Optimal robust control strategy of a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Robust Maneuvering Envelope Estimation Based on Reachability Analysis in an Optimal Control Formulation

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas; Schuet, Stefan R.; Wheeler, Kevin; Acosta, Diana; Kaneshige, John

2013-01-01

This paper discusses an algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. Starting with an optimal control formulation, the optimization problem can be rewritten as a Hamilton- Jacobi-Bellman equation. This equation can be solved by level set methods. This approach has been applied on an aircraft example involving structural airframe damage. Monte Carlo validation tests have confirmed that this approach is successful in estimating the safe maneuvering envelope for damaged aircraft.

Correlation techniques to determine model form in robust nonlinear system realization/identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1991-01-01

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Robust optimization modelling with applications to industry and environmental problems

NASA Astrophysics Data System (ADS)

Chaerani, Diah; Dewanto, Stanley P.; Lesmana, Eman

2017-10-01

Robust Optimization (RO) modeling is one of the existing methodology for handling data uncertainty in optimization problem. The main challenge in this RO methodology is how and when we can reformulate the robust counterpart of uncertain problems as a computationally tractable optimization problem or at least approximate the robust counterpart by a tractable problem. Due to its definition the robust counterpart highly depends on how we choose the uncertainty set. As a consequence we can meet this challenge only if this set is chosen in a suitable way. The development on RO grows fast, since 2004, a new approach of RO called Adjustable Robust Optimization (ARO) is introduced to handle uncertain problems when the decision variables must be decided as a ”wait and see” decision variables. Different than the classic Robust Optimization (RO) that models decision variables as ”here and now”. In ARO, the uncertain problems can be considered as a multistage decision problem, thus decision variables involved are now become the wait and see decision variables. In this paper we present the applications of both RO and ARO. We present briefly all results to strengthen the importance of RO and ARO in many real life problems.

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.

Robust Spatial Approximation of Laser Scanner Point Clouds by Means of Free-form Curve Approaches in Deformation Analysis

NASA Astrophysics Data System (ADS)

Bureick, Johannes; Alkhatib, Hamza; Neumann, Ingo

2016-03-01

In many geodetic engineering applications it is necessary to solve the problem of describing a measured data point cloud, measured, e. g. by laser scanner, by means of free-form curves or surfaces, e. g., with B-Splines as basis functions. The state of the art approaches to determine B-Splines yields results which are seriously manipulated by the occurrence of data gaps and outliers. Optimal and robust B-Spline fitting depend, however, on optimal selection of the knot vector. Hence we combine in our approach Monte-Carlo methods and the location and curvature of the measured data in order to determine the knot vector of the B-Spline in such a way that no oscillating effects at the edges of data gaps occur. We introduce an optimized approach based on computed weights by means of resampling techniques. In order to minimize the effect of outliers, we apply robust M-estimators for the estimation of control points. The above mentioned approach will be applied to a multi-sensor system based on kinematic terrestrial laserscanning in the field of rail track inspection.

Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids: Preprint

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

2016-09-01

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Multiobjective Optimization Using a Pareto Differential Evolution Approach

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

Simulation-based robust optimization for signal timing and setting.

DOT National Transportation Integrated Search

2009-12-30

The performance of signal timing plans obtained from traditional approaches for : pre-timed (fixed-time or actuated) control systems is often unstable under fluctuating traffic : conditions. This report develops a general approach for optimizing the ...

A Robust Design Methodology for Optimal Microscale Secondary Flow Control in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

It is the purpose of this study to develop an economical Robust design methodology for microscale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely Maximum Performance and Maximum HCF Life Expectancy. The Maximum Performance mission maximized total pressure recovery while the Maximum HCF Life Expectancy mission minimized the mean of the first five Fourier harmonic amplitudes, i.e., 'collectively' reduced all the harmonic 1/2 amplitudes of engine face distortion. Each of the mission strategies was subject to a low engine face distortion constraint, i.e., DC60<0.10, which is a level acceptable for commercial engines. For each of these missions strategies, an 'Optimal Robust' (open loop control) and an 'Optimal Adaptive' (closed loop control) installation was designed over a twenty degree angle-of-incidence range. The Optimal Robust installation used economical Robust Design methodology to arrive at a single design which operated over the entire angle-of-incident range (open loop control). The Optimal Adaptive installation optimized all the design parameters at each angle-of-incidence. Thus, the Optimal Adaptive installation would require a closed loop control system to sense a proper signal for each effector and modify that effector device, whether mechanical or fluidic, for optimal inlet performance. In general, the performance differences between the Optimal Adaptive and Optimal Robust installation designs were found to be marginal. This suggests, however, that Optimal Robust open loop installation designs can be very competitive with Optimal Adaptive close loop designs. Secondary flow control in inlets is inherently robust, provided it is optimally designed. Therefore, the new methodology presented in this paper, combined array 'Lower Order' approach to Robust DOE, offers the aerodynamicist a very viable and economical way of exploring the concept of Robust inlet design, where the mission variables are brought directly into the inlet design process and insensitivity or robustness to the mission variables becomes a design objective.

Robust Portfolio Optimization Using Pseudodistances.

PubMed

Toma, Aida; Leoni-Aubin, Samuela

2015-01-01

The presence of outliers in financial asset returns is a frequently occurring phenomenon which may lead to unreliable mean-variance optimized portfolios. This fact is due to the unbounded influence that outliers can have on the mean returns and covariance estimators that are inputs in the optimization procedure. In this paper we present robust estimators of mean and covariance matrix obtained by minimizing an empirical version of a pseudodistance between the assumed model and the true model underlying the data. We prove and discuss theoretical properties of these estimators, such as affine equivariance, B-robustness, asymptotic normality and asymptotic relative efficiency. These estimators can be easily used in place of the classical estimators, thereby providing robust optimized portfolios. A Monte Carlo simulation study and applications to real data show the advantages of the proposed approach. We study both in-sample and out-of-sample performance of the proposed robust portfolios comparing them with some other portfolios known in literature.

Robust Portfolio Optimization Using Pseudodistances

PubMed Central

2015-01-01

The presence of outliers in financial asset returns is a frequently occurring phenomenon which may lead to unreliable mean-variance optimized portfolios. This fact is due to the unbounded influence that outliers can have on the mean returns and covariance estimators that are inputs in the optimization procedure. In this paper we present robust estimators of mean and covariance matrix obtained by minimizing an empirical version of a pseudodistance between the assumed model and the true model underlying the data. We prove and discuss theoretical properties of these estimators, such as affine equivariance, B-robustness, asymptotic normality and asymptotic relative efficiency. These estimators can be easily used in place of the classical estimators, thereby providing robust optimized portfolios. A Monte Carlo simulation study and applications to real data show the advantages of the proposed approach. We study both in-sample and out-of-sample performance of the proposed robust portfolios comparing them with some other portfolios known in literature. PMID:26468948

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

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

Optimal strategy analysis based on robust predictive control for inventory system with random demand

NASA Astrophysics Data System (ADS)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems.

PubMed

Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong

2014-12-01

In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach.

Robust Operation of Soft Open Points in Active Distribution Networks with High Penetration of Photovoltaic Integration

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

Ding, Fei; Ji, Haoran; Wang, Chengshan

Distributed generators (DGs) including photovoltaic panels (PVs) have been integrated dramatically in active distribution networks (ADNs). Due to the strong volatility and uncertainty, the high penetration of PV generation immensely exacerbates the conditions of voltage violation in ADNs. However, the emerging flexible interconnection technology based on soft open points (SOPs) provides increased controllability and flexibility to the system operation. For fully exploiting the regulation ability of SOPs to address the problems caused by PV, this paper proposes a robust optimization method to achieve the robust optimal operation of SOPs in ADNs. A two-stage adjustable robust optimization model is built tomore » tackle the uncertainties of PV outputs, in which robust operation strategies of SOPs are generated to eliminate the voltage violations and reduce the power losses of ADNs. A column-and-constraint generation (C&CG) algorithm is developed to solve the proposed robust optimization model, which are formulated as second-order cone program (SOCP) to facilitate the accuracy and computation efficiency. Case studies on the modified IEEE 33-node system and comparisons with the deterministic optimization approach are conducted to verify the effectiveness and robustness of the proposed method.« less

Robust optimization methods for cardiac sparing in tangential breast IMRT

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

Mahmoudzadeh, Houra, E-mail: houra@mie.utoronto.ca; Lee, Jenny; Chan, Timothy C. Y.

Purpose: In left-sided tangential breast intensity modulated radiation therapy (IMRT), the heart may enter the radiation field and receive excessive radiation while the patient is breathing. The patient’s breathing pattern is often irregular and unpredictable. We verify the clinical applicability of a heart-sparing robust optimization approach for breast IMRT. We compare robust optimized plans with clinical plans at free-breathing and clinical plans at deep inspiration breath-hold (DIBH) using active breathing control (ABC). Methods: Eight patients were included in the study with each patient simulated using 4D-CT. The 4D-CT image acquisition generated ten breathing phase datasets. An average scan was constructedmore » using all the phase datasets. Two of the eight patients were also imaged at breath-hold using ABC. The 4D-CT datasets were used to calculate the accumulated dose for robust optimized and clinical plans based on deformable registration. We generated a set of simulated breathing probability mass functions, which represent the fraction of time patients spend in different breathing phases. The robust optimization method was applied to each patient using a set of dose-influence matrices extracted from the 4D-CT data and a model of the breathing motion uncertainty. The goal of the optimization models was to minimize the dose to the heart while ensuring dose constraints on the target were achieved under breathing motion uncertainty. Results: Robust optimized plans were improved or equivalent to the clinical plans in terms of heart sparing for all patients studied. The robust method reduced the accumulated heart dose (D10cc) by up to 801 cGy compared to the clinical method while also improving the coverage of the accumulated whole breast target volume. On average, the robust method reduced the heart dose (D10cc) by 364 cGy and improved the optBreast dose (D99%) by 477 cGy. In addition, the robust method had smaller deviations from the planned dose to the accumulated dose. The deviation of the accumulated dose from the planned dose for the optBreast (D99%) was 12 cGy for robust versus 445 cGy for clinical. The deviation for the heart (D10cc) was 41 cGy for robust and 320 cGy for clinical. Conclusions: The robust optimization approach can reduce heart dose compared to the clinical method at free-breathing and can potentially reduce the need for breath-hold techniques.« less

Accounting for range uncertainties in the optimization of intensity modulated proton therapy.

PubMed

Unkelbach, Jan; Chan, Timothy C Y; Bortfeld, Thomas

2007-05-21

Treatment plans optimized for intensity modulated proton therapy (IMPT) may be sensitive to range variations. The dose distribution may deteriorate substantially when the actual range of a pencil beam does not match the assumed range. We present two treatment planning concepts for IMPT which incorporate range uncertainties into the optimization. The first method is a probabilistic approach. The range of a pencil beam is assumed to be a random variable, which makes the delivered dose and the value of the objective function a random variable too. We then propose to optimize the expectation value of the objective function. The second approach is a robust formulation that applies methods developed in the field of robust linear programming. This approach optimizes the worst case dose distribution that may occur, assuming that the ranges of the pencil beams may vary within some interval. Both methods yield treatment plans that are considerably less sensitive to range variations compared to conventional treatment plans optimized without accounting for range uncertainties. In addition, both approaches--although conceptually different--yield very similar results on a qualitative level.

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

Baker, Kyri; Dall'Anese, Emiliano; Summers, Tyler

This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flowmore » equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.« less

Robust, Optimal Water Infrastructure Planning Under Deep Uncertainty Using Metamodels

NASA Astrophysics Data System (ADS)

Maier, H. R.; Beh, E. H. Y.; Zheng, F.; Dandy, G. C.; Kapelan, Z.

2015-12-01

Optimal long-term planning plays an important role in many water infrastructure problems. However, this task is complicated by deep uncertainty about future conditions, such as the impact of population dynamics and climate change. One way to deal with this uncertainty is by means of robustness, which aims to ensure that water infrastructure performs adequately under a range of plausible future conditions. However, as robustness calculations require computationally expensive system models to be run for a large number of scenarios, it is generally computationally intractable to include robustness as an objective in the development of optimal long-term infrastructure plans. In order to overcome this shortcoming, an approach is developed that uses metamodels instead of computationally expensive simulation models in robustness calculations. The approach is demonstrated for the optimal sequencing of water supply augmentation options for the southern portion of the water supply for Adelaide, South Australia. A 100-year planning horizon is subdivided into ten equal decision stages for the purpose of sequencing various water supply augmentation options, including desalination, stormwater harvesting and household rainwater tanks. The objectives include the minimization of average present value of supply augmentation costs, the minimization of average present value of greenhouse gas emissions and the maximization of supply robustness. The uncertain variables are rainfall, per capita water consumption and population. Decision variables are the implementation stages of the different water supply augmentation options. Artificial neural networks are used as metamodels to enable all objectives to be calculated in a computationally efficient manner at each of the decision stages. The results illustrate the importance of identifying optimal staged solutions to ensure robustness and sustainability of water supply into an uncertain long-term future.

Execution of Multidisciplinary Design Optimization Approaches on Common Test Problems

NASA Technical Reports Server (NTRS)

Balling, R. J.; Wilkinson, C. A.

1997-01-01

A class of synthetic problems for testing multidisciplinary design optimization (MDO) approaches is presented. These test problems are easy to reproduce because all functions are given as closed-form mathematical expressions. They are constructed in such a way that the optimal value of all variables and the objective is unity. The test problems involve three disciplines and allow the user to specify the number of design variables, state variables, coupling functions, design constraints, controlling design constraints, and the strength of coupling. Several MDO approaches were executed on two sample synthetic test problems. These approaches included single-level optimization approaches, collaborative optimization approaches, and concurrent subspace optimization approaches. Execution results are presented, and the robustness and efficiency of these approaches an evaluated for these sample problems.

SU-F-T-187: Quantifying Normal Tissue Sparing with 4D Robust Optimization of Intensity Modulated Proton Therapy

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

Newpower, M; Ge, S; Mohan, R

Purpose: To report an approach to quantify the normal tissue sparing for 4D robustly-optimized versus PTV-optimized IMPT plans. Methods: We generated two sets of 90 DVHs from a patient’s 10-phase 4D CT set; one by conventional PTV-based optimization done in the Eclipse treatment planning system, and the other by an in-house robust optimization algorithm. The 90 DVHs were created for the following scenarios in each of the ten phases of the 4DCT: ± 5mm shift along x, y, z; ± 3.5% range uncertainty and a nominal scenario. A Matlab function written by Gay and Niemierko was modified to calculate EUDmore » for each DVH for the following structures: esophagus, heart, ipsilateral lung and spinal cord. An F-test determined whether or not the variances of each structure’s DVHs were statistically different. Then a t-test determined if the average EUDs for each optimization algorithm were statistically significantly different. Results: T-test results showed each structure had a statistically significant difference in average EUD when comparing robust optimization versus PTV-based optimization. Under robust optimization all structures except the spinal cord received lower EUDs than PTV-based optimization. Using robust optimization the average EUDs decreased 1.45% for the esophagus, 1.54% for the heart and 5.45% for the ipsilateral lung. The average EUD to the spinal cord increased 24.86% but was still well below tolerance. Conclusion: This work has helped quantify a qualitative relationship noted earlier in our work: that robust optimization leads to plans with greater normal tissue sparing compared to PTV-based optimization. Except in the case of the spinal cord all structures received a lower EUD under robust optimization and these results are statistically significant. While the average EUD to the spinal cord increased to 25.06 Gy under robust optimization it is still well under the TD50 value of 66.5 Gy from Emami et al. Supported in part by the NCI U19 CA021239.« less

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

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

Robust optimization-based DC optimal power flow for managing wind generation uncertainty

NASA Astrophysics Data System (ADS)

Boonchuay, Chanwit; Tomsovic, Kevin; Li, Fangxing; Ongsakul, Weerakorn

2012-11-01

Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.

Optimization of Robust HPLC Method for Quantitation of Ambroxol Hydrochloride and Roxithromycin Using a DoE Approach.

PubMed

Patel, Rashmin B; Patel, Nilay M; Patel, Mrunali R; Solanki, Ajay B

2017-03-01

The aim of this work was to develop and optimize a robust HPLC method for the separation and quantitation of ambroxol hydrochloride and roxithromycin utilizing Design of Experiment (DoE) approach. The Plackett-Burman design was used to assess the impact of independent variables (concentration of organic phase, mobile phase pH, flow rate and column temperature) on peak resolution, USP tailing and number of plates. A central composite design was utilized to evaluate the main, interaction, and quadratic effects of independent variables on the selected dependent variables. The optimized HPLC method was validated based on ICH Q2R1 guideline and was used to separate and quantify ambroxol hydrochloride and roxithromycin in tablet formulations. The findings showed that DoE approach could be effectively applied to optimize a robust HPLC method for quantification of ambroxol hydrochloride and roxithromycin in tablet formulations. Statistical comparison between results of proposed and reported HPLC method revealed no significant difference; indicating the ability of proposed HPLC method for analysis of ambroxol hydrochloride and roxithromycin in pharmaceutical formulations. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Design optimization for cost and quality: The robust design approach

NASA Technical Reports Server (NTRS)

Unal, Resit

1990-01-01

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

A Scalable and Robust Multi-Agent Approach to Distributed Optimization

NASA Technical Reports Server (NTRS)

Tumer, Kagan

2005-01-01

Modularizing a large optimization problem so that the solutions to the subproblems provide a good overall solution is a challenging problem. In this paper we present a multi-agent approach to this problem based on aligning the agent objectives with the system objectives, obviating the need to impose external mechanisms to achieve collaboration among the agents. This approach naturally addresses scaling and robustness issues by ensuring that the agents do not rely on the reliable operation of other agents We test this approach in the difficult distributed optimization problem of imperfect device subset selection [Challet and Johnson, 2002]. In this problem, there are n devices, each of which has a "distortion", and the task is to find the subset of those n devices that minimizes the average distortion. Our results show that in large systems (1000 agents) the proposed approach provides improvements of over an order of magnitude over both traditional optimization methods and traditional multi-agent methods. Furthermore, the results show that even in extreme cases of agent failures (i.e., half the agents fail midway through the simulation) the system remains coordinated and still outperforms a failure-free and centralized optimization algorithm.

Robust Learning Control Design for Quantum Unitary Transformations.

PubMed

Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

2017-12-01

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Robust portfolio selection based on asymmetric measures of variability of stock returns

NASA Astrophysics Data System (ADS)

Chen, Wei; Tan, Shaohua

2009-10-01

This paper addresses a new uncertainty set--interval random uncertainty set for robust optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust mean-variance portfolio selection under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance.

A robust approach to optimal matched filter design in ultrasonic non-destructive evaluation (NDE)

NASA Astrophysics Data System (ADS)

Li, Minghui; Hayward, Gordon

2017-02-01

The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method.

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

NASA Astrophysics Data System (ADS)

Chebbi, A.; Bargaoui, Z. K.; da Conceição Cunha, M.

2013-10-01

Based on rainfall intensity-duration-frequency (IDF) curves, fitted in several locations of a given area, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimization can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables, and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short- and a long-term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. Therefore, it is proposed to augment it by 25, 50, 100 and 160% virtually, which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

Feasibility and robustness of dose painting by numbers in proton therapy with contour-driven plan optimization

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

Barragán, A. M., E-mail: ana.barragan@uclouvain.be; Differding, S.; Lee, J. A.

