulsion thickness, minimum spacing of lines and curing times. The reliability and reproducibility of the sensors are evaluated using scotch tape test, resistivity and profile-meter measurements. It was found that inkjet printing is superior because it is mask-less, has minimum resolution of 100 µm compared to 200 µm for screen printing and higher reproducibility rate of 90% compared to 78% for screen printing.

Nuclear Electric Vehicle Optimization Toolset (NEVOT): Integrated System Design Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Tinker, Michael L.; Steincamp, James W.; Stewart, Eric T.; Patton, Bruce W.; Pannell, William P.; Newby, Ronald L.; Coffman, Mark E.; Qualls, A. L.; Bancroft, S.; Molvik, Greg

2003-01-01

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major Nuclear Electric Propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a Genetic Algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be conceived of through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Complex motion measurement using genetic algorithm

NASA Astrophysics Data System (ADS)

Shen, Jianjun; Tu, Dan; Shen, Zhenkang

1997-12-01

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Distributed Parallel Processing and Dynamic Load Balancing Techniques for Multidisciplinary High Speed Aircraft Design

NASA Technical Reports Server (NTRS)

Krasteva, Denitza T.

1998-01-01

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Comparison between DCA - SSO - VDR and VMAT dose delivery techniques for 15 SRS/SRT patients

NASA Astrophysics Data System (ADS)

Tas, B.; Durmus, I. F.

2018-02-01

To evaluate dose delivery between Dynamic Conformal Arc (DCA) - Segment Shape Optimization (SSO) - Variation Dose Rate (VDR) and Volumetric Modulated Arc Therapy (VMAT) techniques for fifteen SRS patients using Versa HD® lineer accelerator. Fifteen SRS / SRT patient's optimum treatment planning were performed using Monaco5.11® treatment planning system (TPS) with 1 coplanar and 3 non-coplanar fields for VMAT technique, then the plans were reoptimized with the same optimization parameters for DCA - SSO - VDR technique. The advantage of DCA - SSO - VDR technique were determined less MUs and beam on time, also larger segments decrease dosimetric uncertainities of small fields quality assurance. The advantage of VMAT technique were determined a little better GI, CI, PCI, brain V12Gy and brain mean dose. The results show that the clinical objectives and plans for both techniques satisfied all organs at risks (OARs) dose constraints. Depends on the shape and localization of target, we could choose one of these techniques for linear accelerator based SRS / SRT treatment.

A mathematical model for the generation and control of a pH gradient in an immobilized enzyme system involving acid generation.

PubMed

Chen, G; Fournier, R L; Varanasi, S

1998-02-20

An optimal pH control technique has been developed for multistep enzymatic synthesis reactions where the optimal pH differs by several units for each step. This technique separates an acidic environment from a basic environment by the hydrolysis of urea within a thin layer of immobilized urease. With this technique, a two-step enzymatic reaction can take place simultaneously, in proximity to each other, and at their respective optimal pH. Because a reaction system involving an acid generation represents a more challenging test of this pH control technique, a number of factors that affect the generation of such a pH gradient are considered in this study. The mathematical model proposed is based on several simplifying assumptions and represents a first attempt to provide an analysis of this complex problem. The results show that, by choosing appropriate parameters, the pH control technique still can generate the desired pH gradient even if there is an acid-generating reaction in the system. Copyright 1998 John Wiley & Sons, Inc.

Investigations of calcium spectral lines in laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Ching, Sim Yit; Tariq, Usman; Haider, Zuhaib; Tufail, Kashif; Sabri, Salwanie; Imran, Muhammad; Ali, Jalil

2017-03-01

Laser-induced breakdown spectroscopy (LIBS) is a direct and versatile analytical technique that performs the elemental composition analysis based on optical emission produced by laser induced-plasma, with a little or no sample preparation. The performance of the LIBS technique relies on the choice of experimental conditions which must be thoroughly explored and optimized for each application. The main parameters affecting the LIBS performance are the laser energy, laser wavelength, pulse duration, gate delay, geometrical set-up of the focusing and collecting optics. In LIBS quantitative analysis, the gate delay and laser energy are very important parameters that have pronounced impact on the accuracy of the elemental composition information of the materials. The determination of calcium elements in the pelletized samples was investigated and served for the purpose of optimizing the gate delay and laser energy by studying and analyzing the results from emission intensities collected and signal to background ratio (S/B) for the specified wavelengths.

Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid.

PubMed

Arefi-Oskoui, Samira; Khataee, Alireza; Vatanpour, Vahid

2017-07-10

In this research, MgAl-CO 3 2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

Reconstruction of Atmospheric Tracer Releases with Optimal Resolution Features: Concentration Data Assimilation

NASA Astrophysics Data System (ADS)

Singh, Sarvesh Kumar; Turbelin, Gregory; Issartel, Jean-Pierre; Kumar, Pramod; Feiz, Amir Ali

2015-04-01

The fast growing urbanization, industrialization and military developments increase the risk towards the human environment and ecology. This is realized in several past mortality incidents, for instance, Chernobyl nuclear explosion (Ukraine), Bhopal gas leak (India), Fukushima-Daichi radionuclide release (Japan), etc. To reduce the threat and exposure to the hazardous contaminants, a fast and preliminary identification of unknown releases is required by the responsible authorities for the emergency preparedness and air quality analysis. Often, an early detection of such contaminants is pursued by a distributed sensor network. However, identifying the origin and strength of unknown releases following the sensor reported concentrations is a challenging task. This requires an optimal strategy to integrate the measured concentrations with the predictions given by the atmospheric dispersion models. This is an inverse problem. The measured concentrations are insufficient and atmospheric dispersion models suffer from inaccuracy due to the lack of process understanding, turbulence uncertainties, etc. These lead to a loss of information in the reconstruction process and thus, affect the resolution, stability and uniqueness of the retrieved source. An additional well known issue is the numerical artifact arisen at the measurement locations due to the strong concentration gradient and dissipative nature of the concentration. Thus, assimilation techniques are desired which can lead to an optimal retrieval of the unknown releases. In general, this is facilitated within the Bayesian inference and optimization framework with a suitable choice of a priori information, regularization constraints, measurement and background error statistics. An inversion technique is introduced here for an optimal reconstruction of unknown releases using limited concentration measurements. This is based on adjoint representation of the source-receptor relationship and utilization of a weight function which exhibits a priori information about the unknown releases apparent to the monitoring network. The properties of the weight function provide an optimal data resolution and model resolution to the retrieved source estimates. The retrieved source estimates are proved theoretically to be stable against the random measurement errors and their reliability can be interpreted in terms of the distribution of the weight functions. Further, the same framework can be extended for the identification of the point type releases by utilizing the maximum of the retrieved source estimates. The inversion technique has been evaluated with the several diffusion experiments, like, Idaho low wind diffusion experiment (1974), IIT Delhi tracer experiment (1991), European Tracer Experiment (1994), Fusion Field Trials (2007), etc. In case of point release experiments, the source parameters are mostly retrieved close to the true source parameters with least error. Primarily, the proposed technique overcomes two major difficulties incurred in the source reconstruction: (i) The initialization of the source parameters as required by the optimization based techniques. The converged solution depends on their initialization. (ii) The statistical knowledge about the measurement and background errors as required by the Bayesian inference based techniques. These are hypothetically assumed in case of no prior knowledge.

Optimization of process parameters for RF sputter deposition of tin-nitride thin-films

NASA Astrophysics Data System (ADS)

Jangid, Teena; Rao, G. Mohan

2018-05-01

Radio frequency Magnetron sputtering technique was employed to deposit Tin-nitride thin films on Si and glass substrate at different process parameters. Influence of varying parameters like substrate temperature, target-substrate distance and RF power is studied in detail. X-ray diffraction method is used as a key technique for analyzing the changes in the stoichiometric and structural properties of the deposited films. Depending on the combination of deposition parameters, crystalline as well as amorphous films were obtained. Pure tin-nitride thin films were deposited at 15W RF power and 600°C substrate temperature with target-substrate distance fixed at 10cm. Bandgap value of 1.6 eV calculated for the film deposited at optimum process conditions matches well with reported values.

Enhancing magnetic nanoparticle-based DNA transfection: Intracellular-active cassette features

NASA Astrophysics Data System (ADS)

Vernon, Matthew Martin

Efficient plasmid DNA transfection of embryonic stem cells, mesenchymal stem cells, neural cell lines and the majority of primary cell lines is a current challenge in gene therapy research. Magnetic nanoparticle-based DNA transfection is a gene vectoring technique that is promising because it is capable of outperforming most other non-viral transfection methods in terms of both transfection efficiency and cell viability. The nature of the DNA vector implemented depends on the target cell phenotype, where the particle surface chemistry and DNA binding/unbinding kinetics of the DNA carrier molecule play a critical role in the many steps required for successful gene transfection. Accordingly, Neuromag, an iron oxide/polymer nanoparticle optimized for transfection of neural phenotypes, outperforms many other nanoparticles and lipidbased DNA carriers. Up to now, improvements to nanomagnetic transfection techniques have focused mostly on particle functionalization and transfection parameter optimization (cell confluence, growth media, serum starvation, magnet oscillation parameters, etc.). None of these parameters are capable of assisting the nuclear translocation of delivered plasmid DNA once the particle-DNA complex is released from the endosome and dissociates in the cell's cytoplasm. In this study, incorporation of a DNA targeting sequence (DTS) feature in the transfecting plasmid DNA confers improved nuclear translocation, demonstrating significant improvement in nanomagnetic transfection efficiency in differentiated SH-SY5Y neuroblastoma cells. Other parameters, such as days in vitro, are also found to play a role and represent potential targets for further optimization.

Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J. D.

2017-09-01

Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes.

Non-destructive Magnetic Evaluation of Laser Weld Quality in Hot Rolled Coils

NASA Astrophysics Data System (ADS)

Mohapatra, J. N.; Chakradhar, I.; Rao, K. R. C.; Rao, V. V. L.; Kaza, Marutiram

2015-06-01

Weld quality evaluation was conducted on laser welded thin sectsions (2 mm) of hot-rolled (HR) low-carbon steel coils during cold rolling process. The analysis revealed that the poor welds consisting of the weld defects like incomplete fusion, cluster of porosity, and large difference in hardness between the weld zone and base metal were responsible for the weld failures. Experiments were conducted by varying the welding parameters; laser power and welding speed to optimize the parameters for minimizing the weld defects. The optimized weld process parameters have helped elimination of weld defects and the results are verified with microscopy and microhardness measurements. As destructive evaluation techniques are time consuming and not always permitted in industrial applications, attempts have been made in the present investigation for the utilization of suitable non-destructive techniques for the evaluation of weld quality. Non-destructive magnetic techniques of magnetic hysteresis loop and magnetic Barkhausen emissions were used in the present investigation to establish possible correlations of magnetic properties across the weld seam with the mechanical property (microhardness) for evaluation of weld quality. It is inferred that the magnetic properties of coercivity and inverse of root mean square voltage can be effectively utilized to determine weld quality in HR steel coils.

Determination of calibration parameters of a VRX CT system using an “Amoeba” algorithm

PubMed Central

Jordan, Lawrence M.; DiBianca, Frank A.; Melnyk, Roman; Choudhary, Apoorva; Shukla, Hemant; Laughter, Joseph; Gaber, M. Waleed

2008-01-01

Efforts to improve the spatial resolution of CT scanners have focused mainly on reducing the source and detector element sizes, ignoring losses from the size of the secondary-ionization charge “clouds” created by the detected x-ray photons, i.e., the “physics limit.” This paper focuses on implementing a technique called “projective compression.” which allows further reduction in effective cell size while overcoming the physics limit as well. Projective compression signifies detector geometries in which the apparent cell size is smaller than the physical cell size, allowing large resolution boosts. A realization of this technique has been developed with a dual-arm “variable-resolution x-ray” (VRX) detector. Accurate values of the geometrical parameters are needed to convert VRX outputs to formats ready for optimal image reconstruction by standard CT techniques. The required calibrating data are obtained by scanning a rotating pin and fitting a theoretical parametric curve (using a multi-parameter minimization algorithm) to the resulting pin sinogram. Excellent fits are obtained for both detector-arm sections with an average (maximum) fit deviation of ~0.05 (0.1) detector cell width. Fit convergence and sensitivity to starting conditions are considered. Pre- and post-optimization reconstructions of the alignment pin and a biological subject reconstruction after calibration are shown. PMID:19430581

Determination of calibration parameters of a VRX CT system using an "Amoeba" algorithm.

PubMed

Jordan, Lawrence M; Dibianca, Frank A; Melnyk, Roman; Choudhary, Apoorva; Shukla, Hemant; Laughter, Joseph; Gaber, M Waleed

2004-01-01

Efforts to improve the spatial resolution of CT scanners have focused mainly on reducing the source and detector element sizes, ignoring losses from the size of the secondary-ionization charge "clouds" created by the detected x-ray photons, i.e., the "physics limit." This paper focuses on implementing a technique called "projective compression." which allows further reduction in effective cell size while overcoming the physics limit as well. Projective compression signifies detector geometries in which the apparent cell size is smaller than the physical cell size, allowing large resolution boosts. A realization of this technique has been developed with a dual-arm "variable-resolution x-ray" (VRX) detector. Accurate values of the geometrical parameters are needed to convert VRX outputs to formats ready for optimal image reconstruction by standard CT techniques. The required calibrating data are obtained by scanning a rotating pin and fitting a theoretical parametric curve (using a multi-parameter minimization algorithm) to the resulting pin sinogram. Excellent fits are obtained for both detector-arm sections with an average (maximum) fit deviation of ~0.05 (0.1) detector cell width. Fit convergence and sensitivity to starting conditions are considered. Pre- and post-optimization reconstructions of the alignment pin and a biological subject reconstruction after calibration are shown.

Time-Varying Delay Estimation Applied to the Surface Electromyography Signals Using the Parametric Approach

NASA Astrophysics Data System (ADS)

Luu, Gia Thien; Boualem, Abdelbassit; Duy, Tran Trung; Ravier, Philippe; Butteli, Olivier

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over time, which induces time-varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In this paper, the problem of TVD estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a deterministic optimization technique (Newton) and stochastic optimization technique, called simulated annealing (SA). The performance of the two techniques is also compared. We also derive two appropriate Cramer-Rao Lower Bounds (CRLB) for the estimated TVD model parameters and for the TVD waveforms. Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the derived CRBs. A comparison with non-parametric approaches of the TVD estimation is also presented and shows the superiority of the method proposed.

Feedback System Control Optimized Electrospinning for Fabrication of an Excellent Superhydrophobic Surface.

PubMed

Yang, Jian; Liu, Chuangui; Wang, Boqian; Ding, Xianting

2017-10-13

Superhydrophobic surface, as a promising micro/nano material, has tremendous applications in biological and artificial investigations. The electrohydrodynamics (EHD) technique is a versatile and effective method for fabricating micro- to nanoscale fibers and particles from a variety of materials. A combination of critical parameters, such as mass fraction, ratio of N, N-Dimethylformamide (DMF) to Tetrahydrofuran (THF), inner diameter of needle, feed rate, receiving distance, applied voltage as well as temperature, during electrospinning process, to determine the morphology of the electrospun membranes, which in turn determines the superhydrophobic property of the membrane. In this study, we applied a recently developed feedback system control (FSC) scheme for rapid identification of the optimal combination of these controllable parameters to fabricate superhydrophobic surface by one-step electrospinning method without any further modification. Within five rounds of experiments by testing totally forty-six data points, FSC scheme successfully identified an optimal parameter combination that generated electrospun membranes with a static water contact angle of 160 degrees or larger. Scanning electron microscope (SEM) imaging indicates that the FSC optimized surface attains unique morphology. The optimized setup introduced here therefore serves as a one-step, straightforward, and economic approach to fabricate superhydrophobic surface with electrospinning approach.

Optimal pricing and marketing planning for deteriorating items

PubMed Central

Moosavi Tabatabaei, Seyed Reza; Sadjadi, Seyed Jafar; Makui, Ahmad

2017-01-01

Optimal pricing and marketing planning plays an essential role in production decisions on deteriorating items. This paper presents a mathematical model for a three-level supply chain, which includes one producer, one distributor and one retailer. The proposed study considers the production of a deteriorating item where demand is influenced by price, marketing expenditure, quality of product and after-sales service expenditures. The proposed model is formulated as a geometric programming with 5 degrees of difficulty and the problem is solved using the recent advances in optimization techniques. The study is supported by several numerical examples and sensitivity analysis is performed to analyze the effects of the changes in different parameters on the optimal solution. The preliminary results indicate that with the change in parameters influencing on demand, inventory holding, inventory deteriorating and set-up costs change and also significantly affect total revenue. PMID:28306750

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.

A Computational approach in optimizing process parameters of GTAW for SA 106 Grade B steel pipes using Response surface methodology

NASA Astrophysics Data System (ADS)

Sumesh, A.; Sai Ramnadh, L. V.; Manish, P.; Harnath, V.; Lakshman, V.

2016-09-01

Welding is one of the most common metal joining techniques used in industry for decades. As in the global manufacturing scenario the products should be more cost effective. Therefore the selection of right process with optimal parameters will help the industry in minimizing their cost of production. SA 106 Grade B steel has a wide application in Automobile chassis structure, Boiler tubes and pressure vessels industries. Employing central composite design the process parameters for Gas Tungsten Arc Welding was optimized. The input parameters chosen were weld current, peak current and frequency. The joint tensile strength was the response considered in this study. Analysis of variance was performed to determine the statistical significance of the parameters and a Regression analysis was performed to determine the effect of input parameters over the response. From the experiment the maximum tensile strength obtained was 95 KN reported for a weld current of 95 Amp, frequency of 50 Hz and peak current of 100 Amp. With an aim of maximizing the joint strength using Response optimizer a target value of 100 KN is selected and regression models were optimized. The output results are achievable with a Weld current of 62.6148 Amp, Frequency of 23.1821 Hz, and Peak current of 65.9104 Amp. Using Die penetration test the weld joints were also classified in to 2 categories as good weld and weld with defect. This will also help in getting a defect free joint when welding is performed using GTAW process.

Quantum Optimization of Fully Connected Spin Glasses

NASA Astrophysics Data System (ADS)

Venturelli, Davide; Mandrà, Salvatore; Knysh, Sergey; O'Gorman, Bryan; Biswas, Rupak; Smelyanskiy, Vadim

2015-07-01

Many NP-hard problems can be seen as the task of finding a ground state of a disordered highly connected Ising spin glass. If solutions are sought by means of quantum annealing, it is often necessary to represent those graphs in the annealer's hardware by means of the graph-minor embedding technique, generating a final Hamiltonian consisting of coupled chains of ferromagnetically bound spins, whose binding energy is a free parameter. In order to investigate the effect of embedding on problems of interest, the fully connected Sherrington-Kirkpatrick model with random ±1 couplings is programmed on the D-Wave TwoTM annealer using up to 270 qubits interacting on a Chimera-type graph. We present the best embedding prescriptions for encoding the Sherrington-Kirkpatrick problem in the Chimera graph. The results indicate that the optimal choice of embedding parameters could be associated with the emergence of the spin-glass phase of the embedded problem, whose presence was previously uncertain. This optimal parameter setting allows the performance of the quantum annealer to compete with (and potentially outperform, in the absence of analog control errors) optimized simulated annealing algorithms.

Topology Synthesis of Structures Using Parameter Relaxation and Geometric Refinement

NASA Technical Reports Server (NTRS)

Hull, P. V.; Tinker, M. L.

2007-01-01

Typically, structural topology optimization problems undergo relaxation of certain design parameters to allow the existence of intermediate variable optimum topologies. Relaxation permits the use of a variety of gradient-based search techniques and has been shown to guarantee the existence of optimal solutions and eliminate mesh dependencies. This Technical Publication (TP) will demonstrate the application of relaxation to a control point discretization of the design workspace for the structural topology optimization process. The control point parameterization with subdivision has been offered as an alternative to the traditional method of discretized finite element design domain. The principle of relaxation demonstrates the increased utility of the control point parameterization. One of the significant results of the relaxation process offered in this TP is that direct manufacturability of the optimized design will be maintained without the need for designer intervention or translation. In addition, it will be shown that relaxation of certain parameters may extend the range of problems that can be addressed; e.g., in permitting limited out-of-plane motion to be included in a path generation problem.

Adaptive synchronized switch damping on an inductor: a self-tuning switching law

NASA Astrophysics Data System (ADS)

Kelley, Christopher R.; Kauffman, Jeffrey L.

2017-03-01

Synchronized switch damping (SSD) techniques exploit low-power switching between passive circuits connected to piezoelectric material to reduce structural vibration. In the classical implementation of SSD, the piezoelectric material remains in an open circuit for the majority of the vibration cycle and switches briefly to a shunt circuit at every displacement extremum. Recent research indicates that this switch timing is only optimal for excitation exactly at resonance and points to more general optimal switch criteria based on the phase of the displacement and the system parameters. This work proposes a self-tuning approach that implements the more general optimal switch timing for synchronized switch damping on an inductor (SSDI) without needing any knowledge of the system parameters. The law involves a gradient-based search optimization that is robust to noise and uncertainties in the system. Testing of a physical implementation confirms this law successfully adapts to the frequency and parameters of the system. Overall, the adaptive SSDI controller provides better off-resonance steady-state vibration reduction than classical SSDI while matching performance at resonance.

Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

2016-05-01

One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.

The application of artificial intelligence in the optimal design of mechanical systems

NASA Astrophysics Data System (ADS)

Poteralski, A.; Szczepanik, M.

2016-11-01

The paper is devoted to new computational techniques in mechanical optimization where one tries to study, model, analyze and optimize very complex phenomena, for which more precise scientific tools of the past were incapable of giving low cost and complete solution. Soft computing methods differ from conventional (hard) computing in that, unlike hard computing, they are tolerant of imprecision, uncertainty, partial truth and approximation. The paper deals with an application of the bio-inspired methods, like the evolutionary algorithms (EA), the artificial immune systems (AIS) and the particle swarm optimizers (PSO) to optimization problems. Structures considered in this work are analyzed by the finite element method (FEM), the boundary element method (BEM) and by the method of fundamental solutions (MFS). The bio-inspired methods are applied to optimize shape, topology and material properties of 2D, 3D and coupled 2D/3D structures, to optimize the termomechanical structures, to optimize parameters of composites structures modeled by the FEM, to optimize the elastic vibrating systems to identify the material constants for piezoelectric materials modeled by the BEM and to identify parameters in acoustics problem modeled by the MFS.

Nonlinear programming extensions to rational function approximations of unsteady aerodynamics

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1987-01-01

This paper deals with approximating unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft. Two methods of formulating these approximations are extended to include both the same flexibility in constraining them and the same methodology in optimizing nonlinear parameters as another currently used 'extended least-squares' method. Optimal selection of 'nonlinear' parameters is made in each of the three methods by use of the same nonlinear (nongradient) optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is of lower order than that required when no optimization of the nonlinear terms is performed. The free 'linear' parameters are determined using least-squares matrix techniques on a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from the different approaches are described, and results are presented which show comparative evaluations from application of each of the extended methods to a numerical example. The results obtained for the example problem show a significant (up to 63 percent) reduction in the number of differential equations used to represent the unsteady aerodynamic forces in linear time-invariant equations of motion as compared to a conventional method in which nonlinear terms are not optimized.

Applying model abstraction techniques to optimize monitoring networks for detecting subsurface contaminant transport

USDA-ARS?s Scientific Manuscript database

Improving strategies for monitoring subsurface contaminant transport includes performance comparison of competing models, developed independently or obtained via model abstraction. Model comparison and parameter discrimination involve specific performance indicators selected to better understand s...

Optimization of Machining Parameters of Milling Operation by Application of Semi-synthetic oil based Nano cutting Fluids

NASA Astrophysics Data System (ADS)

Giri Prasad, M. J.; Abhishek Raaj, A. S.; Rishi Kumar, R.; Gladson, Frank; M, Gautham

2016-09-01

The present study is concerned with resolving the problems pertaining to the conventional cutting fluids. Two samples of nano cutting fluids were prepared by dispersing 0.01 vol% of MWCNTs and a mixture of 0.01 vol% of MWCNTs and 0.01 vol% of nano ZnO in the soluble oil. The thermophysical properties such as the kinematic viscosity, density, flash point and the tribological properties of the prepared nano cutting fluid samples were experimentally investigated and were compared with those of plain soluble oil. In addition to this, a milling process was carried by varying the process parameters and by application of different samples of cutting fluids and an attempt was made to determine optimal cutting condition using the Taguchi optimization technique.

Results and Error Estimates from GRACE Forward Modeling over Greenland, Canada, and Alaska

NASA Astrophysics Data System (ADS)

Bonin, J. A.; Chambers, D. P.

2012-12-01

Forward modeling using a weighted least squares technique allows GRACE information to be projected onto a pre-determined collection of local basins. This decreases the impact of spatial leakage, allowing estimates of mass change to be better localized. The technique is especially valuable where models of current-day mass change are poor, such as over Greenland and Antarctica. However, the accuracy of the forward model technique has not been determined, nor is it known how the distribution of the local basins affects the results. We use a "truth" model composed of hydrology and ice-melt slopes as an example case, to estimate the uncertainties of this forward modeling method and expose those design parameters which may result in an incorrect high-resolution mass distribution. We then apply these optimal parameters in a forward model estimate created from RL05 GRACE data. We compare the resulting mass slopes with the expected systematic errors from the simulation, as well as GIA and basic trend-fitting uncertainties. We also consider whether specific regions (such as Ellesmere Island and Baffin Island) can be estimated reliably using our optimal basin layout.

Automated prescription of oblique brain 3D magnetic resonance spectroscopic imaging.

PubMed

Ozhinsky, Eugene; Vigneron, Daniel B; Chang, Susan M; Nelson, Sarah J

2013-04-01

Two major difficulties encountered in implementing Magnetic Resonance Spectroscopic Imaging (MRSI) in a clinical setting are limited coverage and difficulty in prescription. The goal of this project was to automate completely the process of 3D PRESS MRSI prescription, including placement of the selection box, saturation bands and shim volume, while maximizing the coverage of the brain. The automated prescription technique included acquisition of an anatomical MRI image, optimization of the oblique selection box parameters, optimization of the placement of outer-volume suppression saturation bands, and loading of the calculated parameters into a customized 3D MRSI pulse sequence. To validate the technique and compare its performance with existing protocols, 3D MRSI data were acquired from six exams from three healthy volunteers. To assess the performance of the automated 3D MRSI prescription for patients with brain tumors, the data were collected from 16 exams from 8 subjects with gliomas. This technique demonstrated robust coverage of the tumor, high consistency of prescription and very good data quality within the T2 lesion. Copyright © 2012 Wiley Periodicals, Inc.