Purpose: To prove the ability of protons to reproduce a dose gradient that matches a dose painting by numbers (DPBN) prescription in the presence of setup and range errors, by using contours and structure-based optimization in a commercial treatment planning system. Methods: For two patients with head and neck cancer, voxel-by-voxel prescription to the target volume (GTV{sub PET}) was calculated from {sup 18}FDG-PET images and approximated with several discrete prescription subcontours. Treatments were planned with proton pencil beam scanning. In order to determine the optimal plan parameters to approach the DPBN prescription, the effects of the scanning pattern, number ofmore » fields, number of subcontours, and use of range shifter were separately tested on each patient. Different constant scanning grids (i.e., spot spacing = Δx = Δy = 3.5, 4, and 5 mm) and uniform energy layer separation [4 and 5 mm WED (water equivalent distance)] were analyzed versus a dynamic and automatic selection of the spots grid. The number of subcontours was increased from 3 to 11 while the number of beams was set to 3, 5, or 7. Conventional PTV-based and robust clinical target volumes (CTV)-based optimization strategies were considered and their robustness against range and setup errors assessed. Because of the nonuniform prescription, ensuring robustness for coverage of GTV{sub PET} inevitably leads to overdosing, which was compared for both optimization schemes. Results: The optimal number of subcontours ranged from 5 to 7 for both patients. All considered scanning grids achieved accurate dose painting (1% average difference between the prescribed and planned doses). PTV-based plans led to nonrobust target coverage while robust-optimized plans improved it considerably (differences between worst-case CTV dose and the clinical constraint was up to 3 Gy for PTV-based plans and did not exceed 1 Gy for robust CTV-based plans). Also, only 15% of the points in the GTV{sub PET} (worst case) were above 5% of DPBN prescription for robust-optimized plans, while they were more than 50% for PTV plans. Low dose to organs at risk (OARs) could be achieved for both PTV and robust-optimized plans. Conclusions: DPBN in proton therapy is feasible with the use of a sufficient number subcontours, automatically generated scanning patterns, and no more than three beams are needed. Robust optimization ensured the required target coverage and minimal overdosing, while PTV-approach led to nonrobust plans with excessive overdose. Low dose to OARs can be achieved even in the presence of a high-dose escalation as in DPBN.« less

Online optimization of storage ring nonlinear beam dynamics

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

Huang, Xiaobiao; Safranek, James

2015-08-01

We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.

Dynamic, stochastic models for congestion pricing and congestion securities.

DOT National Transportation Integrated Search

2010-12-01

This research considers congestion pricing under demand uncertainty. In particular, a robust optimization (RO) approach is applied to optimal congestion pricing problems under user equilibrium. A mathematical model is developed and an analysis perfor...

Feedforward/feedback control synthesis for performance and robustness

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1990-01-01

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

Optimal flexible sample size design with robust power.

PubMed

Zhang, Lanju; Cui, Lu; Yang, Bo

2016-08-30

It is well recognized that sample size determination is challenging because of the uncertainty on the treatment effect size. Several remedies are available in the literature. Group sequential designs start with a sample size based on a conservative (smaller) effect size and allow early stop at interim looks. Sample size re-estimation designs start with a sample size based on an optimistic (larger) effect size and allow sample size increase if the observed effect size is smaller than planned. Different opinions favoring one type over the other exist. We propose an optimal approach using an appropriate optimality criterion to select the best design among all the candidate designs. Our results show that (1) for the same type of designs, for example, group sequential designs, there is room for significant improvement through our optimization approach; (2) optimal promising zone designs appear to have no advantages over optimal group sequential designs; and (3) optimal designs with sample size re-estimation deliver the best adaptive performance. We conclude that to deal with the challenge of sample size determination due to effect size uncertainty, an optimal approach can help to select the best design that provides most robust power across the effect size range of interest. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

NASA Astrophysics Data System (ADS)

Chebbi, A.; Bargaoui, Z. K.; da Conceição Cunha, M.

2012-12-01

Based on rainfall intensity-duration-frequency (IDF) curves, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimisation can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short and a long term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. So, it is proposed to virtually augment it by 25, 50, 100 and 160% which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

Petroleum refinery operational planning using robust optimization

NASA Astrophysics Data System (ADS)

Leiras, A.; Hamacher, S.; Elkamel, A.

2010-12-01

In this article, the robust optimization methodology is applied to deal with uncertainties in the prices of saleable products, operating costs, product demand, and product yield in the context of refinery operational planning. A numerical study demonstrates the effectiveness of the proposed robust approach. The benefits of incorporating uncertainty in the different model parameters were evaluated in terms of the cost of ignoring uncertainty in the problem. The calculations suggest that this benefit is equivalent to 7.47% of the deterministic solution value, which indicates that the robust model may offer advantages to those involved with refinery operational planning. In addition, the probability bounds of constraint violation are calculated to help the decision-maker adopt a more appropriate parameter to control robustness and judge the tradeoff between conservatism and total profit.

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

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1992-01-01

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

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

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

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

HPLC-MS/MS method for dexmedetomidine quantification with Design of Experiments approach: application to pediatric pharmacokinetic study.

PubMed

Szerkus, Oliwia; Struck-Lewicka, Wiktoria; Kordalewska, Marta; Bartosińska, Ewa; Bujak, Renata; Borsuk, Agnieszka; Bienert, Agnieszka; Bartkowska-Śniatkowska, Alicja; Warzybok, Justyna; Wiczling, Paweł; Nasal, Antoni; Kaliszan, Roman; Markuszewski, Michał Jan; Siluk, Danuta

2017-02-01

The purpose of this work was to develop and validate a rapid and robust LC-MS/MS method for the determination of dexmedetomidine (DEX) in plasma, suitable for analysis of a large number of samples. Systematic approach, Design of Experiments, was applied to optimize ESI source parameters and to evaluate method robustness, therefore, a rapid, stable and cost-effective assay was developed. The method was validated according to US FDA guidelines. LLOQ was determined at 5 pg/ml. The assay was linear over the examined concentration range (5-2500 pg/ml), Results: Experimental design approach was applied for optimization of ESI source parameters and evaluation of method robustness. The method was validated according to the US FDA guidelines. LLOQ was determined at 5 pg/ml. The assay was linear over the examined concentration range (R 2 > 0.98). The accuracies, intra- and interday precisions were less than 15%. The stability data confirmed reliable behavior of DEX under tested conditions. Application of Design of Experiments approach allowed for fast and efficient analytical method development and validation as well as for reduced usage of chemicals necessary for regular method optimization. The proposed technique was applied to determination of DEX pharmacokinetics in pediatric patients undergoing long-term sedation in the intensive care unit.

Adaptive torque estimation of robot joint with harmonic drive transmission

NASA Astrophysics Data System (ADS)

Shi, Zhiguo; Li, Yuankai; Liu, Guangjun

2017-11-01

Robot joint torque estimation using input and output position measurements is a promising technique, but the result may be affected by the load variation of the joint. In this paper, a torque estimation method with adaptive robustness and optimality adjustment according to load variation is proposed for robot joint with harmonic drive transmission. Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust. The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor and two representative filtering methods. The results have demonstrated the effectiveness of the proposed torque estimation technique.

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.

Robust and transferable quantification of NMR spectral quality using IROC analysis

NASA Astrophysics Data System (ADS)

Zambrello, Matthew A.; Maciejewski, Mark W.; Schuyler, Adam D.; Weatherby, Gerard; Hoch, Jeffrey C.

2017-12-01

Non-Fourier methods are increasingly utilized in NMR spectroscopy because of their ability to handle nonuniformly-sampled data. However, non-Fourier methods present unique challenges due to their nonlinearity, which can produce nonrandom noise and render conventional metrics for spectral quality such as signal-to-noise ratio unreliable. The lack of robust and transferable metrics (i.e. applicable to methods exhibiting different nonlinearities) has hampered comparison of non-Fourier methods and nonuniform sampling schemes, preventing the identification of best practices. We describe a novel method, in situ receiver operating characteristic analysis (IROC), for characterizing spectral quality based on the Receiver Operating Characteristic curve. IROC utilizes synthetic signals added to empirical data as "ground truth", and provides several robust scalar-valued metrics for spectral quality. This approach avoids problems posed by nonlinear spectral estimates, and provides a versatile quantitative means of characterizing many aspects of spectral quality. We demonstrate applications to parameter optimization in Fourier and non-Fourier spectral estimation, critical comparison of different methods for spectrum analysis, and optimization of nonuniform sampling schemes. The approach will accelerate the discovery of optimal approaches to nonuniform sampling experiment design and non-Fourier spectrum analysis for multidimensional NMR.

Robust stochastic optimization for reservoir operation

NASA Astrophysics Data System (ADS)

Pan, Limeng; Housh, Mashor; Liu, Pan; Cai, Ximing; Chen, Xin

2015-01-01

Optimal reservoir operation under uncertainty is a challenging engineering problem. Application of classic stochastic optimization methods to large-scale problems is limited due to computational difficulty. Moreover, classic stochastic methods assume that the estimated distribution function or the sample inflow data accurately represents the true probability distribution, which may be invalid and the performance of the algorithms may be undermined. In this study, we introduce a robust optimization (RO) approach, Iterative Linear Decision Rule (ILDR), so as to provide a tractable approximation for a multiperiod hydropower generation problem. The proposed approach extends the existing LDR method by accommodating nonlinear objective functions. It also provides users with the flexibility of choosing the accuracy of ILDR approximations by assigning a desired number of piecewise linear segments to each uncertainty. The performance of the ILDR is compared with benchmark policies including the sampling stochastic dynamic programming (SSDP) policy derived from historical data. The ILDR solves both the single and multireservoir systems efficiently. The single reservoir case study results show that the RO method is as good as SSDP when implemented on the original historical inflows and it outperforms SSDP policy when tested on generated inflows with the same mean and covariance matrix as those in history. For the multireservoir case study, which considers water supply in addition to power generation, numerical results show that the proposed approach performs as well as in the single reservoir case study in terms of optimal value and distributional robustness.

Robust Frequency Invariant Beamforming with Low Sidelobe for Speech Enhancement

NASA Astrophysics Data System (ADS)

Zhu, Yiting; Pan, Xiang

2018-01-01

Frequency invariant beamformers (FIBs) are widely used in speech enhancement and source localization. There are two traditional optimization methods for FIB design. The first one is convex optimization, which is simple but the frequency invariant characteristic of the beam pattern is poor with respect to frequency band of five octaves. The least squares (LS) approach using spatial response variation (SRV) constraint is another optimization method. Although, it can provide good frequency invariant property, it usually couldn’t be used in speech enhancement for its lack of weight norm constraint which is related to the robustness of a beamformer. In this paper, a robust wideband beamforming method with a constant beamwidth is proposed. The frequency invariant beam pattern is achieved by resolving an optimization problem of the SRV constraint to cover speech frequency band. With the control of sidelobe level, it is available for the frequency invariant beamformer (FIB) to prevent distortion of interference from the undesirable direction. The approach is completed in time-domain by placing tapped delay lines(TDL) and finite impulse response (FIR) filter at the output of each sensor which is more convenient than the Frost processor. By invoking the weight norm constraint, the robustness of the beamformer is further improved against random errors. Experiment results show that the proposed method has a constant beamwidth and almost the same white noise gain as traditional delay-and-sum (DAS) beamformer.

An improved robust buffer allocation method for the project scheduling problem

NASA Astrophysics Data System (ADS)

Ghoddousi, Parviz; Ansari, Ramin; Makui, Ahmad

2017-04-01

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.

An H(∞) control approach to robust learning of feedforward neural networks.

PubMed

Jing, Xingjian

2011-09-01

A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise. Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H(∞) "noise" attenuation level. Several existing BP-type algorithms are shown to be special cases of the new H(∞)-learning algorithm. Theoretical analysis and several examples are provided to show the advantages of the new method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Using spatial information about recurrence risk for robust optimization of dose-painting prescription functions

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

Bender, Edward T.

Purpose: To develop a robust method for deriving dose-painting prescription functions using spatial information about the risk for disease recurrence. Methods: Spatial distributions of radiobiological model parameters are derived from distributions of recurrence risk after uniform irradiation. These model parameters are then used to derive optimal dose-painting prescription functions given a constant mean biologically effective dose. Results: An estimate for the optimal dose distribution can be derived based on spatial information about recurrence risk. Dose painting based on imaging markers that are moderately or poorly correlated with recurrence risk are predicted to potentially result in inferior disease control when comparedmore » the same mean biologically effective dose delivered uniformly. A robust optimization approach may partially mitigate this issue. Conclusions: The methods described here can be used to derive an estimate for a robust, patient-specific prescription function for use in dose painting. Two approximate scaling relationships were observed: First, the optimal choice for the maximum dose differential when using either a linear or two-compartment prescription function is proportional to R, where R is the Pearson correlation coefficient between a given imaging marker and recurrence risk after uniform irradiation. Second, the predicted maximum possible gain in tumor control probability for any robust optimization technique is nearly proportional to the square of R.« less

Anatomical robust optimization to account for nasal cavity filling variation during intensity-modulated proton therapy: a comparison with conventional and adaptive planning strategies

NASA Astrophysics Data System (ADS)

van de Water, Steven; Albertini, Francesca; Weber, Damien C.; Heijmen, Ben J. M.; Hoogeman, Mischa S.; Lomax, Antony J.

2018-01-01

The aim of this study is to develop an anatomical robust optimization method for intensity-modulated proton therapy (IMPT) that accounts for interfraction variations in nasal cavity filling, and to compare it with conventional single-field uniform dose (SFUD) optimization and online plan adaptation. We included CT data of five patients with tumors in the sinonasal region. Using the planning CT, we generated for each patient 25 ‘synthetic’ CTs with varying nasal cavity filling. The robust optimization method available in our treatment planning system ‘Erasmus-iCycle’ was extended to also account for anatomical uncertainties by including (synthetic) CTs with varying patient anatomy as error scenarios in the inverse optimization. For each patient, we generated treatment plans using anatomical robust optimization and, for benchmarking, using SFUD optimization and online plan adaptation. Clinical target volume (CTV) and organ-at-risk (OAR) doses were assessed by recalculating the treatment plans on the synthetic CTs, evaluating dose distributions individually and accumulated over an entire fractionated 50 GyRBE treatment, assuming each synthetic CT to correspond to a 2 GyRBE fraction. Treatment plans were also evaluated using actual repeat CTs. Anatomical robust optimization resulted in adequate CTV doses (V95%  ⩾  98% and V107%  ⩽  2%) if at least three synthetic CTs were included in addition to the planning CT. These CTV requirements were also fulfilled for online plan adaptation, but not for the SFUD approach, even when applying a margin of 5 mm. Compared with anatomical robust optimization, OAR dose parameters for the accumulated dose distributions were on average 5.9 GyRBE (20%) higher when using SFUD optimization and on average 3.6 GyRBE (18%) lower for online plan adaptation. In conclusion, anatomical robust optimization effectively accounted for changes in nasal cavity filling during IMPT, providing substantially improved CTV and OAR doses compared with conventional SFUD optimization. OAR doses can be further reduced by using online plan adaptation.

Robust on-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi

1993-01-01

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

Inverse Optimization: A New Perspective on the Black-Litterman Model.

PubMed

Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch

2012-12-11

The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct "BL"-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new "BL"-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.

Multi-criteria multi-stakeholder decision analysis using a fuzzy-stochastic approach for hydrosystem management

NASA Astrophysics Data System (ADS)

Subagadis, Y. H.; Schütze, N.; Grundmann, J.

2014-09-01

The conventional methods used to solve multi-criteria multi-stakeholder problems are less strongly formulated, as they normally incorporate only homogeneous information at a time and suggest aggregating objectives of different decision-makers avoiding water-society interactions. In this contribution, Multi-Criteria Group Decision Analysis (MCGDA) using a fuzzy-stochastic approach has been proposed to rank a set of alternatives in water management decisions incorporating heterogeneous information under uncertainty. The decision making framework takes hydrologically, environmentally, and socio-economically motivated conflicting objectives into consideration. The criteria related to the performance of the physical system are optimized using multi-criteria simulation-based optimization, and fuzzy linguistic quantifiers have been used to evaluate subjective criteria and to assess stakeholders' degree of optimism. The proposed methodology is applied to find effective and robust intervention strategies for the management of a coastal hydrosystem affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. Preliminary results show that the MCGDA based on a fuzzy-stochastic approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

An optimization program based on the method of feasible directions: Theory and users guide

NASA Technical Reports Server (NTRS)

Belegundu, Ashok D.; Berke, Laszlo; Patnaik, Surya N.

1994-01-01

The theory and user instructions for an optimization code based on the method of feasible directions are presented. The code was written for wide distribution and ease of attachment to other simulation software. Although the theory of the method of feasible direction was developed in the 1960's, many considerations are involved in its actual implementation as a computer code. Included in the code are a number of features to improve robustness in optimization. The search direction is obtained by solving a quadratic program using an interior method based on Karmarkar's algorithm. The theory is discussed focusing on the important and often overlooked role played by the various parameters guiding the iterations within the program. Also discussed is a robust approach for handling infeasible starting points. The code was validated by solving a variety of structural optimization test problems that have known solutions obtained by other optimization codes. It has been observed that this code is robust: it has solved a variety of problems from different starting points. However, the code is inefficient in that it takes considerable CPU time as compared with certain other available codes. Further work is required to improve its efficiency while retaining its robustness.

Robustness-Based Design Optimization Under Data Uncertainty

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

Integration of the Response Surface Methodology with the Compromise Decision Support Problem in Developing a General Robust Design Procedure

NASA Technical Reports Server (NTRS)

Chen, Wei; Tsui, Kwok-Leung; Allen, Janet K.; Mistree, Farrokh

1994-01-01

In this paper we introduce a comprehensive and rigorous robust design procedure to overcome some limitations of the current approaches. A comprehensive approach is general enough to model the two major types of robust design applications, namely, robust design associated with the minimization of the deviation of performance caused by the deviation of noise factors (uncontrollable parameters), and robust design due to the minimization of the deviation of performance caused by the deviation of control factors (design variables). We achieve mathematical rigor by using, as a foundation, principles from the design of experiments and optimization. Specifically, we integrate the Response Surface Method (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example. Our focus in this paper is on illustrating our approach rather than on the results per se.

Robust patella motion tracking using intensity-based 2D-3D registration on dynamic bi-plane fluoroscopy: towards quantitative assessment in MPFL reconstruction surgery

NASA Astrophysics Data System (ADS)

Otake, Yoshito; Esnault, Matthieu; Grupp, Robert; Kosugi, Shinichi; Sato, Yoshinobu

2016-03-01

The determination of in vivo motion of multiple-bones using dynamic fluoroscopic images and computed tomography (CT) is useful for post-operative assessment of orthopaedic surgeries such as medial patellofemoral ligament reconstruction. We propose a robust method to measure the 3D motion of multiple rigid objects with high accuracy using a series of bi-plane fluoroscopic images and a multi-resolution, intensity-based, 2D-3D registration. A Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimizer was used with a gradient correlation similarity metric. Four approaches to register three rigid objects (femur, tibia-fibula and patella) were implemented: 1) an individual bone approach registering one bone at a time, each with optimization of a six degrees of freedom (6DOF) parameter, 2) a sequential approach registering one bone at a time but using the previous bone results as the background in DRR generation, 3) a simultaneous approach registering all the bones together (18DOF) and 4) a combination of the sequential and the simultaneous approaches. These approaches were compared in experiments using simulated images generated from the CT of a healthy volunteer and measured fluoroscopic images. Over the 120 simulated frames of motion, the simultaneous approach showed improved registration accuracy compared to the individual approach: with less than 0.68mm root-mean-square error (RMSE) for translation and less than 1.12° RMSE for rotation. A robustness evaluation was conducted with 45 trials of a randomly perturbed initialization showed that the sequential approach improved robustness significantly (74% success rate) compared to the individual bone approach (34% success) for patella registration (femur and tibia-fibula registration had a 100% success rate with each approach).

Robust 2DPCA with non-greedy l1 -norm maximization for image analysis.

PubMed

Wang, Rong; Nie, Feiping; Yang, Xiaojun; Gao, Feifei; Yao, Minli

2015-05-01

2-D principal component analysis based on l1 -norm (2DPCA-L1) is a recently developed approach for robust dimensionality reduction and feature extraction in image domain. Normally, a greedy strategy is applied due to the difficulty of directly solving the l1 -norm maximization problem, which is, however, easy to get stuck in local solution. In this paper, we propose a robust 2DPCA with non-greedy l1 -norm maximization in which all projection directions are optimized simultaneously. Experimental results on face and other datasets confirm the effectiveness of the proposed approach.

Numerical Nonlinear Robust Control with Applications to Humanoid Robots

DTIC Science & Technology

2015-07-01

automatically. While optimization and optimal control theory have been widely applied in humanoid robot control, it is not without drawbacks . A blind... drawback of Galerkin-based approaches is the need to successively produce discrete forms, which is difficult to implement in practice. Related...universal function approx- imation ability, these approaches are not without drawbacks . In practice, while a single hidden layer neural network can

Towards robust optimal design of storm water systems

NASA Astrophysics Data System (ADS)

Marquez Calvo, Oscar; Solomatine, Dimitri

2015-04-01

In this study the focus is on the design of a storm water or a combined sewer system. Such a system should be capable to handle properly most of the storm to minimize the damages caused by flooding due to the lack of capacity of the system to cope with rain water at peak times. This problem is a multi-objective optimization problem: we have to take into account the minimization of the construction costs, the minimization of damage costs due to flooding, and possibly other criteria. One of the most important factors influencing the design of storm water systems is the expected amount of water to deal with. It is common that this infrastructure is developed with the capacity to cope with events that occur once in, say 10 or 20 years - so-called design rainfall events. However, rainfall is a random variable and such uncertainty typically is not taken explicitly into account in optimization. Rainfall design data is based on historical information of rainfalls, but many times this data is based on unreliable measures; or in not enough historical information; or as we know, the patterns of rainfall are changing regardless of historical information. There are also other sources of uncertainty influencing design, for example, leakages in the pipes and accumulation of sediments in pipes. In the context of storm water or combined sewer systems design or rehabilitation, robust optimization technique should be able to find the best design (or rehabilitation plan) within the available budget but taking into account uncertainty in those variables that were used to design the system. In this work we consider various approaches to robust optimization proposed by various authors (Gabrel, Murat, Thiele 2013; Beyer, Sendhoff 2007) and test a novel method ROPAR (Solomatine 2012) to analyze robustness. References Beyer, H.G., & Sendhoff, B. (2007). Robust optimization - A comprehensive survey. Comput. Methods Appl. Mech. Engrg., 3190-3218. Gabrel, V.; Murat, C., Thiele, A. (2014). Recent advances in robust optimization: An overview. European Journal of Operational Research. 471-483. Solomatine, D.P. (2012). Robust Optimization and Probabilistic Analysis of Robustness (ROPAR). http://www.unesco-ihe.org/hi/sol/papers/ ROPAR.pdf.

Fast state transfer in a Λ-system: a shortcut-to-adiabaticity approach to robust and resource optimized control

NASA Astrophysics Data System (ADS)

Mortensen, Henrik Lund; Sørensen, Jens Jakob W. H.; Mølmer, Klaus; Sherson, Jacob Friis

2018-02-01

We propose an efficient strategy to find optimal control functions for state-to-state quantum control problems. Our procedure first chooses an input state trajectory, that can realize the desired transformation by adiabatic variation of the system Hamiltonian. The shortcut-to-adiabaticity formalism then provides a control Hamiltonian that realizes the reference trajectory exactly but on a finite time scale. As the final state is achieved with certainty, we define a cost functional that incorporates the resource requirements and a perturbative expression for robustness. We optimize this functional by systematically varying the reference trajectory. We demonstrate the method by application to population transfer in a laser driven three-level Λ-system, where we find solutions that are fast and robust against perturbations while maintaining a low peak laser power.

Distribution-dependent robust linear optimization with applications to inventory control

PubMed Central

Kang, Seong-Cheol; Brisimi, Theodora S.

2014-01-01

This paper tackles linear programming problems with data uncertainty and applies it to an important inventory control problem. Each element of the constraint matrix is subject to uncertainty and is modeled as a random variable with a bounded support. The classical robust optimization approach to this problem yields a solution with guaranteed feasibility. As this approach tends to be too conservative when applications can tolerate a small chance of infeasibility, one would be interested in obtaining a less conservative solution with a certain probabilistic guarantee of feasibility. A robust formulation in the literature produces such a solution, but it does not use any distributional information on the uncertain data. In this work, we show that the use of distributional information leads to an equally robust solution (i.e., under the same probabilistic guarantee of feasibility) but with a better objective value. In particular, by exploiting distributional information, we establish stronger upper bounds on the constraint violation probability of a solution. These bounds enable us to “inject” less conservatism into the formulation, which in turn yields a more cost-effective solution (by 50% or more in some numerical instances). To illustrate the effectiveness of our methodology, we consider a discrete-time stochastic inventory control problem with certain quality of service constraints. Numerical tests demonstrate that the use of distributional information in the robust optimization of the inventory control problem results in 36%–54% cost savings, compared to the case where such information is not used. PMID:26347579

Topology optimization under stochastic stiffness

NASA Astrophysics Data System (ADS)

Asadpoure, Alireza

Topology optimization is a systematic computational tool for optimizing the layout of materials within a domain for engineering design problems. It allows variation of structural boundaries and connectivities. This freedom in the design space often enables discovery of new, high performance designs. However, solutions obtained by performing the optimization in a deterministic setting may be impractical or suboptimal when considering real-world engineering conditions with inherent variabilities including (for example) variabilities in fabrication processes and operating conditions. The aim of this work is to provide a computational methodology for topology optimization in the presence of uncertainties associated with structural stiffness, such as uncertain material properties and/or structural geometry. Existing methods for topology optimization under deterministic conditions are first reviewed. Modifications are then proposed to improve the numerical performance of the so-called Heaviside Projection Method (HPM) in continuum domains. Next, two approaches, perturbation and Polynomial Chaos Expansion (PCE), are proposed to account for uncertainties in the optimization procedure. These approaches are intrusive, allowing tight and efficient coupling of the uncertainty quantification with the optimization sensitivity analysis. The work herein develops a robust topology optimization framework aimed at reducing the sensitivity of optimized solutions to uncertainties. The perturbation-based approach combines deterministic topology optimization with a perturbation method for the quantification of uncertainties. The use of perturbation transforms the problem of topology optimization under uncertainty to an augmented deterministic topology optimization problem. The PCE approach combines the spectral stochastic approach for the representation and propagation of uncertainties with an existing deterministic topology optimization technique. The resulting compact representations for the response quantities allow for efficient and accurate calculation of sensitivities of response statistics with respect to the design variables. The proposed methods are shown to be successful at generating robust optimal topologies. Examples from topology optimization in continuum and discrete domains (truss structures) under uncertainty are presented. It is also shown that proposed methods lead to significant computational savings when compared to Monte Carlo-based optimization which involve multiple formations and inversions of the global stiffness matrix and that results obtained from the proposed method are in excellent agreement with those obtained from a Monte Carlo-based optimization algorithm.

Optimized tomography of continuous variable systems using excitation counting

NASA Astrophysics Data System (ADS)

Shen, Chao; Heeres, Reinier W.; Reinhold, Philip; Jiang, Luyao; Liu, Yi-Kai; Schoelkopf, Robert J.; Jiang, Liang

2016-11-01

We propose a systematic procedure to optimize quantum state tomography protocols for continuous variable systems based on excitation counting preceded by a displacement operation. Compared with conventional tomography based on Husimi or Wigner function measurement, the excitation counting approach can significantly reduce the number of measurement settings. We investigate both informational completeness and robustness, and provide a bound of reconstruction error involving the condition number of the sensing map. We also identify the measurement settings that optimize this error bound, and demonstrate that the improved reconstruction robustness can lead to an order-of-magnitude reduction of estimation error with given resources. This optimization procedure is general and can incorporate prior information of the unknown state to further simplify the protocol.

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been provEn effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. Several approaches that have proven effective for other evolutionary algorithms are modified and implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for standard test optimization problems and for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane

NASA Technical Reports Server (NTRS)

Lu, Ping

1992-01-01

The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.

Inverse Optimization: A New Perspective on the Black-Litterman Model

PubMed Central

Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch.

2014-01-01

The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views. PMID:25382873

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

PubMed

Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

2017-01-01

In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.

Bayesian Regression with Network Prior: Optimal Bayesian Filtering Perspective

PubMed Central

Qian, Xiaoning; Dougherty, Edward R.

2017-01-01

The recently introduced intrinsically Bayesian robust filter (IBRF) provides fully optimal filtering relative to a prior distribution over an uncertainty class ofjoint random process models, whereas formerly the theory was limited to model-constrained Bayesian robust filters, for which optimization was limited to the filters that are optimal for models in the uncertainty class. This paper extends the IBRF theory to the situation where there are both a prior on the uncertainty class and sample data. The result is optimal Bayesian filtering (OBF), where optimality is relative to the posterior distribution derived from the prior and the data. The IBRF theories for effective characteristics and canonical expansions extend to the OBF setting. A salient focus of the present work is to demonstrate the advantages of Bayesian regression within the OBF setting over the classical Bayesian approach in the context otlinear Gaussian models. PMID:28824268

Robust optimization based energy dispatch in smart grids considering demand uncertainty

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Design of Optimally Robust Control Systems.

DTIC Science & Technology

1980-01-01

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

TU-AB-BRB-01: Coverage Evaluation and Probabilistic Treatment Planning as a Margin Alternative

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

Siebers, J.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

TU-AB-BRB-03: Coverage-Based Treatment Planning to Accommodate Organ Deformable Motions and Contouring Uncertainties for Prostate Treatment

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

Xu, H.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

TU-AB-BRB-02: Stochastic Programming Methods for Handling Uncertainty and Motion in IMRT Planning

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

Unkelbach, J.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

TU-AB-BRB-00: New Methods to Ensure Target Coverage

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

NONE

2015-06-15

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

Exploratory High-Fidelity Aerostructural Optimization Using an Efficient Monolithic Solution Method

NASA Astrophysics Data System (ADS)

Zhang, Jenmy Zimi

This thesis is motivated by the desire to discover fuel efficient aircraft concepts through exploratory design. An optimization methodology based on tightly integrated high-fidelity aerostructural analysis is proposed, which has the flexibility, robustness, and efficiency to contribute to this goal. The present aerostructural optimization methodology uses an integrated geometry parameterization and mesh movement strategy, which was initially proposed for aerodynamic shape optimization. This integrated approach provides the optimizer with a large amount of geometric freedom for conducting exploratory design, while allowing for efficient and robust mesh movement in the presence of substantial shape changes. In extending this approach to aerostructural optimization, this thesis has addressed a number of important challenges. A structural mesh deformation strategy has been introduced to translate consistently the shape changes described by the geometry parameterization to the structural model. A three-field formulation of the discrete steady aerostructural residual couples the mesh movement equations with the three-dimensional Euler equations and a linear structural analysis. Gradients needed for optimization are computed with a three-field coupled adjoint approach. A number of investigations have been conducted to demonstrate the suitability and accuracy of the present methodology for use in aerostructural optimization involving substantial shape changes. Robustness and efficiency in the coupled solution algorithms is crucial to the success of an exploratory optimization. This thesis therefore also focuses on the design of an effective monolithic solution algorithm for the proposed methodology. This involves using a Newton-Krylov method for the aerostructural analysis and a preconditioned Krylov subspace method for the coupled adjoint solution. Several aspects of the monolithic solution method have been investigated. These include appropriate strategies for scaling and matrix-vector product evaluation, as well as block preconditioning techniques that preserve the modularity between subproblems. The monolithic solution method is applied to problems with varying degrees of fluid-structural coupling, as well as a wing span optimization study. The monolithic solution algorithm typically requires 20%-70% less computing time than its partitioned counterpart. This advantage increases with increasing wing flexibility. The performance of the monolithic solution method is also much less sensitive to the choice of the solution parameter.

Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

NASA Astrophysics Data System (ADS)

Lim, Yeerang; Jung, Youeyun; Bang, Hyochoong

2018-05-01

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.

Robust design of configurations and parameters of adaptable products

NASA Astrophysics Data System (ADS)

Zhang, Jian; Chen, Yongliang; Xue, Deyi; Gu, Peihua

2014-03-01

An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architecture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertainties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candidates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.

Strong stabilization servo controller with optimization of performance criteria.

PubMed

Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor

2011-07-01

Synthesis of a simple robust controller with a pole placement technique and a H(∞) metrics is the method used for control of a servo mechanism with BLDC and BDC electric motors. The method includes solving a polynomial equation on the basis of the chosen characteristic polynomial using the Manabe standard polynomial form and parametric solutions. Parametric solutions are introduced directly into the structure of the servo controller. On the basis of the chosen parametric solutions the robustness of a closed-loop system is assessed through uncertainty models and assessment of the norm ‖•‖(∞). The design procedure and the optimization are performed with a genetic algorithm differential evolution - DE. The DE optimization method determines a suboptimal solution throughout the optimization on the basis of a spectrally square polynomial and Šiljak's absolute stability test. The stability of the designed controller during the optimization is being checked with Lipatov's stability condition. Both utilized approaches: Šiljak's test and Lipatov's condition, check the robustness and stability characteristics on the basis of the polynomial's coefficients, and are very convenient for automated design of closed-loop control and for application in optimization algorithms such as DE. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Approach to method development and validation in capillary electrophoresis for enantiomeric purity testing of active basic pharmaceutical ingredients.

PubMed

Sokoliess, Torsten; Köller, Gerhard

2005-06-01

A chiral capillary electrophoresis system allowing the determination of the enantiomeric purity of an investigational new drug was developed using a generic method development approach for basic analytes. The method was optimized in terms of type and concentration of both cyclodextrin (CD) and electrolyte, buffer pH, temperature, voltage, and rinsing procedure. Optimal chiral separation of the analyte was obtained using an electrolyte with 2.5% carboxymethyl-beta-CD in 25 mM NaH2PO4 (pH 4.0). Interchanging the inlet and outlet vials after each run improved the method's precision. To assure the method's suitability for the control of enantiomeric impurities in pharmaceutical quality control, its specificity, linearity, precision, accuracy, and robustness were validated according to the requirements of the International Conference on Harmonization. The usefulness of our generic method development approach for the validation of robustness was demonstrated.

Optimal design for robust control of uncertain flexible joint manipulators: a fuzzy dynamical system approach

NASA Astrophysics Data System (ADS)

Han, Jiang; Chen, Ye-Hwa; Zhao, Xiaomin; Dong, Fangfang

2018-04-01

A novel fuzzy dynamical system approach to the control design of flexible joint manipulators with mismatched uncertainty is proposed. Uncertainties of the system are assumed to lie within prescribed fuzzy sets. The desired system performance includes a deterministic phase and a fuzzy phase. First, by creatively implanting a fictitious control, a robust control scheme is constructed to render the system uniformly bounded and uniformly ultimately bounded. Both the manipulator modelling and control scheme are deterministic and not IF-THEN heuristic rules-based. Next, a fuzzy-based performance index is proposed. An optimal design problem for a control design parameter is formulated as a constrained optimisation problem. The global solution to this problem can be obtained from solving two quartic equations. The fuzzy dynamical system approach is systematic and is able to assure the deterministic performance as well as to minimise the fuzzy performance index.

Selective robust optimization: A new intensity-modulated proton therapy optimization strategy

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

Li, Yupeng; Niemela, Perttu; Siljamaki, Sami

2015-08-15

Purpose: To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications. Methods: In selective robust optimization, a worst-case-based robust optimization algorithm is extended, and terms of the objective function are selectively computed from either the worst-case dose or the nominal dose. Two lung cancer cases and one head and neck cancer case were used to demonstrate the practical significance of the proposed robust planning strategy. The lung cancer cases had minimal tumor motion less than 5 mm, and, for the demonstration of the methodology,more » are assumed to be static. Results: Selective robust optimization achieved robust clinical target volume (CTV) coverage and at the same time increased nominal planning target volume coverage to 95.8%, compared to the 84.6% coverage achieved with CTV-based robust optimization in one of the lung cases. In the other lung case, the maximum dose in selective robust optimization was lowered from a dose of 131.3% in the CTV-based robust optimization to 113.6%. Selective robust optimization provided robust CTV coverage in the head and neck case, and at the same time improved controls over isodose distribution so that clinical requirements may be readily met. Conclusions: Selective robust optimization may provide the flexibility and capability necessary for meeting various clinical requirements in addition to achieving the required plan robustness in practical proton treatment planning settings.« less

Application of multi-factorial design of experiments to successfully optimize immunoassays for robust measurements of therapeutic proteins.

PubMed

Ray, Chad A; Patel, Vimal; Shih, Judy; Macaraeg, Chris; Wu, Yuling; Thway, Theingi; Ma, Mark; Lee, Jean W; Desilva, Binodh

2009-02-20

Developing a process that generates robust immunoassays that can be used to support studies with tight timelines is a common challenge for bioanalytical laboratories. Design of experiments (DOEs) is a tool that has been used by many industries for the purpose of optimizing processes. The approach is capable of identifying critical factors and their interactions with a minimal number of experiments. The challenge for implementing this tool in the bioanalytical laboratory is to develop a user-friendly approach that scientists can understand and apply. We have successfully addressed these challenges by eliminating the screening design, introducing automation, and applying a simple mathematical approach for the output parameter. A modified central composite design (CCD) was applied to three ligand binding assays. The intra-plate factors selected were coating, detection antibody concentration, and streptavidin-HRP concentrations. The inter-plate factors included incubation times for each step. The objective was to maximize the logS/B (S/B) of the low standard to the blank. The maximum desirable conditions were determined using JMP 7.0. To verify the validity of the predictions, the logS/B prediction was compared against the observed logS/B during pre-study validation experiments. The three assays were optimized using the multi-factorial DOE. The total error for all three methods was less than 20% which indicated method robustness. DOE identified interactions in one of the methods. The model predictions for logS/B were within 25% of the observed pre-study validation values for all methods tested. The comparison between the CCD and hybrid screening design yielded comparable parameter estimates. The user-friendly design enables effective application of multi-factorial DOE to optimize ligand binding assays for therapeutic proteins. The approach allows for identification of interactions between factors, consistency in optimal parameter determination, and reduced method development time.

Validation of the Chinese Version of the Life Orientation Test with a Robust Weighted Least Squares Approach

ERIC Educational Resources Information Center

Li, Cheng-Hsien

2012-01-01

Of the several measures of optimism presently available in the literature, the Life Orientation Test (LOT; Scheier & Carver, 1985) has been the most widely used in empirical research. This article explores, confirms, and cross-validates the factor structure of the Chinese version of the LOT with ordinal data by using robust weighted least…

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, M.; Klimeck, G.; Hanks, D.

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment.

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.