Estimating the Celestial Reference Frame via Intra-Technique Combination

NASA Astrophysics Data System (ADS)

Iddink, Andreas; Artz, Thomas; Halsig, Sebastian; Nothnagel, Axel

2016-12-01

One of the primary goals of Very Long Baseline Interferometry (VLBI) is the determination of the International Celestial Reference Frame (ICRF). Currently the third realization of the internationally adopted CRF, the ICRF3, is under preparation. In this process, various optimizations are planned to realize a CRF that does not benefit only from the increased number of observations since the ICRF2 was published. The new ICRF can also benefit from an intra-technique combination as is done for the Terrestrial Reference Frame (TRF). Here, we aim at estimating an optimized CRF by means of an intra-technique combination. The solutions are based on the input to the official combined product of the International VLBI Service for Geodesy and Astrometry (IVS), also providing the radio source parameters. We discuss the differences in the setup using a different number of contributions and investigate the impact on TRF and CRF as well as on the Earth Orientation Parameters (EOPs). Here, we investigate the differences between the combined CRF and the individual CRFs from the different analysis centers.

Optimization and Characterization of the Friction Stir Welded Sheets of AA 5754-H111: Monitoring of the Quality of Joints with Thermographic Techniques

PubMed Central

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Galietti, Umberto

2017-01-01

Friction Stir Welding (FSW) is a solid-state welding process, based on frictional and stirring phenomena, that offers many advantages with respect to the traditional welding methods. However, several parameters can affect the quality of the produced joints. In this work, an experimental approach has been used for studying and optimizing the FSW process, applied on 5754-H111 aluminum plates. In particular, the thermal behavior of the material during the process has been investigated and two thermal indexes, the maximum temperature and the heating rate of the material, correlated to the frictional power input, were investigated for different process parameters (the travel and rotation tool speeds) configurations. Moreover, other techniques (micrographs, macrographs and destructive tensile tests) were carried out for supporting in a quantitative way the analysis of the quality of welded joints. The potential of thermographic technique has been demonstrated both for monitoring the FSW process and for predicting the quality of joints in terms of tensile strength. PMID:29019948

Development of a shock wave adhesion test for composite bonds by laser pulsed and mechanical impacts

NASA Astrophysics Data System (ADS)

Ecault, Romain; Boustie, Michel; Touchard, Fabienne; Arrigoni, Michel; Berthe, Laurent; CNRS Collaboration

2013-06-01

Evaluating the bonding quality of composite material is becoming one of the main challenges faced by aeronautic industries. This work aims the development of a technique using shock wave, which would enable to quantify the bonding mechanical quality. Laser shock experiments were carried out. This technique enables high tensile stress generation in the thickness of composite bond without any mechanical contact. The resulting damage has been quantified using different method such as confocal microscopy, ultrasound and cross section observation. The discrimination between a correct bond and a weak bond was possible thanks to these experiments. Nevertheless, laser sources are not well adapted for optimization of such a test since it has often fixed parameters. That is why mechanical impacts bonded composites were also performed in this work. By changing the thickness of aluminum projectiles, the tensile stresses generated by the shock wave propagation were moved toward the composite/bond interface. The observations made prove that the optimization of the technique is possible. The key parameters for the development of a bonding test using shock wave have been identified.

Optimum data weighting and error calibration for estimation of gravitational parameters

NASA Technical Reports Server (NTRS)

Lerch, F. J.

1989-01-01

A new technique was developed for the weighting of data from satellite tracking systems in order to obtain an optimum least squares solution and an error calibration for the solution parameters. Data sets from optical, electronic, and laser systems on 17 satellites in GEM-T1 (Goddard Earth Model, 36x36 spherical harmonic field) were employed toward application of this technique for gravity field parameters. Also, GEM-T2 (31 satellites) was recently computed as a direct application of the method and is summarized here. The method employs subset solutions of the data associated with the complete solution and uses an algorithm to adjust the data weights by requiring the differences of parameters between solutions to agree with their error estimates. With the adjusted weights the process provides for an automatic calibration of the error estimates for the solution parameters. The data weights derived are generally much smaller than corresponding weights obtained from nominal values of observation accuracy or residuals. Independent tests show significant improvement for solutions with optimal weighting as compared to the nominal weighting. The technique is general and may be applied to orbit parameters, station coordinates, or other parameters than the gravity model.

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.

Current status of the real-time processing of complex radar signatures

NASA Astrophysics Data System (ADS)

Clay, E.

The real-time processing technique developed by ONERA to characterize radar signatures at the Brahms station is described. This technique is used for the real-time analysis of the RCS of airframes and rotating parts, the one-dimensional tomography of aircraft, and the RCS of electromagnetic decoys. Using this technique, it is also possible to optimize the experimental parameters, i.e., the analysis band, the microwave-network gain, and the electromagnetic window of the analysis.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology.

PubMed

Faltermeier, Rupert; Proescholdt, Martin A; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

PubMed Central

Faltermeier, Rupert; Proescholdt, Martin A.; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses. PMID:26693250

Using evolutionary computation to optimize an SVM used in detecting buried objects in FLIR imagery

NASA Astrophysics Data System (ADS)

Paino, Alex; Popescu, Mihail; Keller, James M.; Stone, Kevin

2013-06-01

In this paper we describe an approach for optimizing the parameters of a Support Vector Machine (SVM) as part of an algorithm used to detect buried objects in forward looking infrared (FLIR) imagery captured by a camera installed on a moving vehicle. The overall algorithm consists of a spot-finding procedure (to look for potential targets) followed by the extraction of several features from the neighborhood of each spot. The features include local binary pattern (LBP) and histogram of oriented gradients (HOG) as these are good at detecting texture classes. Finally, we project and sum each hit into UTM space along with its confidence value (obtained from the SVM), producing a confidence map for ROC analysis. In this work, we use an Evolutionary Computation Algorithm (ECA) to optimize various parameters involved in the system, such as the combination of features used, parameters on the Canny edge detector, the SVM kernel, and various HOG and LBP parameters. To validate our approach, we compare results obtained from an SVM using parameters obtained through our ECA technique with those previously selected by hand through several iterations of "guess and check".

Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

PubMed

Pozzobon, Victor; Perre, Patrick

2018-01-21

This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimal feedback control infinite dimensional parabolic evolution systems: Approximation techniques

NASA Technical Reports Server (NTRS)

Banks, H. T.; Wang, C.

1989-01-01

A general approximation framework is discussed for computation of optimal feedback controls in linear quadratic regular problems for nonautonomous parabolic distributed parameter systems. This is done in the context of a theoretical framework using general evolution systems in infinite dimensional Hilbert spaces. Conditions are discussed for preservation under approximation of stabilizability and detectability hypotheses on the infinite dimensional system. The special case of periodic systems is also treated.

Empirical simulations of materials

NASA Astrophysics Data System (ADS)

Jogireddy, Vasantha

2011-12-01

Molecular dynamics is a specialized discipline of molecular modelling and computer techniques. In this work, first we presented simulation results from a study carried out on silicon nanowires. In the second part of the work, we presented an electrostatic screened coulomb potential developed for studying metal alloys and metal oxides. In particular, we have studied aluminum-copper alloys, aluminum oxides and copper oxides. Parameter optimization for the potential is done using multiobjective optimization algorithms.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

PubMed

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Real data assimilation for optimization of frictional parameters and prediction of afterslip in the 2003 Tokachi-oki earthquake inferred from slip velocity by an adjoint method

NASA Astrophysics Data System (ADS)

Kano, Masayuki; Miyazaki, Shin'ichi; Ishikawa, Yoichi; Hiyoshi, Yoshihisa; Ito, Kosuke; Hirahara, Kazuro

2015-10-01

Data assimilation is a technique that optimizes the parameters used in a numerical model with a constraint of model dynamics achieving the better fit to observations. Optimized parameters can be utilized for the subsequent prediction with a numerical model and predicted physical variables are presumably closer to observations that will be available in the future, at least, comparing to those obtained without the optimization through data assimilation. In this work, an adjoint data assimilation system is developed for optimizing a relatively large number of spatially inhomogeneous frictional parameters during the afterslip period in which the physical constraints are a quasi-dynamic equation of motion and a laboratory derived rate and state dependent friction law that describe the temporal evolution of slip velocity at subduction zones. The observed variable is estimated slip velocity on the plate interface. Before applying this method to the real data assimilation for the afterslip of the 2003 Tokachi-oki earthquake, a synthetic data assimilation experiment is conducted to examine the feasibility of optimizing the frictional parameters in the afterslip area. It is confirmed that the current system is capable of optimizing the frictional parameters A-B, A and L by adopting the physical constraint based on a numerical model if observations capture the acceleration and decaying phases of slip on the plate interface. On the other hand, it is unlikely to constrain the frictional parameters in the region where the amplitude of afterslip is less than 1.0 cm d-1. Next, real data assimilation for the 2003 Tokachi-oki earthquake is conducted to incorporate slip velocity data inferred from time dependent inversion of Global Navigation Satellite System time-series. The optimized values of A-B, A and L are O(10 kPa), O(102 kPa) and O(10 mm), respectively. The optimized frictional parameters yield the better fit to the observations and the better prediction skill of slip velocity afterwards. Also, further experiment shows the importance of employing a fine-mesh model. It will contribute to the further understanding of the frictional properties on plate interfaces and lead to the forecasting system that provides useful information on the possibility of consequent earthquakes.

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

NASA Astrophysics Data System (ADS)

Zeng, Rongping; Badano, Aldo; Myers, Kyle J.

2017-04-01

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

Application of Modified Particle Swarm Optimization Method for Parameter Extraction of 2-D TEC Mapping

NASA Astrophysics Data System (ADS)

Toker, C.; Gokdag, Y. E.; Arikan, F.; Arikan, O.

2012-04-01

Ionosphere is a very important part of Space Weather. Modeling and monitoring of ionospheric variability is a major part of satellite communication, navigation and positioning systems. Total Electron Content (TEC), which is defined as the line integral of the electron density along a ray path, is one of the parameters to investigate the ionospheric variability. Dual-frequency GPS receivers, with their world wide availability and efficiency in TEC estimation, have become a major source of global and regional TEC modeling. When Global Ionospheric Maps (GIM) of International GPS Service (IGS) centers (http://iono.jpl.nasa.gov/gim.html) are investigated, it can be observed that regional ionosphere along the midlatitude regions can be modeled as a constant, linear or a quadratic surface. Globally, especially around the magnetic equator, the TEC surfaces resemble twisted and dispersed single centered or double centered Gaussian functions. Particle Swarm Optimization (PSO) proved itself as a fast converging and an effective optimization tool in various diverse fields. Yet, in order to apply this optimization technique into TEC modeling, the method has to be modified for higher efficiency and accuracy in extraction of geophysical parameters such as model parameters of TEC surfaces. In this study, a modified PSO (mPSO) method is applied to regional and global synthetic TEC surfaces. The synthetic surfaces that represent the trend and small scale variability of various ionospheric states are necessary to compare the performance of mPSO over number of iterations, accuracy in parameter estimation and overall surface reconstruction. The Cramer-Rao bounds for each surface type and model are also investigated and performance of mPSO are tested with respect to these bounds. For global models, the sample points that are used in optimization are obtained using IGS receiver network. For regional TEC models, regional networks such as Turkish National Permanent GPS Network (TNPGN-Active) receiver sites are used. The regional TEC models are grouped into constant (one parameter), linear (two parameters), and quadratic (six parameters) surfaces which are functions of latitude and longitude. Global models require seven parameters for single centered Gaussian and 13 parameters for double centered Gaussian function. The error criterion is the normalized percentage error for both the surface and the parameters. It is observed that mPSO is very successful in parameter extraction of various regional and global models. The normalized reconstruction error varies from 10-4 for constant surfaces to 10-3 for quadratic surfaces in regional models, sampled with regional networks. Even for the cases of a severe geomagnetic storm that affects measurements globally, with IGS network, the reconstruction error is on the order of 10-1 even though individual parameters have higher normalized errors. The modified PSO technique proved itself to be a useful tool for parameter extraction of more complicated TEC models. This study is supported by TUBITAK EEEAG under Grant No: 109E055.

Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method

NASA Astrophysics Data System (ADS)

Matin, F.; Jeong, Y.; Kim, K.; Park, K.

2018-01-01

This paper introduces, a novel method for the image enhancement using multiscale retinex and practical swarm optimization. Multiscale retinex is widely used image enhancement technique which intemperately pertains on parameters such as Gaussian scales, gain and offset, etc. To achieve the privileged effect, the parameters need to be tuned manually according to the image. In order to handle this matter, a developed retinex algorithm based on PSO has been used. The PSO method adjusted the parameters for multiscale retinex with chromaticity preservation (MSRCP) attains better outcome to compare with other existing methods. The experimental result indicates that the proposed algorithm is an efficient one and not only provides true color loyalty in low light conditions but also avoid color distortion at the same time.

Inverse optimal self-tuning PID control design for an autonomous underwater vehicle

NASA Astrophysics Data System (ADS)

Rout, Raja; Subudhi, Bidyadhar

2017-01-01

This paper presents a new approach to path following control design for an autonomous underwater vehicle (AUV). A NARMAX model of the AUV is derived first and then its parameters are adapted online using the recursive extended least square algorithm. An adaptive Propotional-Integral-Derivative (PID) controller is developed using the derived parameters to accomplish the path following task of an AUV. The gain parameters of the PID controller are tuned using an inverse optimal control technique, which alleviates the problem of solving Hamilton-Jacobian equation and also satisfies an error cost function. Simulation studies were pursued to verify the efficacy of the proposed control algorithm. From the obtained results, it is envisaged that the proposed NARMAX model-based self-tuning adaptive PID control provides good path following performance even in the presence of uncertainty arising due to ocean current or hydrodynamic parameter.

Multiobjective optimization in structural design with uncertain parameters and stochastic processes

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The application of multiobjective optimization techniques to structural design problems involving uncertain parameters and random processes is studied. The design of a cantilever beam with a tip mass subjected to a stochastic base excitation is considered for illustration. Several of the problem parameters are assumed to be random variables and the structural mass, fatigue damage, and negative of natural frequency of vibration are considered for minimization. The solution of this three-criteria design problem is found by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It is observed that the game theory approach is superior in finding a better optimum solution, assuming the proper balance of the various objective functions. The procedures used in the present investigation are expected to be useful in the design of general dynamic systems involving uncertain parameters, stochastic process, and multiple objectives.

Effect of Process Parameter on Barium Titanate Stannate (BTS) Materials Sintered at Low Sintering

NASA Astrophysics Data System (ADS)

Shukla, Alok; Bajpai, P. K.

2011-11-01

Ba(Ti1-xSnx)O3 solid solutions with (x = 0.15, 0.20, 0.30 and 0.40) are synthesized using conventional solid state reaction method. Formation of solid solutions in the range 0 ≤ x ≤0.40 is confirmed using X-ray diffraction technique. Single phase solid solutions with homogeneous grain distribution are observed at relatively low sintering by controlling process parameters viz. sintering time. Composition at optimized temperature (1150 °C) sintered by varying the sintering time, stabilize in cubic perovskite phase. The % experimental density increase with increasing the time of sintering instead of increasing sintering temperature. The lattice parameter increases by increasing the tin composition in the material. This demonstrates that process parameter optimization can lead to single phase at relatively lower sintering-a major advantage for the materials used as capacitor element in MLCC.

Aircraft wing structural design optimization based on automated finite element modelling and ground structure approach

NASA Astrophysics Data System (ADS)

Yang, Weizhu; Yue, Zhufeng; Li, Lei; Wang, Peiyan

2016-01-01

An optimization procedure combining an automated finite element modelling (AFEM) technique with a ground structure approach (GSA) is proposed for structural layout and sizing design of aircraft wings. The AFEM technique, based on CATIA VBA scripting and PCL programming, is used to generate models automatically considering the arrangement of inner systems. GSA is used for local structural topology optimization. The design procedure is applied to a high-aspect-ratio wing. The arrangement of the integral fuel tank, landing gear and control surfaces is considered. For the landing gear region, a non-conventional initial structural layout is adopted. The positions of components, the number of ribs and local topology in the wing box and landing gear region are optimized to obtain a minimum structural weight. Constraints include tank volume, strength, buckling and aeroelastic parameters. The results show that the combined approach leads to a greater weight saving, i.e. 26.5%, compared with three additional optimizations based on individual design approaches.

Lunar Habitat Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.

2007-01-01

Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.

Studies on the Parametric Effects of Plasma Arc Welding of 2205 Duplex Stainless Steel

NASA Astrophysics Data System (ADS)

Selva Bharathi, R.; Siva Shanmugam, N.; Murali Kannan, R.; Arungalai Vendan, S.

2018-03-01

This research study attempts to create an optimized parametric window by employing Taguchi algorithm for Plasma Arc Welding (PAW) of 2 mm thick 2205 duplex stainless steel. The parameters considered for experimentation and optimization are the welding current, welding speed and pilot arc length respectively. The experimentation involves the parameters variation and subsequently recording the depth of penetration and bead width. Welding current of 60-70 A, welding speed of 250-300 mm/min and pilot arc length of 1-2 mm are the range between which the parameters are varied. Design of experiments is used for the experimental trials. Back propagation neural network, Genetic algorithm and Taguchi techniques are used for predicting the bead width, depth of penetration and validated with experimentally achieved results which were in good agreement. Additionally, micro-structural characterizations are carried out to examine the weld quality. The extrapolation of these optimized parametric values yield enhanced weld strength with cost and time reduction.

Experimental Investigation and Optimization of TIG Welding Parameters on Aluminum 6061 Alloy Using Firefly Algorithm

NASA Astrophysics Data System (ADS)

Kumar, Rishi; Mevada, N. Ramesh; Rathore, Santosh; Agarwal, Nitin; Rajput, Vinod; Sinh Barad, AjayPal

2017-08-01

To improve Welding quality of aluminum (Al) plate, the TIG Welding system has been prepared, by which Welding current, Shielding gas flow rate and Current polarity can be controlled during Welding process. In the present work, an attempt has been made to study the effect of Welding current, current polarity, and shielding gas flow rate on the tensile strength of the weld joint. Based on the number of parameters and their levels, the Response Surface Methodology technique has been selected as the Design of Experiment. For understanding the influence of input parameters on Ultimate tensile strength of weldment, ANOVA analysis has been carried out. Also to describe and optimize TIG Welding using a new metaheuristic Nature - inspired algorithm which is called as Firefly algorithm which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of firefly algorithm is presented together with an analytical, mathematical modeling to optimize the TIG Welding process by a single equivalent objective function.

Computer-oriented synthesis of wide-band non-uniform negative resistance amplifiers

NASA Technical Reports Server (NTRS)

Branner, G. R.; Chan, S.-P.

1975-01-01

This paper presents a synthesis procedure which provides design values for broad-band amplifiers using non-uniform negative resistance devices. Employing a weighted least squares optimization scheme, the technique, based on an extension of procedures for uniform negative resistance devices, is capable of providing designs for a variety of matching network topologies. It also provides, for the first time, quantitative results for predicting the effects of parameter element variations on overall amplifier performance. The technique is also unique in that it employs exact partial derivatives for optimization and sensitivity computation. In comparison with conventional procedures, significantly improved broad-band designs are shown to result.

Optimizing for Large Planar Fractures in Multistage Horizontal Wells in Enhanced Geothermal Systems Using a Coupled Fluid and Geomechanics Simulator

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

Hu, Xiexiaomen; Tutuncu, Azra; Eustes, Alfred

Enhanced Geothermal Systems (EGS) could potentially use technological advancements in coupled implementation of horizontal drilling and multistage hydraulic fracturing techniques in tight oil and shale gas reservoirs along with improvements in reservoir simulation techniques to design and create EGS reservoirs. In this study, a commercial hydraulic fracture simulation package, Mangrove by Schlumberger, was used in an EGS model with largely distributed pre-existing natural fractures to model fracture propagation during the creation of a complex fracture network. The main goal of this study is to investigate optimum treatment parameters in creating multiple large, planar fractures to hydraulically connect a horizontal injectionmore » well and a horizontal production well that are 10,000 ft. deep and spaced 500 ft. apart from each other. A matrix of simulations for this study was carried out to determine the influence of reservoir and treatment parameters on preventing (or aiding) the creation of large planar fractures. The reservoir parameters investigated during the matrix simulations include the in-situ stress state and properties of the natural fracture set such as the primary and secondary fracture orientation, average fracture length, and average fracture spacing. The treatment parameters investigated during the simulations were fluid viscosity, proppant concentration, pump rate, and pump volume. A final simulation with optimized design parameters was performed. The optimized design simulation indicated that high fluid viscosity, high proppant concentration, large pump volume and pump rate tend to minimize the complexity of the created fracture network. Additionally, a reservoir with 'friendly' formation characteristics such as large stress anisotropy, natural fractures set parallel to the maximum horizontal principal stress (SHmax), and large natural fracture spacing also promote the creation of large planar fractures while minimizing fracture complexity.« less

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.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

NASA Astrophysics Data System (ADS)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm.

PubMed

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

A Computational Approach for Model Update of an LS-DYNA Energy Absorbing Cell

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Jackson, Karen E.; Kellas, Sotiris

2008-01-01

NASA and its contractors are working on structural concepts for absorbing impact energy of aerospace vehicles. Recently, concepts in the form of multi-cell honeycomb-like structures designed to crush under load have been investigated for both space and aeronautics applications. Efforts to understand these concepts are progressing from tests of individual cells to tests of systems with hundreds of cells. Because of fabrication irregularities, geometry irregularities, and material properties uncertainties, the problem of reconciling analytical models, in particular LS-DYNA models, with experimental data is a challenge. A first look at the correlation results between single cell load/deflection data with LS-DYNA predictions showed problems which prompted additional work in this area. This paper describes a computational approach that uses analysis of variance, deterministic sampling techniques, response surface modeling, and genetic optimization to reconcile test with analysis results. Analysis of variance provides a screening technique for selection of critical parameters used when reconciling test with analysis. In this study, complete ignorance of the parameter distribution is assumed and, therefore, the value of any parameter within the range that is computed using the optimization procedure is considered to be equally likely. Mean values from tests are matched against LS-DYNA solutions by minimizing the square error using a genetic optimization. The paper presents the computational methodology along with results obtained using this approach.

Rational positive real approximations for LQG optimal compensators arising in active stabilization of flexible structures

NASA Technical Reports Server (NTRS)

Desantis, A.

1994-01-01

In this paper the approximation problem for a class of optimal compensators for flexible structures is considered. The particular case of a simply supported truss with an offset antenna is dealt with. The nonrational positive real optimal compensator transfer function is determined, and it is proposed that an approximation scheme based on a continued fraction expansion method be used. Comparison with the more popular modal expansion technique is performed in terms of stability margin and parameters sensitivity of the relative approximated closed loop transfer functions.

Two-speed phacoemulsification for soft cataracts using optimized parameters and procedure step toolbar with the CENTURION Vision System and Balanced Tip.

PubMed

Davison, James A

2015-01-01

To present a cause of posterior capsule aspiration and a technique using optimized parameters to prevent it from happening when operating soft cataracts. A prospective list of posterior capsule aspiration cases was kept over 4,062 consecutive cases operated with the Alcon CENTURION machine and Balanced Tip. Video analysis of one case of posterior capsule aspiration was accomplished. A surgical technique was developed using empirically derived machine parameters and customized setting-selection procedure step toolbar to reduce the pace of aspiration of soft nuclear quadrants in order to prevent capsule aspiration. Two cases out of 3,238 experienced posterior capsule aspiration before use of the soft quadrant technique. Video analysis showed an attractive vortex effect with capsule aspiration occurring in 1/5 of a second. A soft quadrant removal setting was empirically derived which had a slower pace and seemed more controlled with no capsule aspiration occurring in the subsequent 824 cases. The setting featured simultaneous linear control from zero to preset maximums for: aspiration flow, 20 mL/min; and vacuum, 400 mmHg, with the addition of torsional tip amplitude up to 20% after the fluidic maximums were achieved. A new setting selection procedure step toolbar was created to increase intraoperative flexibility by providing instantaneous shifting between the soft and normal settings. A technique incorporating a reduced pace for soft quadrant acquisition and aspiration can be accomplished through the use of a dedicated setting of integrated machine parameters. Toolbar placement of the procedure button next to the normal setting procedure button provides the opportunity to instantaneously alternate between the two settings. Simultaneous surgeon control over vacuum, aspiration flow, and torsional tip motion may make removal of soft nuclear quadrants more efficient and safer.

Optimization of capillary zone electrophoresis for charge heterogeneity testing of biopharmaceuticals using enhanced method development principles.

PubMed

Moritz, Bernd; Locatelli, Valentina; Niess, Michele; Bathke, Andrea; Kiessig, Steffen; Entler, Barbara; Finkler, Christof; Wegele, Harald; Stracke, Jan

2017-12-01

CZE is a well-established technique for charge heterogeneity testing of biopharmaceuticals. It is based on the differences between the ratios of net charge and hydrodynamic radius. In an extensive intercompany study, it was recently shown that CZE is very robust and can be easily implemented in labs that did not perform it before. However, individual characteristics of some examined proteins resulted in suboptimal resolution. Therefore, enhanced method development principles were applied here to investigate possibilities for further method optimization. For this purpose, a high number of different method parameters was evaluated with the aim to improve CZE separation. For the relevant parameters, design of experiments (DoE) models were generated and optimized in several ways for different sets of responses like resolution, peak width and number of peaks. In spite of product specific DoE optimization it was found that the resulting combination of optimized parameters did result in significant improvement of separation for 13 out of 16 different antibodies and other molecule formats. These results clearly demonstrate generic applicability of the optimized CZE method. Adaptation to individual molecular properties may sometimes still be required in order to achieve optimal separation but the set screws discussed in this study [mainly pH, identity of the polymer additive (HPC versus HPMC) and the concentrations of additives like acetonitrile, butanolamine and TETA] are expected to significantly reduce the effort for specific optimization. 2017 The Authors. Electrophoresis published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Integrated light-sheet imaging and flow-based enquiry (iLIFE) system for 3D in-vivo imaging of multicellular organism

NASA Astrophysics Data System (ADS)

Rasmi, Chelur K.; Padmanabhan, Sreedevi; Shirlekar, Kalyanee; Rajan, Kanhirodan; Manjithaya, Ravi; Singh, Varsha; Mondal, Partha Pratim

2017-12-01

We propose and demonstrate a light-sheet-based 3D interrogation system on a microfluidic platform for screening biological specimens during flow. To achieve this, a diffraction-limited light-sheet (with a large field-of-view) is employed to optically section the specimens flowing through the microfluidic channel. This necessitates optimization of the parameters for the illumination sub-system (illumination intensity, light-sheet width, and thickness), microfluidic specimen platform (channel-width and flow-rate), and detection sub-system (camera exposure time and frame rate). Once optimized, these parameters facilitate cross-sectional imaging and 3D reconstruction of biological specimens. The proposed integrated light-sheet imaging and flow-based enquiry (iLIFE) imaging technique enables single-shot sectional imaging of a range of specimens of varying dimensions, ranging from a single cell (HeLa cell) to a multicellular organism (C. elegans). 3D reconstruction of the entire C. elegans is achieved in real-time and with an exposure time of few hundred micro-seconds. A maximum likelihood technique is developed and optimized for the iLIFE imaging system. We observed an intracellular resolution for mitochondria-labeled HeLa cells, which demonstrates the dynamic resolution of the iLIFE system. The proposed technique is a step towards achieving flow-based 3D imaging. We expect potential applications in diverse fields such as structural biology and biophysics.