Intelligent and robust optimization frameworks for smart grids

NASA Astrophysics Data System (ADS)

Dhansri, Naren Reddy

A smart grid implies a cyberspace real-time distributed power control system to optimally deliver electricity based on varying consumer characteristics. Although smart grids solve many of the contemporary problems, they give rise to new control and optimization problems with the growing role of renewable energy sources such as wind or solar energy. Under highly dynamic nature of distributed power generation and the varying consumer demand and cost requirements, the total power output of the grid should be controlled such that the load demand is met by giving a higher priority to renewable energy sources. Hence, the power generated from renewable energy sources should be optimized while minimizing the generation from non renewable energy sources. This research develops a demand-based automatic generation control and optimization framework for real-time smart grid operations by integrating conventional and renewable energy sources under varying consumer demand and cost requirements. Focusing on the renewable energy sources, the intelligent and robust control frameworks optimize the power generation by tracking the consumer demand in a closed-loop control framework, yielding superior economic and ecological benefits and circumvent nonlinear model complexities and handles uncertainties for superior real-time operations. The proposed intelligent system framework optimizes the smart grid power generation for maximum economical and ecological benefits under an uncertain renewable wind energy source. The numerical results demonstrate that the proposed framework is a viable approach to integrate various energy sources for real-time smart grid implementations. The robust optimization framework results demonstrate the effectiveness of the robust controllers under bounded power plant model uncertainties and exogenous wind input excitation while maximizing economical and ecological performance objectives. Therefore, the proposed framework offers a new worst-case deterministic optimization algorithm for smart grid automatic generation control.

Sample size calculation in cost-effectiveness cluster randomized trials: optimal and maximin approaches.

PubMed

Manju, Md Abu; Candel, Math J J M; Berger, Martijn P F

2014-07-10

In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost-effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra-cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost-effectiveness of an intervention.

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Multi-Satellite Observation Scheduling for Large Area Disaster Emergency Response

NASA Astrophysics Data System (ADS)

Niu, X. N.; Tang, H.; Wu, L. X.

2018-04-01

an optimal imaging plan, plays a key role in coordinating multiple satellites to monitor the disaster area. In the paper, to generate imaging plan dynamically according to the disaster relief, we propose a dynamic satellite task scheduling method for large area disaster response. First, an initial robust scheduling scheme is generated by a robust satellite scheduling model in which both the profit and the robustness of the schedule are simultaneously maximized. Then, we use a multi-objective optimization model to obtain a series of decomposing schemes. Based on the initial imaging plan, we propose a mixed optimizing algorithm named HA_NSGA-II to allocate the decomposing results thus to obtain an adjusted imaging schedule. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of rapid response using satellite resources and used to evaluate the performance of the proposed method with state-of-the-art approaches. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

Robust optimization of supersonic ORC nozzle guide vanes

NASA Astrophysics Data System (ADS)

Bufi, Elio A.; Cinnella, Paola

2017-03-01

An efficient Robust Optimization (RO) strategy is developed for the design of 2D supersonic Organic Rankine Cycle turbine expanders. The dense gas effects are not-negligible for this application and they are taken into account describing the thermodynamics by means of the Peng-Robinson-Stryjek-Vera equation of state. The design methodology combines an Uncertainty Quantification (UQ) loop based on a Bayesian kriging model of the system response to the uncertain parameters, used to approximate statistics (mean and variance) of the uncertain system output, a CFD solver, and a multi-objective non-dominated sorting algorithm (NSGA), also based on a Kriging surrogate of the multi-objective fitness function, along with an adaptive infill strategy for surrogate enrichment at each generation of the NSGA. The objective functions are the average and variance of the isentropic efficiency. The blade shape is parametrized by means of a Free Form Deformation (FFD) approach. The robust optimal blades are compared to the baseline design (based on the Method of Characteristics) and to a blade obtained by means of a deterministic CFD-based optimization.

On the robust optimization to the uncertain vaccination strategy problem

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

Chaerani, D., E-mail: d.chaerani@unpad.ac.id; Anggriani, N., E-mail: d.chaerani@unpad.ac.id; Firdaniza, E-mail: d.chaerani@unpad.ac.id

2014-02-21

In order to prevent an epidemic of infectious diseases, the vaccination coverage needs to be minimized and also the basic reproduction number needs to be maintained below 1. This means that as we get the vaccination coverage as minimum as possible, thus we need to prevent the epidemic to a small number of people who already get infected. In this paper, we discuss the case of vaccination strategy in term of minimizing vaccination coverage, when the basic reproduction number is assumed as an uncertain parameter that lies between 0 and 1. We refer to the linear optimization model for vaccinationmore » strategy that propose by Becker and Starrzak (see [2]). Assuming that there is parameter uncertainty involved, we can see Tanner et al (see [9]) who propose the optimal solution of the problem using stochastic programming. In this paper we discuss an alternative way of optimizing the uncertain vaccination strategy using Robust Optimization (see [3]). In this approach we assume that the parameter uncertainty lies within an ellipsoidal uncertainty set such that we can claim that the obtained result will be achieved in a polynomial time algorithm (as it is guaranteed by the RO methodology). The robust counterpart model is presented.« less

Group Counseling Optimization: A Novel Approach

NASA Astrophysics Data System (ADS)

Eita, M. A.; Fahmy, M. M.

A new population-based search algorithm, which we call Group Counseling Optimizer (GCO), is presented. It mimics the group counseling behavior of humans in solving their problems. The algorithm is tested using seven known benchmark functions: Sphere, Rosenbrock, Griewank, Rastrigin, Ackley, Weierstrass, and Schwefel functions. A comparison is made with the recently published comprehensive learning particle swarm optimizer (CLPSO). The results demonstrate the efficiency and robustness of the proposed algorithm.

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction

PubMed Central

Li, Zukui; Floudas, Christodoulos A.

2012-01-01

Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, “interval+ellipsoidal” and “interval+polyhedral” uncertainty sets (Li, Z., Ding, R., and Floudas, C.A., A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization, Ind. Eng. Chem. Res, 2011, 50, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented. PMID:23329868

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

Tripathi, Ashish; McNulty, Ian; Munson, Todd

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

Adaptive Wing Camber Optimization: A Periodic Perturbation Approach

NASA Technical Reports Server (NTRS)

Espana, Martin; Gilyard, Glenn

1994-01-01

Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.

The Problem of Size in Robust Design

NASA Technical Reports Server (NTRS)

Koch, Patrick N.; Allen, Janet K.; Mistree, Farrokh; Mavris, Dimitri

1997-01-01

To facilitate the effective solution of multidisciplinary, multiobjective complex design problems, a departure from the traditional parametric design analysis and single objective optimization approaches is necessary in the preliminary stages of design. A necessary tradeoff becomes one of efficiency vs. accuracy as approximate models are sought to allow fast analysis and effective exploration of a preliminary design space. In this paper we apply a general robust design approach for efficient and comprehensive preliminary design to a large complex system: a high speed civil transport (HSCT) aircraft. Specifically, we investigate the HSCT wing configuration design, incorporating life cycle economic uncertainties to identify economically robust solutions. The approach is built on the foundation of statistical experimentation and modeling techniques and robust design principles, and is specialized through incorporation of the compromise Decision Support Problem for multiobjective design. For large problems however, as in the HSCT example, this robust design approach developed for efficient and comprehensive design breaks down with the problem of size - combinatorial explosion in experimentation and model building with number of variables -and both efficiency and accuracy are sacrificed. Our focus in this paper is on identifying and discussing the implications and open issues associated with the problem of size for the preliminary design of large complex systems.

Robust control of systems with real parameter uncertainty and unmodelled dynamics

NASA Technical Reports Server (NTRS)

Chang, Bor-Chin; Fischl, Robert

1991-01-01

During this research period we have made significant progress in the four proposed areas: (1) design of robust controllers via H infinity optimization; (2) design of robust controllers via mixed H2/H infinity optimization; (3) M-delta structure and robust stability analysis for structured uncertainties; and (4) a study on controllability and observability of perturbed plant. It is well known now that the two-Riccati-equation solution to the H infinity control problem can be used to characterize all possible stabilizing optimal or suboptimal H infinity controllers if the optimal H infinity norm or gamma, an upper bound of a suboptimal H infinity norm, is given. In this research, we discovered some useful properties of these H infinity Riccati solutions. Among them, the most prominent one is that the spectral radius of the product of these two Riccati solutions is a continuous, nonincreasing, convex function of gamma in the domain of interest. Based on these properties, quadratically convergent algorithms are developed to compute the optimal H infinity norm. We also set up a detailed procedure for applying the H infinity theory to robust control systems design. The desire to design controllers with H infinity robustness but H(exp 2) performance has recently resulted in mixed H(exp 2) and H infinity control problem formulation. The mixed H(exp 2)/H infinity problem have drawn the attention of many investigators. However, solution is only available for special cases of this problem. We formulated a relatively realistic control problem with H(exp 2) performance index and H infinity robustness constraint into a more general mixed H(exp 2)/H infinity problem. No optimal solution yet is available for this more general mixed H(exp 2)/H infinity problem. Although the optimal solution for this mixed H(exp 2)/H infinity control has not yet been found, we proposed a design approach which can be used through proper choice of the available design parameters to influence both robustness and performance. For a large class of linear time-invariant systems with real parametric perturbations, the coefficient vector of the characteristic polynomial is a multilinear function of the real parameter vector. Based on this multilinear mapping relationship together with the recent developments for polytopic polynomials and parameter domain partition technique, we proposed an iterative algorithm for coupling the real structured singular value.

Self-tuning bistable parametric feedback oscillator: Near-optimal amplitude maximization without model information

NASA Astrophysics Data System (ADS)

Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu

2017-01-01

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.

Robust surveillance and control of invasive species using a scenario optimization approach

Treesearch

Denys Yemshanov; Robert G. Haight; Frank H. Koch; Bo Lu; Robert C. Venette; Ronald E. Fournier; Jean J. Turgeon

2017-01-01

Uncertainty about future outcomes of invasions is a major hurdle in the planning of invasive species management programs. We present a scenario optimization model that incorporates uncertainty about the spread of an invasive species and allocates survey and eradication measures to minimize the number of infested or potentially infested host plants on the landscape. We...

Robust estimation approach for blind denoising.

PubMed

Rabie, Tamer

2005-11-01

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

Strain-Dependent Transcriptome Signatures for Robustness in Lactococcus lactis

PubMed Central

Dijkstra, Annereinou R.; Alkema, Wynand; Starrenburg, Marjo J. C.; van Hijum, Sacha A. F. T.; Bron, Peter A.

2016-01-01

Recently, we demonstrated that fermentation conditions have a strong impact on subsequent survival of Lactococcus lactis strain MG1363 during heat and oxidative stress, two important parameters during spray drying. Moreover, employment of a transcriptome-phenotype matching approach revealed groups of genes associated with robustness towards heat and/or oxidative stress. To investigate if other strains have similar or distinct transcriptome signatures for robustness, we applied an identical transcriptome-robustness phenotype matching approach on the L. lactis strains IL1403, KF147 and SK11, which have previously been demonstrated to display highly diverse robustness phenotypes. These strains were subjected to an identical fermentation regime as was performed earlier for strain MG1363 and consisted of twelve conditions, varying in the level of salt and/or oxygen, as well as fermentation temperature and pH. In the exponential phase of growth, cells were harvested for transcriptome analysis and assessment of heat and oxidative stress survival phenotypes. The variation in fermentation conditions resulted in differences in heat and oxidative stress survival of up to five 10-log units. Effects of the fermentation conditions on stress survival of the L. lactis strains were typically strain-dependent, although the fermentation conditions had mainly similar effects on the growth characteristics of the different strains. By association of the transcriptomes and robustness phenotypes highly strain-specific transcriptome signatures for robustness towards heat and oxidative stress were identified, indicating that multiple mechanisms exist to increase robustness and, as a consequence, robustness of each strain requires individual optimization. However, a relatively small overlap in the transcriptome responses of the strains was also identified and this generic transcriptome signature included genes previously associated with stress (ctsR and lplL) and novel genes, including nanE and genes encoding transport proteins. The transcript levels of these genes can function as indicators of robustness and could aid in selection of fermentation parameters, potentially resulting in more optimal robustness during spray drying. PMID:27973578

Direct Multiple Shooting Optimization with Variable Problem Parameters

NASA Technical Reports Server (NTRS)

Whitley, Ryan J.; Ocampo, Cesar A.

2009-01-01

Taking advantage of a novel approach to the design of the orbital transfer optimization problem and advanced non-linear programming algorithms, several optimal transfer trajectories are found for problems with and without known analytic solutions. This method treats the fixed known gravitational constants as optimization variables in order to reduce the need for an advanced initial guess. Complex periodic orbits are targeted with very simple guesses and the ability to find optimal transfers in spite of these bad guesses is successfully demonstrated. Impulsive transfers are considered for orbits in both the 2-body frame as well as the circular restricted three-body problem (CRTBP). The results with this new approach demonstrate the potential for increasing robustness for all types of orbit transfer problems.

Trajectory optimization for the National Aerospace Plane

NASA Technical Reports Server (NTRS)

Lu, Ping

1992-01-01

The primary objective of this research is to develop an efficient and robust trajectory optimization tool for the optimal ascent problem of the National Aerospace Plane (NASP). This report is organized in the following order to summarize the complete work: Section two states the formulation and models of the trajectory optimization problem. An inverse dynamics approach to the problem is introduced in Section three. Optimal trajectories corresponding to various conditions and performance parameters are presented in Section four. A midcourse nonlinear feedback controller is developed in Section five. Section six demonstrates the performance of the inverse dynamics approach and midcourse controller during disturbances. Section seven discusses rocket assisted ascent which may be beneficial when orbital altitude is high. Finally, Section eight recommends areas of future research.

Efficient Robust Optimization of Metal Forming Processes using a Sequential Metamodel Based Strategy

NASA Astrophysics Data System (ADS)

Wiebenga, J. H.; Klaseboer, G.; van den Boogaard, A. H.

2011-08-01

The coupling of Finite Element (FE) simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a new and generally applicable structured methodology for modeling and solving robust optimization problems. Stochastic design variables or noise variables are taken into account explicitly in the optimization procedure. The metamodel-based strategy is combined with a sequential improvement algorithm to efficiently increase the accuracy of the objective function prediction. This is only done at regions of interest containing the optimal robust design. Application of the methodology to an industrial V-bending process resulted in valuable process insights and an improved robust process design. Moreover, a significant improvement of the robustness (>2σ) was obtained by minimizing the deteriorating effects of several noise variables. The robust optimization results demonstrate the general applicability of the robust optimization strategy and underline the importance of including uncertainty and robustness explicitly in the numerical optimization procedure.

An Optimization-Based Approach to Determine Requirements and Aircraft Design under Multi-domain Uncertainties

NASA Astrophysics Data System (ADS)

Govindaraju, Parithi

Determining the optimal requirements for and design variable values of new systems, which operate along with existing systems to provide a set of overarching capabilities, as a single task is challenging due to the highly interconnected effects that setting requirements on a new system's design can have on how an operator uses this newly designed system. This task of determining the requirements and the design variable values becomes even more difficult because of the presence of uncertainties in the new system design and in the operational environment. This research proposed and investigated aspects of a framework that generates optimum design requirements of new, yet-to-be-designed systems that, when operating alongside other systems, will optimize fleet-level objectives while considering the effects of various uncertainties. Specifically, this research effort addresses the issues of uncertainty in the design of the new system through reliability-based design optimization methods, and uncertainty in the operations of the fleet through descriptive sampling methods and robust optimization formulations. In this context, fleet-level performance metrics result from using the new system alongside other systems to accomplish an overarching objective or mission. This approach treats the design requirements of a new system as decision variables in an optimization problem formulation that a user in the position of making an acquisition decision could solve. This solution would indicate the best new system requirements-and an associated description of the best possible design variable variables for that new system-to optimize the fleet level performance metric(s). Using a problem motivated by recorded operations of the United States Air Force Air Mobility Command for illustration, the approach is demonstrated first for a simplified problem that only considers demand uncertainties in the service network and the proposed methodology is used to identify the optimal design requirements and optimal aircraft sizing variables of new, yet-to-be-introduced aircraft. With this new aircraft serving alongside other existing aircraft, the fleet of aircraft satisfy the desired demand for cargo transportation, while maximizing fleet productivity and minimizing fuel consumption via a multi-objective problem formulation. The approach is then extended to handle uncertainties in both the design of the new system and in the operations of the fleet. The propagation of uncertainties associated with the conceptual design of the new aircraft to the uncertainties associated with the subsequent operations of the new and existing aircraft in the fleet presents some unique challenges. A computationally tractable hybrid robust counterpart formulation efficiently handles the confluence of the two types of domain-specific uncertainties. This hybrid formulation is tested on a larger route network problem to demonstrate the scalability of the approach. Following the presentation of the results obtained, a summary discussion indicates how decision-makers might use these results to set requirements for new aircraft that meet operational needs while balancing the environmental impact of the fleet with fleet-level performance. Comparing the solutions from the uncertainty-based and deterministic formulations via a posteriori analysis demonstrates the efficacy of the robust and reliability-based optimization formulations in addressing the different domain-specific uncertainties. Results suggest that the aircraft design requirements and design description determined through the hybrid robust counterpart formulation approach differ from solutions obtained from the simplistic deterministic approach, and leads to greater fleet-level fuel savings, when subjected to real-world uncertain scenarios (more robust to uncertainty). The research, though applied to a specific air cargo application, is technically agnostic in nature and can be applied to other facets of policy and acquisition management, to explore capability trade spaces for different vehicle systems, mitigate risks, define policy and potentially generate better returns on investment. Other domains relevant to policy and acquisition decisions could utilize the problem formulation and solution approach proposed in this dissertation provided that the problem can be split into a non-linear programming problem to describe the new system sizing and the fleet operations problem can be posed as a linear/integer programming problem.

Reducing Design Risk Using Robust Design Methods: A Dual Response Surface Approach

NASA Technical Reports Server (NTRS)

Unal, Resit; Yeniay, Ozgur; Lepsch, Roger A. (Technical Monitor)

2003-01-01

Space transportation system conceptual design is a multidisciplinary process containing considerable element of risk. Risk here is defined as the variability in the estimated (output) performance characteristic of interest resulting from the uncertainties in the values of several disciplinary design and/or operational parameters. Uncertainties from one discipline (and/or subsystem) may propagate to another, through linking parameters and the final system output may have a significant accumulation of risk. This variability can result in significant deviations from the expected performance. Therefore, an estimate of variability (which is called design risk in this study) together with the expected performance characteristic value (e.g. mean empty weight) is necessary for multidisciplinary optimization for a robust design. Robust design in this study is defined as a solution that minimizes variability subject to a constraint on mean performance characteristics. Even though multidisciplinary design optimization has gained wide attention and applications, the treatment of uncertainties to quantify and analyze design risk has received little attention. This research effort explores the dual response surface approach to quantify variability (risk) in critical performance characteristics (such as weight) during conceptual design.

Field-based optimal-design of an electric motor: a new sensitivity formulation

NASA Astrophysics Data System (ADS)

Barba, Paolo Di; Mognaschi, Maria Evelina; Lowther, David Alister; Wiak, Sławomir

2017-12-01

In this paper, a new approach to robust optimal design is proposed. The idea is to consider the sensitivity by means of two auxiliary criteria A and D, related to the magnitude and isotropy of the sensitivity, respectively. The optimal design of a switched-reluctance motor is considered as a case study: since the case study exhibits two design criteria, the relevant Pareto front is approximated by means of evolutionary computing.

Sensitivity regularization of the Cramér-Rao lower bound to minimize B1 nonuniformity effects in quantitative magnetization transfer imaging.