Inverse Estimation of Parameters for a Coupled Photosynthesis and Stomatal Conductance Model Using Eddy Covariance Measurements at a Black Spruce Forest in Alaska

NASA Astrophysics Data System (ADS)

Ueyama, M.; Tahara, N.; Iwata, H.; Nagano, H.; Harazono, Y.

2014-12-01

For better understanding high-latitude carbon and water cycles, parameters of a coupled photosynthesis and stomatal conductance big-leaf model (Farquhar et al., 1980; Ball and Berry, 1987; Baldocchi, 1994) were inversely estimated using gross primary productivity (GPP) and evapotranspiration by eddy covariance measurements at a black spruce forest in interior Alaska (Iwata et al., 2012; Ueyama et al., 2014). We developed a sequential optimization method based on a global optimization technique; shuffled complex evolution (SCE-UA) method (Duan et al., 1993). First, photosynthetic parameters (maximum carboxylation and maximum electron transfer rate at 25oC; Vcmax25 and Jmax25) were optimized for GPP, and then stomatal conductance parameters (m and b in the Ball-Berry model) were optimized for evapotranspiration. Based on our optimization, Vcmax25, Jmax25, and m varied seasonally, but b value was almost constant throughout seasons. Vcmax25 and Jmax25 were higher in summer months than other months, which related to understory leaf area index. m was higher in winter months than other months, but did not significantly change throughout the growing season. Our results indicated that simulations using constant ecophysiological parameters could underestimate photosynthesis and evapotranspiration of high-latitude ecosystems. References Ball and Berry, 1987: Progress in Photosynthesis Research, pp 221-224. Baldocchi, 1994: Tree Physiol., 14, 1069-1079. Duan et al., 1993: J. Optimization Theory and Applications, 76, 501-521. Farquhar et al., 1980: Planta, 149, 78-90. Iwata et al., 2012: Agric. For. Meteorol., 161, 107-115. Ueyama et al., 2014: Global Change Biol., 20, 1161-1173.

Optimization of hybrid laser - TIG welding of 316LN steel using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Ragavendran, M.; Chandrasekhar, N.; Ravikumar, R.; Saxena, Rajesh; Vasudevan, M.; Bhaduri, A. K.

2017-07-01

In the present study, the hybrid laser - TIG welding parameters for welding of 316LN austenitic stainless steel have been investigated by combining a pulsed laser beam with a TIG welding heat source at the weld pool. Laser power, pulse frequency, pulse duration, TIG current were presumed as the welding process parameters whereas weld bead width, weld cross-sectional area and depth of penetration (DOP) were considered as the process responses. Central composite design was used to complete the design matrix and welding experiments were conducted based on the design matrix. Weld bead measurements were then carried out to generate the dataset. Multiple regression models correlating the process parameters with the responses have been developed. The accuracy of the models were found to be good. Then, the desirability approach optimization technique was employed for determining the optimum process parameters to obtain the desired weld bead profile. Validation experiments were then carried out from the determined optimum process parameters. There was good agreement between the predicted and measured values.

Electrodeposition of organic-inorganic tri-halide perovskites solar cell

NASA Astrophysics Data System (ADS)

Charles, U. A.; Ibrahim, M. A.; Teridi, M. A. M.

2018-02-01

Perovskite (CH3NH3PbI3) semiconductor materials are promising high-performance light energy absorber for solar cell application. However, the power conversion efficiency of perovskite solar cell is severely affected by the surface quality of the deposited thin film. Spin coating is a low-cost and widely used deposition technique for perovskite solar cell. Notably, film deposited by spin coating evolves surface hydroxide and defeats from uncontrolled precipitation and inter-diffusion reaction. Alternatively, vapor deposition (VD) method produces uniform thin film but requires precise control of complex thermodynamic parameters which makes the technique unsuitable for large scale production. Most deposition techniques for perovskite require tedious surface optimization to improve the surface quality of deposits. Optimization of perovskite surface is necessary to significantly improve device structure and electrical output. In this review, electrodeposition of perovskite solar cell is demonstrated as a scalable and reproducible technique to fabricate uniform and smooth thin film surface that circumvents the need for high vacuum environment. Electrodeposition is achieved at low temperatures, supports precise control and optimization of deposits for efficient charge transfer.

An approximation theory for nonlinear partial differential equations with applications to identification and control

NASA Technical Reports Server (NTRS)

Banks, H. T.; Kunisch, K.

1982-01-01

Approximation results from linear semigroup theory are used to develop a general framework for convergence of approximation schemes in parameter estimation and optimal control problems for nonlinear partial differential equations. These ideas are used to establish theoretical convergence results for parameter identification using modal (eigenfunction) approximation techniques. Results from numerical investigations of these schemes for both hyperbolic and parabolic systems are given.

Impact of the Z potential technique on reducing the sperm DNA fragmentation index, fertilization rate and embryo development.

PubMed

Duarte, Carlos; Núñez, Víctor; Wong, Yat; Vivar, Carlos; Benites, Elder; Rodriguez, Urso; Vergara, Carlos; Ponce, Jorge

2017-12-01

In assisted reproduction procedures, we need to develop and enhance new protocols to optimize sperm selection. The aim of this study is to evaluate the ability of the Z potential technique to select sperm with intact DNA in non-normospermic patients and evaluate the impact of this selection on embryonic development. We analyzed a total of 174 human seminal samples with at least one altered parameter. We measured basal, post density gradients, and post density gradients + Z potential DNA fragmentation index. To evaluate the impact of this technique on embryo development, 54 cases were selected. The embryo development parameters evaluated were fertilization rate, cleavage rate, top quality embryos at the third day and blastocysts rate. We found significant differences in the study groups when we compared the sperm fragmentation index by adding the Z potential technique to density gradient selection vs. density gradients alone. Furthermore, there was no significant difference in the embryo development parameters between the low sperm fragmentation index group vs. the moderate and high sperm fragmentation index groups, when selecting sperms with this new technique. The Z potential technique is a very useful tool for sperm selection; it significantly reduces the DNA fragmentation index and improves the parameters of embryo development. This technique could be considered routine for its simplicity and low cost.

Optimized positioning of autonomous surgical lamps

NASA Astrophysics Data System (ADS)

Teuber, Jörn; Weller, Rene; Kikinis, Ron; Oldhafer, Karl-Jürgen; Lipp, Michael J.; Zachmann, Gabriel

2017-03-01

We consider the problem of finding automatically optimal positions of surgical lamps throughout the whole surgical procedure, where we assume that future lamps could be robotized. We propose a two-tiered optimization technique for the real-time autonomous positioning of those robotized surgical lamps. Typically, finding optimal positions for surgical lamps is a multi-dimensional problem with several, in part conflicting, objectives, such as optimal lighting conditions at every point in time while minimizing the movement of the lamps in order to avoid distractions of the surgeon. Consequently, we use multi-objective optimization (MOO) to find optimal positions in real-time during the entire surgery. Due to the conflicting objectives, there is usually not a single optimal solution for such kinds of problems, but a set of solutions that realizes a Pareto-front. When our algorithm selects a solution from this set it additionally has to consider the individual preferences of the surgeon. This is a highly non-trivial task because the relationship between the solution and the parameters is not obvious. We have developed a novel meta-optimization that considers exactly this challenge. It delivers an easy to understand set of presets for the parameters and allows a balance between the lamp movement and lamp obstruction. This metaoptimization can be pre-computed for different kinds of operations and it then used by our online optimization for the selection of the appropriate Pareto solution. Both optimization approaches use data obtained by a depth camera that captures the surgical site but also the environment around the operating table. We have evaluated our algorithms with data recorded during a real open abdominal surgery. It is available for use for scientific purposes. The results show that our meta-optimization produces viable parameter sets for different parts of an intervention even when trained on a small portion of it.

Neighboring extremals of dynamic optimization problems with path equality constraints

NASA Technical Reports Server (NTRS)

Lee, A. Y.

1988-01-01

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique.

Parameter extraction using global particle swarm optimization approach and the influence of polymer processing temperature on the solar cell parameters

NASA Astrophysics Data System (ADS)

Kumar, S.; Singh, A.; Dhar, A.

2017-08-01

The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.

Automatic design of optical systems by digital computer

NASA Technical Reports Server (NTRS)

Casad, T. A.; Schmidt, L. F.

1967-01-01

Computer program uses geometrical optical techniques and a least squares optimization method employing computing equipment for the automatic design of optical systems. It evaluates changes in various optical parameters, provides comprehensive ray-tracing, and generally determines the acceptability of the optical system characteristics.

Analysis of laser surgery in non-melanoma skin cancer for optimal tissue removal

NASA Astrophysics Data System (ADS)

Fanjul-Vélez, Félix; Salas-García, Irene; Arce-Diego, José Luis

2015-02-01

Laser surgery is a commonly used technique for tissue ablation or the resection of malignant tumors. It presents advantages over conventional non-optical ablation techniques, like a scalpel or electrosurgery, such as the increased precision of the resected volume, minimization of scars and shorter recovery periods. Laser surgery is employed in medical branches such as ophthalmology or dermatology. The application of laser surgery requires the optimal adjustment of laser beam parameters, taking into account the particular patient and lesion. In this work we present a predictive tool for tissue resection in biological tissue after laser surgery, which allows an a priori knowledge of the tissue ablation volume, area and depth. The model employs a Monte Carlo 3D approach for optical propagation and a rate equation for plasma-induced ablation. The tool takes into account characteristics of the specific lesion to be ablated, mainly the geometric, optical and ablation properties. It also considers the parameters of the laser beam, such as the radius, spatial profile, pulse width, total delivered energy or wavelength. The predictive tool is applied to dermatology tumor resection, particularly to different types of non-melanoma skin cancer tumors: basocellular carcinoma, squamous cell carcinoma and infiltrative carcinoma. The ablation volume, area and depth are calculated for healthy skin and for each type of tumor as a function of the laser beam parameters. The tool could be used for laser surgery planning before the clinical application. The laser parameters could be adjusted for optimal resection volume, by personalizing the process to the particular patient and lesion.

Subsurface water parameters: optimization approach to their determination from remotely sensed water color data.

PubMed

Jain, S C; Miller, J R

1976-04-01

A method, using an optimization scheme, has been developed for the interpretation of spectral albedo (or spectral reflectance) curves obtained from remotely sensed water color data. This method used a two-flow model of the radiation flow and solves for the albedo. Optimization fitting of predicted to observed reflectance data is performed by a quadratic interpolation method for the variables chlorophyll concentration and scattering coefficient. The technique is applied to airborne water color data obtained from Kawartha Lakes, Sargasso Sea, and Nova Scotia coast. The modeled spectral albedo curves are compared to those obtained experimentally, and the computed optimum water parameters are compared to ground truth values. It is shown that the backscattered spectral signal contains information that can be interpreted to give quantitative estimates of the chlorophyll concentration and turbidity in the waters studied.

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

Camingue, Pamela; Christian, Rochelle; Ng, Davin

The purpose of this study was to compare 4 different external beam radiation therapy treatment techniques for the treatment of T1-2, N0, M0 glottic cancers: traditional lateral beams with wedges (3D), 5-field intensity-modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), and proton therapy. Treatment plans in each technique were created for 10 patients using consistent planning parameters. The photon treatment plans were optimized using Philips Pinnacle{sub 3} v.9 and the IMRT and VMAT plans used the Direct Machine Parameter Optimization algorithm. The proton treatment plans were optimized using Varian Eclipse Proton v.8.9. The prescription used for each plan wasmore » 63 Gy in 28 fractions. The contours for spinal cord, right carotid artery, left carotid artery, and normal tissue were created with respect to the patient's bony anatomy so that proper comparisons of doses could be made with respect to volume. An example of the different isodose distributions will be shown. The data collection for comparison purposes includes: clinical treatment volume coverage, dose to spinal cord, dose to carotid arteries, and dose to normal tissue. Data comparisons will be displayed graphically showing the maximum, mean, median, and ranges of doses.« less

Optimal non-linear health insurance.

PubMed

Blomqvist, A

1997-06-01

Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.

Optimization and Analysis of Laser Beam Machining Parameters for Al7075-TiB2 In-situ Composite

NASA Astrophysics Data System (ADS)

Manjoth, S.; Keshavamurthy, R.; Pradeep Kumar, G. S.

2016-09-01

The paper focuses on laser beam machining (LBM) of In-situ synthesized Al7075-TiB2 metal matrix composite. Optimization and influence of laser machining process parameters on surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy of composites were studied. Al7075-TiB2 metal matrix composite was synthesized by in-situ reaction technique using stir casting process. Taguchi's L9 orthogonal array was used to design experimental trials. Standoff distance (SOD) (0.3 - 0.5mm), Cutting Speed (1000 - 1200 m/hr) and Gas pressure (0.5 - 0.7 bar) were considered as variable input parameters at three different levels, while power and nozzle diameter were maintained constant with air as assisting gas. Optimized process parameters for surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy were calculated by generating the main effects plot for signal noise ratio (S/N ratio) for surface roughness, VMRR and dimensional error using Minitab software (version 16). The Significant of standoff distance (SOD), cutting speed and gas pressure on surface roughness, volumetric material removal rate (VMRR) and dimensional error were calculated using analysis of variance (ANOVA) method. Results indicate that, for surface roughness, cutting speed (56.38%) is most significant parameter followed by standoff distance (41.03%) and gas pressure (2.6%). For volumetric material removal (VMRR), gas pressure (42.32%) is most significant parameter followed by cutting speed (33.60%) and standoff distance (24.06%). For dimensional error, Standoff distance (53.34%) is most significant parameter followed by cutting speed (34.12%) and gas pressure (12.53%). Further, verification experiments were carried out to confirm performance of optimized process parameters.

PERIODIC AUTOREGRESSIVE-MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES.

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

Results involving correlation properties and parameter estimation for autogressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.

Taguchi optimization of bismuth-telluride based thermoelectric cooler

NASA Astrophysics Data System (ADS)

Anant Kishore, Ravi; Kumar, Prashant; Sanghadasa, Mohan; Priya, Shashank

2017-07-01

In the last few decades, considerable effort has been made to enhance the figure-of-merit (ZT) of thermoelectric (TE) materials. However, the performance of commercial TE devices still remains low due to the fact that the module figure-of-merit not only depends on the material ZT, but also on the operating conditions and configuration of TE modules. This study takes into account comprehensive set of parameters to conduct the numerical performance analysis of the thermoelectric cooler (TEC) using a Taguchi optimization method. The Taguchi method is a statistical tool that predicts the optimal performance with a far less number of experimental runs than the conventional experimental techniques. Taguchi results are also compared with the optimized parameters obtained by a full factorial optimization method, which reveals that the Taguchi method provides optimum or near-optimum TEC configuration using only 25 experiments against 3125 experiments needed by the conventional optimization method. This study also shows that the environmental factors such as ambient temperature and cooling coefficient do not significantly affect the optimum geometry and optimum operating temperature of TECs. The optimum TEC configuration for simultaneous optimization of cooling capacity and coefficient of performance is also provided.

Optimal placement of tuning masses for vibration reduction in helicopter rotor blades

NASA Technical Reports Server (NTRS)

Pritchard, Jocelyn I.; Adelman, Howard M.

1988-01-01

Described are methods for reducing vibration in helicopter rotor blades by determining optimum sizes and locations of tuning masses through formal mathematical optimization techniques. An optimization procedure is developed which employs the tuning masses and corresponding locations as design variables which are systematically changed to achieve low values of shear without a large mass penalty. The finite-element structural analysis of the blade and the optimization formulation require development of discretized expressions for two performance parameters: modal shaping parameter and modal shear amplitude. Matrix expressions for both quantities and their sensitivity derivatives are developed. Three optimization strategies are developed and tested. The first is based on minimizing the modal shaping parameter which indirectly reduces the modal shear amplitudes corresponding to each harmonic of airload. The second strategy reduces these amplitudes directly, and the third strategy reduces the shear as a function of time during a revolution of the blade. The first strategy works well for reducing the shear for one mode responding to a single harmonic of the airload, but has been found in some cases to be ineffective for more than one mode. The second and third strategies give similar results and show excellent reduction of the shear with a low mass penalty.

Trajectory optimization for an asymmetric launch vehicle. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Sullivan, Jeanne Marie

1990-01-01

A numerical optimization technique is used to fully automate the trajectory design process for an symmetric configuration of the proposed Advanced Launch System (ALS). The objective of the ALS trajectory design process is the maximization of the vehicle mass when it reaches the desired orbit. The trajectories used were based on a simple shape that could be described by a small set of parameters. The use of a simple trajectory model can significantly reduce the computation time required for trajectory optimization. A predictive simulation was developed to determine the on-orbit mass given an initial vehicle state, wind information, and a set of trajectory parameters. This simulation utilizes an idealized control system to speed computation by increasing the integration time step. The conjugate gradient method is used for the numerical optimization of on-orbit mass. The method requires only the evaluation of the on-orbit mass function using the predictive simulation, and the gradient of the on-orbit mass function with respect to the trajectory parameters. The gradient is approximated with finite differencing. Prelaunch trajectory designs were carried out using the optimization procedure. The predictive simulation is used in flight to redesign the trajectory to account for trajectory deviations produced by off-nominal conditions, e.g., stronger than expected head winds.

A four parameter optimization and troubleshooting of a RPLC - charged aerosol detection stability indicating method for determination of S-lysophosphatidylcholines in a phospholipid formulation.

PubMed

Tam, James; Ahmad, Imad A Haidar; Blasko, Andrei

2018-06-05

A four parameter optimization of a stability indicating method for non-chromophoric degradation products of 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), 1-stearoyl-sn-glycero-3-phosphocholine and 2-stearoyl-sn-glycero-3-phosphocholine was achieved using a reverse phase liquid chromatography-charged aerosol detection (RPLC-CAD) technique. Using the hydrophobic subtraction model of selectivity, a core-shell, polar embedded RPLC column was selected followed by gradient-temperature optimization, resulting in ideal relative peak placements for a robust, stability indicating separation. The CAD instrument parameters, power function value (PFV) and evaporator temperature were optimized for lysophosphatidylcholines to give UV absorbance detector-like linearity performance within a defined concentration range. The two lysophosphatidylcholines gave the same response factor in the selected conditions. System specific power function values needed to be set for the two RPLC-CAD instruments used. A custom flow-divert profile, sending only a portion of the column effluent to the detector, was necessary to mitigate detector response drifting effects. The importance of the PFV optimization for each instrument of identical build and how to overcome recovery issues brought on by the matrix effects from the lipid-RP stationary phase interaction is reported. Copyright © 2018 Elsevier B.V. All rights reserved.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

PubMed

Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

2018-04-10

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

Carbon-13 and proton nuclear magnetic resonance analysis of shale-derived refinery products and jet fuels and of experimental referee broadened-specification jet fuels

NASA Technical Reports Server (NTRS)

Dalling, D. K.; Bailey, B. K.; Pugmire, R. J.

1984-01-01

A proton and carbon-13 nuclear magnetic resonance (NMR) study was conducted of Ashland shale oil refinery products, experimental referee broadened-specification jet fuels, and of related isoprenoid model compounds. Supercritical fluid chromatography techniques using carbon dioxide were developed on a preparative scale, so that samples could be quantitatively separated into saturates and aromatic fractions for study by NMR. An optimized average parameter treatment was developed, and the NMR results were analyzed in terms of the resulting average parameters; formulation of model mixtures was demonstrated. Application of novel spectroscopic techniques to fuel samples was investigated.

Optimization of the GBMV2 implicit solvent force field for accurate simulation of protein conformational equilibria.

PubMed

Lee, Kuo Hao; Chen, Jianhan

2017-06-15

Accurate treatment of solvent environment is critical for reliable simulations of protein conformational equilibria. Implicit treatment of solvation, such as using the generalized Born (GB) class of models arguably provides an optimal balance between computational efficiency and physical accuracy. Yet, GB models are frequently plagued by a tendency to generate overly compact structures. The physical origins of this drawback are relatively well understood, and the key to a balanced implicit solvent protein force field is careful optimization of physical parameters to achieve a sufficient level of cancellation of errors. The latter has been hampered by the difficulty of generating converged conformational ensembles of non-trivial model proteins using the popular replica exchange sampling technique. Here, we leverage improved sampling efficiency of a newly developed multi-scale enhanced sampling technique to re-optimize the generalized-Born with molecular volume (GBMV2) implicit solvent model with the CHARMM36 protein force field. Recursive optimization of key GBMV2 parameters (such as input radii) and protein torsion profiles (via the CMAP torsion cross terms) has led to a more balanced GBMV2 protein force field that recapitulates the structures and stabilities of both helical and β-hairpin model peptides. Importantly, this force field appears to be free of the over-compaction bias, and can generate structural ensembles of several intrinsically disordered proteins of various lengths that seem highly consistent with available experimental data. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Load Balancing in Multi Cloud Computing Environment with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Vhansure, Fularani; Deshmukh, Apurva; Sumathy, S.

2017-11-01

Cloud is a pool of resources that is available on pay per use model. It provides services to the user which is increasing rapidly. Load balancing is an issue because it cannot handle so many requests at a time. It is also known as NP complete problem. In traditional system the functions consist of various parameter values to maximise it in order to achieve best optimal individualsolutions. Challenge is when there are many parameters of solutionsin the system space. Another challenge is to optimize the function which is much more complex. In this paper, various techniques to handle load balancing virtually (VM) as well as physically (nodes) using genetic algorithm is discussed.

Improved aortic enhancement in CT angiography using slope-based triggering with table speed optimization: a pilot study.

PubMed

Bashir, Mustafa R; Weber, Paul W; Husarik, Daniela B; Howle, Laurens E; Nelson, Rendon C

2012-08-01

To assess whether a scan triggering technique based on the slope of the time-attenuation curve combined with table speed optimization may improve arterial enhancement in aortic CT angiography compared to conventional threshold-based triggering techniques. Measurements of arterial enhancement were performed in a physiologic flow phantom over a range of simulated cardiac outputs (2.2-8.1 L/min) using contrast media boluses of 80 and 150 mL injected at 4 mL/s. These measurements were used to construct computer models of aortic attenuation in CT angiography, using cardiac output, aortic diameter, and CT table speed as input parameters. In-plane enhancement was calculated for normal and aneurysmal aortic diameters. Calculated arterial enhancement was poor (<150 HU) along most of the scan length using the threshold-based triggering technique for low cardiac outputs and the aneurysmal aorta model. Implementation of the slope-based triggering technique with table speed optimization improved enhancement in all scenarios and yielded good- (>200 HU; 13/16 scenarios) to excellent-quality (>300 HU; 3/16 scenarios) enhancement in all cases. Slope-based triggering with table speed optimization may improve the technical quality of aortic CT angiography over conventional threshold-based techniques, and may reduce technical failures related to low cardiac output and slow flow through an aneurysmal aorta.

Optimisation of phase ratio in the triple jump using computer simulation.

PubMed

Allen, Sam J; King, Mark A; Yeadon, M R Fred

2016-04-01

The triple jump is an athletic event comprising three phases in which the optimal proportion of each phase to the total distance jumped, termed the phase ratio, is unknown. This study used a whole-body torque-driven computer simulation model of all three phases of the triple jump to investigate optimal technique. The technique of the simulation model was optimised by varying torque generator activation parameters using a Genetic Algorithm in order to maximise total jump distance, resulting in a hop-dominated technique (35.7%:30.8%:33.6%) and a distance of 14.05m. Optimisations were then run with penalties forcing the model to adopt hop and jump phases of 33%, 34%, 35%, 36%, and 37% of the optimised distance, resulting in total distances of: 13.79m, 13.87m, 13.95m, 14.05m, and 14.02m; and 14.01m, 14.02m, 13.97m, 13.84m, and 13.67m respectively. These results indicate that in this subject-specific case there is a plateau in optimum technique encompassing balanced and hop-dominated techniques, but that a jump-dominated technique is associated with a decrease in performance. Hop-dominated techniques are associated with higher forces than jump-dominated techniques; therefore optimal phase ratio may be related to a combination of strength and approach velocity. Copyright © 2016 Elsevier B.V. All rights reserved.

Parameterization of Shape and Compactness in Object-based Image Classification Using Quickbird-2 Imagery

NASA Astrophysics Data System (ADS)

Tonbul, H.; Kavzoglu, T.

2016-12-01

In recent years, object based image analysis (OBIA) has spread out and become a widely accepted technique for the analysis of remotely sensed data. OBIA deals with grouping pixels into homogenous objects based on spectral, spatial and textural features of contiguous pixels in an image. The first stage of OBIA, named as image segmentation, is the most prominent part of object recognition. In this study, multiresolution segmentation, which is a region-based approach, was employed to construct image objects. In the application of multi-resolution, three parameters, namely shape, compactness and scale must be set by the analyst. Segmentation quality remarkably influences the fidelity of the thematic maps and accordingly the classification accuracy. Therefore, it is of great importance to search and set optimal values for the segmentation parameters. In the literature, main focus has been on the definition of scale parameter, assuming that the effect of shape and compactness parameters is limited in terms of achieved classification accuracy. The aim of this study is to deeply analyze the influence of shape/compactness parameters by varying their values while using the optimal scale parameter determined by the use of Estimation of Scale Parameter (ESP-2) approach. A pansharpened Qickbird-2 image covering Trabzon, Turkey was employed to investigate the objectives of the study. For this purpose, six different combinations of shape/compactness were utilized to make deductions on the behavior of shape and compactness parameters and optimal setting for all parameters as a whole. Objects were assigned to classes using nearest neighbor classifier in all segmentation observations and equal number of pixels was randomly selected to calculate accuracy metrics. The highest overall accuracy (92.3%) was achieved by setting the shape/compactness criteria to 0.3/0.3. The results of this study indicate that shape/compactness parameters can have significant effect on classification accuracy with 4% change in overall accuracy. Also, statistical significance of differences in accuracy was tested using the McNemar's test and found that the difference between poor and optimal setting of shape/compactness parameters was statistically significant, suggesting a search for optimal parameterization instead of default setting.

Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

NASA Astrophysics Data System (ADS)

Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

2017-05-01

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

Global optimization for motion estimation with applications to ultrasound videos of carotid artery plaques

NASA Astrophysics Data System (ADS)

Murillo, Sergio; Pattichis, Marios; Soliz, Peter; Barriga, Simon; Loizou, C. P.; Pattichis, C. S.

2010-03-01

Motion estimation from digital video is an ill-posed problem that requires a regularization approach. Regularization introduces a smoothness constraint that can reduce the resolution of the velocity estimates. The problem is further complicated for ultrasound videos (US), where speckle noise levels can be significant. Motion estimation using optical flow models requires the modification of several parameters to satisfy the optical flow constraint as well as the level of imposed smoothness. Furthermore, except in simulations or mostly unrealistic cases, there is no ground truth to use for validating the velocity estimates. This problem is present in all real video sequences that are used as input to motion estimation algorithms. It is also an open problem in biomedical applications like motion analysis of US of carotid artery (CA) plaques. In this paper, we study the problem of obtaining reliable ultrasound video motion estimates for atherosclerotic plaques for use in clinical diagnosis. A global optimization framework for motion parameter optimization is presented. This framework uses actual carotid artery motions to provide optimal parameter values for a variety of motions and is tested on ten different US videos using two different motion estimation techniques.

Investigation of Parametric Influence on the Properties of Al6061-SiCp Composite

NASA Astrophysics Data System (ADS)

Adebisi, A. A.; Maleque, M. A.; Bello, K. A.

2017-03-01

The influence of process parameter in stir casting play a major role on the development of aluminium reinforced silicon carbide particle (Al-SiCp) composite. This study aims to investigate the influence of process parameters on wear and density properties of Al-SiCp composite using stir casting technique. Experimental data are generated based on a four-factors-five-level central composite design of response surface methodology. Analysis of variance is utilized to confirm the adequacy and validity of developed models considering the significant model terms. Optimization of the process parameters adequately predicts the Al-SiCp composite properties with stirring speed as the most influencing factor. The aim of optimization process is to minimize wear and maximum density. The multiple objective optimization (MOO) achieved an optimal value of 14 wt% reinforcement fraction (RF), 460 rpm stirring speed (SS), 820 °C processing temperature (PTemp) and 150 secs processing time (PT). Considering the optimum parametric combination, wear mass loss achieved a minimum of 1 x 10-3 g and maximum density value of 2.780g/mm3 with a confidence and desirability level of 95.5%.

Optimization of kinetic parameters for the degradation of plasmid DNA in rat plasma

NASA Astrophysics Data System (ADS)

Chaudhry, Q. A.

2014-12-01

Biotechnology is a rapidly growing area of research work in the field of pharmaceutical sciences. The study of pharmacokinetics of plasmid DNA (pDNA) is an important area of research work. It has been observed that the process of gene delivery faces many troubles on the transport of pDNA towards their target sites. The topoforms of pDNA has been termed as super coiled (S-C), open circular (O-C) and linear (L), the kinetic model of which will be presented in this paper. The kinetic model gives rise to system of ordinary differential equations (ODEs), the exact solution of which has been found. The kinetic parameters, which are responsible for the degradation of super coiled, and the formation of open circular and linear topoforms have a great significance not only in vitro but for modeling of further processes as well, therefore need to be addressed in great detail. For this purpose, global optimization techniques have been adopted, thus finding the optimal results for the said model. The results of the model, while using the optimal parameters, were compared against the measured data, which gives a nice agreement.

Parameterized CAD techniques implementation for the fatigue behaviour optimization of a service chamber

NASA Astrophysics Data System (ADS)

Sánchez, H. T.; Estrems, M.; Franco, P.; Faura, F.

2009-11-01

In recent years, the market of heat exchangers is increasingly demanding new products in short cycle time, which means that both the design and manufacturing stages must be extremely reduced. The design stage can be reduced by means of CAD-based parametric design techniques. The methodology presented in this proceeding is based on the optimized control of geometric parameters of a service chamber of a heat exchanger by means of the Application Programming Interface (API) provided by the Solidworks CAD package. Using this implementation, a set of different design configurations of the service chamber made of stainless steel AISI 316 are studied by means of the FE method. As a result of this study, a set of knowledge rules based on the fatigue behaviour are constructed and integrated into the design optimization process.

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

PubMed Central

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

PubMed

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

Integrator Windup Protection-Techniques and a STOVL Aircraft Engine Controller Application

NASA Technical Reports Server (NTRS)

KrishnaKumar, K.; Narayanaswamy, S.

1997-01-01

Integrators are included in the feedback loop of a control system to eliminate the steady state errors in the commanded variables. The integrator windup problem arises if the control actuators encounter operational limits before the steady state errors are driven to zero by the integrator. The typical effects of windup are large system oscillations, high steady state error, and a delayed system response following the windup. In this study, methods to prevent the integrator windup are examined to provide Integrator Windup Protection (IW) for an engine controller of a Short Take-Off and Vertical Landing (STOVL) aircraft. An unified performance index is defined to optimize the performance of the Conventional Anti-Windup (CAW) and the Modified Anti-Windup (MAW) methods. A modified Genetic Algorithm search procedure with stochastic parameter encoding is implemented to obtain the optimal parameters of the CAW scheme. The advantages and drawbacks of the CAW and MAW techniques are discussed and recommendations are made for the choice of the IWP scheme, given some characteristics of the system.

Engineering Parameters in Bioreactor's Design: A Critical Aspect in Tissue Engineering

PubMed Central

Amoabediny, Ghassem; Pouran, Behdad; Tabesh, Hadi; Shokrgozar, Mohammad Ali; Haghighipour, Nooshin; Khatibi, Nahid; Mottaghy, Khosrow; Zandieh-Doulabi, Behrouz

2013-01-01

Bioreactors are important inevitable part of any tissue engineering (TE) strategy as they aid the construction of three-dimensional functional tissues. Since the ultimate aim of a bioreactor is to create a biological product, the engineering parameters, for example, internal and external mass transfer, fluid velocity, shear stress, electrical current distribution, and so forth, are worth to be thoroughly investigated. The effects of such engineering parameters on biological cultures have been addressed in only a few preceding studies. Furthermore, it would be highly inefficient to determine the optimal engineering parameters by trial and error method. A solution is provided by emerging modeling and computational tools and by analyzing oxygen, carbon dioxide, and nutrient and metabolism waste material transports, which can simulate and predict the experimental results. Discovering the optimal engineering parameters is crucial not only to reduce the cost and time of experiments, but also to enhance efficacy and functionality of the tissue construct. This review intends to provide an inclusive package of the engineering parameters together with their calculation procedure in addition to the modeling techniques in TE bioreactors. PMID:24000327

Engineering parameters in bioreactor's design: a critical aspect in tissue engineering.

PubMed

Salehi-Nik, Nasim; Amoabediny, Ghassem; Pouran, Behdad; Tabesh, Hadi; Shokrgozar, Mohammad Ali; Haghighipour, Nooshin; Khatibi, Nahid; Anisi, Fatemeh; Mottaghy, Khosrow; Zandieh-Doulabi, Behrouz

2013-01-01

Bioreactors are important inevitable part of any tissue engineering (TE) strategy as they aid the construction of three-dimensional functional tissues. Since the ultimate aim of a bioreactor is to create a biological product, the engineering parameters, for example, internal and external mass transfer, fluid velocity, shear stress, electrical current distribution, and so forth, are worth to be thoroughly investigated. The effects of such engineering parameters on biological cultures have been addressed in only a few preceding studies. Furthermore, it would be highly inefficient to determine the optimal engineering parameters by trial and error method. A solution is provided by emerging modeling and computational tools and by analyzing oxygen, carbon dioxide, and nutrient and metabolism waste material transports, which can simulate and predict the experimental results. Discovering the optimal engineering parameters is crucial not only to reduce the cost and time of experiments, but also to enhance efficacy and functionality of the tissue construct. This review intends to provide an inclusive package of the engineering parameters together with their calculation procedure in addition to the modeling techniques in TE bioreactors.

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network.

PubMed

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

2016-11-10

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations

PubMed Central

Duarte, Belmiro P.M.; Wong, Weng Kee; Oliveira, Nuno M.C.

2015-01-01

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D–, A– and E–optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D–optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice. PMID:26949279

Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee; Oliveira, Nuno M C

2016-02-15

We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D -, A - and E -optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D -optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice.

Photovoltaic characteristics of diffused P/+N bulk GaAs solar cells

NASA Technical Reports Server (NTRS)

Borrego, J. M.; Keeney, R. P.; Bhat, I. B.; Bhat, K. N.; Sundaram, L. G.; Ghandhi, S. K.

1982-01-01

The photovoltaic characteristics of P(+)N junction solar cells fabricated on bulk GaAs by an open tube diffusion technique are described in this paper.Spectral response measurements were analyzed in detail and compared to a computer simulation in order to determine important material parameters. It is projected that proper optimization of the cell parameters can increase the efficiency of the cells from 12.2 percent to close to 20 percent.

Adaptive statistical pattern classifiers for remotely sensed data

NASA Technical Reports Server (NTRS)

Gonzalez, R. C.; Pace, M. O.; Raulston, H. S.

1975-01-01

A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data.

Vortex generator design for aircraft inlet distortion as a numerical optimization problem

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Levy, Ralph

1991-01-01

Aerodynamic compatibility of aircraft/inlet/engine systems is a difficult design problem for aircraft that must operate in many different flight regimes. Takeoff, subsonic cruise, supersonic cruise, transonic maneuvering, and high altitude loiter each place different constraints on inlet design. Vortex generators, small wing like sections mounted on the inside surfaces of the inlet duct, are used to control flow separation and engine face distortion. The design of vortex generator installations in an inlet is defined as a problem addressable by numerical optimization techniques. A performance parameter is suggested to account for both inlet distortion and total pressure loss at a series of design flight conditions. The resulting optimization problem is difficult since some of the design parameters take on integer values. If numerical procedures could be used to reduce multimillion dollar development test programs to a small set of verification tests, numerical optimization could have a significant impact on both cost and elapsed time to design new aircraft.

Optimization Strategies for Single-Stage, Multi-Stage and Continuous ADRs

NASA Technical Reports Server (NTRS)

Shirron, Peter J.

2014-01-01

Adiabatic Demagnetization Refrigerators (ADR) have many advantages that are prompting a resurgence in their use in spaceflight and laboratory applications. They are solid-state coolers capable of very high efficiency and very wide operating range. However, their low energy storage density translates to larger mass for a given cooling capacity than is possible with other refrigeration techniques. The interplay between refrigerant mass and other parameters such as magnetic field and heat transfer points in multi-stage ADRs gives rise to a wide parameter space for optimization. This paper first presents optimization strategies for single ADR stages, focusing primarily on obtaining the largest cooling capacity per stage mass, then discusses the optimization of multi-stage and continuous ADRs in the context of the coordinated heat transfer that must occur between stages. The goal for the latter is usually to obtain the largest cooling power per mass or volume, but there can also be many secondary objectives, such as limiting instantaneous heat rejection rates and producing intermediate temperatures for cooling of other instrument components.

CPU time optimization and precise adjustment of the Geant4 physics parameters for a VARIAN 2100 C/D gamma radiotherapy linear accelerator simulation using GAMOS.

PubMed

Arce, Pedro; Lagares, Juan Ignacio

2018-01-25

We have verified the GAMOS/Geant4 simulation model of a 6 MV VARIAN Clinac 2100 C/D linear accelerator by the procedure of adjusting the initial beam parameters to fit the percentage depth dose and cross-profile dose experimental data at different depths in a water phantom. Thanks to the use of a wide range of field sizes, from 2  ×  2 cm 2 to 40  ×  40 cm 2 , a small phantom voxel size and high statistics, fine precision in the determination of the beam parameters has been achieved. This precision has allowed us to make a thorough study of the different physics models and parameters that Geant4 offers. The three Geant4 electromagnetic physics sets of models, i.e. Standard, Livermore and Penelope, have been compared to the experiment, testing the four different models of angular bremsstrahlung distributions as well as the three available multiple-scattering models, and optimizing the most relevant Geant4 electromagnetic physics parameters. Before the fitting, a comprehensive CPU time optimization has been done, using several of the Geant4 efficiency improvement techniques plus a few more developed in GAMOS.

Performance evaluation of laser induced breakdown spectroscopy in the measurement of liquid and solid samples

NASA Astrophysics Data System (ADS)

Bilge, Gonca; Sezer, Banu; Boyaci, Ismail Hakki; Eseller, Kemal Efe; Berberoglu, Halil

2018-07-01

Liquid analysis by using LIBS is a complicated process due to difficulties encountered during the collection of light and formation of plasma in liquid. To avoid these, some applications are performed such as aerosol formation and transforming liquid into solid state. However, performance of LIBS in liquid samples still remains a challenging issue. In this study, performance evaluation of LIBS and parameter optimizations in liquid and solid phase samples were performed. For this purpose, milk was chosen as model sample; milk powder was used as solid sample, and milk was used as liquid sample in the experiments. Different experimental setups have been constructed for each sampling technique, and optimizations were performed to determine suitable parameters such as delay time, laser energy, repetition rate and speed of rotary table for solid sampling technique, and flow rate of carrier gas for liquid sampling technique. Target element was determined as Ca, which is a critically important element in milk for determining its nutritional value and Ca addition. In optimum parameters, limit of detection (LOD), limit of quantification (LOQ) and relative standard deviation (RSD) values were calculated as 0.11%, 0.36% and 8.29% respectively for milk powders samples; while LOD, LOQ and RSD values were calculated as 0.24%, 0.81%, and 10.93% respectively for milk samples. It can be said that LIBS is an applicable method in both liquid and solid samples with suitable systems and parameters. However, liquid analysis requires much more developed systems for more accurate results.

Measurement of the main and critical parameters for optimal laser treatment of heart disease

NASA Astrophysics Data System (ADS)

Kabeya, FB; Abrahamse, H.; Karsten, AE

2017-10-01

Laser light is frequently used in the diagnosis and treatment of patients. As in traditional treatments such as medication, bypass surgery, and minimally invasive ways, laser treatment can also fail and present serious side effects. The true reason for laser treatment failure or the side effects thereof, remains unknown. From the literature review conducted, and experimental results generated we conclude that an optimal laser treatment for coronary artery disease (named heart disease) can be obtained if certain critical parameters are correctly measured and understood. These parameters include the laser power, the laser beam profile, the fluence rate, the treatment time, as well as the absorption and scattering coefficients of the target treatment tissue. Therefore, this paper proposes different, accurate methods for the measurement of these critical parameters to determine the optimal laser treatment of heart disease with a minimal risk of side effects. The results from the measurement of absorption and scattering properties can be used in a computer simulation package to predict the fluence rate. The computing technique is a program based on the random number (Monte Carlo) process and probability statistics to track the propagation of photons through a biological tissue.

Cutting and coagulation during intraoral soft tissue surgery using Er: YAG laser.

PubMed

Onisor, I; Pecie, R; Chaskelis, I; Krejci, I

2013-06-01

To find the optimal techniques and parameters that enables Er:YAG laser to be used successfully for small intraoral soft tissue interventions, in respect to its cutting and coagulation abilities. In vitro pre-tests: 4 different Er:YAG laser units and one CO2 unit as the control were used for incision and coagulation on porcine lower jaws and optimal parameters were established for each type of intervention and each laser unit: energy, frequency, type, pulse duration and distance. 3 different types of intervention using Er:YAG units are presented: crown lengthening, gingivoplasty and maxillary labial frenectomy with parameters found in the in vitro pre-tests. The results showed a great decrease of the EMG activity of masseter and anterior temporalis muscles. Moreover, the height and width of the chewing cycles in the frontal plane increased after therapy. Er:YAG is able to provide good cutting and coagulation effects on soft tissues. Specific parameters have to be defined for each laser unit in order to obtain the desired effect. Reduced or absent water spray, defocused light beam, local anaesthesia and the most effective use of long pulses are methods to obtain optimal coagulation and bleeding control.

Reflecting metallic metasurfaces designed with stochastic optimization as waveplates for manipulating light polarization

NASA Astrophysics Data System (ADS)

Haberko, Jakub; Wasylczyk, Piotr

2018-03-01

We demonstrate that a stochastic optimization algorithm with a properly chosen, weighted fitness function, following a global variation of parameters upon each step can be used to effectively design reflective polarizing optical elements. Two sub-wavelength metallic metasurfaces, corresponding to broadband half- and quarter-waveplates are demonstrated with simple structure topology, a uniform metallic coating and with the design suited for the currently available microfabrication techniques, such as ion milling or 3D printing.

Optimal design of solenoid valve to minimize cavitation by numerical analysis

NASA Astrophysics Data System (ADS)

Ko, Seungbin; Jang, Ilhoon; Song, Simon

2012-11-01

Keeping pace with the development of clean energy, hybrid cars and electric vehicles are getting extensive attention recently. In an electronic-control brake system which is essential to those vehicles, a solenoid valve is used to control external hydraulic pressure that boosts up the driver's braking force. However, strong cavitation occurs at the narrow passage between the ball and seat of a solenoid valve due to sudden decrease in pressure, leading to severe damage to the valve. In this study, we investigate the cavitation numerically to discover geometric parameters to affect the cavitation, and an optimal design to minimize the cavitation using optimization technique. As a result, we found four parameters: seat inner radius, seat angle, seat length, and ball radius. Among them, the seat inner radius affects the cavitation most. Also, we found that preventing a sudden reduction in a flow passage is important to reduce cavitation. Finally using an evolutionary algorithm for optimization we minimized cavitation. The optimal design resulted in the maximum vapor volume of fraction of 0.04 while it was 0.7 for reference geometry.

Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

PubMed

Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan

2009-02-01

The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

NASA Astrophysics Data System (ADS)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Optimal Inversion Parameters for Full Waveform Inversion using OBS Data Set

NASA Astrophysics Data System (ADS)

Kim, S.; Chung, W.; Shin, S.; Kim, D.; Lee, D.

2017-12-01

In recent years, full Waveform Inversion (FWI) has been the most researched technique in seismic data processing. It uses the residuals between observed and modeled data as an objective function; thereafter, the final subsurface velocity model is generated through a series of iterations meant to minimize the residuals.Research on FWI has expanded from acoustic media to elastic media. In acoustic media, the subsurface property is defined by P-velocity; however, in elastic media, properties are defined by multiple parameters, such as P-velocity, S-velocity, and density. Further, the elastic media can also be defined by Lamé constants, density or impedance PI, SI; consequently, research is being carried out to ascertain the optimal parameters.From results of advanced exploration equipment and Ocean Bottom Seismic (OBS) survey, it is now possible to obtain multi-component seismic data. However, to perform FWI on these data and generate an accurate subsurface model, it is important to determine optimal inversion parameters among (Vp, Vs, ρ), (λ, μ, ρ), and (PI, SI) in elastic media. In this study, staggered grid finite difference method was applied to simulate OBS survey. As in inversion, l2-norm was set as objective function. Further, the accurate computation of gradient direction was performed using the back-propagation technique and its scaling was done using the Pseudo-hessian matrix.In acoustic media, only Vp is used as the inversion parameter. In contrast, various sets of parameters, such as (Vp, Vs, ρ) and (λ, μ, ρ) can be used to define inversion in elastic media. Therefore, it is important to ascertain the parameter that gives the most accurate result for inversion with OBS data set.In this study, we generated Vp and Vs subsurface models by using (λ, μ, ρ) and (Vp, Vs, ρ) as inversion parameters in every iteration, and compared the final two FWI results.This research was supported by the Basic Research Project(17-3312) of the Korea Institute of Geoscience and Mineral Resources(KIGAM) funded by the Ministry of Science, ICT and Future Planning of Korea.

Optimization of turning process through the analytic flank wear modelling

NASA Astrophysics Data System (ADS)

Del Prete, A.; Franchi, R.; De Lorenzis, D.

2018-05-01

In the present work, the approach used for the optimization of the process capabilities for Oil&Gas components machining will be described. These components are machined by turning of stainless steel castings workpieces. For this purpose, a proper Design Of Experiments (DOE) plan has been designed and executed: as output of the experimentation, data about tool wear have been collected. The DOE has been designed starting from the cutting speed and feed values recommended by the tools manufacturer; the depth of cut parameter has been maintained as a constant. Wear data has been obtained by means the observation of the tool flank wear under an optical microscope: the data acquisition has been carried out at regular intervals of working times. Through a statistical data and regression analysis, analytical models of the flank wear and the tool life have been obtained. The optimization approach used is a multi-objective optimization, which minimizes the production time and the number of cutting tools used, under the constraint on a defined flank wear level. The technique used to solve the optimization problem is a Multi Objective Particle Swarm Optimization (MOPS). The optimization results, validated by the execution of a further experimental campaign, highlighted the reliability of the work and confirmed the usability of the optimized process parameters and the potential benefit for the company.

High dynamic range fringe acquisition: A novel 3-D scanning technique for high-reflective surfaces

NASA Astrophysics Data System (ADS)

Jiang, Hongzhi; Zhao, Huijie; Li, Xudong

2012-10-01

This paper presents a novel 3-D scanning technique for high-reflective surfaces based on phase-shifting fringe projection method. High dynamic range fringe acquisition (HDRFA) technique is developed to process the fringe images reflected from the shiny surfaces, and generates a synthetic fringe image by fusing the raw fringe patterns, acquired with different camera exposure time and the illumination fringe intensity from the projector. Fringe image fusion algorithm is introduced to avoid saturation and under-illumination phenomenon by choosing the pixels in the raw fringes with the highest fringe modulation intensity. A method of auto-selection of HDRFA parameters is developed and largely increases the measurement automation. The synthetic fringes have higher signal-to-noise ratio (SNR) under ambient light by optimizing HDRFA parameters. Experimental results show that the proposed technique can successfully measure objects with high-reflective surfaces and is insensitive to ambient light.

Importance of double-pole CFS-PML for broad-band seismic wave simulation and optimal parameters selection

NASA Astrophysics Data System (ADS)

Feng, Haike; Zhang, Wei; Zhang, Jie; Chen, Xiaofei

2017-05-01

The perfectly matched layer (PML) is an efficient absorbing technique for numerical wave simulation. The complex frequency-shifted PML (CFS-PML) introduces two additional parameters in the stretching function to make the absorption frequency dependent. This can help to suppress converted evanescent waves from near grazing incident waves, but does not efficiently absorb low-frequency waves below the cut-off frequency. To absorb both the evanescent wave and the low-frequency wave, the double-pole CFS-PML having two poles in the coordinate stretching function was developed in computational electromagnetism. Several studies have investigated the performance of the double-pole CFS-PML for seismic wave simulations in the case of a narrowband seismic wavelet and did not find significant difference comparing to the CFS-PML. Another difficulty to apply the double-pole CFS-PML for real problems is that a practical strategy to set optimal parameter values has not been established. In this work, we study the performance of the double-pole CFS-PML for broad-band seismic wave simulation. We find that when the maximum to minimum frequency ratio is larger than 16, the CFS-PML will either fail to suppress the converted evanescent waves for grazing incident waves, or produce visible low-frequency reflection, depending on the value of α. In contrast, the double-pole CFS-PML can simultaneously suppress the converted evanescent waves and avoid low-frequency reflections with proper parameter values. We analyse the different roles of the double-pole CFS-PML parameters and propose optimal selections of these parameters. Numerical tests show that the double-pole CFS-PML with the optimal parameters can generate satisfactory results for broad-band seismic wave simulations.

Identification of laboratory techniques to optimize Superpave HMA surface friction characteristics : final report, April 2010.

DOT National Transportation Integrated Search

2010-04-15

Wet pavement friction is known to be one of the most important roadway safety parameters. In this : research, frictional properties of flexible (asphalt) pavements were investigated. : As a part of this study, a laboratory device to polish asphalt sp...

Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

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

Pereira, Ana I.; ALGORITMI,University of Minho; Lima, José

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Finalmore » results demonstrate that optimization is a valid gait planning technique.« less

Optimization of Wireless Power Transfer Systems Enhanced by Passive Elements and Metasurfaces

NASA Astrophysics Data System (ADS)

Lang, Hans-Dieter; Sarris, Costas D.

2017-10-01

This paper presents a rigorous optimization technique for wireless power transfer (WPT) systems enhanced by passive elements, ranging from simple reflectors and intermedi- ate relays all the way to general electromagnetic guiding and focusing structures, such as metasurfaces and metamaterials. At its core is a convex semidefinite relaxation formulation of the otherwise nonconvex optimization problem, of which tightness and optimality can be confirmed by a simple test of its solutions. The resulting method is rigorous, versatile, and general -- it does not rely on any assumptions. As shown in various examples, it is able to efficiently and reliably optimize such WPT systems in order to find their physical limitations on performance, optimal operating parameters and inspect their working principles, even for a large number of active transmitters and passive elements.

Current techniques for the real-time processing of complex radar signatures

NASA Astrophysics Data System (ADS)

Clay, E.

A real-time processing technique has been developed for the microwave receiver of the Brahms radar station. The method allows such target signatures as the radar cross section (RCS) of the airframes and rotating parts, the one-dimensional tomography of aircraft, and the RCS of electromagnetic decoys to be characterized. The method allows optimization of experimental parameters including the analysis frequency band, the receiver gain, and the wavelength range of EM analysis.

Task-driven imaging in cone-beam computed tomography.