PubMed

Boudreau, Mathieu; Pike, G Bruce

2018-05-07

To develop and validate a regularization approach of optimizing B 1 insensitivity of the quantitative magnetization transfer (qMT) pool-size ratio (F). An expression describing the impact of B 1 inaccuracies on qMT fitting parameters was derived using a sensitivity analysis. To simultaneously optimize for robustness against noise and B 1 inaccuracies, the optimization condition was defined as the Cramér-Rao lower bound (CRLB) regularized by the B 1 -sensitivity expression for the parameter of interest (F). The qMT protocols were iteratively optimized from an initial search space, with and without B 1 regularization. Three 10-point qMT protocols (Uniform, CRLB, CRLB+B 1 regularization) were compared using Monte Carlo simulations for a wide range of conditions (e.g., SNR, B 1 inaccuracies, tissues). The B 1 -regularized CRLB optimization protocol resulted in the best robustness of F against B 1 errors, for a wide range of SNR and for both white matter and gray matter tissues. For SNR = 100, this protocol resulted in errors of less than 1% in mean F values for B 1 errors ranging between -10 and 20%, the range of B 1 values typically observed in vivo in the human head at field strengths of 3 T and less. Both CRLB-optimized protocols resulted in the lowest σ F values for all SNRs and did not increase in the presence of B 1 inaccuracies. This work demonstrates a regularized optimization approach for improving the robustness of auxiliary measurements (e.g., B 1 ) sensitivity of qMT parameters, particularly the pool-size ratio (F). Predicting substantially less B 1 sensitivity using protocols optimized with this method, B 1 mapping could even be omitted for qMT studies primarily interested in F. © 2018 International Society for Magnetic Resonance in Medicine.

Robust Transceiver Design for Multiuser MIMO Downlink with Channel Uncertainties

NASA Astrophysics Data System (ADS)

Miao, Wei; Li, Yunzhou; Chen, Xiang; Zhou, Shidong; Wang, Jing

This letter addresses the problem of robust transceiver design for the multiuser multiple-input-multiple-output (MIMO) downlink where the channel state information at the base station (BS) is imperfect. A stochastic approach which minimizes the expectation of the total mean square error (MSE) of the downlink conditioned on the channel estimates under a total transmit power constraint is adopted. The iterative algorithm reported in [2] is improved to handle the proposed robust optimization problem. Simulation results show that our proposed robust scheme effectively reduces the performance loss due to channel uncertainties and outperforms existing methods, especially when the channel errors of the users are different.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

NASA Astrophysics Data System (ADS)

Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

2016-10-01

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization

PubMed Central

Li, Zukui; Ding, Ran; Floudas, Christodoulos A.

2011-01-01

Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263

Measure of robustness for complex networks

NASA Astrophysics Data System (ADS)

Youssef, Mina Nabil

Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance (VCSIS ) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible (SIS) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, VCSIS provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barabasi-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric VCSIR is introduced to assess the robustness of networks with respect to the spread of susceptible/infected/recovered (SIR) epidemics. To compute VCSIR, we propose a novel individual-based approach to model the spread of SIR epidemics in networks, which captures the infection size for a given effective infection rate. Thus, VCSIR quantitatively integrates the infection strength with the corresponding infection size. To optimize the VCSIR metric, a new mitigation strategy is proposed, based on a temporary reduction of contacts in social networks. The social contact network is modeled as a weighted graph that describes the frequency of contacts among the individuals. Thus, we consider the spread of an epidemic as a dynamical system, and the total number of infection cases as the state of the system, while the weight reduction in the social network is the controller variable leading to slow/reduce the spread of epidemics. Using optimal control theory, the obtained solution represents an optimal adaptive weighted network defined over a finite time interval. Moreover, given the high complexity of the optimization problem, we propose two heuristics to find the near optimal solutions by reducing the contacts among the individuals in a decentralized way. Finally, the cascading failures that can take place in power grids and have recently caused several blackouts are studied. We propose a new metric to assess the robustness of the power grid with respect to the cascading failures. The power grid topology is modeled as a network, which consists of nodes and links representing power substations and transmission lines, respectively. We also propose an optimal islanding strategy to protect the power grid when a cascading failure event takes place in the grid. The robustness metrics are numerically evaluated using real and synthetic networks to quantify their robustness with respect to disturbing dynamics. We show that the proposed metrics outperform the classical metrics in quantifying the robustness of networks and the efficiency of the mitigation strategies. In summary, our work advances the network science field in assessing the robustness of complex networks with respect to various disturbing dynamics.

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.

Enabling School Structure, Collective Responsibility, and a Culture of Academic Optimism: Toward a Robust Model of School Performance in Taiwan

ERIC Educational Resources Information Center

Wu, Jason H.; Hoy, Wayne K.; Tarter, C. John

2013-01-01

Purpose: The purpose of this research is twofold: to test a theory of academic optimism in Taiwan elementary schools and to expand the theory by adding new variables, collective responsibility and enabling school structure, to the model. Design/methodology/approach: Structural equation modeling was used to test, refine, and expand an…

A stochastic optimal feedforward and feedback control methodology for superagility

NASA Technical Reports Server (NTRS)

Halyo, Nesim; Direskeneli, Haldun; Taylor, Deborah B.

1992-01-01

A new control design methodology is developed: Stochastic Optimal Feedforward and Feedback Technology (SOFFT). Traditional design techniques optimize a single cost function (which expresses the design objectives) to obtain both the feedforward and feedback control laws. This approach places conflicting demands on the control law such as fast tracking versus noise atttenuation/disturbance rejection. In the SOFFT approach, two cost functions are defined. The feedforward control law is designed to optimize one cost function, the feedback optimizes the other. By separating the design objectives and decoupling the feedforward and feedback design processes, both objectives can be achieved fully. A new measure of command tracking performance, Z-plots, is also developed. By analyzing these plots at off-nominal conditions, the sensitivity or robustness of the system in tracking commands can be predicted. Z-plots provide an important tool for designing robust control systems. The Variable-Gain SOFFT methodology was used to design a flight control system for the F/A-18 aircraft. It is shown that SOFFT can be used to expand the operating regime and provide greater performance (flying/handling qualities) throughout the extended flight regime. This work was performed under the NASA SBIR program. ICS plans to market the software developed as a new module in its commercial CACSD software package: ACET.

Stochastic Control Synthesis of Systems with Structured Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L. (Technical Monitor); Crespo, Luis G.

2003-01-01

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

DOE PAGES

Tripathi, Ashish; McNulty, Ian; Munson, Todd; ...

2016-10-14

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

A protocatechuate biosensor for Pseudomonas putida KT2440 via promoter and protein evolution.

PubMed

Jha, Ramesh K; Bingen, Jeremy M; Johnson, Christopher W; Kern, Theresa L; Khanna, Payal; Trettel, Daniel S; Strauss, Charlie E M; Beckham, Gregg T; Dale, Taraka

2018-06-01

Robust fluorescence-based biosensors are emerging as critical tools for high-throughput strain improvement in synthetic biology. Many biosensors are developed in model organisms where sophisticated synthetic biology tools are also well established. However, industrial biochemical production often employs microbes with phenotypes that are advantageous for a target process, and biosensors may fail to directly transition outside the host in which they are developed. In particular, losses in sensitivity and dynamic range of sensing often occur, limiting the application of a biosensor across hosts. Here we demonstrate the optimization of an Escherichia coli- based biosensor in a robust microbial strain for the catabolism of aromatic compounds, Pseudomonas putida KT2440, through a generalizable approach of modulating interactions at the protein-DNA interface in the promoter and the protein-protein dimer interface. The high-throughput biosensor optimization approach demonstrated here is readily applicable towards other allosteric regulators.

A protocatechuate biosensor for Pseudomonas putida KT2440 via promoter and protein evolution

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

Jha, Ramesh K.; Bingen, Jeremy M.; Johnson, Christopher W.

Robust fluorescence-based biosensors are emerging as critical tools for high-throughput strain improvement in synthetic biology. Many biosensors are developed in model organisms where sophisticated synthetic biology tools are also well established. However, industrial biochemical production often employs microbes with phenotypes that are advantageous for a target process, and biosensors may fail to directly transition outside the host in which they are developed. In particular, losses in sensitivity and dynamic range of sensing often occur, limiting the application of a biosensor across hosts. In this study, we demonstrate the optimization of an Escherichia coli-based biosensor in a robust microbial strain formore » the catabolism of aromatic compounds, Pseudomonas putida KT2440, through a generalizable approach of modulating interactions at the protein-DNA interface in the promoter and the protein-protein dimer interface. The high-throughput biosensor optimization approach demonstrated here is readily applicable towards other allosteric regulators.« less

A protocatechuate biosensor for Pseudomonas putida KT2440 via promoter and protein evolution

DOE PAGES

Jha, Ramesh K.; Bingen, Jeremy M.; Johnson, Christopher W.; ...

2018-06-01

Robust fluorescence-based biosensors are emerging as critical tools for high-throughput strain improvement in synthetic biology. Many biosensors are developed in model organisms where sophisticated synthetic biology tools are also well established. However, industrial biochemical production often employs microbes with phenotypes that are advantageous for a target process, and biosensors may fail to directly transition outside the host in which they are developed. In particular, losses in sensitivity and dynamic range of sensing often occur, limiting the application of a biosensor across hosts. In this study, we demonstrate the optimization of an Escherichia coli-based biosensor in a robust microbial strain formore » the catabolism of aromatic compounds, Pseudomonas putida KT2440, through a generalizable approach of modulating interactions at the protein-DNA interface in the promoter and the protein-protein dimer interface. The high-throughput biosensor optimization approach demonstrated here is readily applicable towards other allosteric regulators.« less

Improving Zernike moments comparison for optimal similarity and rotation angle retrieval.

PubMed

Revaud, Jérôme; Lavoué, Guillaume; Baskurt, Atilla

2009-04-01

Zernike moments constitute a powerful shape descriptor in terms of robustness and description capability. However the classical way of comparing two Zernike descriptors only takes into account the magnitude of the moments and loses the phase information. The novelty of our approach is to take advantage of the phase information in the comparison process while still preserving the invariance to rotation. This new Zernike comparator provides a more accurate similarity measure together with the optimal rotation angle between the patterns, while keeping the same complexity as the classical approach. This angle information is particularly of interest for many applications, including 3D scene understanding through images. Experiments demonstrate that our comparator outperforms the classical one in terms of similarity measure. In particular the robustness of the retrieval against noise and geometric deformation is greatly improved. Moreover, the rotation angle estimation is also more accurate than state-of-the-art algorithms.

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

NASA Astrophysics Data System (ADS)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

An experimental study of nonlinear dynamic system identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1990-01-01

A technique for robust identification of nonlinear dynamic systems is developed and illustrated using both simulations and analog experiments. The technique is based on the Minimum Model Error optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature of the current work is the ability to identify nonlinear dynamic systems without prior assumptions regarding the form of the nonlinearities, in constrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

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

PubMed

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

2015-07-01

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

Reliability based design optimization: Formulations and methodologies

NASA Astrophysics Data System (ADS)

Agarwal, Harish

Modern products ranging from simple components to complex systems should be designed to be optimal and reliable. The challenge of modern engineering is to ensure that manufacturing costs are reduced and design cycle times are minimized while achieving requirements for performance and reliability. If the market for the product is competitive, improved quality and reliability can generate very strong competitive advantages. Simulation based design plays an important role in designing almost any kind of automotive, aerospace, and consumer products under these competitive conditions. Single discipline simulations used for analysis are being coupled together to create complex coupled simulation tools. This investigation focuses on the development of efficient and robust methodologies for reliability based design optimization in a simulation based design environment. Original contributions of this research are the development of a novel efficient and robust unilevel methodology for reliability based design optimization, the development of an innovative decoupled reliability based design optimization methodology, the application of homotopy techniques in unilevel reliability based design optimization methodology, and the development of a new framework for reliability based design optimization under epistemic uncertainty. The unilevel methodology for reliability based design optimization is shown to be mathematically equivalent to the traditional nested formulation. Numerical test problems show that the unilevel methodology can reduce computational cost by at least 50% as compared to the nested approach. The decoupled reliability based design optimization methodology is an approximate technique to obtain consistent reliable designs at lesser computational expense. Test problems show that the methodology is computationally efficient compared to the nested approach. A framework for performing reliability based design optimization under epistemic uncertainty is also developed. A trust region managed sequential approximate optimization methodology is employed for this purpose. Results from numerical test studies indicate that the methodology can be used for performing design optimization under severe uncertainty.

Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.

PubMed

Van Daele, Timothy; Gernaey, Krist V; Ringborg, Rolf H; Börner, Tim; Heintz, Søren; Van Hauwermeiren, Daan; Grey, Carl; Krühne, Ulrich; Adlercreutz, Patrick; Nopens, Ingmar

2017-09-01

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278-1293, 2017. © 2017 American Institute of Chemical Engineers.

Reduced conservatism in stability robustness bounds by state transformation

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.; Liang, Z.

1986-01-01

This note addresses the issue of 'conservatism' in the time domain stability robustness bounds obtained by the Liapunov approach. A state transformation is employed to improve the upper bounds on the linear time-varying perturbation of an asymptotically stable linear time-invariant system for robust stability. This improvement is due to the variance of the conservatism of the Liapunov approach with respect to the basis of the vector space in which the Liapunov function is constructed. Improved bounds are obtained, using a transformation, on elemental and vector norms of perturbations (i.e., structured perturbations) as well as on a matrix norm of perturbations (i.e., unstructured perturbations). For the case of a diagonal transformation, an algorithm is proposed to find the 'optimal' transformation. Several examples are presented to illustrate the proposed analysis.

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.

Chemometric optimization of the robustness of the near infrared spectroscopic method in wheat quality control.

PubMed

Pojić, Milica; Rakić, Dušan; Lazić, Zivorad

2015-01-01

A chemometric approach was applied for the optimization of the robustness of the NIRS method for wheat quality control. Due to the high number of experimental (n=6) and response variables to be studied (n=7) the optimization experiment was divided into two stages: screening stage in order to evaluate which of the considered variables were significant, and optimization stage to optimize the identified factors in the previously selected experimental domain. The significant variables were identified by using fractional factorial experimental design, whilst Box-Wilson rotatable central composite design (CCRD) was run to obtain the optimal values for the significant variables. The measured responses included: moisture, protein and wet gluten content, Zeleny sedimentation value and deformation energy. In order to achieve the minimal variation in responses, the optimal factor settings were found by minimizing the propagation of error (POE). The simultaneous optimization of factors was conducted by desirability function. The highest desirability of 87.63% was accomplished by setting up experimental conditions as follows: 19.9°C for sample temperature, 19.3°C for ambient temperature and 240V for instrument voltage. Copyright © 2014 Elsevier B.V. All rights reserved.

Application of polynomial control to design a robust oscillation-damping controller in a multimachine power system.

PubMed

Hasanvand, Hamed; Mozafari, Babak; Arvan, Mohammad R; Amraee, Turaj

2015-11-01

This paper addresses the application of a static Var compensator (SVC) to improve the damping of interarea oscillations. Optimal location and size of SVC are defined using bifurcation and modal analysis to satisfy its primary application. Furthermore, the best-input signal for damping controller is selected using Hankel singular values and right half plane-zeros. The proposed approach is aimed to design a robust PI controller based on interval plants and Kharitonov's theorem. The objective here is to determine the stability region to attain robust stability, the desired phase margin, gain margin, and bandwidth. The intersection of the resulting stability regions yields the set of kp-ki parameters. In addition, optimal multiobjective design of PI controller using particle swarm optimization (PSO) algorithm is presented. The effectiveness of the suggested controllers in damping of local and interarea oscillation modes of a multimachine power system, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear time domain simulation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

SU-E-T-452: Impact of Respiratory Motion On Robustly-Optimized Intensity-Modulated Proton Therapy to Treat Lung Cancers

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

Liu, W; Schild, S; Bues, M

Purpose: We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case robustly optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods: For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional Method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from themore » internal target volume (ITV). The second employed the worst-case robust optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests. Results: Without respiratory motion considered, we affirmed that worst-case robust optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, robust optimization still leads to more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% - D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)]. Conclusion: Worst-case robust optimization led to superior solutions for lung IMPT. Despite of the fact that robust optimization did not explicitly account for respiratory motion it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.« less

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

NASA Astrophysics Data System (ADS)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

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

PubMed

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

2014-11-01

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

Big Data-Based Approach to Detect, Locate, and Enhance the Stability of an Unplanned Microgrid Islanding

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

Jiang, Huaiguang; Li, Yan; Zhang, Yingchen

In this paper, a big data-based approach is proposed for the security improvement of an unplanned microgrid islanding (UMI). The proposed approach contains two major steps: the first step is big data analysis of wide-area monitoring to detect a UMI and locate it; the second step is particle swarm optimization (PSO)-based stability enhancement for the UMI. First, an optimal synchrophasor measurement device selection (OSMDS) and matching pursuit decomposition (MPD)-based spatial-temporal analysis approach is proposed to significantly reduce the volume of data while keeping appropriate information from the synchrophasor measurements. Second, a random forest-based ensemble learning approach is trained to detectmore » the UMI. When combined with grid topology, the UMI can be located. Then the stability problem of the UMI is formulated as an optimization problem and the PSO is used to find the optimal operational parameters of the UMI. An eigenvalue-based multiobjective function is proposed, which aims to improve the damping and dynamic characteristics of the UMI. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed approach.« less

SU-E-T-07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy

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

Liao, L; Department of Industrial Engineering, University of Houston, Houston, TX; Yu, J

2015-06-15

Purpose: To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer. Methods: Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst-case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non-robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used tomore » evaluate the robustness of the plans. Results: Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non-robust plans. Conclusion: A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.« less

Using 50 years of soil radiocarbon data to identify optimal approaches for estimating soil carbon residence times

NASA Astrophysics Data System (ADS)

Baisden, W. T.; Canessa, S.

2013-01-01

In 1959, Athol Rafter began a substantial programme of systematically monitoring the flow of 14C produced by atmospheric thermonuclear tests through organic matter in New Zealand soils under stable land use. A database of ∼500 soil radiocarbon measurements spanning 50 years has now been compiled, and is used here to identify optimal approaches for soil C-cycle studies. Our results confirm the potential of 14C to determine residence times, by estimating the amount of ‘bomb 14C’ incorporated. High-resolution time series confirm this approach is appropriate, and emphasise that residence times can be calculated routinely with two or more time points as little as 10 years apart. This approach is generally robust to the key assumptions that can create large errors when single time-point 14C measurements are modelled. The three most critical assumptions relate to: (1) the distribution of turnover times, and particularly the proportion of old C (‘passive fraction’), (2) the lag time between photosynthesis and C entering the modelled pool, (3) changes in the rates of C input. When carrying out approaches using robust assumptions on time-series samples, multiple soil layers can be aggregated using a mixing equation. Where good archived samples are available, AMS measurements can develop useful understanding for calibrating models of the soil C cycle at regional to continental scales with sample numbers on the order of hundreds rather than thousands. Sample preparation laboratories and AMS facilities can play an important role in coordinating the efficient delivery of robust calculated residence times for soil carbon.

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

NASA Astrophysics Data System (ADS)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.

PubMed

Song, Qi; Chen, Mingqing; Bai, Junjie; Sonka, Milan; Wu, Xiaodong

2011-01-01

Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.

A robust approach to chance constrained optimal power flow with renewable generation

DOE PAGES

Lubin, Miles; Dvorkin, Yury; Backhaus, Scott N.

2016-09-01

Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a robust chance constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved usingmore » a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. In conclusion, deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.« less

n-D shape/texture optimal synthetic description and modeling by GEOGINE

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco F.

2004-12-01

GEOGINE(GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for multidimensional shape/texture optimal synthetic description and learning, is presented. Usually elementary geometric shape robust characterization, subjected to geometric transformation, on a rigorous mathematical level is a key problem in many computer applications in different interest areas. The past four decades have seen solutions almost based on the use of n-Dimensional Moment and Fourier descriptor invariants. The present paper introduces a new approach for automatic model generation based on n -Dimensional Tensor Invariants as formal dictionary. An ontological model is the kernel used for specifying ontologies so that how close an ontology can be from the real world depends on the possibilities offered by the ontological model. By this approach even chromatic information content can be easily and reliably decoupled from target geometric information and computed into robus colour shape parameter attributes. Main GEOGINEoperational advantages over previous approaches are: 1) Automated Model Generation, 2) Invariant Minimal Complete Set for computational efficiency, 3) Arbitrary Model Precision for robust object description.

A Robust Statistics Approach to Minimum Variance Portfolio Optimization

NASA Astrophysics Data System (ADS)

Yang, Liusha; Couillet, Romain; McKay, Matthew R.

2015-12-01

We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that the sample covariance matrix performs poorly as a covariance estimator. Additionally, financial market data often contain outliers which, if not correctly handled, may further corrupt the covariance estimation. We address these shortcomings by studying the performance of a hybrid covariance matrix estimator based on Tyler's robust M-estimator and on Ledoit-Wolf's shrinkage estimator while assuming samples with heavy-tailed distribution. Employing recent results from random matrix theory, we develop a consistent estimator of (a scaled version of) the realized portfolio risk, which is minimized by optimizing online the shrinkage intensity. Our portfolio optimization method is shown via simulations to outperform existing methods both for synthetic and real market data.

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

DOE PAGES

Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab; ...

2016-11-21

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach tomore » address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.« less

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

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

Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach tomore » address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.« less

Optimal full motion video registration with rigorous error propagation

NASA Astrophysics Data System (ADS)

Dolloff, John; Hottel, Bryant; Doucette, Peter; Theiss, Henry; Jocher, Glenn

2014-06-01

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor's trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to "down-stream" Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object's 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

Reliable numerical computation in an optimal output-feedback design

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm is presented for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters. The algorithm is a part of a design algorithm for optimal linear dynamic output-feedback controller that minimizes a finite-time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control-law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed-loop eigensystem. This approach through the use of an accurate Pade series approximation does not require the closed-loop system matrix to be diagonalizable. The algorithm was included in a control design package for optimal robust low-order controllers. Usefulness of the proposed numerical algorithm was demonstrated using numerous practical design cases where degeneracies occur frequently in the closed-loop system under an arbitrary controller design initialization and during the numerical search.

Advanced rotorcraft control using parameter optimization

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters is presented. The algorithm is part of a design algorithm for an optimal linear dynamic output feedback controller that minimizes a finite time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed loop eigensystem. This approach through the use of a accurate Pade series approximation does not require the closed loop system matrix to be diagonalizable. The algorithm has been included in a control design package for optimal robust low order controllers. Usefulness of the proposed numerical algorithm has been demonstrated using numerous practical design cases where degeneracies occur frequently in the closed loop system under an arbitrary controller design initialization and during the numerical search.

Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

Robust Frequency-Domain Constrained Feedback Design via a Two-Stage Heuristic Approach.

PubMed

Li, Xianwei; Gao, Huijun

2015-10-01

Based on a two-stage heuristic method, this paper is concerned with the design of robust feedback controllers with restricted frequency-domain specifications (RFDSs) for uncertain linear discrete-time systems. Polytopic uncertainties are assumed to enter all the system matrices, while RFDSs are motivated by the fact that practical design specifications are often described in restricted finite frequency ranges. Dilated multipliers are first introduced to relax the generalized Kalman-Yakubovich-Popov lemma for output feedback controller synthesis and robust performance analysis. Then a two-stage approach to output feedback controller synthesis is proposed: at the first stage, a robust full-information (FI) controller is designed, which is used to construct a required output feedback controller at the second stage. To improve the solvability of the synthesis method, heuristic iterative algorithms are further formulated for exploring the feedback gain and optimizing the initial FI controller at the individual stage. The effectiveness of the proposed design method is finally demonstrated by the application to active control of suspension systems.

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

DOE PAGES

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

2017-11-09

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

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

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

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

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

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

PubMed

Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

2015-08-01

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm.

PubMed

Pal, Partha S; Kar, R; Mandal, D; Ghoshal, S P

2015-11-01

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Klimeck, Gerhard; Hanks, David; Hua, Hook

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Out preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.

Robust model predictive control for multi-step short range spacecraft rendezvous

NASA Astrophysics Data System (ADS)

Zhu, Shuyi; Sun, Ran; Wang, Jiaolong; Wang, Jihe; Shao, Xiaowei

2018-07-01

This work presents a robust model predictive control (MPC) approach for the multi-step short range spacecraft rendezvous problem. During the specific short range phase concerned, the chaser is supposed to be initially outside the line-of-sight (LOS) cone. Therefore, the rendezvous process naturally includes two steps: the first step is to transfer the chaser into the LOS cone and the second step is to transfer the chaser into the aimed region with its motion confined within the LOS cone. A novel MPC framework named after Mixed MPC (M-MPC) is proposed, which is the combination of the Variable-Horizon MPC (VH-MPC) framework and the Fixed-Instant MPC (FI-MPC) framework. The M-MPC framework enables the optimization for the two steps to be implemented jointly rather than to be separated factitiously, and its computation workload is acceptable for the usually low-power processors onboard spacecraft. Then considering that disturbances including modeling error, sensor noise and thrust uncertainty may induce undesired constraint violations, a robust technique is developed and it is attached to the above M-MPC framework to form a robust M-MPC approach. The robust technique is based on the chance-constrained idea, which ensures that constraints can be satisfied with a prescribed probability. It improves the robust technique proposed by Gavilan et al., because it eliminates the unnecessary conservativeness by explicitly incorporating known statistical properties of the navigation uncertainty. The efficacy of the robust M-MPC approach is shown in a simulation study.

Integrated approaches to the application of advanced modeling technology in process development and optimization

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

Allgor, R.J.; Feehery, W.F.; Tolsma, J.E.

The batch process development problem serves as good candidate to guide the development of process modeling environments. It demonstrates that very robust numerical techniques are required within an environment that can collect, organize, and maintain the data and models required to address the batch process development problem. This paper focuses on improving the robustness and efficiency of the numerical algorithms required in such a modeling environment through the development of hybrid numerical and symbolic strategies.

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location-Specific Premiums in the Netherlands.

PubMed

Ermolieva, T; Filatova, T; Ermoliev, Y; Obersteiner, M; de Bruijn, K M; Jeuken, A

2017-01-01

As flood risks grow worldwide, a well-designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood-loss-sharing program involving private insurance based on location-specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS-based flood model and a stochastic optimization procedure with respect to location-specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile-related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures. © 2016 Society for Risk Analysis.

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Research on robust optimization of emergency logistics network considering the time dependence characteristic

NASA Astrophysics Data System (ADS)

WANG, Qingrong; ZHU, Changfeng; LI, Ying; ZHANG, Zhengkun

2017-06-01

Considering the time dependence of emergency logistic network and complexity of the environment that the network exists in, in this paper the time dependent network optimization theory and robust discrete optimization theory are combined, and the emergency logistics dynamic network optimization model with characteristics of robustness is built to maximize the timeliness of emergency logistics. On this basis, considering the complexity of dynamic network and the time dependence of edge weight, an improved ant colony algorithm is proposed to realize the coupling of the optimization algorithm and the network time dependence and robustness. Finally, a case study has been carried out in order to testify validity of this robustness optimization model and its algorithm, and the value of different regulation factors was analyzed considering the importance of the value of the control factor in solving the optimal path. Analysis results show that this model and its algorithm above-mentioned have good timeliness and strong robustness.

Optimization of Operations Resources via Discrete Event Simulation Modeling

NASA Technical Reports Server (NTRS)

Joshi, B.; Morris, D.; White, N.; Unal, R.

1996-01-01

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

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

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

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

RootGraph: a graphic optimization tool for automated image analysis of plant roots

PubMed Central

Cai, Jinhai; Zeng, Zhanghui; Connor, Jason N.; Huang, Chun Yuan; Melino, Vanessa; Kumar, Pankaj; Miklavcic, Stanley J.

2015-01-01

This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions. PMID:26224880

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

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

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Absolute Stability Analysis of a Phase Plane Controlled Spacecraft

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; Plummer, Michael; Bedrossian, Nazareth; Hall, Charles; Jackson, Mark; Spanos, Pol

2010-01-01

Many aerospace attitude control systems utilize phase plane control schemes that include nonlinear elements such as dead zone and ideal relay. To evaluate phase plane control robustness, stability margin prediction methods must be developed. Absolute stability is extended to predict stability margins and to define an abort condition. A constrained optimization approach is also used to design flex filters for roll control. The design goal is to optimize vehicle tracking performance while maintaining adequate stability margins. Absolute stability is shown to provide satisfactory stability constraints for the optimization.

A device-oriented optimizer for solving ground state problems on an approximate quantum computer, Part II: Experiments for interacting spin and molecular systems

NASA Astrophysics Data System (ADS)

Kandala, Abhinav; Mezzacapo, Antonio; Temme, Kristan; Bravyi, Sergey; Takita, Maika; Chavez-Garcia, Jose; Córcoles, Antonio; Smolin, John; Chow, Jerry; Gambetta, Jay

Hybrid quantum-classical algorithms can be used to find variational solutions to generic quantum problems. Here, we present an experimental implementation of a device-oriented optimizer that uses superconducting quantum hardware. The experiment relies on feedback between the quantum device and classical optimization software which is robust to measurement noise. Our device-oriented approach uses naturally available interactions for the preparation of trial states. We demonstrate the application of this technique for solving interacting spin and molecular structure problems.

Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

PubMed Central

Dokeroglu, Tansel; Sert, Seyyit Alper; Cinar, Muhammet Serkan

2014-01-01

With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose. PMID:24892048

Some Approaches Towards Constructing Optimally Efficient Multigrid Solvers for the Inviscid Flow Equations

NASA Technical Reports Server (NTRS)

Sidilkover, David

1997-01-01

Some important advances took place during the last several years in the development of genuinely multidimensional upwind schemes for the compressible Euler equations. In particular, a robust, high-resolution genuinely multidimensional scheme which can be used for any of the flow regimes computations was constructed. This paper summarizes briefly these developments and outlines the fundamental advantages of this approach.

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques.

PubMed

Vilas, Carlos; Balsa-Canto, Eva; García, Maria-Sonia G; Banga, Julio R; Alonso, Antonio A

2012-07-02

Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.

Reliability- and performance-based robust design optimization of MEMS structures considering technological uncertainties

NASA Astrophysics Data System (ADS)

Martowicz, Adam; Uhl, Tadeusz

2012-10-01

The paper discusses the applicability of a reliability- and performance-based multi-criteria robust design optimization technique for micro-electromechanical systems, considering their technological uncertainties. Nowadays, micro-devices are commonly applied systems, especially in the automotive industry, taking advantage of utilizing both the mechanical structure and electronic control circuit on one board. Their frequent use motivates the elaboration of virtual prototyping tools that can be applied in design optimization with the introduction of technological uncertainties and reliability. The authors present a procedure for the optimization of micro-devices, which is based on the theory of reliability-based robust design optimization. This takes into consideration the performance of a micro-device and its reliability assessed by means of uncertainty analysis. The procedure assumes that, for each checked design configuration, the assessment of uncertainty propagation is performed with the meta-modeling technique. The described procedure is illustrated with an example of the optimization carried out for a finite element model of a micro-mirror. The multi-physics approach allowed the introduction of several physical phenomena to correctly model the electrostatic actuation and the squeezing effect present between electrodes. The optimization was preceded by sensitivity analysis to establish the design and uncertain domains. The genetic algorithms fulfilled the defined optimization task effectively. The best discovered individuals are characterized by a minimized value of the multi-criteria objective function, simultaneously satisfying the constraint on material strength. The restriction of the maximum equivalent stresses was introduced with the conditionally formulated objective function with a penalty component. The yielded results were successfully verified with a global uniform search through the input design domain.

A robust optimization methodology for preliminary aircraft design

NASA Astrophysics Data System (ADS)

Prigent, S.; Maréchal, P.; Rondepierre, A.; Druot, T.; Belleville, M.

2016-05-01

This article focuses on a robust optimization of an aircraft preliminary design under operational constraints. According to engineers' know-how, the aircraft preliminary design problem can be modelled as an uncertain optimization problem whose objective (the cost or the fuel consumption) is almost affine, and whose constraints are convex. It is shown that this uncertain optimization problem can be approximated in a conservative manner by an uncertain linear optimization program, which enables the use of the techniques of robust linear programming of Ben-Tal, El Ghaoui, and Nemirovski [Robust Optimization, Princeton University Press, 2009]. This methodology is then applied to two real cases of aircraft design and numerical results are presented.

Systems biology of the modified branched Entner-Doudoroff pathway in Sulfolobus solfataricus

PubMed Central

Figueiredo, Ana Sofia; Esser, Dominik; Haferkamp, Patrick; Wieloch, Patricia; Schomburg, Dietmar; Siebers, Bettina; Schaber, Jörg

2017-01-01

Sulfolobus solfataricus is a thermoacidophilic Archaeon that thrives in terrestrial hot springs (solfatares) with optimal growth at 80°C and pH 2–4. It catabolizes specific carbon sources, such as D-glucose, to pyruvate via the modified Entner-Doudoroff (ED) pathway. This pathway has two parallel branches, the semi-phosphorylative and the non-phosphorylative. However, the strategy of S.solfataricus to endure in such an extreme environment in terms of robustness and adaptation is not yet completely understood. Here, we present the first dynamic mathematical model of the ED pathway parameterized with quantitative experimental data. These data consist of enzyme activities of the branched pathway at 70°C and 80°C and of metabolomics data at the same temperatures for the wild type and for a metabolic engineered knockout of the semi-phosphorylative branch. We use the validated model to address two questions: 1. Is this system more robust to perturbations at its optimal growth temperature? 2. Is the ED robust to deletion and perturbations? We employed a systems biology approach to answer these questions and to gain further knowledge on the emergent properties of this biological system. Specifically, we applied deterministic and stochastic approaches to study the sensitivity and robustness of the system, respectively. The mathematical model we present here, shows that: 1. Steady state metabolite concentrations of the ED pathway are consistently more robust to stochastic internal perturbations at 80°C than at 70°C; 2. These metabolite concentrations are highly robust when faced with the knockout of either branch. Connected with this observation, these two branches show different properties at the level of metabolite production and flux control. These new results reveal how enzyme kinetics and metabolomics synergizes with mathematical modelling to unveil new systemic properties of the ED pathway in S.solfataricus in terms of its adaptation and robustness. PMID:28692669

Marker optimization for facial motion acquisition and deformation.

PubMed

Le, Binh H; Zhu, Mingyang; Deng, Zhigang

2013-11-01

A long-standing problem in marker-based facial motion capture is what are the optimal facial mocap marker layouts. Despite its wide range of potential applications, this problem has not yet been systematically explored to date. This paper describes an approach to compute optimized marker layouts for facial motion acquisition as optimization of characteristic control points from a set of high-resolution, ground-truth facial mesh sequences. Specifically, the thin-shell linear deformation model is imposed onto the example pose reconstruction process via optional hard constraints such as symmetry and multiresolution constraints. Through our experiments and comparisons, we validate the effectiveness, robustness, and accuracy of our approach. Besides guiding minimal yet effective placement of facial mocap markers, we also describe and demonstrate its two selected applications: marker-based facial mesh skinning and multiresolution facial performance capture.

Learning Robust and Discriminative Subspace With Low-Rank Constraints.

PubMed

Li, Sheng; Fu, Yun

2016-11-01

In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization-based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The experimental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.

An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling computations

PubMed Central

Sankaran, Sethuraman; Humphrey, Jay D.; Marsden, Alison L.

2013-01-01

Computational models for vascular growth and remodeling (G&R) are used to predict the long-term response of vessels to changes in pressure, flow, and other mechanical loading conditions. Accurate predictions of these responses are essential for understanding numerous disease processes. Such models require reliable inputs of numerous parameters, including material properties and growth rates, which are often experimentally derived, and inherently uncertain. While earlier methods have used a brute force approach, systematic uncertainty quantification in G&R models promises to provide much better information. In this work, we introduce an efficient framework for uncertainty quantification and optimal parameter selection, and illustrate it via several examples. First, an adaptive sparse grid stochastic collocation scheme is implemented in an established G&R solver to quantify parameter sensitivities, and near-linear scaling with the number of parameters is demonstrated. This non-intrusive and parallelizable algorithm is compared with standard sampling algorithms such as Monte-Carlo. Second, we determine optimal arterial wall material properties by applying robust optimization. We couple the G&R simulator with an adaptive sparse grid collocation approach and a derivative-free optimization algorithm. We show that an artery can achieve optimal homeostatic conditions over a range of alterations in pressure and flow; robustness of the solution is enforced by including uncertainty in loading conditions in the objective function. We then show that homeostatic intramural and wall shear stress is maintained for a wide range of material properties, though the time it takes to achieve this state varies. We also show that the intramural stress is robust and lies within 5% of its mean value for realistic variability of the material parameters. We observe that prestretch of elastin and collagen are most critical to maintaining homeostasis, while values of the material properties are most critical in determining response time. Finally, we outline several challenges to the G&R community for future work. We suggest that these tools provide the first systematic and efficient framework to quantify uncertainties and optimally identify G&R model parameters. PMID:23626380

Distribution path robust optimization of electric vehicle with multiple distribution centers

PubMed Central

Hao, Wei; He, Ruichun; Jia, Xiaoyan; Pan, Fuquan; Fan, Jing; Xiong, Ruiqi

2018-01-01

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas’ theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model. PMID:29518169

Failure detection system design methodology. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chow, E. Y.

1980-01-01

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.

Applications of Coding in Network Communications

ERIC Educational Resources Information Center

Chang, Christopher SungWook

2012-01-01

This thesis uses the tool of network coding to investigate fast peer-to-peer file distribution, anonymous communication, robust network construction under uncertainty, and prioritized transmission. In a peer-to-peer file distribution system, we use a linear optimization approach to show that the network coding framework significantly simplifies…

Design of Robust Adaptive Unbalance Response Controllers for Rotors with Magnetic Bearings

NASA Technical Reports Server (NTRS)

Knospe, Carl R.; Tamer, Samir M.; Fedigan, Stephen J.

1996-01-01

Experimental results have recently demonstrated that an adaptive open loop control strategy can be highly effective in the suppression of unbalance induced vibration on rotors supported in active magnetic bearings. This algorithm, however, relies upon a predetermined gain matrix. Typically, this matrix is determined by an optimal control formulation resulting in the choice of the pseudo-inverse of the nominal influence coefficient matrix as the gain matrix. This solution may result in problems with stability and performance robustness since the estimated influence coefficient matrix is not equal to the actual influence coefficient matrix. Recently, analysis tools have been developed to examine the robustness of this control algorithm with respect to structured uncertainty. Herein, these tools are extended to produce a design procedure for determining the adaptive law's gain matrix. The resulting control algorithm has a guaranteed convergence rate and steady state performance in spite of the uncertainty in the rotor system. Several examples are presented which demonstrate the effectiveness of this approach and its advantages over the standard optimal control formulation.

A robust active control system for shimmy damping in the presence of free play and uncertainties

NASA Astrophysics Data System (ADS)

Orlando, Calogero; Alaimo, Andrea

2017-02-01

Shimmy vibration is the oscillatory motion of the fork-wheel assembly about the steering axis. It represents one of the major problem of aircraft landing gear because it can lead to excessive wear, discomfort as well as safety concerns. Based on the nonlinear model of the mechanics of a single wheel nose landing gear (NLG), electromechanical actuator and tire elasticity, a robust active controller capable of damping shimmy vibration is designed and investigated in this study. A novel Decline Population Swarm Optimization (PDSO) procedure is introduced and used to select the optimal parameters for the controller. The PDSO procedure is based on a decline demographic model and shows high global search capability with reduced computational costs. The open and closed loop system behavior is analyzed under different case studies of aeronautical interest and the effects of torsional free play on the nose landing gear response are also studied. Plant parameters probabilistic uncertainties are then taken into account to assess the active controller robustness using a stochastic approach.