PubMed

Gang, G J; Stayman, J W; Ouadah, S; Ehtiati, T; Siewerdsen, J H

Conventional workflow in interventional imaging often ignores a wealth of prior information of the patient anatomy and the imaging task. This work introduces a task-driven imaging framework that utilizes such information to prospectively design acquisition and reconstruction techniques for cone-beam CT (CBCT) in a manner that maximizes task-based performance in subsequent imaging procedures. The framework is employed in jointly optimizing tube current modulation, orbital tilt, and reconstruction parameters in filtered backprojection reconstruction for interventional imaging. Theoretical predictors of noise and resolution relates acquisition and reconstruction parameters to task-based detectability. Given a patient-specific prior image and specification of the imaging task, an optimization algorithm prospectively identifies the combination of imaging parameters that maximizes task-based detectability. Initial investigations were performed for a variety of imaging tasks in an elliptical phantom and an anthropomorphic head phantom. Optimization of tube current modulation and view-dependent reconstruction kernel was shown to have greatest benefits for a directional task (e.g., identification of device or tissue orientation). The task-driven approach yielded techniques in which the dose and sharp kernels were concentrated in views contributing the most to the signal power associated with the imaging task. For example, detectability of a line pair detection task was improved by at least three fold compared to conventional approaches. For radially symmetric tasks, the task-driven strategy yielded results similar to a minimum variance strategy in the absence of kernel modulation. Optimization of the orbital tilt successfully avoided highly attenuating structures that can confound the imaging task by introducing noise correlations masquerading at spatial frequencies of interest. This work demonstrated the potential of a task-driven imaging framework to improve image quality and reduce dose beyond that achievable with conventional imaging approaches.

IOTA: integration optimization, triage and analysis tool for the processing of XFEL diffraction images.

PubMed

Lyubimov, Artem Y; Uervirojnangkoorn, Monarin; Zeldin, Oliver B; Brewster, Aaron S; Murray, Thomas D; Sauter, Nicholas K; Berger, James M; Weis, William I; Brunger, Axel T

2016-06-01

Serial femtosecond crystallography (SFX) uses an X-ray free-electron laser to extract diffraction data from crystals not amenable to conventional X-ray light sources owing to their small size or radiation sensitivity. However, a limitation of SFX is the high variability of the diffraction images that are obtained. As a result, it is often difficult to determine optimal indexing and integration parameters for the individual diffraction images. Presented here is a software package, called IOTA , which uses a grid-search technique to determine optimal spot-finding parameters that can in turn affect the success of indexing and the quality of integration on an image-by-image basis. Integration results can be filtered using a priori information about the Bravais lattice and unit-cell dimensions and analyzed for unit-cell isomorphism, facilitating an improvement in subsequent data-processing steps.

Optimal sampling theory and population modelling - Application to determination of the influence of the microgravity environment on drug distribution and elimination

NASA Technical Reports Server (NTRS)

Drusano, George L.

1991-01-01

The optimal sampling theory is evaluated in applications to studies related to the distribution and elimination of several drugs (including ceftazidime, piperacillin, and ciprofloxacin), using the SAMPLE module of the ADAPT II package of programs developed by D'Argenio and Schumitzky (1979, 1988) and comparing the pharmacokinetic parameter values with results obtained by traditional ten-sample design. The impact of the use of optimal sampling was demonstrated in conjunction with NONMEM (Sheiner et al., 1977) approach, in which the population is taken as the unit of analysis, allowing even fragmentary patient data sets to contribute to population parameter estimates. It is shown that this technique is applicable in both the single-dose and the multiple-dose environments. The ability to study real patients made it possible to show that there was a bimodal distribution in ciprofloxacin nonrenal clearance.

Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm

NASA Astrophysics Data System (ADS)

Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel

2011-09-01

The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.

Photogrammetry and ballistic analysis of a high-flying projectile in the STS-124 space shuttle launch

NASA Astrophysics Data System (ADS)

Metzger, Philip T.; Lane, John E.; Carilli, Robert A.; Long, Jason M.; Shawn, Kathy L.

2010-07-01

A method combining photogrammetry with ballistic analysis is demonstrated to identify flying debris in a rocket launch environment. Debris traveling near the STS-124 Space Shuttle was captured on cameras viewing the launch pad within the first few seconds after launch. One particular piece of debris caught the attention of investigators studying the release of flame trench fire bricks because its high trajectory could indicate a flight risk to the Space Shuttle. Digitized images from two pad perimeter high-speed 16-mm film cameras were processed using photogrammetry software based on a multi-parameter optimization technique. Reference points in the image were found from 3D CAD models of the launch pad and from surveyed points on the pad. The three-dimensional reference points were matched to the equivalent two-dimensional camera projections by optimizing the camera model parameters using a gradient search optimization technique. Using this method of solving the triangulation problem, the xyz position of the object's path relative to the reference point coordinate system was found for every set of synchronized images. This trajectory was then compared to a predicted trajectory while performing regression analysis on the ballistic coefficient and other parameters. This identified, with a high degree of confidence, the object's material density and thus its probable origin within the launch pad environment. Future extensions of this methodology may make it possible to diagnose the underlying causes of debris-releasing events in near-real time, thus improving flight safety.

Stochastic parameter estimation in nonlinear time-delayed vibratory systems with distributed delay

NASA Astrophysics Data System (ADS)

Torkamani, Shahab; Butcher, Eric A.

2013-07-01

The stochastic estimation of parameters and states in linear and nonlinear time-delayed vibratory systems with distributed delay is explored. The approach consists of first employing a continuous time approximation to approximate the delayed integro-differential system with a large set of ordinary differential equations having stochastic excitations. Then the problem of state and parameter estimation in the resulting stochastic ordinary differential system is represented as an optimal filtering problem using a state augmentation technique. By adapting the extended Kalman-Bucy filter to the augmented filtering problem, the unknown parameters of the time-delayed system are estimated from noise-corrupted, possibly incomplete measurements of the states. Similarly, the upper bound of the distributed delay can also be estimated by the proposed technique. As an illustrative example to a practical problem in vibrations, the parameter, delay upper bound, and state estimation from noise-corrupted measurements in a distributed force model widely used for modeling machine tool vibrations in the turning operation is investigated.

Fast Appearance Modeling for Automatic Primary Video Object Segmentation.

PubMed

Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong

2016-02-01

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.

Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

PubMed Central

Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

2016-01-01

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

Sci-Thur PM - Colourful Interactions: Highlights 08: ARC TBI using Single-Step Optimized VMAT Fields

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

Hudson, Alana; Gordon, Deborah; Moore, Roseanne

Purpose: This work outlines a new TBI delivery technique to replace a lateral POP full bolus technique. The new technique is done with VMAT arc delivery, without bolus, treating the patient prone and supine. The benefits of the arc technique include: increased patient experience and safety, better dose conformity, better organ at risk sparing, decreased therapist time and reduction of therapist injuries. Methods: In this work we build on a technique developed by Jahnke et al. We use standard arc fields with gantry speeds corrected for varying distance to the patient followed by a single step VMAT optimization on amore » patient CT to increase dose inhomogeneity and to reduce dose to the lungs (vs. blocks). To compare the arc TBI technique to our full bolus technique, we produced plans on patient CTs for both techniques and evaluated several dosimetric parameters using an ANOVA test. Results and Conclusions: The arc technique is able reduce both the hot areas to the body (D2% reduced from 122.2% to 111.8% p<0.01) and the lungs (mean lung dose reduced from 107.5% to 99.1%, p<0.01), both statistically significant, while maintaining coverage (D98% = 97.8% vs. 94.6%, p=0.313, not statistically significant). We developed a more patient and therapist-friendly TBI treatment technique that utilizes single-step optimized VMAT plans. It was found that this technique was dosimetrically equivalent to our previous lateral technique in terms of coverage and statistically superior in terms of reduced lung dose.« less

An Optimal Control Strategy for DC Bus Voltage Regulation in Photovoltaic System with Battery Energy Storage

PubMed Central

Daud, Muhamad Zalani; Mohamed, Azah; Hannan, M. A.

2014-01-01

This paper presents an evaluation of an optimal DC bus voltage regulation strategy for grid-connected photovoltaic (PV) system with battery energy storage (BES). The BES is connected to the PV system DC bus using a DC/DC buck-boost converter. The converter facilitates the BES power charge/discharge to compensate for the DC bus voltage deviation during severe disturbance conditions. In this way, the regulation of DC bus voltage of the PV/BES system can be enhanced as compared to the conventional regulation that is solely based on the voltage-sourced converter (VSC). For the grid side VSC (G-VSC), two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. A new set of optimized parameters are obtained for each of the power converters for comparison purposes. The PSCAD/EMTDC-based simulation case studies are presented to evaluate the performance of the proposed optimized control scheme in comparison to the conventional methods. PMID:24883374

An optimal control strategy for DC bus voltage regulation in photovoltaic system with battery energy storage.

PubMed

Daud, Muhamad Zalani; Mohamed, Azah; Hannan, M A

2014-01-01

This paper presents an evaluation of an optimal DC bus voltage regulation strategy for grid-connected photovoltaic (PV) system with battery energy storage (BES). The BES is connected to the PV system DC bus using a DC/DC buck-boost converter. The converter facilitates the BES power charge/discharge to compensate for the DC bus voltage deviation during severe disturbance conditions. In this way, the regulation of DC bus voltage of the PV/BES system can be enhanced as compared to the conventional regulation that is solely based on the voltage-sourced converter (VSC). For the grid side VSC (G-VSC), two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. A new set of optimized parameters are obtained for each of the power converters for comparison purposes. The PSCAD/EMTDC-based simulation case studies are presented to evaluate the performance of the proposed optimized control scheme in comparison to the conventional methods.

Space Weather Activities of IONOLAB Group: TEC Mapping

NASA Astrophysics Data System (ADS)

Arikan, F.; Yilmaz, A.; Arikan, O.; Sayin, I.; Gurun, M.; Akdogan, K. E.; Yildirim, S. A.

2009-04-01

Being a key player in Space Weather, ionospheric variability affects the performance of both communication and navigation systems. To improve the performance of these systems, ionosphere has to be monitored. Total Electron Content (TEC), line integral of the electron density along a ray path, is an important parameter to investigate the ionospheric variability. A cost-effective way of obtaining TEC is by using dual-frequency GPS receivers. Since these measurements are sparse in space, accurate and robust interpolation techniques are needed to interpolate (or map) the TEC distribution for a given region in space. However, the TEC data derived from GPS measurements contain measurement noise, model and computational errors. Thus, it is necessary to analyze the interpolation performance of the techniques on synthetic data sets that can represent various ionospheric states. By this way, interpolation performance of the techniques can be compared over many parameters that can be controlled to represent the desired ionospheric states. In this study, Multiquadrics, Inverse Distance Weighting (IDW), Cubic Splines, Ordinary and Universal Kriging, Random Field Priors (RFP), Multi-Layer Perceptron Neural Network (MLP-NN), and Radial Basis Function Neural Network (RBF-NN) are employed as the spatial interpolation algorithms. These mapping techniques are initially tried on synthetic TEC surfaces for parameter and coefficient optimization and determination of error bounds. Interpolation performance of these methods are compared on synthetic TEC surfaces over the parameters of sampling pattern, number of samples, the variability of the surface and the trend type in the TEC surfaces. By examining the performance of the interpolation methods, it is observed that both Kriging, RFP and NN have important advantages and possible disadvantages depending on the given constraints. It is also observed that the determining parameter in the error performance is the trend in the Ionosphere. Optimization of the algorithms in terms of their performance parameters (like the choice of the semivariogram function for Kriging algorithms and the hidden layer and neuron numbers for MLP-NN) mostly depend on the behavior of the ionosphere at that given time instant for the desired region. The sampling pattern and number of samples are the other important parameters that may contribute to the higher errors in reconstruction. For example, for all of the above listed algorithms, hexagonal regular sampling of the ionosphere provides the lowest reconstruction error and the performance significantly degrades as the samples in the region become sparse and clustered. The optimized models and coefficients are applied to regional GPS-TEC mapping using the IONOLAB-TEC data (www.ionolab.org). Both Kriging combined with Kalman Filter and dynamic modeling of NN are also implemented as first trials of TEC and space weather predictions.

Optimized MLAA for quantitative non-TOF PET/MR of the brain

NASA Astrophysics Data System (ADS)

Benoit, Didier; Ladefoged, Claes N.; Rezaei, Ahmadreza; Keller, Sune H.; Andersen, Flemming L.; Højgaard, Liselotte; Hansen, Adam E.; Holm, Søren; Nuyts, Johan

2016-12-01

For quantitative tracer distribution in positron emission tomography, attenuation correction is essential. In a hybrid PET/CT system the CT images serve as a basis for generation of the attenuation map, but in PET/MR, the MR images do not have a similarly simple relationship with the attenuation map. Hence attenuation correction in PET/MR systems is more challenging. Typically either of two MR sequences are used: the Dixon or the ultra-short time echo (UTE) techniques. However these sequences have some well-known limitations. In this study, a reconstruction technique based on a modified and optimized non-TOF MLAA is proposed for PET/MR brain imaging. The idea is to tune the parameters of the MLTR applying some information from an attenuation image computed from the UTE sequences and a T1w MR image. In this MLTR algorithm, an {αj} parameter is introduced and optimized in order to drive the algorithm to a final attenuation map most consistent with the emission data. Because the non-TOF MLAA is used, a technique to reduce the cross-talk effect is proposed. In this study, the proposed algorithm is compared to the common reconstruction methods such as OSEM using a CT attenuation map, considered as the reference, and OSEM using the Dixon and UTE attenuation maps. To show the robustness and the reproducibility of the proposed algorithm, a set of 204 [18F]FDG patients, 35 [11C]PiB patients and 1 [18F]FET patient are used. The results show that by choosing an optimized value of {αj} in MLTR, the proposed algorithm improves the results compared to the standard MR-based attenuation correction methods (i.e. OSEM using the Dixon or the UTE attenuation maps), and the cross-talk and the scale problem are limited.

Experimental investigation and optimization of welding process parameters for various steel grades using NN tool and Taguchi method

NASA Astrophysics Data System (ADS)

Soni, Sourabh Kumar; Thomas, Benedict

2018-04-01

The term "weldability" has been used to describe a wide variety of characteristics when a material is subjected to welding. In our analysis we perform experimental investigation to estimate the tensile strength of welded joint strength and then optimization of welding process parameters by using taguchi method and Artificial Neural Network (ANN) tool in MINITAB and MATLAB software respectively. The study reveals the influence on weldability of steel by varying composition of steel by mechanical characterization. At first we prepare the samples of different grades of steel (EN8, EN 19, EN 24). The samples were welded together by metal inert gas welding process and then tensile testing on Universal testing machine (UTM) was conducted for the same to evaluate the tensile strength of the welded steel specimens. Further comparative study was performed to find the effects of welding parameter on quality of weld strength by employing Taguchi method and Neural Network tool. Finally we concluded that taguchi method and Neural Network Tool is much efficient technique for optimization.

SiC-VJFETs power switching devices: an improved model and parameter optimization technique

NASA Astrophysics Data System (ADS)

Ben Salah, T.; Lahbib, Y.; Morel, H.

2009-12-01

Silicon carbide junction field effect transistor (SiC-JFETs) is a mature power switch newly applied in several industrial applications. SiC-JFETs are often simulated by Spice model in order to predict their electrical behaviour. Although such a model provides sufficient accuracy for some applications, this paper shows that it presents serious shortcomings in terms of the neglect of the body diode model, among many others in circuit model topology. Simulation correction is then mandatory and a new model should be proposed. Moreover, this paper gives an enhanced model based on experimental dc and ac data. New devices are added to the conventional circuit model giving accurate static and dynamic behaviour, an effect not accounted in the Spice model. The improved model is implemented into VHDL-AMS language and steady-state dynamic and transient responses are simulated for many SiC-VJFETs samples. Very simple and reliable optimization algorithm based on the optimization of a cost function is proposed to extract the JFET model parameters. The obtained parameters are verified by comparing errors between simulations results and experimental data.

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Convergence in parameters and predictions using computational experimental design.

PubMed

Hagen, David R; White, Jacob K; Tidor, Bruce

2013-08-06

Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

Analysis, optimization, and implementation of a hybrid DS/FFH spread-spectrum technique for smart grid communications

DOE PAGES

Olama, Mohammed M.; Ma, Xiao; Killough, Stephen M.; ...

2015-03-12

In recent years, there has been great interest in using hybrid spread-spectrum (HSS) techniques for commercial applications, particularly in the Smart Grid, in addition to their inherent uses in military communications. This is because HSS can accommodate high data rates with high link integrity, even in the presence of significant multipath effects and interfering signals. A highly useful form of this transmission technique for many types of command, control, and sensing applications is the specific code-related combination of standard direct sequence modulation with fast frequency hopping, denoted hybrid DS/FFH, wherein multiple frequency hops occur within a single data-bit time. Inmore » this paper, error-probability analyses are performed for a hybrid DS/FFH system over standard Gaussian and fading-type channels, progressively including the effects from wide- and partial-band jamming, multi-user interference, and varying degrees of Rayleigh and Rician fading. In addition, an optimization approach is formulated that minimizes the bit-error performance of a hybrid DS/FFH communication system and solves for the resulting system design parameters. The optimization objective function is non-convex and can be solved by applying the Karush-Kuhn-Tucker conditions. We also present our efforts toward exploring the design, implementation, and evaluation of a hybrid DS/FFH radio transceiver using a single FPGA. Numerical and experimental results are presented under widely varying design parameters to demonstrate the adaptability of the waveform for varied harsh smart grid RF signal environments.« less

Modified extrusion-spheronization as a technique of microencapsulation for stabilization of choline bitartrate using hydrogenated soya bean oil.

PubMed

Gangurde, Avinash Bhaskar; Sav, Ajay Kumar; Javeer, Sharadchandra Dagadu; Moravkar, Kailas K; Pawar, Jaywant N; Amin, Purnima D

2015-01-01

Choline bitartrate (CBT) is a vital nutrient for fetal brain development and memory function. It is hygroscopic in nature which is associated with stability related problem during storage such as development of fishy odor and discoloration. Microencapsulation method was adopted to resolve the stability problem and for this hydrogenated soya bean oil (HSO) was used as encapsulating agent. Industrially feasible modified extrusion-spheronization technique was selected for microencapsulation. HSO was used as encapsulating agent, hydroxypropyl methyl cellulose E5/E15 as binder and microcrystalline cellulose as spheronization aid. Formulated pellets were evaluated for parameters such as flow property, morphological characteristics, hardness-friability index (HFI), drug content, encapsulation efficiency, and in vitro drug release. The optimized formulations were also characterized for particle size (by laser diffractometry), differential scanning calorimetry, powder X-ray diffractometry (PXRD), Fourier transform infrared spectroscopy, and scanning electron microscopy. The results from the study showed that coating of 90% and 60% CBT was successful with respect to all desired evaluation parameters. Optimized formulation was kept for 6 months stability study as per ICH guidelines, and there was no change in color, moisture content, drug content, and no fishy odor was observed. Microencapsulated pellets of CBT using HSO as encapsulating agent were developed using modified extrusion spheronization technique. Optimized formulations, CBT 90% (F5), and CBT 60% (F10), were found to be stable for 4M and 6M, respectively, at accelerated conditions.

Rocket ascent G-limited moment-balanced optimization program (RAGMOP)

NASA Technical Reports Server (NTRS)

Lyons, J. T.; Woltosz, W. S.; Abercrombie, G. E.; Gottlieb, R. G.

1972-01-01

This document describes the RAGMOP (Rocket Ascent G-limited Momentbalanced Optimization Program) computer program for parametric ascent trajectory optimization. RAGMOP computes optimum polynomial-form attitude control histories, launch azimuth, engine burn-time, and gross liftoff weight for space shuttle type vehicles using a search-accelerated, gradient projection parameter optimization technique. The trajectory model available in RAGMOP includes a rotating oblate earth model, the option of input wind tables, discrete and/or continuous throttling for the purposes of limiting the thrust acceleration and/or the maximum dynamic pressure, limitation of the structural load indicators (the product of dynamic pressure with angle-of-attack and sideslip angle), and a wide selection of intermediate and terminal equality constraints.

SEEK: A FORTRAN optimization program using a feasible directions gradient search

NASA Technical Reports Server (NTRS)

Savage, M.

1995-01-01

This report describes the use of computer program 'SEEK' which works in conjunction with two user-written subroutines and an input data file to perform an optimization procedure on a user's problem. The optimization method uses a modified feasible directions gradient technique. SEEK is written in ANSI standard Fortran 77, has an object size of about 46K bytes, and can be used on a personal computer running DOS. This report describes the use of the program and discusses the optimizing method. The program use is illustrated with four example problems: a bushing design, a helical coil spring design, a gear mesh design, and a two-parameter Weibull life-reliability curve fit.

Program manual for ASTOP, an Arbitrary space trajectory optimization program

NASA Technical Reports Server (NTRS)

Horsewood, J. L.

1974-01-01

The ASTOP program (an Arbitrary Space Trajectory Optimization Program) designed to generate optimum low-thrust trajectories in an N-body field while satisfying selected hardware and operational constraints is presented. The trajectory is divided into a number of segments or arcs over which the control is held constant. This constant control over each arc is optimized using a parameter optimization scheme based on gradient techniques. A modified Encke formulation of the equations of motion is employed. The program provides a wide range of constraint, end conditions, and performance index options. The basic approach is conducive to future expansion of features such as the incorporation of new constraints and the addition of new end conditions.

A Particle Smoother with Sequential Importance Resampling for soil hydraulic parameter estimation: A lysimeter experiment

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Hendricks Franssen, Harrie-Jan; Moradkhani, Hamid; Pütz, Thomas; Han, Xujun; Vereecken, Harry

2013-04-01

An adequate description of soil hydraulic properties is essential for a good performance of hydrological forecasts. So far, several studies showed that data assimilation could reduce the parameter uncertainty by considering soil moisture observations. However, these observations and also the model forcings were recorded with a specific measurement error. It seems a logical step to base state updating and parameter estimation on observations made at multiple time steps, in order to reduce the influence of outliers at single time steps given measurement errors and unknown model forcings. Such outliers could result in erroneous state estimation as well as inadequate parameters. This has been one of the reasons to use a smoothing technique as implemented for Bayesian data assimilation methods such as the Ensemble Kalman Filter (i.e. Ensemble Kalman Smoother). Recently, an ensemble-based smoother has been developed for state update with a SIR particle filter. However, this method has not been used for dual state-parameter estimation. In this contribution we present a Particle Smoother with sequentially smoothing of particle weights for state and parameter resampling within a time window as opposed to the single time step data assimilation used in filtering techniques. This can be seen as an intermediate variant between a parameter estimation technique using global optimization with estimation of single parameter sets valid for the whole period, and sequential Monte Carlo techniques with estimation of parameter sets evolving from one time step to another. The aims are i) to improve the forecast of evaporation and groundwater recharge by estimating hydraulic parameters, and ii) to reduce the impact of single erroneous model inputs/observations by a smoothing method. In order to validate the performance of the proposed method in a real world application, the experiment is conducted in a lysimeter environment.

Experiences with Probabilistic Analysis Applied to Controlled Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Giesy, Daniel P.

2004-01-01

This paper presents a semi-analytic method for computing frequency dependent means, variances, and failure probabilities for arbitrarily large-order closed-loop dynamical systems possessing a single uncertain parameter or with multiple highly correlated uncertain parameters. The approach will be shown to not suffer from the same computational challenges associated with computing failure probabilities using conventional FORM/SORM techniques. The approach is demonstrated by computing the probabilistic frequency domain performance of an optimal feed-forward disturbance rejection scheme.

Space Shuttle propulsion parameter estimation using optimal estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

The fifth monthly progress report includes corrections and additions to the previously submitted reports. The addition of the SRB propellant thickness as a state variable is included with the associated partial derivatives. During this reporting period, preliminary results of the estimation program checkout was presented to NASA technical personnel.

Rapid performance modeling and parameter regression of geodynamic models

NASA Astrophysics Data System (ADS)

Brown, J.; Duplyakin, D.

2016-12-01

Geodynamic models run in a parallel environment have many parameters with complicated effects on performance and scientifically-relevant functionals. Manually choosing an efficient machine configuration and mapping out the parameter space requires a great deal of expert knowledge and time-consuming experiments. We propose an active learning technique based on Gaussion Process Regression to automatically select experiments to map out the performance landscape with respect to scientific and machine parameters. The resulting performance model is then used to select optimal experiments for improving the accuracy of a reduced order model per unit of computational cost. We present the framework and evaluate its quality and capability using popular lithospheric dynamics models.

Evaluation of random errors in Williams’ series coefficients obtained with digital image correlation

NASA Astrophysics Data System (ADS)

Lychak, Oleh V.; Holyns'kiy, Ivan S.

2016-03-01

The use of the Williams’ series parameters for fracture analysis requires valid information about their error values. The aim of this investigation is the development of the method for estimation of the standard deviation of random errors of the Williams’ series parameters, obtained from the measured components of the stress field. Also, the criteria for choosing the optimal number of terms in the truncated Williams’ series for derivation of their parameters with minimal errors is proposed. The method was used for the evaluation of the Williams’ parameters, obtained from the data, and measured by the digital image correlation technique for testing a three-point bending specimen.

Study on Silicon Microstructure Processing Technology Based on Porous Silicon

NASA Astrophysics Data System (ADS)

Shang, Yingqi; Zhang, Linchao; Qi, Hong; Wu, Yalin; Zhang, Yan; Chen, Jing

2018-03-01

Aiming at the heterogeneity of micro - sealed cavity in silicon microstructure processing technology, the technique of preparing micro - sealed cavity of porous silicon is proposed. The effects of different solutions, different substrate doping concentrations, different current densities, and different etching times on the rate, porosity, thickness and morphology of the prepared porous silicon were studied. The porous silicon was prepared by different process parameters and the prepared porous silicon was tested and analyzed. For the test results, optimize the process parameters and experiments. The experimental results show that the porous silicon can be controlled by optimizing the parameters of the etching solution and the doping concentration of the substrate, and the preparation of porous silicon with different porosity can be realized by different doping concentration, so as to realize the preparation of silicon micro-sealed cavity, to solve the sensor sensitive micro-sealed cavity structure heterogeneous problem, greatly increasing the application of the sensor.

Examining Mechanical Strength Characteristics of Selective Inhibition Sintered HDPE Specimens Using RSM and Desirability Approach

NASA Astrophysics Data System (ADS)

Rajamani, D.; Esakki, Balasubramanian

2017-09-01

Selective inhibition sintering (SIS) is a powder based additive manufacturing (AM) technique to produce functional parts with an inexpensive system compared with other AM processes. Mechanical properties of SIS fabricated parts are of high dependence on various process parameters importantly layer thickness, heat energy, heater feedrate, and printer feedrate. In this paper, examining the influence of these process parameters on evaluating mechanical properties such as tensile and flexural strength using Response Surface Methodology (RSM) is carried out. The test specimens are fabricated using high density polyethylene (HDPE) and mathematical models are developed to correlate the control factors to the respective experimental design response. Further, optimal SIS process parameters are determined using desirability approach to enhance the mechanical properties of HDPE specimens. Optimization studies reveal that, combination of high heat energy, low layer thickness, medium heater feedrate and printer feedrate yielded superior mechanical strength characteristics.