Influence of robust optimization in intensity-modulated proton therapy with different dose delivery techniques

PubMed Central

Liu, Wei; Li, Yupeng; Li, Xiaoqiang; Cao, Wenhua; Zhang, Xiaodong

2012-01-01

Purpose: The distal edge tracking (DET) technique in intensity-modulated proton therapy (IMPT) allows for high energy efficiency, fast and simple delivery, and simple inverse treatment planning; however, it is highly sensitive to uncertainties. In this study, the authors explored the application of DET in IMPT (IMPT-DET) and conducted robust optimization of IMPT-DET to see if the planning technique’s sensitivity to uncertainties was reduced. They also compared conventional and robust optimization of IMPT-DET with three-dimensional IMPT (IMPT-3D) to gain understanding about how plan robustness is achieved. Methods: They compared the robustness of IMPT-DET and IMPT-3D plans to uncertainties by analyzing plans created for a typical prostate cancer case and a base of skull (BOS) cancer case (using data for patients who had undergone proton therapy at our institution). Spots with the highest and second highest energy layers were chosen so that the Bragg peak would be at the distal edge of the targets in IMPT-DET using 36 equally spaced angle beams; in IMPT-3D, 3 beams with angles chosen by a beam angle optimization algorithm were planned. Dose contributions for a number of range and setup uncertainties were calculated, and a worst-case robust optimization was performed. A robust quantification technique was used to evaluate the plans’ sensitivity to uncertainties. Results: With no uncertainties considered, the DET is less robust to uncertainties than is the 3D method but offers better normal tissue protection. With robust optimization to account for range and setup uncertainties, robust optimization can improve the robustness of IMPT plans to uncertainties; however, our findings show the extent of improvement varies. Conclusions: IMPT’s sensitivity to uncertainties can be improved by using robust optimization. They found two possible mechanisms that made improvements possible: (1) a localized single-field uniform dose distribution (LSFUD) mechanism, in which the optimization algorithm attempts to produce a single-field uniform dose distribution while minimizing the patching field as much as possible; and (2) perturbed dose distribution, which follows the change in anatomical geometry. Multiple-instance optimization has more knowledge of the influence matrices; this greater knowledge improves IMPT plans’ ability to retain robustness despite the presence of uncertainties. PMID:22755694

Trade-offs between robustness and small-world effect in complex networks

PubMed Central

Peng, Guan-Sheng; Tan, Suo-Yi; Wu, Jun; Holme, Petter

2016-01-01

Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail. PMID:27853301

Exploratory Study of 4D Versus 3D Robust Optimization in Intensity-Modulated Proton Therapy for Lung Cancer

PubMed Central

Liu, Wei; Schild, Steven E.; Chang, Joe Y.; Liao, Zhongxing; Chang, Yu-Hui; Wen, Zhifei; Shen, Jiajian; Stoker, Joshua B.; Ding, Xiaoning; Hu, Yanle; Sahoo, Narayan; Herman, Michael G.; Vargas, Carlos; Keole, Sameer; Wong, William; Bues, Martin

2015-01-01

Background To compare the impact of uncertainties and interplay effect on 3D and 4D robustly optimized intensity-modulated proton therapy (IMPT) plans for lung cancer in an exploratory methodology study. Methods IMPT plans were created for 11 non-randomly selected non-small-cell lung cancer (NSCLC) cases: 3D robustly optimized plans on average CTs with internal gross tumor volume density overridden to irradiate internal target volume, and 4D robustly optimized plans on 4D CTs to irradiate clinical target volume (CTV). Regular fractionation (66 Gy[RBE] in 33 fractions) were considered. In 4D optimization, the CTV of individual phases received non-uniform doses to achieve a uniform cumulative dose. The root-mean-square-dose volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under the RVH curve (AUCs) were used to evaluate plan robustness. Dose evaluation software modeled time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Dose-volume histogram indices comparing CTV coverage, homogeneity, and normal tissue sparing were evaluated using Wilcoxon signed-rank test. Results 4D robust optimization plans led to smaller AUC for CTV (14.26 vs. 18.61 (p=0.001), better CTV coverage (Gy[RBE]) [D95% CTV: 60.6 vs 55.2 (p=0.001)], and better CTV homogeneity [D5%–D95% CTV: 10.3 vs 17.7 (p=0.002)] in the face of uncertainties. With interplay effect considered, 4D robust optimization produced plans with better target coverage [D95% CTV: 64.5 vs 63.8 (p=0.0068)], comparable target homogeneity, and comparable normal tissue protection. The benefits from 4D robust optimization were most obvious for the 2 typical stage III lung cancer patients. Conclusions Our exploratory methodology study showed that, compared to 3D robust optimization, 4D robust optimization produced significantly more robust and interplay-effect-resistant plans for targets with comparable dose distributions for normal tissues. A further study with a larger and more realistic patient population is warranted to generalize the conclusions. PMID:26725727

Robust optimization in lung treatment plans accounting for geometric uncertainty.

PubMed

Zhang, Xin; Rong, Yi; Morrill, Steven; Fang, Jian; Narayanasamy, Ganesh; Galhardo, Edvaldo; Maraboyina, Sanjay; Croft, Christopher; Xia, Fen; Penagaricano, Jose

2018-05-01

Robust optimization generates scenario-based plans by a minimax optimization method to find optimal scenario for the trade-off between target coverage robustness and organ-at-risk (OAR) sparing. In this study, 20 lung cancer patients with tumors located at various anatomical regions within the lungs were selected and robust optimization photon treatment plans including intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans were generated. The plan robustness was analyzed using perturbed doses with setup error boundary of ±3 mm in anterior/posterior (AP), ±3 mm in left/right (LR), and ±5 mm in inferior/superior (IS) directions from isocenter. Perturbed doses for D 99 , D 98 , and D 95 were computed from six shifted isocenter plans to evaluate plan robustness. Dosimetric study was performed to compare the internal target volume-based robust optimization plans (ITV-IMRT and ITV-VMAT) and conventional PTV margin-based plans (PTV-IMRT and PTV-VMAT). The dosimetric comparison parameters were: ITV target mean dose (D mean ), R 95 (D 95 /D prescription ), Paddick's conformity index (CI), homogeneity index (HI), monitor unit (MU), and OAR doses including lung (D mean , V 20 Gy and V 15 Gy ), chest wall, heart, esophagus, and maximum cord doses. A comparison of optimization results showed the robust optimization plan had better ITV dose coverage, better CI, worse HI, and lower OAR doses than conventional PTV margin-based plans. Plan robustness evaluation showed that the perturbed doses of D 99 , D 98 , and D 95 were all satisfied at least 99% of the ITV to received 95% of prescription doses. It was also observed that PTV margin-based plans had higher MU than robust optimization plans. The results also showed robust optimization can generate plans that offer increased OAR sparing, especially for normal lungs and OARs near or abutting the target. Weak correlation was found between normal lung dose and target size, and no other correlation was observed in this study. © 2018 University of Arkansas for Medical Sciences. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

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.

A practical globalization of one-shot optimization for optimal design of tokamak divertors

NASA Astrophysics Data System (ADS)

Blommaert, Maarten; Dekeyser, Wouter; Baelmans, Martine; Gauger, Nicolas R.; Reiter, Detlev

2017-01-01

In past studies, nested optimization methods were successfully applied to design of the magnetic divertor configuration in nuclear fusion reactors. In this paper, so-called one-shot optimization methods are pursued. Due to convergence issues, a globalization strategy for the one-shot solver is sought. Whereas Griewank introduced a globalization strategy using a doubly augmented Lagrangian function that includes primal and adjoint residuals, its practical usability is limited by the necessity of second order derivatives and expensive line search iterations. In this paper, a practical alternative is offered that avoids these drawbacks by using a regular augmented Lagrangian merit function that penalizes only state residuals. Additionally, robust rank-two Hessian estimation is achieved by adaptation of Powell's damped BFGS update rule. The application of the novel one-shot approach to magnetic divertor design is considered in detail. For this purpose, the approach is adapted to be complementary with practical in parts adjoint sensitivities. Using the globalization strategy, stable convergence of the one-shot approach is achieved.

SU-E-T-625: Robustness Evaluation and Robust Optimization of IMPT Plans Based on Per-Voxel Standard Deviation of Dose Distributions.

PubMed

Liu, W; Mohan, R

2012-06-01

Proton dose distributions, IMPT in particular, are highly sensitive to setup and range uncertainties. We report a novel method, based on per-voxel standard deviation (SD) of dose distributions, to evaluate the robustness of proton plans and to robustly optimize IMPT plans to render them less sensitive to uncertainties. For each optimization iteration, nine dose distributions are computed - the nominal one, and one each for ± setup uncertainties along x, y and z axes and for ± range uncertainty. SD of dose in each voxel is used to create SD-volume histogram (SVH) for each structure. SVH may be considered a quantitative representation of the robustness of the dose distribution. For optimization, the desired robustness may be specified in terms of an SD-volume (SV) constraint on the CTV and incorporated as a term in the objective function. Results of optimization with and without this constraint were compared in terms of plan optimality and robustness using the so called'worst case' dose distributions; which are obtained by assigning the lowest among the nine doses to each voxel in the clinical target volume (CTV) and the highest to normal tissue voxels outside the CTV. The SVH curve and the area under it for each structure were used as quantitative measures of robustness. Penalty parameter of SV constraint may be varied to control the tradeoff between robustness and plan optimality. We applied these methods to one case each of H&N and lung. In both cases, we found that imposing SV constraint improved plan robustness but at the cost of normal tissue sparing. SVH-based optimization and evaluation is an effective tool for robustness evaluation and robust optimization of IMPT plans. Studies need to be conducted to test the methods for larger cohorts of patients and for other sites. This research is supported by National Cancer Institute (NCI) grant P01CA021239, the University Cancer Foundation via the Institutional Research Grant program at the University of Texas MD Anderson Cancer Center, and MD Anderson’s cancer center support grant CA016672. © 2012 American Association of Physicists in Medicine.

Possibility-based robust design optimization for the structural-acoustic system with fuzzy parameters

NASA Astrophysics Data System (ADS)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2018-03-01

The conventional engineering optimization problems considering uncertainties are based on the probabilistic model. However, the probabilistic model may be unavailable because of the lack of sufficient objective information to construct the precise probability distribution of uncertainties. This paper proposes a possibility-based robust design optimization (PBRDO) framework for the uncertain structural-acoustic system based on the fuzzy set model, which can be constructed by expert opinions. The objective of robust design is to optimize the expectation and variability of system performance with respect to uncertainties simultaneously. In the proposed PBRDO, the entropy of the fuzzy system response is used as the variability index; the weighted sum of the entropy and expectation of the fuzzy response is used as the objective function, and the constraints are established in the possibility context. The computations for the constraints and objective function of PBRDO are a triple-loop and a double-loop nested problem, respectively, whose computational costs are considerable. To improve the computational efficiency, the target performance approach is introduced to transform the calculation of the constraints into a double-loop nested problem. To further improve the computational efficiency, a Chebyshev fuzzy method (CFM) based on the Chebyshev polynomials is proposed to estimate the objective function, and the Chebyshev interval method (CIM) is introduced to estimate the constraints, thereby the optimization problem is transformed into a single-loop one. Numerical results on a shell structural-acoustic system verify the effectiveness and feasibility of the proposed methods.

A reliable algorithm for optimal control synthesis

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1992-01-01

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

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

PubMed

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

2018-01-01

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

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

NASA Technical Reports Server (NTRS)

Englander, Arnold C.; Englander, Jacob A.

2017-01-01

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

Nuclear-relaxed elastic and piezoelectric constants of materials: Computational aspects of two quantum-mechanical approaches.

PubMed

Erba, Alessandro; Caglioti, Dominique; Zicovich-Wilson, Claudio Marcelo; Dovesi, Roberto

2017-02-15

Two alternative approaches for the quantum-mechanical calculation of the nuclear-relaxation term of elastic and piezoelectric tensors of crystalline materials are illustrated and their computational aspects discussed: (i) a numerical approach based on the geometry optimization of atomic positions at strained lattice configurations and (ii) a quasi-analytical approach based on the evaluation of the force- and displacement-response internal-strain tensors as combined with the interatomic force-constant matrix. The two schemes are compared both as regards their computational accuracy and performance. The latter approach, not being affected by the many numerical parameters and procedures of a typical quasi-Newton geometry optimizer, constitutes a more reliable and robust mean to the evaluation of such properties, at a reduced computational cost for most crystalline systems. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States while Preserving Local Energy Minima.

PubMed

Perthold, Jan Walther; Oostenbrink, Chris

2018-05-17

Enveloping distribution sampling (EDS) is an efficient approach to calculate multiple free-energy differences from a single molecular dynamics (MD) simulation. However, the construction of an appropriate reference-state Hamiltonian that samples all states efficiently is not straightforward. We propose a novel approach for the construction of the EDS reference-state Hamiltonian, related to a previously described procedure to smoothen energy landscapes. In contrast to previously suggested EDS approaches, our reference-state Hamiltonian preserves local energy minima of the combined end-states. Moreover, we propose an intuitive, robust and efficient parameter optimization scheme to tune EDS Hamiltonian parameters. We demonstrate the proposed method with established and novel test systems and conclude that our approach allows for the automated calculation of multiple free-energy differences from a single simulation. Accelerated EDS promises to be a robust and user-friendly method to compute free-energy differences based on solid statistical mechanics.

Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

PubMed

Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

2013-11-04

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

PubMed Central

Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

2013-01-01

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

DTIC Science & Technology

2009-03-10

pp. 594-606. 8. Inverse Approaches to Drying of Thin Bodies With Significant Shrinkage Effects (with G. H. Kanevce, L. P. Kanevce, V. B. Mitrevski ...Kanevce, L. Kanevce, V. Mitrevski ), ICCES󈧌: International Conference on Computational & Experimental Engineering and Sciences, Honolulu, Hawaii, March 17...Miami Beach, FL, April 16-18, 2007. 16. Inverse Approaches to Drying of Sliced Foods (with Kanevce, G. H., Kanevce, Lj. P., and Mitrevski , V. B

Synthesis of Optimal Constant-Gain Positive-Real Controllers for Passive Systems

NASA Technical Reports Server (NTRS)

Mao, Y.; Kelkar, A. G.; Joshi, S. M.

1999-01-01

This paper presents synthesis methods for the design of constant-gain positive real controllers for passive systems. The results presented in this paper, in conjunction with the previous work by the authors on passification of non-passive systems, offer a useful synthesis tool for the design of passivity-based robust controllers for non-passive systems as well. Two synthesis approaches are given for minimizing an LQ-type performance index, resulting in optimal controller gains. Two separate algorithms, one for each of these approaches, are given. The synthesis techniques are demonstrated using two numerical examples: control of a flexible structure and longitudinal control of a fighter aircraft.

Reliability-Based Control Design for Uncertain Systems

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a robust control design methodology for systems with probabilistic parametric uncertainty. Control design is carried out by solving a reliability-based multi-objective optimization problem where the probability of violating design requirements is minimized. Simultaneously, failure domains are optimally enlarged to enable global improvements in the closed-loop performance. To enable an efficient numerical implementation, a hybrid approach for estimating reliability metrics is developed. This approach, which integrates deterministic sampling and asymptotic approximations, greatly reduces the numerical burden associated with complex probabilistic computations without compromising the accuracy of the results. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

Optimal and robust control of quantum state transfer by shaping the spectral phase of ultrafast laser pulses.

PubMed

Guo, Yu; Dong, Daoyi; Shu, Chuan-Cun

2018-04-04

Achieving fast and efficient quantum state transfer is a fundamental task in physics, chemistry and quantum information science. However, the successful implementation of the perfect quantum state transfer also requires robustness under practically inevitable perturbative defects. Here, we demonstrate how an optimal and robust quantum state transfer can be achieved by shaping the spectral phase of an ultrafast laser pulse in the framework of frequency domain quantum optimal control theory. Our numerical simulations of the single dibenzoterrylene molecule as well as in atomic rubidium show that optimal and robust quantum state transfer via spectral phase modulated laser pulses can be achieved by incorporating a filtering function of the frequency into the optimization algorithm, which in turn has potential applications for ultrafast robust control of photochemical reactions.

Safe Maneuvering Envelope Estimation Based on a Physical Approach

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas J. J.; Schuet, Stefan R.; Wheeler, Kevin R.; Acosta, Diana; Kaneshige, John T.

2013-01-01

This paper discusses a computationally efficient algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. This approach differs from others since it is physically inspired. This more transparent approach allows interpreting data in each step, and it is assumed that these physical models based upon flight dynamics theory will therefore facilitate certification for future real life applications.

Vectorial mask optimization methods for robust optical lithography

NASA Astrophysics Data System (ADS)

Ma, Xu; Li, Yanqiu; Guo, Xuejia; Dong, Lisong; Arce, Gonzalo R.

2012-10-01

Continuous shrinkage of critical dimension in an integrated circuit impels the development of resolution enhancement techniques for low k1 lithography. Recently, several pixelated optical proximity correction (OPC) and phase-shifting mask (PSM) approaches were developed under scalar imaging models to account for the process variations. However, the lithography systems with larger-NA (NA>0.6) are predominant for current technology nodes, rendering the scalar models inadequate to describe the vector nature of the electromagnetic field that propagates through the optical lithography system. In addition, OPC and PSM algorithms based on scalar models can compensate for wavefront aberrations, but are incapable of mitigating polarization aberrations in practical lithography systems, which can only be dealt with under the vector model. To this end, we focus on developing robust pixelated gradient-based OPC and PSM optimization algorithms aimed at canceling defocus, dose variation, wavefront and polarization aberrations under a vector model. First, an integrative and analytic vector imaging model is applied to formulate the optimization problem, where the effects of process variations are explicitly incorporated in the optimization framework. A steepest descent algorithm is then used to iteratively optimize the mask patterns. Simulations show that the proposed algorithms can effectively improve the process windows of the optical lithography systems.

Profile Optimization Method for Robust Airfoil Shape Optimization in Viscous Flow

NASA Technical Reports Server (NTRS)

Li, Wu

2003-01-01

Simulation results obtained by using FUN2D for robust airfoil shape optimization in transonic viscous flow are included to show the potential of the profile optimization method for generating fairly smooth optimal airfoils with no off-design performance degradation.

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

PubMed

Liu, Meiqin; Zhang, Senlin

2008-10-01

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

An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm.

PubMed

Xue, Dingyü; Li, Tingxue

2017-04-27

The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The impact of the topology on cascading failures in a power grid model

NASA Astrophysics Data System (ADS)

Koç, Yakup; Warnier, Martijn; Mieghem, Piet Van; Kooij, Robert E.; Brazier, Frances M. T.

2014-05-01

Cascading failures are one of the main reasons for large scale blackouts in power transmission grids. Secure electrical power supply requires, together with careful operation, a robust design of the electrical power grid topology. Currently, the impact of the topology on grid robustness is mainly assessed by purely topological approaches, that fail to capture the essence of electric power flow. This paper proposes a metric, the effective graph resistance, to relate the topology of a power grid to its robustness against cascading failures by deliberate attacks, while also taking the fundamental characteristics of the electric power grid into account such as power flow allocation according to Kirchhoff laws. Experimental verification on synthetic power systems shows that the proposed metric reflects the grid robustness accurately. The proposed metric is used to optimize a grid topology for a higher level of robustness. To demonstrate its applicability, the metric is applied on the IEEE 118 bus power system to improve its robustness against cascading failures.