Optimization of spectroscopic surveys for testing non-Gaussianity

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

Raccanelli, Alvise; Doré, Olivier; Dalal, Neal, E-mail: alvise@caltech.edu, E-mail: Olivier.P.Dore@jpl.nasa.gov, E-mail: dalaln@illinois.edu

We investigate optimization strategies to measure primordial non-Gaussianity with future spectroscopic surveys. We forecast measurements coming from the 3D galaxy power spectrum and compute constraints on primordial non-Gaussianity parameters f{sub NL} and n{sub NG}. After studying the dependence on those parameters upon survey specifications such as redshift range, area, number density, we assume a reference mock survey and investigate the trade-off between number density and area surveyed. We then define the observational requirements to reach the detection of f{sub NL} of order 1. Our results show that power spectrum constraints on non-Gaussianity from future spectroscopic surveys can improve on currentmore » CMB limits, but the multi-tracer technique and higher order correlations will be needed in order to reach an even better precision in the measurements of the non-Gaussianity parameter f{sub NL}.« less

Automatic Sleep Stage Determination by Multi-Valued Decision Making Based on Conditional Probability with Optimal Parameters

NASA Astrophysics Data System (ADS)

Wang, Bei; Sugi, Takenao; Wang, Xingyu; Nakamura, Masatoshi

Data for human sleep study may be affected by internal and external influences. The recorded sleep data contains complex and stochastic factors, which increase the difficulties for the computerized sleep stage determination techniques to be applied for clinical practice. The aim of this study is to develop an automatic sleep stage determination system which is optimized for variable sleep data. The main methodology includes two modules: expert knowledge database construction and automatic sleep stage determination. Visual inspection by a qualified clinician is utilized to obtain the probability density function of parameters during the learning process of expert knowledge database construction. Parameter selection is introduced in order to make the algorithm flexible. Automatic sleep stage determination is manipulated based on conditional probability. The result showed close agreement comparing with the visual inspection by clinician. The developed system can meet the customized requirements in hospitals and institutions.

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.

Inverse problems and optimal experiment design in unsteady heat transfer processes identification

NASA Technical Reports Server (NTRS)

Artyukhin, Eugene A.

1991-01-01

Experimental-computational methods for estimating characteristics of unsteady heat transfer processes are analyzed. The methods are based on the principles of distributed parameter system identification. The theoretical basis of such methods is the numerical solution of nonlinear ill-posed inverse heat transfer problems and optimal experiment design problems. Numerical techniques for solving problems are briefly reviewed. The results of the practical application of identification methods are demonstrated when estimating effective thermophysical characteristics of composite materials and thermal contact resistance in two-layer systems.

Fitting aerodynamic forces in the Laplace domain: An application of a nonlinear nongradient technique to multilevel constrained optimization

NASA Technical Reports Server (NTRS)

Tiffany, S. H.; Adams, W. M., Jr.

1984-01-01

A technique which employs both linear and nonlinear methods in a multilevel optimization structure to best approximate generalized unsteady aerodynamic forces for arbitrary motion is described. Optimum selection of free parameters is made in a rational function approximation of the aerodynamic forces in the Laplace domain such that a best fit is obtained, in a least squares sense, to tabular data for purely oscillatory motion. The multilevel structure and the corresponding formulation of the objective models are presented which separate the reduction of the fit error into linear and nonlinear problems, thus enabling the use of linear methods where practical. Certain equality and inequality constraints that may be imposed are identified; a brief description of the nongradient, nonlinear optimizer which is used is given; and results which illustrate application of the method are presented.

Design of Life Extending Controls Using Nonlinear Parameter Optimization

NASA Technical Reports Server (NTRS)

Lorenzo, Carl F.; Holmes, Michael S.; Ray, Asok

1998-01-01

This report presents the conceptual development of a life extending control system where the objective is to achieve high performance and structural durability of the plant. A life extending controller is designed for a reusable rocket engine via damage mitigation in both the fuel and oxidizer turbines while achieving high performance for transient responses of the combustion chamber pressure and the O2/H2 mixture ratio. This design approach makes use of a combination of linear and nonlinear controller synthesis techniques and also allows adaptation of the life extending controller module to augment a conventional performance controller of a rocket engine. The nonlinear aspect of the design is achieved using nonlinear parameter optimization of a prescribed control structure.

Angular filter refractometry analysis using simulated annealing.

PubMed

Angland, P; Haberberger, D; Ivancic, S T; Froula, D H

2017-10-01

Angular filter refractometry (AFR) is a novel technique used to characterize the density profiles of laser-produced, long-scale-length plasmas [Haberberger et al., Phys. Plasmas 21, 056304 (2014)]. A new method of analysis for AFR images was developed using an annealing algorithm to iteratively converge upon a solution. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on the minimization of the χ 2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in an average uncertainty in the density profile of 5%-20% in the region of interest.

Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

NASA Astrophysics Data System (ADS)

Negro Maggio, Valentina; Iocchi, Luca

2015-02-01

Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

Channel modeling, signal processing and coding for perpendicular magnetic recording

NASA Astrophysics Data System (ADS)

Wu, Zheng

With the increasing areal density in magnetic recording systems, perpendicular recording has replaced longitudinal recording to overcome the superparamagnetic limit. Studies on perpendicular recording channels including aspects of channel modeling, signal processing and coding techniques are presented in this dissertation. To optimize a high density perpendicular magnetic recording system, one needs to know the tradeoffs between various components of the system including the read/write transducers, the magnetic medium, and the read channel. We extend the work by Chaichanavong on the parameter optimization for systems via design curves. Different signal processing and coding techniques are studied. Information-theoretic tools are utilized to determine the acceptable region for the channel parameters when optimal detection and linear coding techniques are used. Our results show that a considerable gain can be achieved by the optimal detection and coding techniques. The read-write process in perpendicular magnetic recording channels includes a number of nonlinear effects. Nonlinear transition shift (NLTS) is one of them. The signal distortion induced by NLTS can be reduced by write precompensation during data recording. We numerically evaluate the effect of NLTS on the read-back signal and examine the effectiveness of several write precompensation schemes in combating NLTS in a channel characterized by both transition jitter noise and additive white Gaussian electronics noise. We also present an analytical method to estimate the bit-error-rate and use it to help determine the optimal write precompensation values in multi-level precompensation schemes. We propose a mean-adjusted pattern-dependent noise predictive (PDNP) detection algorithm for use on the channel with NLTS. We show that this detector can offer significant improvements in bit-error-rate (BER) compared to conventional Viterbi and PDNP detectors. Moreover, the system performance can be further improved by combining the new detector with a simple write precompensation scheme. Soft-decision decoding for algebraic codes can improve performance for magnetic recording systems. In this dissertation, we propose two soft-decision decoding methods for tensor-product parity codes. We also present a list decoding algorithm for generalized error locating codes.

The impact of different dose response parameters on biologically optimized IMRT in breast cancer

NASA Astrophysics Data System (ADS)

Costa Ferreira, Brigida; Mavroidis, Panayiotis; Adamus-Górka, Magdalena; Svensson, Roger; Lind, Bengt K.

2008-05-01

The full potential of biologically optimized radiation therapy can only be maximized with the prediction of individual patient radiosensitivity prior to treatment. Unfortunately, the available biological parameters, derived from clinical trials, reflect an average radiosensitivity of the examined populations. In the present study, a breast cancer patient of stage I II with positive lymph nodes was chosen in order to analyse the effect of the variation of individual radiosensitivity on the optimal dose distribution. Thus, deviations from the average biological parameters, describing tumour, heart and lung response, were introduced covering the range of patient radiosensitivity reported in the literature. Two treatment configurations of three and seven biologically optimized intensity-modulated beams were employed. The different dose distributions were analysed using biological and physical parameters such as the complication-free tumour control probability (P+), the biologically effective uniform dose (\\bar{\\bar{D}} ), dose volume histograms, mean doses, standard deviations, maximum and minimum doses. In the three-beam plan, the difference in P+ between the optimal dose distribution (when the individual patient radiosensitivity is known) and the reference dose distribution, which is optimal for the average patient biology, ranges up to 13.9% when varying the radiosensitivity of the target volume, up to 0.9% when varying the radiosensitivity of the heart and up to 1.3% when varying the radiosensitivity of the lung. Similarly, in the seven-beam plan, the differences in P+ are up to 13.1% for the target, up to 1.6% for the heart and up to 0.9% for the left lung. When the radiosensitivity of the most important tissues in breast cancer radiation therapy was simultaneously changed, the maximum gain in outcome was as high as 7.7%. The impact of the dose response uncertainties on the treatment outcome was clinically insignificant for the majority of the simulated patients. However, the jump from generalized to individualized radiation therapy may significantly increase the therapeutic window for patients with extreme radio sensitivity or radioresistance, provided that these are identified. Even for radiosensitive patients a simple treatment technique is sufficient to maximize the outcome, since no significant benefits were obtained with a more complex technique using seven intensity-modulated beams portals.

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

PubMed

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

Sci-Thur AM: YIS – 07: Optimizing dual-energy x-ray parameters using a single filter for both high and low-energy images to enhance soft-tissue imaging

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

Bowman, Wesley; Sattarivand, Mike

Objective: To optimize dual-energy parameters of ExacTrac stereoscopic x-ray imaging system for lung SBRT patients Methods: Simulated spectra and a lung phantom were used to optimize filter material, thickness, kVps, and weighting factors to obtain bone subtracted dual-energy images. Spektr simulations were used to identify material in the atomic number (Z) range [3–83] based on a metric defined to separate spectrums of high and low energies. Both energies used the same filter due to time constraints of image acquisition in lung SBRT imaging. A lung phantom containing bone, soft tissue, and a tumor mimicking material was imaged with filter thicknessesmore » range [0–1] mm and kVp range [60–140]. A cost function based on contrast-to-noise-ratio of bone, soft tissue, and tumor, as well as image noise content, was defined to optimize filter thickness and kVp. Using the optimized parameters, dual-energy images of anthropomorphic Rando phantom were acquired and evaluated for bone subtraction. Imaging dose was measured with dual-energy technique using tin filtering. Results: Tin was the material of choice providing the best energy separation, non-toxicity, and non-reactiveness. The best soft-tissue-only image in the lung phantom was obtained using 0.3 mm tin and [140, 80] kVp pair. Dual-energy images of the Rando phantom had noticeable bone elimination when compared to no filtration. Dose was lower with tin filtering compared to no filtration. Conclusions: Dual-energy soft-tissue imaging is feasible using ExacTrac stereoscopic imaging system utilizing a single tin filter for both high and low energies and optimized acquisition parameters.« less

New evaluation parameter for wearable thermoelectric generators

NASA Astrophysics Data System (ADS)

Wijethunge, Dimuthu; Kim, Woochul

2018-04-01

Wearable devices constitute a key application area for thermoelectric devices. However, owing to new constraints in wearable applications, a few conventional device optimization techniques are not appropriate and material evaluation parameters, such as figure of merit (zT) and power factor (PF), tend to be inadequate. We illustrated the incompleteness of zT and PF by performing simulations and considering different thermoelectric materials. The results indicate a weak correlation between device performance and zT and PF. In this study, we propose a new evaluation parameter, zTwearable, which is better suited for wearable applications compared to conventional zT. Owing to size restrictions, gap filler based device optimization is extremely critical in wearable devices. With respect to the occasions in which gap fillers are used, expressions for power, effective thermal conductivity (keff), and optimum load electrical ratio (mopt) are derived. According to the new parameters, the thermal conductivity of the material has become much more critical now. The proposed new evaluation parameter, namely, zTwearable, is extremely useful in the selection of an appropriate thermoelectric material among various candidates prior to the commencement of the actual design process.

Sensitivity of Space Station alpha joint robust controller to structural modal parameter variations

NASA Technical Reports Server (NTRS)

Kumar, Renjith R.; Cooper, Paul A.; Lim, Tae W.

1991-01-01

The photovoltaic array sun tracking control system of Space Station Freedom is described. A synthesis procedure for determining optimized values of the design variables of the control system is developed using a constrained optimization technique. The synthesis is performed to provide a given level of stability margin, to achieve the most responsive tracking performance, and to meet other design requirements. Performance of the baseline design, which is synthesized using predicted structural characteristics, is discussed and the sensitivity of the stability margin is examined for variations of the frequencies, mode shapes and damping ratios of dominant structural modes. The design provides enough robustness to tolerate a sizeable error in the predicted modal parameters. A study was made of the sensitivity of performance indicators as the modal parameters of the dominant modes vary. The design variables are resynthesized for varying modal parameters in order to achieve the most responsive tracking performance while satisfying the design requirements. This procedure of reoptimization design parameters would be useful in improving the control system performance if accurate model data are provided.

Machine-learned and codified synthesis parameters of oxide materials

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa

2017-09-01

Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

Determination of full piezoelectric complex parameters using gradient-based optimization algorithm

NASA Astrophysics Data System (ADS)

Kiyono, C. Y.; Pérez, N.; Silva, E. C. N.

2016-02-01

At present, numerical techniques allow the precise simulation of mechanical structures, but the results are limited by the knowledge of the material properties. In the case of piezoelectric ceramics, the full model determination in the linear range involves five elastic, three piezoelectric, and two dielectric complex parameters. A successful solution to obtaining piezoceramic properties consists of comparing the experimental measurement of the impedance curve and the results of a numerical model by using the finite element method (FEM). In the present work, a new systematic optimization method is proposed to adjust the full piezoelectric complex parameters in the FEM model. Once implemented, the method only requires the experimental data (impedance modulus and phase data acquired by an impedometer), material density, geometry, and initial values for the properties. This method combines a FEM routine implemented using an 8-noded axisymmetric element with a gradient-based optimization routine based on the method of moving asymptotes (MMA). The main objective of the optimization procedure is minimizing the quadratic difference between the experimental and numerical electrical conductance and resistance curves (to consider resonance and antiresonance frequencies). To assure the convergence of the optimization procedure, this work proposes restarting the optimization loop whenever the procedure ends in an undesired or an unfeasible solution. Two experimental examples using PZ27 and APC850 samples are presented to test the precision of the method and to check the dependency of the frequency range used, respectively.

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Hou, Zeyu; Lu, Wenxi

2018-05-01

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.

ToTem: a tool for variant calling pipeline optimization.

PubMed

Tom, Nikola; Tom, Ondrej; Malcikova, Jitka; Pavlova, Sarka; Kubesova, Blanka; Rausch, Tobias; Kolarik, Miroslav; Benes, Vladimir; Bystry, Vojtech; Pospisilova, Sarka

2018-06-26

High-throughput bioinformatics analyses of next generation sequencing (NGS) data often require challenging pipeline optimization. The key problem is choosing appropriate tools and selecting the best parameters for optimal precision and recall. Here we introduce ToTem, a tool for automated pipeline optimization. ToTem is a stand-alone web application with a comprehensive graphical user interface (GUI). ToTem is written in Java and PHP with an underlying connection to a MySQL database. Its primary role is to automatically generate, execute and benchmark different variant calling pipeline settings. Our tool allows an analysis to be started from any level of the process and with the possibility of plugging almost any tool or code. To prevent an over-fitting of pipeline parameters, ToTem ensures the reproducibility of these by using cross validation techniques that penalize the final precision, recall and F-measure. The results are interpreted as interactive graphs and tables allowing an optimal pipeline to be selected, based on the user's priorities. Using ToTem, we were able to optimize somatic variant calling from ultra-deep targeted gene sequencing (TGS) data and germline variant detection in whole genome sequencing (WGS) data. ToTem is a tool for automated pipeline optimization which is freely available as a web application at  https://totem.software .

Automatic parameter selection for feature-based multi-sensor image registration

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

2006-05-01

Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

Space Shuttle propulsion parameter estimation using optimal estimation techniques, volume 1

NASA Technical Reports Server (NTRS)

1983-01-01

The mathematical developments and their computer program implementation for the Space Shuttle propulsion parameter estimation project are summarized. The estimation approach chosen is the extended Kalman filtering with a modified Bryson-Frazier smoother. Its use here is motivated by the objective of obtaining better estimates than those available from filtering and to eliminate the lag associated with filtering. The estimation technique uses as the dynamical process the six degree equations-of-motion resulting in twelve state vector elements. In addition to these are mass and solid propellant burn depth as the ""system'' state elements. The ""parameter'' state elements can include aerodynamic coefficient, inertia, center-of-gravity, atmospheric wind, etc. deviations from referenced values. Propulsion parameter state elements have been included not as options just discussed but as the main parameter states to be estimated. The mathematical developments were completed for all these parameters. Since the systems dynamics and measurement processes are non-linear functions of the states, the mathematical developments are taken up almost entirely by the linearization of these equations as required by the estimation algorithms.

Dynamic positioning configuration and its first-order optimization

NASA Astrophysics Data System (ADS)

Xue, Shuqiang; Yang, Yuanxi; Dang, Yamin; Chen, Wu

2014-02-01

Traditional geodetic network optimization deals with static and discrete control points. The modern space geodetic network is, on the other hand, composed of moving control points in space (satellites) and on the Earth (ground stations). The network configuration composed of these facilities is essentially dynamic and continuous. Moreover, besides the position parameter which needs to be estimated, other geophysical information or signals can also be extracted from the continuous observations. The dynamic (continuous) configuration of the space network determines whether a particular frequency of signals can be identified by this system. In this paper, we employ the functional analysis and graph theory to study the dynamic configuration of space geodetic networks, and mainly focus on the optimal estimation of the position and clock-offset parameters. The principle of the D-optimization is introduced in the Hilbert space after the concept of the traditional discrete configuration is generalized from the finite space to the infinite space. It shows that the D-optimization developed in the discrete optimization is still valid in the dynamic configuration optimization, and this is attributed to the natural generalization of least squares from the Euclidean space to the Hilbert space. Then, we introduce the principle of D-optimality invariance under the combination operation and rotation operation, and propose some D-optimal simplex dynamic configurations: (1) (Semi) circular configuration in 2-dimensional space; (2) the D-optimal cone configuration and D-optimal helical configuration which is close to the GPS constellation in 3-dimensional space. The initial design of GPS constellation can be approximately treated as a combination of 24 D-optimal helixes by properly adjusting the ascending node of different satellites to realize a so-called Walker constellation. In the case of estimating the receiver clock-offset parameter, we show that the circular configuration, the symmetrical cone configuration and helical curve configuration are still D-optimal. It shows that the given total observation time determines the optimal frequency (repeatability) of moving known points and vice versa, and one way to improve the repeatability is to increase the rotational speed. Under the Newton's law of motion, the frequency of satellite motion determines the orbital altitude. Furthermore, we study three kinds of complex dynamic configurations, one of which is the combination of D-optimal cone configurations and a so-called Walker constellation composed of D-optimal helical configuration, the other is the nested cone configuration composed of n cones, and the last is the nested helical configuration composed of n orbital planes. It shows that an effective way to achieve high coverage is to employ the configuration composed of a certain number of moving known points instead of the simplex configuration (such as D-optimal helical configuration), and one can use the D-optimal simplex solutions or D-optimal complex configurations in any combination to achieve powerful configurations with flexile coverage and flexile repeatability. Alternately, how to optimally generate and assess the discrete configurations sampled from the continuous one is discussed. The proposed configuration optimization framework has taken the well-known regular polygons (such as equilateral triangle and quadrangular) in two-dimensional space and regular polyhedrons (regular tetrahedron, cube, regular octahedron, regular icosahedron, or regular dodecahedron) into account. It shows that the conclusions made by the proposed technique are more general and no longer limited by different sampling schemes. By the conditional equation of D-optimal nested helical configuration, the relevance issues of GNSS constellation optimization are solved and some examples are performed by GPS constellation to verify the validation of the newly proposed optimization technique. The proposed technique is potentially helpful in maintenance and quadratic optimization of single GNSS of which the orbital inclination and the orbital altitude change under the precession, as well as in optimally nesting GNSSs to perform global homogeneous coverage of the Earth.

Multi-length scale tomography for the determination and optimization of the effective microstructural properties in novel hierarchical solid oxide fuel cell anodes

NASA Astrophysics Data System (ADS)

Lu, Xuekun; Taiwo, Oluwadamilola O.; Bertei, Antonio; Li, Tao; Li, Kang; Brett, Dan J. L.; Shearing, Paul R.

2017-11-01

Effective microstructural properties are critical in determining the electrochemical performance of solid oxide fuel cells (SOFCs), particularly when operating at high current densities. A novel tubular SOFC anode with a hierarchical microstructure, composed of self-organized micro-channels and sponge-like regions, has been fabricated by a phase inversion technique to mitigate concentration losses. However, since pore sizes span over two orders of magnitude, the determination of the effective transport parameters using image-based techniques remains challenging. Pioneering steps are made in this study to characterize and optimize the microstructure by coupling multi-length scale 3D tomography and modeling. The results conclusively show that embedding finger-like micro-channels into the tubular anode can improve the mass transport by 250% and the permeability by 2-3 orders of magnitude. Our parametric study shows that increasing the porosity in the spongy layer beyond 10% enhances the effective transport parameters of the spongy layer at an exponential rate, but linearly for the full anode. For the first time, local and global mass transport properties are correlated to the microstructure, which is of wide interest for rationalizing the design optimization of SOFC electrodes and more generally for hierarchical materials in batteries and membranes.

Application of Monte Carlo techniques to optimization of high-energy beam transport in a stochastic environment

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Dieudonne, J. E.; Filippas, T. A.

1971-01-01

An algorithm employing a modified sequential random perturbation, or creeping random search, was applied to the problem of optimizing the parameters of a high-energy beam transport system. The stochastic solution of the mathematical model for first-order magnetic-field expansion allows the inclusion of state-variable constraints, and the inclusion of parameter constraints allowed by the method of algorithm application eliminates the possibility of infeasible solutions. The mathematical model and the algorithm were programmed for a real-time simulation facility; thus, two important features are provided to the beam designer: (1) a strong degree of man-machine communication (even to the extent of bypassing the algorithm and applying analog-matching techniques), and (2) extensive graphics for displaying information concerning both algorithm operation and transport-system behavior. Chromatic aberration was also included in the mathematical model and in the optimization process. Results presented show this method as yielding better solutions (in terms of resolutions) to the particular problem than those of a standard analog program as well as demonstrating flexibility, in terms of elements, constraints, and chromatic aberration, allowed by user interaction with both the algorithm and the stochastic model. Example of slit usage and a limited comparison of predicted results and actual results obtained with a 600 MeV cyclotron are given.

Optimization of magnetization transfer measurements: statistical analysis by stochastic simulation. Application to creatine kinase kinetics.

PubMed

Rydzy, M; Deslauriers, R; Smith, I C; Saunders, J K

1990-08-01

A systematic study was performed to optimize the accuracy of kinetic parameters derived from magnetization transfer measurements. Three techniques were investigated: time-dependent saturation transfer (TDST), saturation recovery (SRS), and inversion recovery (IRS). In the last two methods, one of the resonances undergoing exchange is saturated throughout the experiment. The three techniques were compared with respect to the accuracy of the kinetic parameters derived from experiments performed in a given, fixed, amount of time. Stochastic simulation of magnetization transfer experiments was performed to optimize experimental design. General formulas for the relative accuracies of the unidirectional rate constant (k) were derived for each of the three experimental methods. It was calculated that for k values between 0.1 and 1.0 s-1, T1 values between 1 and 10 s, and relaxation delays appropriate for the creatine kinase reaction, the SRS method yields more accurate values of k than does the IRS method. The TDST method is more accurate than the SRS method for reactions where T1 is long and k is large, within the range of k and T1 values examined. Experimental verification of the method was carried out on a solution in which the forward (PCr----ATP) rate constant (kf) of the creatine kinase reaction was measured.

Zinc oxide nanostructures and its nano-compounds for efficient visible light photo-catalytic processes

NASA Astrophysics Data System (ADS)

Adam, Rania E.; Alnoor, Hatim; Elhag, Sami; Nur, Omer; Willander, Magnus

2017-02-01

Zinc oxide (ZnO) in its nanostructure form is a promising material for visible light emission/absorption and utilization in different energy efficient photocatalytic processes. We will first present our recent results on the effect of varying the molar ratio of the synthesis nutrients on visible light emission. Further we will use the optimized conditions from the molar ration experiments to vary the synthesis processing parameters like stirring time etc. and the effect of all these parameters in order to optimize the efficiency and control the emission spectrum are investigated using different complementary techniques. Cathodoluminescence (CL) is combined with photoluminescence (PL) and electroluminescence (EL) as the techniques to investigate and optimizes visible light emission from ZnO/GaN light emitting diodes. We will then show and discuss our recent finding of the use of high quality ZnO nanoparticles (NPs) for efficient photo-degradation of toxic dyes using the visible spectra, namely with a wavelength up to 800 nm. In the end, we show how ZnO nanorods (NRs) are used as the first template to be transferred to bismuth zinc vanadate (BiZn2VO6). The BiZn2VO6 is then used to demonstrate efficient and cost effective hydrogen production through photoelectrochemical water splitting using solar radiation.

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters.

PubMed

Riches, S F; Payne, G S; Morgan, V A; Dearnaley, D; Morgan, S; Partridge, M; Livni, N; Ogden, C; deSouza, N M

2015-05-01

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist. Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (K(trans),Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer. Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively. The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data. • The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters • The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI • The computed model improves tumour detection compared to an expert viewing parametric maps.

Doubly Strange Hypernuclei Physics with antiprotons at PANDA

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

Szymanska, K.; Iazzi, F.

2010-04-26

The study of the double hypernuclei will be possible inside the future facility FAIR. A new technique for their production was recently proposed, based on high intensity antiproton beams in connection with a two-target set-up, for the future PANDA experiment at HESR. In particular, the production technique and optimized parameters for the primary target where the hyperon XI{sup -} is produced as well as the expected rates for the stoped XI{sup -} will be discussed.

Optimization of LC-Orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments.

PubMed

Hu, Meng; Krauss, Martin; Brack, Werner; Schulze, Tobias

2016-11-01

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a well-established technique for nontarget screening of contaminants in complex environmental samples. Automatic peak detection is essential, but its performance has only rarely been assessed and optimized so far. With the aim to fill this gap, we used pristine water extracts spiked with 78 contaminants as a test case to evaluate and optimize chromatogram and spectral data processing. To assess whether data acquisition strategies have a significant impact on peak detection, three values of MS cycle time (CT) of an LTQ Orbitrap instrument were tested. Furthermore, the key parameter settings of the data processing software MZmine 2 were optimized to detect the maximum number of target peaks from the samples by the design of experiments (DoE) approach and compared to a manual evaluation. The results indicate that short CT significantly improves the quality of automatic peak detection, which means that full scan acquisition without additional MS 2 experiments is suggested for nontarget screening. MZmine 2 detected 75-100 % of the peaks compared to manual peak detection at an intensity level of 10 5 in a validation dataset on both spiked and real water samples under optimal parameter settings. Finally, we provide an optimization workflow of MZmine 2 for LC-HRMS data processing that is applicable for environmental samples for nontarget screening. The results also show that the DoE approach is useful and effort-saving for optimizing data processing parameters. Graphical Abstract ᅟ.

D-Optimal Experimental Design for Contaminant Source Identification

NASA Astrophysics Data System (ADS)

Sai Baba, A. K.; Alexanderian, A.

2016-12-01

Contaminant source identification seeks to estimate the release history of a conservative solute given point concentration measurements at some time after the release. This can be mathematically expressed as an inverse problem, with a linear observation operator or a parameter-to-observation map, which we tackle using a Bayesian approach. Acquisition of experimental data can be laborious and expensive. The goal is to control the experimental parameters - in our case, the sparsity of the sensors, to maximize the information gain subject to some physical or budget constraints. This is known as optimal experimental design (OED). D-optimal experimental design seeks to maximize the expected information gain, and has long been considered the gold standard in the statistics community. Our goal is to develop scalable methods for D-optimal experimental designs involving large-scale PDE constrained problems with high-dimensional parameter fields. A major challenge for the OED, is that a nonlinear optimization algorithm for the D-optimality criterion requires repeated evaluation of objective function and gradient involving the determinant of large and dense matrices - this cost can be prohibitively expensive for applications of interest. We propose novel randomized matrix techniques that bring down the computational costs of the objective function and gradient evaluations by several orders of magnitude compared to the naive approach. The effect of randomized estimators on the accuracy and the convergence of the optimization solver will be discussed. The features and benefits of our new approach will be demonstrated on a challenging model problem from contaminant source identification involving the inference of the initial condition from spatio-temporal observations in a time-dependent advection-diffusion problem.

Simultaneous detection of resolved glutamate, glutamine, and γ-aminobutyric acid at 4 T

NASA Astrophysics Data System (ADS)

Hu, Jiani; Yang, Shaolin; Xuan, Yang; Jiang, Quan; Yang, Yihong; Haacke, E. Mark

2007-04-01

A new approach is introduced to simultaneously detect resolved glutamate (Glu), glutamine (Gln), and γ-aminobutyric acid (GABA) using a standard STEAM localization pulse sequence with the optimized sequence timing parameters. This approach exploits the dependence of the STEAM spectra of the strongly coupled spin systems of Glu, Gln, and GABA on the echo time TE and the mixing time TM at 4 T to find an optimized sequence parameter set, i.e., {TE, TM}, where the outer-wings of the Glu C4 multiplet resonances around 2.35 ppm, the Gln C4 multiplet resonances around 2.45 ppm, and the GABA C2 multiplet resonance around 2.28 ppm are significantly suppressed and the three resonances become virtual singlets simultaneously and thus resolved. Spectral simulation and optimization were conducted to find the optimized sequence parameters, and phantom and in vivo experiments (on normal human brains, one patient with traumatic brain injury, and one patient with brain tumor) were carried out for verification. The results have demonstrated that the Gln, Glu, and GABA signals at 2.2-2.5 ppm can be well resolved using a standard STEAM sequence with the optimized sequence timing parameters around {82 ms, 48 ms} at 4 T, while the other main metabolites, such as N-acetyl aspartate (NAA), choline (tCho), and creatine (tCr), are still preserved in the same spectrum. The technique can be easily implemented and should prove to be a useful tool for the basic and clinical studies associated with metabolism of Glu, Gln, and/or GABA.

Nurse Scheduling by Cooperative GA with Effective Mutation Operator

NASA Astrophysics Data System (ADS)

Ohki, Makoto

In this paper, we propose an effective mutation operators for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). The nurse scheduling is a very difficult task, because NSP is a complex combinatorial optimizing problem for which many requirements must be considered. In real hospitals, the schedule changes frequently. The changes of the shift schedule yields various problems, for example, a fall in the nursing level. We describe a technique of the reoptimization of the nurse schedule in response to a change. The conventional CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the unfavorable situation because it is inferior to ability for global search. When the optimization stagnates for long generation cycle, a searching point, population in this case, would be caught in a wide local minimum area. To escape such local minimum area, small change in a population should be required. Based on such consideration, we propose a mutation operator activated depending on the optimization speed. When the optimization stagnates, in other words, when the optimization speed decreases, the mutation yields small changes in the population. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator requires two well-defined parameters. This means that user have to consider the value of these parameters carefully. To solve this problem, we propose a periodic mutation operator which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.

Optimization of the design of thick, segmented scintillators for megavoltage cone-beam CT using a novel, hybrid modeling technique

PubMed Central

Liu, Langechuan; Antonuk, Larry E.; El-Mohri, Youcef; Zhao, Qihua; Jiang, Hao

2014-01-01

Purpose: Active matrix flat-panel imagers (AMFPIs) incorporating thick, segmented scintillators have demonstrated order-of-magnitude improvements in detective quantum efficiency (DQE) at radiotherapy energies compared to systems based on conventional phosphor screens. Such improved DQE values facilitate megavoltage cone-beam CT (MV CBCT) imaging at clinically practical doses. However, the MV CBCT performance of such AMFPIs is highly dependent on the design parameters of the scintillators. In this paper, optimization of the design of segmented scintillators was explored using a hybrid modeling technique which encompasses both radiation and optical effects. Methods: Imaging performance in terms of the contrast-to-noise ratio (CNR) and spatial resolution of various hypothetical scintillator designs was examined through a hybrid technique involving Monte Carlo simulation of radiation transport in combination with simulation of optical gain distributions and optical point spread functions. The optical simulations employed optical parameters extracted from a best fit to measurement results reported in a previous investigation of a 1.13 cm thick, 1016 μm pitch prototype BGO segmented scintillator. All hypothetical designs employed BGO material with a thickness and element-to-element pitch ranging from 0.5 to 6 cm and from 0.508 to 1.524 mm, respectively. In the CNR study, for each design, full tomographic scans of a contrast phantom incorporating various soft-tissue inserts were simulated at a total dose of 4 cGy. Results: Theoretical values for contrast, noise, and CNR were found to be in close agreement with empirical results from the BGO prototype, strongly supporting the validity of the modeling technique. CNR and spatial resolution for the various scintillator designs demonstrate complex behavior as scintillator thickness and element pitch are varied—with a clear trade-off between these two imaging metrics up to a thickness of ∼3 cm. Based on these results, an optimization map indicating the regions of design that provide a balance between these metrics was obtained. The map shows that, for a given set of optical parameters, scintillator thickness and pixel pitch can be judiciously chosen to maximize performance without resorting to thicker, more costly scintillators. Conclusions: Modeling radiation and optical effects in thick, segmented scintillators through use of a hybrid technique can provide a practical way to gain insight as to how to optimize the performance of such devices in radiotherapy imaging. Assisted by such modeling, the development of practical designs should greatly facilitate low-dose, soft tissue visualization employing MV CBCT imaging in external beam radiotherapy. PMID:24877827

Analysis of Proportional Integral and Optimized Proportional Integral Controllers for Resistance Spot Welding System (RSWS) - A Performance Perspective

NASA Astrophysics Data System (ADS)

Rama Subbanna, S.; Suryakalavathi, M., Dr.

2017-08-01

This paper is an attempt to accomplish a performance analysis of the different control techniques on spikes reduction method applied on the medium frequency transformer based DC spot welding system. Spike reduction is an important factor to be considered while spot welding systems are concerned. During normal RSWS operation welding transformer’s magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. The current control technique is a piecewise linear control technique that is inspired from the DC-DC converter control algorithms to register a novel spike reduction method in the MFDC spot welding applications. Two controllers that were used for the spike reduction portion of the overall applications involve the traditional PI controller and Optimized PI controller. Care is taken such that the current control technique would maintain a reduced spikes in the primary current of the transformer while it reduces the Total Harmonic Distortion. The performance parameter that is involved in the spikes reduction technique is the THD, Percentage of current spike reduction for both techniques. Matlab/SimulinkTM based simulation is carried out for the MFDC RSWS with KW and results are tabulated for the PI and Optimized PI controllers and a tradeoff analysis is carried out.

Constrained multi-objective optimization of storage ring lattices

NASA Astrophysics Data System (ADS)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Fast Bound Methods for Large Scale Simulation with Application for Engineering Optimization

NASA Technical Reports Server (NTRS)

Patera, Anthony T.; Peraire, Jaime; Zang, Thomas A. (Technical Monitor)

2002-01-01

In this work, we have focused on fast bound methods for large scale simulation with application for engineering optimization. The emphasis is on the development of techniques that provide both very fast turnaround and a certificate of Fidelity; these attributes ensure that the results are indeed relevant to - and trustworthy within - the engineering context. The bound methodology which underlies this work has many different instantiations: finite element approximation; iterative solution techniques; and reduced-basis (parameter) approximation. In this grant we have, in fact, treated all three, but most of our effort has been concentrated on the first and third. We describe these below briefly - but with a pointer to an Appendix which describes, in some detail, the current "state of the art."

Optimal Draft requirement for vibratory tillage equipment using Genetic Algorithm Technique

NASA Astrophysics Data System (ADS)

Rao, Gowripathi; Chaudhary, Himanshu; Singh, Prem

2018-03-01

Agriculture is an important sector of Indian economy. Primary and secondary tillage operations are required for any land preparation process. Conventionally different tractor-drawn implements such as mouldboard plough, disc plough, subsoiler, cultivator and disc harrow, etc. are used for primary and secondary manipulations of soils. Among them, oscillatory tillage equipment is one such type which uses vibratory motion for tillage purpose. Several investigators have reported that the requirement for draft consumption in primary tillage implements is more as compared to oscillating one because they are always in contact with soil. Therefore in this paper, an attempt is made to find out the optimal parameters from the experimental data available in the literature to obtain minimum draft consumption through genetic algorithm technique.

Desensitized Optimal Filtering and Sensor Fusion Toolkit

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.

2015-01-01

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Optimization of auxiliary basis sets for the LEDO expansion and a projection technique for LEDO-DFT.

PubMed

Götz, Andreas W; Kollmar, Christian; Hess, Bernd A

2005-09-01

We present a systematic procedure for the optimization of the expansion basis for the limited expansion of diatomic overlap density functional theory (LEDO-DFT) and report on optimized auxiliary orbitals for the Ahlrichs split valence plus polarization basis set (SVP) for the elements H, Li--F, and Na--Cl. A new method to deal with near-linear dependences in the LEDO expansion basis is introduced, which greatly reduces the computational effort of LEDO-DFT calculations. Numerical results for a test set of small molecules demonstrate the accuracy of electronic energies, structural parameters, dipole moments, and harmonic frequencies. For larger molecular systems the numerical errors introduced by the LEDO approximation can lead to an uncontrollable behavior of the self-consistent field (SCF) process. A projection technique suggested by Löwdin is presented in the framework of LEDO-DFT, which guarantees for SCF convergence. Numerical results on some critical test molecules suggest the general applicability of the auxiliary orbitals presented in combination with this projection technique. Timing results indicate that LEDO-DFT is competitive with conventional density fitting methods. (c) 2005 Wiley Periodicals, Inc.

Biomechanical simulation of vocal fold dynamics in adults based on laryngeal high-speed videoendoscopy

PubMed Central

Gómez, Pablo; Patel, Rita R.; Alexiou, Christoph; Bohr, Christopher; Schützenberger, Anne

2017-01-01

Motivation Human voice is generated in the larynx by the two oscillating vocal folds. Owing to the limited space and accessibility of the larynx, endoscopic investigation of the actual phonatory process in detail is challenging. Hence the biomechanics of the human phonatory process are still not yet fully understood. Therefore, we adapt a mathematical model of the vocal folds towards vocal fold oscillations to quantify gender and age related differences expressed by computed biomechanical model parameters. Methods The vocal fold dynamics are visualized by laryngeal high-speed videoendoscopy (4000 fps). A total of 33 healthy young subjects (16 females, 17 males) and 11 elderly subjects (5 females, 6 males) were recorded. A numerical two-mass model is adapted to the recorded vocal fold oscillations by varying model masses, stiffness and subglottal pressure. For adapting the model towards the recorded vocal fold dynamics, three different optimization algorithms (Nelder–Mead, Particle Swarm Optimization and Simulated Bee Colony) in combination with three cost functions were considered for applicability. Gender differences and age-related kinematic differences reflected by the model parameters were analyzed. Results and conclusion The biomechanical model in combination with numerical optimization techniques allowed phonatory behavior to be simulated and laryngeal parameters involved to be quantified. All three optimization algorithms showed promising results. However, only one cost function seems to be suitable for this optimization task. The gained model parameters reflect the phonatory biomechanics for men and women well and show quantitative age- and gender-specific differences. The model parameters for younger females and males showed lower subglottal pressures, lower stiffness and higher masses than the corresponding elderly groups. Females exhibited higher subglottal pressures, smaller oscillation masses and larger stiffness than the corresponding similar aged male groups. Optimizing numerical models towards vocal fold oscillations is useful to identify underlying laryngeal components controlling the phonatory process. PMID:29121085

Multi-wavelength dual polarisation lidar for monitoring precipitation process in the cloud seeding technique

NASA Astrophysics Data System (ADS)

Sudhakar, P.; Sheela, K. Anitha; Ramakrishna Rao, D.; Malladi, Satyanarayana

2016-05-01

In recent years weather modification activities are being pursued in many countries through cloud seeding techniques to facilitate the increased and timely precipitation from the clouds. In order to induce and accelerate the precipitation process clouds are artificially seeded with suitable materials like silver iodide, sodium chloride or other hygroscopic materials. The success of cloud seeding can be predicted with confidence if the precipitation process involving aerosol, the ice water balance, water vapor content and size of the seeding material in relation to aerosol in the cloud is monitored in real time and optimized. A project on the enhancement of rain fall through cloud seeding is being implemented jointly with Kerala State Electricity Board Ltd. Trivandrum, Kerala, India at the catchment areas of the reservoir of one of the Hydro electric projects. The dual polarization lidar is being used to monitor and measure the microphysical properties, the extinction coefficient, size distribution and related parameters of the clouds. The lidar makes use of the Mie, Rayleigh and Raman scattering techniques for the various measurement proposed. The measurements with the dual polarization lidar as above are being carried out in real time to obtain the various parameters during cloud seeding operations. In this paper we present the details of the multi-wavelength dual polarization lidar being used and the methodology to monitor the various cloud parameters involved in the precipitation process. The necessary retrieval algorithms for deriving the microphysical properties of clouds, aerosols characteristics and water vapor profiles are incorporated as a software package working under Lab-view for online and off line analysis. Details on the simulation studies and the theoretical model developed in this regard for the optimization of various parameters are discussed.

A Designed Experiments Approach to Optimizing MALDI-TOF MS Spectrum Processing Parameters Enhances Detection of Antibiotic Resistance in Campylobacter jejuni

PubMed Central

Penny, Christian; Grothendick, Beau; Zhang, Lin; Borror, Connie M.; Barbano, Duane; Cornelius, Angela J.; Gilpin, Brent J.; Fagerquist, Clifton K.; Zaragoza, William J.; Jay-Russell, Michele T.; Lastovica, Albert J.; Ragimbeau, Catherine; Cauchie, Henry-Michel; Sandrin, Todd R.

2016-01-01

MALDI-TOF MS has been utilized as a reliable and rapid tool for microbial fingerprinting at the genus and species levels. Recently, there has been keen interest in using MALDI-TOF MS beyond the genus and species levels to rapidly identify antibiotic resistant strains of bacteria. The purpose of this study was to enhance strain level resolution for Campylobacter jejuni through the optimization of spectrum processing parameters using a series of designed experiments. A collection of 172 strains of C. jejuni were collected from Luxembourg, New Zealand, North America, and South Africa, consisting of four groups of antibiotic resistant isolates. The groups included: (1) 65 strains resistant to cefoperazone (2) 26 resistant to cefoperazone and beta-lactams (3) 5 strains resistant to cefoperazone, beta-lactams, and tetracycline, and (4) 76 strains resistant to cefoperazone, teicoplanin, amphotericin, B and cephalothin. Initially, a model set of 16 strains (three biological replicates and three technical replicates per isolate, yielding a total of 144 spectra) of C. jejuni was subjected to each designed experiment to enhance detection of antibiotic resistance. The most optimal parameters were applied to the larger collection of 172 isolates (two biological replicates and three technical replicates per isolate, yielding a total of 1,031 spectra). We observed an increase in antibiotic resistance detection whenever either a curve based similarity coefficient (Pearson or ranked Pearson) was applied rather than a peak based (Dice) and/or the optimized preprocessing parameters were applied. Increases in antimicrobial resistance detection were scored using the jackknife maximum similarity technique following cluster analysis. From the first four groups of antibiotic resistant isolates, the optimized preprocessing parameters increased detection respective to the aforementioned groups by: (1) 5% (2) 9% (3) 10%, and (4) 2%. An additional second categorization was created from the collection consisting of 31 strains resistant to beta-lactams and 141 strains sensitive to beta-lactams. Applying optimal preprocessing parameters, beta-lactam resistance detection was increased by 34%. These results suggest that spectrum processing parameters, which are rarely optimized or adjusted, affect the performance of MALDI-TOF MS-based detection of antibiotic resistance and can be fine-tuned to enhance screening performance. PMID:27303397

Modeling, simulation, and estimation of optical turbulence

NASA Astrophysics Data System (ADS)

Formwalt, Byron Paul

This dissertation documents three new contributions to simulation and modeling of optical turbulence. The first contribution is the formalization, optimization, and validation of a modeling technique called successively conditioned rendering (SCR). The SCR technique is empirically validated by comparing the statistical error of random phase screens generated with the technique. The second contribution is the derivation of the covariance delineation theorem, which provides theoretical bounds on the error associated with SCR. It is shown empirically that the theoretical bound may be used to predict relative algorithm performance. Therefore, the covariance delineation theorem is a powerful tool for optimizing SCR algorithms. For the third contribution, we introduce a new method for passively estimating optical turbulence parameters, and demonstrate the method using experimental data. The technique was demonstrated experimentally, using a 100 m horizontal path at 1.25 m above sun-heated tarmac on a clear afternoon. For this experiment, we estimated C2n ≈ 6.01 · 10-9 m-23 , l0 ≈ 17.9 mm, and L0 ≈ 15.5 m.

Continuous piecewise-linear, reduced-order electrochemical model for lithium-ion batteries in real-time applications

NASA Astrophysics Data System (ADS)

Farag, Mohammed; Fleckenstein, Matthias; Habibi, Saeid

2017-02-01

Model-order reduction and minimization of the CPU run-time while maintaining the model accuracy are critical requirements for real-time implementation of lithium-ion electrochemical battery models. In this paper, an isothermal, continuous, piecewise-linear, electrode-average model is developed by using an optimal knot placement technique. The proposed model reduces the univariate nonlinear function of the electrode's open circuit potential dependence on the state of charge to continuous piecewise regions. The parameterization experiments were chosen to provide a trade-off between extensive experimental characterization techniques and purely identifying all parameters using optimization techniques. The model is then parameterized in each continuous, piecewise-linear, region. Applying the proposed technique cuts down the CPU run-time by around 20%, compared to the reduced-order, electrode-average model. Finally, the model validation against real-time driving profiles (FTP-72, WLTP) demonstrates the ability of the model to predict the cell voltage accurately with less than 2% error.

Shape optimization of disc-type flywheels

NASA Technical Reports Server (NTRS)

Nizza, R. S.

1976-01-01

Techniques were developed for presenting an analytical and graphical means for selecting an optimum flywheel system design, based on system requirements, geometric constraints, and weight limitations. The techniques for creating an analytical solution are formulated from energy and structural principals. The resulting flywheel design relates stress and strain pattern distribution, operating speeds, geometry, and specific energy levels. The design techniques incorporate the lowest stressed flywheel for any particular application and achieve the highest specific energy per unit flywheel weight possible. Stress and strain contour mapping and sectional profile plotting reflect the results of the structural behavior manifested under rotating conditions. This approach toward flywheel design is applicable to any metal flywheel, and permits the selection of the flywheel design to be based solely on the criteria of the system requirements that must be met, those that must be optimized, and those system parameters that may be permitted to vary.

Two-speed phacoemulsification for soft cataracts using optimized parameters and procedure step toolbar with the CENTURION Vision System and Balanced Tip

PubMed Central

Davison, James A

2015-01-01

Purpose To present a cause of posterior capsule aspiration and a technique using optimized parameters to prevent it from happening when operating soft cataracts. Patients and methods A prospective list of posterior capsule aspiration cases was kept over 4,062 consecutive cases operated with the Alcon CENTURION machine and Balanced Tip. Video analysis of one case of posterior capsule aspiration was accomplished. A surgical technique was developed using empirically derived machine parameters and customized setting-selection procedure step toolbar to reduce the pace of aspiration of soft nuclear quadrants in order to prevent capsule aspiration. Results Two cases out of 3,238 experienced posterior capsule aspiration before use of the soft quadrant technique. Video analysis showed an attractive vortex effect with capsule aspiration occurring in 1/5 of a second. A soft quadrant removal setting was empirically derived which had a slower pace and seemed more controlled with no capsule aspiration occurring in the subsequent 824 cases. The setting featured simultaneous linear control from zero to preset maximums for: aspiration flow, 20 mL/min; and vacuum, 400 mmHg, with the addition of torsional tip amplitude up to 20% after the fluidic maximums were achieved. A new setting selection procedure step toolbar was created to increase intraoperative flexibility by providing instantaneous shifting between the soft and normal settings. Conclusion A technique incorporating a reduced pace for soft quadrant acquisition and aspiration can be accomplished through the use of a dedicated setting of integrated machine parameters. Toolbar placement of the procedure button next to the normal setting procedure button provides the opportunity to instantaneously alternate between the two settings. Simultaneous surgeon control over vacuum, aspiration flow, and torsional tip motion may make removal of soft nuclear quadrants more efficient and safer. PMID:26355695

A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

PubMed

Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong

2015-05-05

The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

Diffused junction p(+)-n solar cells in bulk GaAs. II - Device characterization and modelling

NASA Technical Reports Server (NTRS)

Keeney, R.; Sundaram, L. M. G.; Rode, H.; Bhat, I.; Ghandhi, S. K.; Borrego, J. M.

1984-01-01

The photovoltaic characteristics of p(+)-n junction solar cells fabricated on bulk GaAs by an open tube diffusion technique are presented in detail. Quantum efficiency measurements were analyzed and compared to computer simulations of the cell structure in order to determine material parameters such as diffusion length, surface recombination velocity and junction depth. From the results obtained it is projected that proper optimization of the cell parameters can increase the efficiency of the cells to close to 20 percent.

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

Pauli, Adam T; Robertson, Raymond E; Branthaver, Jan F

A system for determining parameters and compatibility of a substance such as an asphalt or other petroleum substance uses titration to highly accurately determine one or more flocculation occurrences and is especially applicable to the determination or use of Heithaus parameters and optimal mixing of various asphalt stocks. In a preferred embodiment, automated titration in an oxygen gas exclusive system and further using spectrophotometric analysis (2-8) of solution turbidity is presented. A reversible titration technique enabling in-situ titration measurement of various solution concentrations is also presented.

Backward bifurcation and optimal control of Plasmodium Knowlesi malaria

NASA Astrophysics Data System (ADS)

Abdullahi, Mohammed Baba; Hasan, Yahya Abu; Abdullah, Farah Aini

2014-07-01

A deterministic model for the transmission dynamics of Plasmodium Knowlesi malaria with direct transmission is developed. The model is analyzed using dynamical system techniques and it shows that the backward bifurcation occurs for some range of parameters. The model is extended to assess the impact of time dependent preventive (biological and chemical control) against the mosquitoes and vaccination for susceptible humans, while treatment for infected humans. The existence of optimal control is established analytically by the use of optimal control theory. Numerical simulations of the problem, suggest that applying the four control measure can effectively reduce if not eliminate the spread of Plasmodium Knowlesi in a community.

Fabrication of aluminum-carbon composites

NASA Technical Reports Server (NTRS)

Novak, R. C.

1973-01-01

A screening, optimization, and evaluation program is reported of unidirectional carbon-aluminum composites. During the screening phase both large diameter monofilament and small diameter multifilament reinforcements were utilized to determine optimum precursor tape making and consolidation techniques. Difficulty was encountered in impregnating and consolidating the multifiber reinforcements. Large diameter monofilament reinforcement was found easier to fabricate into composites and was selected to carry into the optimization phase in which the hot pressing parameters were refined and the size of the fabricated panels was scaled up. After process optimization the mechanical properties of the carbon-aluminum composites were characterized in tension, stress-rupture and creep, mechanical fatigue, thermal fatigue, thermal aging, thermal expansion, and impact.

Multilayer perceptron architecture optimization using parallel computing techniques.

PubMed

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

Multilayer perceptron architecture optimization using parallel computing techniques

PubMed Central

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time. PMID:29236744

A pilot study for determining the optimal operation condition for simultaneously controlling the emissions of PCDD/Fs and PAHs from the iron ore sintering process.

PubMed

Chen, Yu-Cheng; Tsai, Perng-Jy; Mou, Jin-Luh; Kuo, Yu-Chieh; Wang, Shih-Min; Young, Li-Hao; Wang, Ya-Fen

2012-09-01

In this study, the cost-benefit analysis technique was developed and incorporated into the Taguchi experimental design to determine the optimal operation combination for the purpose of providing a technique solution for controlling both emissions of PCDD/Fs and PAHs, and increasing both the sinter productivity (SP) and sinter strength (SS) simultaneously. Four operating parameters, including the water content, suction pressure, bed height, and type of hearth layer, were selected and all experimental campaigns were conducted on a pilot-scale sinter pot to simulate various sintering operating conditions of a real-scale sinter plant. The resultant optimal combination could reduce the total carcinogenic emissions arising from both emissions of PCDD/Fs and PAHs by 49.8%, and increase the sinter benefit associated with the increase in both SP and SS by 10.1%, as in comparison with the operation condition currently used in the real plant. The ANOVA results indicate that the suction pressure was the most dominant parameter in determining the optimal operation combination. The above result was theoretically plausible since the higher suction pressure provided more oxygen contents leading to the decrease in both PCDD/F and PAH emissions. But it should be noted that the results obtained from the present study were based on pilot scale experiments, conducting confirmation tests in a real scale plant are still necessary in the future. Copyright © 2012 Elsevier Ltd. All rights reserved.

Parameter optimization for reproducible cardiac 1 H-MR spectroscopy at 3 Tesla.

PubMed

de Heer, Paul; Bizino, Maurice B; Lamb, Hildo J; Webb, Andrew G

2016-11-01

To optimize data acquisition parameters in cardiac proton MR spectroscopy, and to evaluate the intra- and intersession variability in myocardial triglyceride content. Data acquisition parameters at 3 Tesla (T) were optimized and reproducibility measured using, in total, 49 healthy subjects. The signal-to-noise-ratio (SNR) and the variance in metabolite amplitude between averages were measured for: (i) global versus local power optimization; (ii) static magnetic field (B 0 ) shimming performed during free-breathing or within breathholds; (iii) post R-wave peak measurement times between 50 and 900 ms; (iv) without respiratory compensation, with breathholds and with navigator triggering; and (v) frequency selective excitation, Chemical Shift Selective (CHESS) and Multiply Optimized Insensitive Suppression Train (MOIST) water suppression techniques. Using the optimized parameters intra- and intersession myocardial triglyceride content reproducibility was measured. Two cardiac proton spectra were acquired with the same parameters and compared (intrasession reproducibility) after which the subject was removed from the scanner and placed back in the scanner and a third spectrum was acquired which was compared with the first measurement (intersession reproducibility). Local power optimization increased SNR on average by 22% compared with global power optimization (P = 0.0002). The average linewidth was not significantly different for pencil beam B 0 shimming using free-breathing or breathholds (19.1 Hz versus 17.5 Hz; P = 0.15). The highest signal stability occurred at a cardiac trigger delay around 240 ms. The mean amplitude variation was significantly lower for breathholds versus free-breathing (P = 0.03) and for navigator triggering versus free-breathing (P = 0.03) as well as for navigator triggering versus breathhold (P = 0.02). The mean residual water signal using CHESS (1.1%, P = 0.01) or MOIST (0.7%, P = 0.01) water suppression was significantly lower than using frequency selective excitation water suppression (7.0%). Using the optimized parameters an intrasession limits of agreement of the myocardial triglyceride content of -0.11% to +0.04%, and an intersession of -0.15% to +0.9%, were achieved. The coefficient of variation was 5% for the intrasession reproducibility and 6.5% for the intersession reproducibility. Using approaches designed to optimize SNR and minimize the variation in inter-average signal intensities and frequencies/phases, a protocol was developed to perform cardiac MR spectroscopy on a clinical 3T system with high reproducibility. J. Magn. Reson. Imaging 2016;44:1151-1158. © 2016 International Society for Magnetic Resonance in Medicine.

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

PubMed Central

De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

2016-01-01

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration. PMID:28774035

Highly defined 3D printed chitosan scaffolds featuring improved cell growth.

PubMed

Elviri, Lisa; Foresti, Ruben; Bergonzi, Carlo; Zimetti, Francesca; Marchi, Cinzia; Bianchera, Annalisa; Bernini, Franco; Silvestri, Marco; Bettini, Ruggero

2017-07-12

The augmented demand for medical devices devoted to tissue regeneration and possessing a controlled micro-architecture means there is a need for industrial scale-up in the production of hydrogels. A new 3D printing technique was applied to the automation of a freeze-gelation method for the preparation of chitosan scaffolds with controlled porosity. For this aim, a dedicated 3D printer was built in-house: a preliminary effort has been necessary to explore the printing parameter space to optimize the printing results in terms of geometry, tolerances and mechanical properties of the product. Analysed parameters included viscosity of the starting chitosan solution, which was measured with a Brookfield viscometer, and temperature of deposition, which was determined by filming the process with a cryocooled sensor thermal camera. Optimized parameters were applied to the production of scaffolds from solutions of chitosan alone or with the addition of raffinose as a viscosity modifier. Resulting hydrogels were characterized in terms of morphology and porosity. In vitro cell culture studies comparing 3D printed scaffolds with their homologous produced by solution casting evidenced an improvement in biocompatibility deriving from the production technique as well as from the solid state modification of chitosan stemming from the addition of the viscosity modifier.

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bekele, E. G.; Nicklow, J. W.

2005-12-01

Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

PubMed Central

Dura-Bernal, S.; Neymotin, S. A.; Kerr, C. C.; Sivagnanam, S.; Majumdar, A.; Francis, J. T.; Lytton, W. W.

2017-01-01

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics. PMID:29200477

Tomographic inversion techniques incorporating physical constraints for line integrated spectroscopy in stellarators and tokamaks

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

Pablant, N. A.; Bell, R. E.; Bitter, M.

2014-11-15

Accurate tomographic inversion is important for diagnostic systems on stellarators and tokamaks which rely on measurements of line integrated emission spectra. A tomographic inversion technique based on spline optimization with enforcement of constraints is described that can produce unique and physically relevant inversions even in situations with noisy or incomplete input data. This inversion technique is routinely used in the analysis of data from the x-ray imaging crystal spectrometer (XICS) installed at the Large Helical Device. The XICS diagnostic records a 1D image of line integrated emission spectra from impurities in the plasma. Through the use of Doppler spectroscopy andmore » tomographic inversion, XICS can provide profile measurements of the local emissivity, temperature, and plasma flow. Tomographic inversion requires the assumption that these measured quantities are flux surface functions, and that a known plasma equilibrium reconstruction is available. In the case of low signal levels or partial spatial coverage of the plasma cross-section, standard inversion techniques utilizing matrix inversion and linear-regularization often cannot produce unique and physically relevant solutions. The addition of physical constraints, such as parameter ranges, derivative directions, and boundary conditions, allow for unique solutions to be reliably found. The constrained inversion technique described here utilizes a modified Levenberg-Marquardt optimization scheme, which introduces a condition avoidance mechanism by selective reduction of search directions. The constrained inversion technique also allows for the addition of more complicated parameter dependencies, for example, geometrical dependence of the emissivity due to asymmetries in the plasma density arising from fast rotation. The accuracy of this constrained inversion technique is discussed, with an emphasis on its applicability to systems with limited plasma coverage.« less

Tomographic inversion techniques incorporating physical constraints for line integrated spectroscopy in stellarators and tokamaksa)

DOE PAGES

Pablant, N. A.; Bell, R. E.; Bitter, M.; ...

2014-08-08

Accurate tomographic inversion is important for diagnostic systems on stellarators and tokamaks which rely on measurements of line integrated emission spectra. A tomographic inversion technique based on spline optimization with enforcement of constraints is described that can produce unique and physically relevant inversions even in situations with noisy or incomplete input data. This inversion technique is routinely used in the analysis of data from the x-ray imaging crystal spectrometer (XICS) installed at LHD. The XICS diagnostic records a 1D image of line integrated emission spectra from impurities in the plasma. Through the use of Doppler spectroscopy and tomographic inversion, XICSmore » can provide pro file measurements of the local emissivity, temperature and plasma flow. Tomographic inversion requires the assumption that these measured quantities are flux surface functions, and that a known plasma equilibrium reconstruction is available. In the case of low signal levels or partial spatial coverage of the plasma cross-section, standard inversion techniques utilizing matrix inversion and linear-regularization often cannot produce unique and physically relevant solutions. The addition of physical constraints, such as parameter ranges, derivative directions, and boundary conditions, allow for unique solutions to be reliably found. The constrained inversion technique described here utilizes a modifi ed Levenberg-Marquardt optimization scheme, which introduces a condition avoidance mechanism by selective reduction of search directions. The constrained inversion technique also allows for the addition of more complicated parameter dependencies, for example geometrical dependence of the emissivity due to asymmetries in the plasma density arising from fast rotation. The accuracy of this constrained inversion technique is discussed, with an emphasis on its applicability to systems with limited plasma coverage.« less

Determination of Copper by Graphite Furnace Atomic Absorption Spectrophotometry: A Student Exercise in Instrumental Methods of Analysis.

ERIC Educational Resources Information Center

Williamson, Mark A.

1989-01-01

Discusses a student exercise which requires the optimizing of the charring and atomization temperatures by producing a plot of absorbance versus temperature for each temperature parameter. Notes that although the graphite furnace atomic absorption spectroscopy technique has widespread industrial use, there are no published, structured experiments…

Topology-optimization-based design method of flexures for mounting the primary mirror of a large-aperture space telescope.

PubMed

Hu, Rui; Liu, Shutian; Li, Quhao

2017-05-20

For the development of a large-aperture space telescope, one of the key techniques is the method for designing the flexures for mounting the primary mirror, as the flexures are the key components. In this paper, a topology-optimization-based method for designing flexures is presented. The structural performances of the mirror system under multiple load conditions, including static gravity and thermal loads, as well as the dynamic vibration, are considered. The mirror surface shape error caused by gravity and the thermal effect is treated as the objective function, and the first-order natural frequency of the mirror structural system is taken as the constraint. The pattern repetition constraint is added, which can ensure symmetrical material distribution. The topology optimization model for flexure design is established. The substructuring method is also used to condense the degrees of freedom (DOF) of all the nodes of the mirror system, except for the nodes that are linked to the mounting flexures, to reduce the computation effort during the optimization iteration process. A potential optimized configuration is achieved by solving the optimization model and post-processing. A detailed shape optimization is subsequently conducted to optimize its dimension parameters. Our optimization method deduces new mounting structures that significantly enhance the optical performance of the mirror system compared to the traditional methods, which only focus on the parameters of existing structures. Design results demonstrate the effectiveness of the proposed optimization method.

A closed-form solution to tensor voting: theory and applications.

PubMed

Wu, Tai-Pang; Yeung, Sai-Kit; Jia, Jiaya; Tang, Chi-Keung; Medioni, Gérard

2012-08-01

We prove a closed-form solution to tensor voting (CFTV): Given a point set in any dimensions, our closed-form solution provides an exact, continuous, and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation. Using CFTV, we prove the convergence of tensor voting on a Markov random field (MRF), thus termed as MRFTV, where the structure-aware tensor at each input site reaches a stationary state upon convergence in structure propagation. We then embed structure-aware tensor into expectation maximization (EM) for optimizing a single linear structure to achieve efficient and robust parameter estimation. Specifically, our EMTV algorithm optimizes both the tensor and fitting parameters and does not require random sampling consensus typically used in existing robust statistical techniques. We performed quantitative evaluation on its accuracy and robustness, showing that EMTV performs better than the original TV and other state-of-the-art techniques in fundamental matrix estimation for multiview stereo matching. The extensions of CFTV and EMTV for extracting multiple and nonlinear structures are underway.

A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.

PubMed

Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao

2011-08-01

The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.

Computer Program for Analysis, Design and Optimization of Propulsion, Dynamics, and Kinematics of Multistage Rockets

NASA Astrophysics Data System (ADS)

Lali, Mehdi

2009-03-01

A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: "analysis and design" and "optimization." Each section has a GUI (Graphical User Interface) in which the rocket's data are entered by the user and by which the program is run. The first section analyzes the performance of the rocket that is previously devised by the user. Numerous plots and subplots are provided to display the performance of the rocket. The second section of the program finds the "optimum trajectory" via billions of iterations and computations which are done through sophisticated algorithms using numerical methods and incremental integrations. Innovative techniques are applied to calculate the optimal parameters for the engine and designing the "optimal pitch program." This computer program is stand-alone in such a way that it calculates almost every design parameter in regards to rocket propulsion and dynamics. It is meant to be used for actual launch operations as well as educational and research purposes.

Pulse shape optimization for electron-positron production in rotating fields

NASA Astrophysics Data System (ADS)

Fillion-Gourdeau, François; Hebenstreit, Florian; Gagnon, Denis; MacLean, Steve

2017-07-01

We optimize the pulse shape and polarization of time-dependent electric fields to maximize the production of electron-positron pairs via strong field quantum electrodynamics processes. The pulse is parametrized in Fourier space by a B -spline polynomial basis, which results in a relatively low-dimensional parameter space while still allowing for a large number of electric field modes. The optimization is performed by using a parallel implementation of the differential evolution, one of the most efficient metaheuristic algorithms. The computational performance of the numerical method and the results on pair production are compared with a local multistart optimization algorithm. These techniques allow us to determine the pulse shape and field polarization that maximize the number of produced pairs in computationally accessible regimes.

Techniques for designing rotorcraft control systems

NASA Technical Reports Server (NTRS)

Yudilevitch, Gil; Levine, William S.

1994-01-01

Over the last two and a half years we have been demonstrating a new methodology for the design of rotorcraft flight control systems (FCS) to meet handling qualities requirements. This method is based on multicriterion optimization as implemented in the optimization package CONSOL-OPTCAD (C-O). This package has been developed at the Institute for Systems Research (ISR) at the University of Maryland at College Park. This design methodology has been applied to the design of a FCS for the UH-60A helicopter in hover having the ADOCS control structure. The controller parameters have been optimized to meet the ADS-33C specifications. Furthermore, using this approach, an optimal (minimum control energy) controller has been obtained and trade-off studies have been performed.

Optimized open-flow mixing: insights from microbubble streaming

NASA Astrophysics Data System (ADS)

Rallabandi, Bhargav; Wang, Cheng; Guo, Lin; Hilgenfeldt, Sascha

2015-11-01

Microbubble streaming has been developed into a robust and powerful flow actuation technique in microfluidics. Here, we study it as a paradigmatic system for microfluidic mixing under a continuous throughput of fluid (open-flow mixing), providing a systematic optimization of the device parameters in this practically important situation. Focusing on two-dimensional advective stirring (neglecting diffusion), we show through numerical simulation and analytical theory that mixing in steady streaming vortices becomes ineffective beyond a characteristic time scale, necessitating the introduction of unsteadiness. By duty cycling the streaming, such unsteadiness is introduced in a controlled fashion, leading to exponential refinement of the advection structures. The rate of refinement is then optimized for particular parameters of the time modulation, i.e. a particular combination of times for which the streaming is turned ``on'' and ``off''. The optimized protocol can be understood theoretically using the properties of the streaming vortices and the throughput Poiseuille flow. We can thus infer simple design principles for practical open flow micromixing applications, consistent with experiments. Current Address: Mechanical and Aerospace Engineering, Princeton University.

Towards inverse modeling of turbidity currents: The inverse lock-exchange problem

NASA Astrophysics Data System (ADS)

Lesshafft, Lutz; Meiburg, Eckart; Kneller, Ben; Marsden, Alison

2011-04-01

A new approach is introduced for turbidite modeling, leveraging the potential of computational fluid dynamics methods to simulate the flow processes that led to turbidite formation. The practical use of numerical flow simulation for the purpose of turbidite modeling so far is hindered by the need to specify parameters and initial flow conditions that are a priori unknown. The present study proposes a method to determine optimal simulation parameters via an automated optimization process. An iterative procedure matches deposit predictions from successive flow simulations against available localized reference data, as in practice may be obtained from well logs, and aims at convergence towards the best-fit scenario. The final result is a prediction of the entire deposit thickness and local grain size distribution. The optimization strategy is based on a derivative-free, surrogate-based technique. Direct numerical simulations are performed to compute the flow dynamics. A proof of concept is successfully conducted for the simple test case of a two-dimensional lock-exchange turbidity current. The optimization approach is demonstrated to accurately retrieve the initial conditions used in a reference calculation.

Comparison of chirped-probe-pulse and hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering for combustion thermometry.

PubMed

Richardson, Daniel R; Stauffer, Hans U; Roy, Sukesh; Gord, James R

2017-04-10

A comparison is made between two ultrashort-pulse coherent anti-Stokes Raman scattering (CARS) thermometry techniques-hybrid femtosecond/picosecond (fs/ps) CARS and chirped-probe-pulse (CPP) fs-CARS-that have become standards for high-repetition-rate thermometry in the combustion diagnostics community. These two variants of fs-CARS differ only in the characteristics of the ps-duration probe pulse; in hybrid fs/ps CARS a spectrally narrow, time-asymmetric probe pulse is used, whereas a highly chirped, spectrally broad probe pulse is used in CPP fs-CARS. Temperature measurements were performed using both techniques in near-adiabatic flames in the temperature range 1600-2400 K and for probe time delays of 0-30 ps. Under these conditions, both techniques are shown to exhibit similar temperature measurement accuracies and precisions to previously reported values and to each other. However, it is observed that initial calibration fits to the spectrally broad CPP results require more fitting parameters and a more robust optimization algorithm and therefore significantly increased computational cost and complexity compared to the fitting of hybrid fs/ps CARS data. The optimized model parameters varied more for the CPP measurements than for the hybrid fs/ps measurements for different experimental conditions.

Capsule implosion optimization during the indirect-drive National Ignition Campaign

NASA Astrophysics Data System (ADS)

Landen, O. L.; Edwards, J.; Haan, S. W.; Robey, H. F.; Milovich, J.; Spears, B. K.; Weber, S. V.; Clark, D. S.; Lindl, J. D.; MacGowan, B. J.; Moses, E. I.; Atherton, J.; Amendt, P. A.; Boehly, T. R.; Bradley, D. K.; Braun, D. G.; Callahan, D. A.; Celliers, P. M.; Collins, G. W.; Dewald, E. L.; Divol, L.; Frenje, J. A.; Glenzer, S. H.; Hamza, A.; Hammel, B. A.; Hicks, D. G.; Hoffman, N.; Izumi, N.; Jones, O. S.; Kilkenny, J. D.; Kirkwood, R. K.; Kline, J. L.; Kyrala, G. A.; Marinak, M. M.; Meezan, N.; Meyerhofer, D. D.; Michel, P.; Munro, D. H.; Olson, R. E.; Nikroo, A.; Regan, S. P.; Suter, L. J.; Thomas, C. A.; Wilson, D. C.

2011-05-01

Capsule performance optimization campaigns will be conducted at the National Ignition Facility [G. H. Miller, E. I. Moses, and C. R. Wuest, Nucl. Fusion 44, 228 (2004)] to substantially increase the probability of ignition. The campaigns will experimentally correct for residual uncertainties in the implosion and hohlraum physics used in our radiation-hydrodynamic computational models using a variety of ignition capsule surrogates before proceeding to cryogenic-layered implosions and ignition experiments. The quantitative goals and technique options and down selections for the tuning campaigns are first explained. The computationally derived sensitivities to key laser and target parameters are compared to simple analytic models to gain further insight into the physics of the tuning techniques. The results of the validation of the tuning techniques at the OMEGA facility [J. M. Soures et al., Phys. Plasmas 3, 2108 (1996)] under scaled hohlraum and capsule conditions relevant to the ignition design are shown to meet the required sensitivity and accuracy. A roll-up of all expected random and systematic uncertainties in setting the key ignition laser and target parameters due to residual measurement, calibration, cross-coupling, surrogacy, and scale-up errors has been derived that meets the required budget. Finally, we show how the tuning precision will be improved after a number of shots and iterations to meet an acceptable level of residual uncertainty.

Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Shah, Rahul H.

Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.

Performance comparison of extracellular spike sorting algorithms for single-channel recordings.

PubMed

Wild, Jiri; Prekopcsak, Zoltan; Sieger, Tomas; Novak, Daniel; Jech, Robert

2012-01-30

Proper classification of action potentials from extracellular recordings is essential for making an accurate study of neuronal behavior. Many spike sorting algorithms have been presented in the technical literature. However, no comparative analysis has hitherto been performed. In our study, three widely-used publicly-available spike sorting algorithms (WaveClus, KlustaKwik, OSort) were compared with regard to their parameter settings. The algorithms were evaluated using 112 artificial signals (publicly available online) with 2-9 different neurons and varying noise levels between 0.00 and 0.60. An optimization technique based on Adjusted Mutual Information was employed to find near-optimal parameter settings for a given artificial signal and algorithm. All three algorithms performed significantly better (p<0.01) with optimized parameters than with the default ones. WaveClus was the most accurate spike sorting algorithm, receiving the best evaluation score for 60% of all signals. OSort operated at almost five times the speed of the other algorithms. In terms of accuracy, OSort performed significantly less well (p<0.01) than WaveClus for signals with a noise level in the range 0.15-0.30. KlustaKwik achieved similar scores to WaveClus for signals with low noise level 0.00-0.15 and was worse otherwise. In conclusion, none of the three compared algorithms was optimal in general. The accuracy of the algorithms depended on proper choice of the algorithm parameters and also on specific properties of the examined signal. Copyright © 2011 Elsevier B.V. All rights reserved.

The Advantages of Collimator Optimization for Intensity Modulated Radiation Therapy

NASA Astrophysics Data System (ADS)

Doozan, Brian

The goal of this study was to improve dosimetry for pelvic, lung, head and neck, and other cancers sites with aspherical planning target volumes (PTV) using a new algorithm for collimator optimization for intensity modulated radiation therapy (IMRT) that minimizes the x-jaw gap (CAX) and the area of the jaws (CAA) for each treatment field. A retroactive study on the effects of collimator optimization of 20 patients was performed by comparing metric results for new collimator optimization techniques in Eclipse version 11.0. Keeping all other parameters equal, multiple plans are created using four collimator techniques: CA 0, all fields have collimators set to 0°, CAE, using the Eclipse collimator optimization, CAA, minimizing the area of the jaws around the PTV, and CAX, minimizing the x-jaw gap. The minimum area and the minimum x-jaw angles are found by evaluating each field beam's eye view of the PTV with ImageJ and finding the desired parameters with a custom script. The evaluation of the plans included the monitor units (MU), the maximum dose of the plan, the maximum dose to organs at risk (OAR), the conformity index (CI) and the number of fields that are calculated to split. Compared to the CA0 plans, the monitor units decreased on average by 6% for the CAX method with a p-value of 0.01 from an ANOVA test. The average maximum dose remained within 1.1% difference between all four methods with the lowest given by CAX. The maximum dose to the most at risk organ was best spared by the CAA method, which decreased by 0.62% compared to the CA0. Minimizing the x-jaws significantly reduced the number of split fields from 61 to 37. In every metric tested the CAX optimization produced comparable or superior results compared to the other three techniques. For aspherical PTVs, CAX on average reduced the number of split fields, lowered the maximum dose, minimized the dose to the surrounding OAR, and decreased the monitor units. This is achieved while maintaining the same control of the PTV.

Periodic orbits of hybrid systems and parameter estimation via AD.

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

Guckenheimer, John.; Phipps, Eric Todd; Casey, Richard

Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical modelsmore » of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method [GM00, Phi03]. Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance between two given periodic orbits which is then minimized using a trust-region minimization algorithm [DS83] to find optimal fits of the model to a reference orbit [Cas04]. There are two different yet related goals that motivate the algorithmic choices listed above. The first is to provide a simple yet powerful framework for studying periodic motions in mechanical systems. Formulating mechanically correct equations of motion for systems of interconnected rigid bodies, while straightforward, is a time-consuming error prone process. Much of this difficulty stems from computing the acceleration of each rigid body in an inertial reference frame. The acceleration is computed most easily in a redundant set of coordinates giving the spatial positions of each body: since the acceleration is just the second derivative of these positions. Rather than providing explicit formulas for these derivatives, automatic differentiation can be employed to compute these quantities efficiently during the course of a simulation. The feasibility of these ideas was investigated by applying these techniques to the problem of locating stable walking motions for a disc-foot passive walking machine [CGMR01, Gar99, McG91]. The second goal for this work was to investigate the application of smooth optimization methods to periodic orbit parameter estimation problems in neural oscillations. Others [BB93, FUS93, VB99] have favored non-continuous optimization methods such as genetic algorithms, stochastic search methods, simulated annealing and brute-force random searches because of their perceived suitability to the landscape of typical objective functions in parameter space, particularly for multi-compartmental neural models. Here we argue that a carefully formulated optimization problem is amenable to Newton-like methods and has a sufficiently smooth landscape in parameter space that these methods can be an efficient and effective alternative. The plan of this paper is as follows. In Section 1 we provide a definition of hybrid systems that is the basis for modeling systems with discontinuities or discrete transitions. Sections 2, 3, and 4 briefly describe the Taylor series integration, periodic orbit tracking, and parameter estimation algorithms. For full treatments of these algorithms, we refer the reader to [Phi03, Cas04, CPG04]. The software implementation of these algorithms is briefly described in Section 5 with particular emphasis on the automatic differentiation software ADMC++. Finally, these algorithms are applied to the bipedal walking and Hodgkin-Huxley based neural oscillation problems discussed above in Section 6.« less

Compressive properties of passive skeletal muscle-the impact of precise sample geometry on parameter identification in inverse finite element analysis.

PubMed

Böl, Markus; Kruse, Roland; Ehret, Alexander E; Leichsenring, Kay; Siebert, Tobias

2012-10-11

Due to the increasing developments in modelling of biological material, adequate parameter identification techniques are urgently needed. The majority of recent contributions on passive muscle tissue identify material parameters solely by comparing characteristic, compressive stress-stretch curves from experiments and simulation. In doing so, different assumptions concerning e.g. the sample geometry or the degree of friction between the sample and the platens are required. In most cases these assumptions are grossly simplified leading to incorrect material parameters. In order to overcome such oversimplifications, in this paper a more reliable parameter identification technique is presented: we use the inverse finite element method (iFEM) to identify the optimal parameter set by comparison of the compressive stress-stretch response including the realistic geometries of the samples and the presence of friction at the compressed sample faces. Moreover, we judge the quality of the parameter identification by comparing the simulated and experimental deformed shapes of the samples. Besides this, the study includes a comprehensive set of compressive stress-stretch data on rabbit soleus muscle and the determination of static friction coefficients between muscle and PTFE. Copyright © 2012 Elsevier Ltd. All rights reserved.

Crew/computer communications study. Volume 1: Final report. [onboard computerized communications system for spacecrews

NASA Technical Reports Server (NTRS)

Johannes, J. D.

1974-01-01

Techniques, methods, and system requirements are reported for an onboard computerized communications system that provides on-line computing capability during manned space exploration. Communications between man and computer take place by sequential execution of each discrete step of a procedure, by interactive progression through a tree-type structure to initiate tasks or by interactive optimization of a task requiring man to furnish a set of parameters. Effective communication between astronaut and computer utilizes structured vocabulary techniques and a word recognition system.

Production of yarns composed of oriented nanofibers for ophthalmological implants

NASA Astrophysics Data System (ADS)

Shynkarenko, A.; Klapstova, A.; Krotov, A.; Moucka, M.; Lukas, D.

2017-10-01

Parallelized nanofibrous structures are commonly used in medical sector, especially for the ophthalmological implants. In this research self-fabricated device is tested for improved collection and twisting of the parallel nanofibers. Previously manual techniques are used to collect the nanofibers and then twist is given, where as in our device different parameters can be optimized to obtained parallel nanofibers and further twisting can be given. The device is used to bring automation to the technique of achieving parallel fibrous structures for medical applications.