A traditional and a less-invasive robust design: choices in optimizing effort allocation for seabird population studies

USGS Publications Warehouse

Converse, S.J.; Kendall, W.L.; Doherty, P.F.; Naughton, M.B.; Hines, J.E.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

For many animal populations, one or more life stages are not accessible to sampling, and therefore an unobservable state is created. For colonially-breeding populations, this unobservable state could represent the subset of adult breeders that have foregone breeding in a given year. This situation applies to many seabird populations, notably albatrosses, where skipped breeders are either absent from the colony, or are present but difficult to capture or correctly assign to breeding state. Kendall et al. have proposed design strategies for investigations of seabird demography where such temporary emigration occurs, suggesting the use of the robust design to permit the estimation of time-dependent parameters and to increase the precision of estimates from multi-state models. A traditional robust design, where animals are subject to capture multiple times in a sampling season, is feasible in many cases. However, due to concerns that multiple captures per season could cause undue disturbance to animals, Kendall et al. developed a less-invasive robust design (LIRD), where initial captures are followed by an assessment of the ratio of marked-to-unmarked birds in the population or sampled plot. This approach has recently been applied in the Northwestern Hawaiian Islands to populations of Laysan (Phoebastria immutabilis) and black-footed (P. nigripes) albatrosses. In this paper, we outline the LIRD and its application to seabird population studies. We then describe an approach to determining optimal allocation of sampling effort in which we consider a non-robust design option (nRD), and variations of both the traditional robust design (RD), and the LIRD. Variations we considered included the number of secondary sampling occasions for the RD and the amount of total effort allocated to the marked-to-unmarked ratio assessment for the LIRD. We used simulations, informed by early data from the Hawaiian study, to address optimal study design for our example cases. We found that the LIRD performed as well or nearly as well as certain variations of the RD in terms of root mean square error, especially when relatively little of the total effort was allocated to the assessment of the marked-to-unmarked ratio versus to initial captures. For the RD, we found no clear benefit of using 2, 4, or 6 secondary sampling occasions per year, though this result will depend on the relative effort costs of captures versus recaptures and on the length of the study. We also found that field-readable bands, which may be affixed to birds in addition to standard metal bands, will be beneficial in longer-term studies of albatrosses in the Northwestern Hawaiian Islands. Field-readable bands reduce the effort cost of recapturing individuals, and in the long-term this cost reduction can offset the additional effort expended in affixing the bands. Finally, our approach to determining optimal study design can be generally applied by researchers, with little seed data, to design their studies at the outset.

A feedback control for the advanced launch system

NASA Technical Reports Server (NTRS)

Seywald, Hans; Cliff, Eugene M.

1991-01-01

A robust feedback algorithm is presented for a near-minimum-fuel ascent of a two-stage launch vehicle operating in the equatorial plane. The development of the algorithm is based on the ideas of neighboring optimal control and can be derived into three phases. In phase 1, the formalism of optimal control is employed to calculate fuel-optimal ascent trajectories for a simple point-mass model. In phase 2, these trajectories are used to numerically calculate gain functions of time for the control(s), the total flight time, and possibly, for other variables of interest. In phase 3, these gains are used to determine feedback expressions for the controls associated with a more realistic model of a launch vehicle. With the Advanced Launch System in mind, all calculations are performed on a two-stage vehicle with fixed thrust history, but this restriction is by no means important for the approach taken. Performance and robustness of the algorithm is found to be excellent.

Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.

Exploratory Study of 4D versus 3D Robust Optimization in Intensity Modulated Proton Therapy for Lung Cancer

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

Liu, Wei, E-mail: Liu.Wei@mayo.edu; Schild, Steven E.; Chang, Joe Y.

Purpose: The purpose of this study was to compare the impact of uncertainties and interplay on 3-dimensional (3D) and 4D robustly optimized intensity modulated proton therapy (IMPT) plans for lung cancer in an exploratory methodology study. Methods and Materials: IMPT plans were created for 11 nonrandomly selected non-small cell lung cancer (NSCLC) cases: 3D robustly optimized plans on average CTs with internal gross tumor volume density overridden to irradiate internal target volume, and 4D robustly optimized plans on 4D computed tomography (CT) to irradiate clinical target volume (CTV). Regular fractionation (66 Gy [relative biological effectiveness; RBE] in 33 fractions) was considered.more » In 4D optimization, the CTV of individual phases received nonuniform doses to achieve a uniform cumulative dose. The root-mean-square dose-volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under the RVH curve (AUCs) were used to evaluate plan robustness. Dose evaluation software modeled time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Dose-volume histogram (DVH) indices comparing CTV coverage, homogeneity, and normal tissue sparing were evaluated using Wilcoxon signed rank test. Results: 4D robust optimization plans led to smaller AUC for CTV (14.26 vs 18.61, respectively; P=.001), better CTV coverage (Gy [RBE]) (D{sub 95%} CTV: 60.6 vs 55.2, respectively; P=.001), and better CTV homogeneity (D{sub 5%}-D{sub 95%} CTV: 10.3 vs 17.7, resspectively; P=.002) in the face of uncertainties. With interplay effect considered, 4D robust optimization produced plans with better target coverage (D{sub 95%} CTV: 64.5 vs 63.8, respectively; P=.0068), comparable target homogeneity, and comparable normal tissue protection. The benefits from 4D robust optimization were most obvious for the 2 typical stage III lung cancer patients. Conclusions: Our exploratory methodology study showed that, compared to 3D robust optimization, 4D robust optimization produced significantly more robust and interplay-effect-resistant plans for targets with comparable dose distributions for normal tissues. A further study with a larger and more realistic patient population is warranted to generalize the conclusions.« less

Robust design of multiple trailing edge flaps for helicopter vibration reduction: A multi-objective bat algorithm approach

NASA Astrophysics Data System (ADS)

Mallick, Rajnish; Ganguli, Ranjan; Seetharama Bhat, M.

2015-09-01

The objective of this study is to determine an optimal trailing edge flap configuration and flap location to achieve minimum hub vibration levels and flap actuation power simultaneously. An aeroelastic analysis of a soft in-plane four-bladed rotor is performed in conjunction with optimal control. A second-order polynomial response surface based on an orthogonal array (OA) with 3-level design describes both the objectives adequately. Two new orthogonal arrays called MGB2P-OA and MGB4P-OA are proposed to generate nonlinear response surfaces with all interaction terms for two and four parameters, respectively. A multi-objective bat algorithm (MOBA) approach is used to obtain the optimal design point for the mutually conflicting objectives. MOBA is a recently developed nature-inspired metaheuristic optimization algorithm that is based on the echolocation behaviour of bats. It is found that MOBA inspired Pareto optimal trailing edge flap design reduces vibration levels by 73% and flap actuation power by 27% in comparison with the baseline design.

Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad; Janssen, Hans

2015-02-01

The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of midpoints in the hypercube intervals (midpoint LHS). Both approaches have been extensively used, but no attempt has been previously made to compare the efficiency and robustness of their resulting sample designs. In this study we compare the two approaches and show that the space-filling characteristics of OLHS designs are sensitive to the initial design that is fed into the optimization algorithm. It is also illustrated that the space-filling characteristics of OLHS designs based on midpoint LHS are significantly better those based on random LHS. The two approaches are compared by incorporating their resulting sample designs in Monte Carlo simulation (MCS) for uncertainty propagation analysis, and then, by employing the sample designs in the selection of the training set for constructing non-intrusive polynomial chaos expansion (NIPCE) meta-models which subsequently replace the original full model in MCSs. The analysis is based on two case studies involving numerical simulation of density dependent flow and solute transport in porous media within the context of seawater intrusion in coastal aquifers. We show that the use of midpoint LHS as the initial design increases the efficiency and robustness of the resulting MCSs and NIPCE meta-models. The study also illustrates that this relative improvement decreases with increasing number of sample points and input parameter dimensions. Since the computational time and efforts for generating the sample designs in the two approaches are identical, the use of midpoint LHS as the initial design in OLHS is thus recommended.

Controlled grafting of vinylic monomers on polyolefins: a robust mathematical modeling approach

PubMed Central

Saeb, Mohammad Reza; Rezaee, Babak; Shadman, Alireza; Formela, Krzysztof; Ahmadi, Zahed; Hemmati, Farkhondeh; Kermaniyan, Tayebeh Sadat; Mohammadi, Yousef

2017-01-01

Abstract Experimental and mathematical modeling analyses were used for controlling melt free-radical grafting of vinylic monomers on polyolefins and, thereby, reducing the disturbance of undesired cross-linking of polyolefins. Response surface, desirability function, and artificial intelligence methodologies were blended to modeling/optimization of grafting reaction in terms of vinylic monomer content, peroxide initiator concentration, and melt-processing time. An in-house code was developed based on artificial neural network that learns and mimics processing torque and grafting of glycidyl methacrylate (GMA) typical vinylic monomer on high-density polyethylene (HDPE). Application of response surface and desirability function enabled concurrent optimization of processing torque and GMA grafting on HDPE, through which we quantified for the first time competition between parallel reactions taking place during melt processing: (i) desirable grafting of GMA on HDPE; (ii) undesirable cross-linking of HDPE. The proposed robust mathematical modeling approach can precisely learn the behavior of grafting reaction of vinylic monomers on polyolefins and be placed into practice in finding exact operating condition needed for efficient grafting of reactive monomers on polyolefins. PMID:29491797

Controlled grafting of vinylic monomers on polyolefins: a robust mathematical modeling approach.

PubMed

Saeb, Mohammad Reza; Rezaee, Babak; Shadman, Alireza; Formela, Krzysztof; Ahmadi, Zahed; Hemmati, Farkhondeh; Kermaniyan, Tayebeh Sadat; Mohammadi, Yousef

2017-01-01

Experimental and mathematical modeling analyses were used for controlling melt free-radical grafting of vinylic monomers on polyolefins and, thereby, reducing the disturbance of undesired cross-linking of polyolefins. Response surface, desirability function, and artificial intelligence methodologies were blended to modeling/optimization of grafting reaction in terms of vinylic monomer content, peroxide initiator concentration, and melt-processing time. An in-house code was developed based on artificial neural network that learns and mimics processing torque and grafting of glycidyl methacrylate (GMA) typical vinylic monomer on high-density polyethylene (HDPE). Application of response surface and desirability function enabled concurrent optimization of processing torque and GMA grafting on HDPE, through which we quantified for the first time competition between parallel reactions taking place during melt processing: (i) desirable grafting of GMA on HDPE; (ii) undesirable cross-linking of HDPE. The proposed robust mathematical modeling approach can precisely learn the behavior of grafting reaction of vinylic monomers on polyolefins and be placed into practice in finding exact operating condition needed for efficient grafting of reactive monomers on polyolefins.

Robust Decision Making Approach to Managing Water Resource Risks (Invited)

NASA Astrophysics Data System (ADS)

Lempert, R.

2010-12-01

The IPCC and US National Academies of Science have recommended iterative risk management as the best approach for water management and many other types of climate-related decisions. Such an approach does not rely on a single set of judgments at any one time but rather actively updates and refines strategies as new information emerges. In addition, the approach emphasizes that a portfolio of different types of responses, rather than any single action, often provides the best means to manage uncertainty. Implementing an iterative risk management approach can however prove difficult in actual decision support applications. This talk will suggest that robust decision making (RDM) provides a particularly useful set of quantitative methods for implementing iterative risk management. This RDM approach is currently being used in a wide variety of water management applications. RDM employs three key concepts that differentiate it from most types of probabilistic risk analysis: 1) characterizing uncertainty with multiple views of the future (which can include sets of probability distributions) rather than a single probabilistic best-estimate, 2) employing a robustness rather than an optimality criterion to assess alternative policies, and 3) organizing the analysis with a vulnerability and response option framework, rather than a predict-then-act framework. This talk will summarize the RDM approach, describe its use in several different types of water management applications, and compare the results to those obtained with other methods.

Integrating operation design into infrastructure planning to foster robustness of planned water systems

NASA Astrophysics Data System (ADS)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over the past years, many studies have looked at the planning and management of water infrastructure systems as two separate problems, where the dynamic component (i.e., operations) is considered only after the static problem (i.e., planning) has been resolved. Most recent works have started to investigate planning and management as two strictly interconnected faces of the same problem, where the former is solved jointly with the latter in an integrated framework. This brings advantages to multi-purpose water reservoir systems, where several optimal operating strategies exist and similar system designs might perform differently on the long term depending on the considered short-term operating tradeoff. An operationally robust design will be therefore one performing well across multiple feasible tradeoff operating policies. This work aims at studying the interaction between short-term operating strategies and their impacts on long-term structural decisions, when long-lived infrastructures with complex ecological impacts and multi-sectoral demands to satisfy (i.e., reservoirs) are considered. A parametric reinforcement learning approach is adopted for nesting optimization and control yielding to both optimal reservoir design and optimal operational policies for water reservoir systems. The method is demonstrated on a synthetic reservoir that must be designed and operated for ensuring reliable water supply to downstream users. At first, the optimal design capacity derived is compared with the 'no-fail storage' computed through Rippl, a capacity design function that returns the minimum storage needed to satisfy specified water demands without allowing supply shortfall. Then, the optimal reservoir volume is used to simulate the simplified case study under other operating objectives than water supply, in order to assess whether and how the system performance changes. The more robust the infrastructural design, the smaller the difference between the performances of different operating strategies.

Understanding London's Water Supply Tradeoffs When Scheduling Interventions Under Deep Uncertainty

NASA Astrophysics Data System (ADS)

Huskova, I.; Matrosov, E. S.; Harou, J. J.; Kasprzyk, J. R.; Reed, P. M.

2015-12-01

Water supply planning in many major world cities faces several challenges associated with but not limited to climate change, population growth and insufficient land availability for infrastructure development. Long-term plans to maintain supply-demand balance and ecosystem services require careful consideration of uncertainties associated with future conditions. The current approach for London's water supply planning utilizes least cost optimization of future intervention schedules with limited uncertainty consideration. Recently, the focus of the long-term plans has shifted from solely least cost performance to robustness and resilience of the system. Identifying robust scheduling of interventions requires optimizing over a statistically representative sample of stochastic inputs which may be computationally difficult to achieve. In this study we optimize schedules using an ensemble of plausible scenarios and assess how manipulating that ensemble influences the different Pareto-approximate intervention schedules. We investigate how a major stress event's location in time as well as the optimization problem formulation influence the Pareto-approximate schedules. A bootstrapping method that respects the non-stationary trend of climate change scenarios and ensures the even distribution of the major stress event in the scenario ensemble is proposed. Different bootstrapped hydrological scenario ensembles are assessed using many-objective scenario optimization of London's future water supply and demand intervention scheduling. However, such a "fixed" scheduling of interventions approach does not aim to embed flexibility or adapt effectively as the future unfolds. Alternatively, making decisions based on the observations of occurred conditions could help planners who prefer adaptive planning. We will show how rules to guide the implementation of interventions based on observations may result in more flexible strategies.

Robust Optimization Design for Turbine Blade-Tip Radial Running Clearance using Hierarchically Response Surface Method

NASA Astrophysics Data System (ADS)

Zhiying, Chen; Ping, Zhou

2017-11-01

Considering the robust optimization computational precision and efficiency for complex mechanical assembly relationship like turbine blade-tip radial running clearance, a hierarchically response surface robust optimization algorithm is proposed. The distribute collaborative response surface method is used to generate assembly system level approximation model of overall parameters and blade-tip clearance, and then a set samples of design parameters and objective response mean and/or standard deviation is generated by using system approximation model and design of experiment method. Finally, a new response surface approximation model is constructed by using those samples, and this approximation model is used for robust optimization process. The analyses results demonstrate the proposed method can dramatic reduce the computational cost and ensure the computational precision. The presented research offers an effective way for the robust optimization design of turbine blade-tip radial running clearance.

Options for Robust Airfoil Optimization under Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Li, Wu

2002-01-01

A robust optimization method is developed to overcome point-optimization at the sampled design points. This method combines the best features from several preliminary methods proposed by the authors and their colleagues. The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of spline control points as design variables yet the resulting airfoil shape does not need to be smoothed, and (3) it allows the user to make a tradeoff between the level of optimization and the amount of computing time consumed. For illustration purposes, the robust optimization method is used to solve a lift-constrained drag minimization problem for a two-dimensional (2-D) airfoil in Euler flow with 20 geometric design variables.

Optimized swimmer tracking system based on a novel multi-related-targets approach

NASA Astrophysics Data System (ADS)

Benarab, D.; Napoléon, T.; Alfalou, A.; Verney, A.; Hellard, P.

2017-02-01

Robust tracking is a crucial step in automatic swimmer evaluation from video sequences. We designed a robust swimmer tracking system using a new multi-related-targets approach. The main idea is to consider the swimmer as a bloc of connected subtargets that advance at the same speed. If one of the subtargets is partially or totally occluded, it can be localized by knowing the position of the others. In this paper, we first introduce the two-dimensional direct linear transformation technique that we used to calibrate the videos. Then, we present the classical tracking approach based on dynamic fusion. Next, we highlight the main contribution of our work, which is the multi-related-targets tracking approach. This approach, the classical head-only approach and the ground truth are then compared, through testing on a database of high-level swimmers in training, national and international competitions (French National Championships, Limoges 2015, and World Championships, Kazan 2015). Tracking percentage and the accuracy of the instantaneous speed are evaluated and the findings show that our new appraoach is significantly more accurate than the classical approach.

Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis

NASA Astrophysics Data System (ADS)

Jia, Ningning; Y Lam, Edmund

2010-04-01

Inverse lithography technology (ILT) synthesizes photomasks by solving an inverse imaging problem through optimization of an appropriate functional. Much effort on ILT is dedicated to deriving superior masks at a nominal process condition. However, the lower k1 factor causes the mask to be more sensitive to process variations. Robustness to major process variations, such as focus and dose variations, is desired. In this paper, we consider the focus variation as a stochastic variable, and treat the mask design as a machine learning problem. The stochastic gradient descent approach, which is a useful tool in machine learning, is adopted to train the mask design. Compared with previous work, simulation shows that the proposed algorithm is effective in producing robust masks.

Addressing Climate Change in Long-Term Water Planning Using Robust Decisionmaking

NASA Astrophysics Data System (ADS)

Groves, D. G.; Lempert, R.

2008-12-01

Addressing climate change in long-term natural resource planning is difficult because future management conditions are deeply uncertain and the range of possible adaptation options are so extensive. These conditions pose challenges to standard optimization decision-support techniques. This talk will describe a methodology called Robust Decisionmaking (RDM) that can complement more traditional analytic approaches by utilizing screening-level water management models to evaluate large numbers of strategies against a wide range of plausible future scenarios. The presentation will describe a recent application of the methodology to evaluate climate adaptation strategies for the Inland Empire Utilities Agency in Southern California. This project found that RDM can provide a useful way for addressing climate change uncertainty and identify robust adaptation strategies.

Parallax-Robust Surveillance Video Stitching

PubMed Central

He, Botao; Yu, Shaohua

2015-01-01

This paper presents a parallax-robust video stitching technique for timely synchronized surveillance video. An efficient two-stage video stitching procedure is proposed in this paper to build wide Field-of-View (FOV) videos for surveillance applications. In the stitching model calculation stage, we develop a layered warping algorithm to align the background scenes, which is location-dependent and turned out to be more robust to parallax than the traditional global projective warping methods. On the selective seam updating stage, we propose a change-detection based optimal seam selection approach to avert ghosting and artifacts caused by moving foregrounds. Experimental results demonstrate that our procedure can efficiently stitch multi-view videos into a wide FOV video output without ghosting and noticeable seams. PMID:26712756

Handling Uncertain Gross Margin and Water Demand in Agricultural Water Resources Management using Robust Optimization

NASA Astrophysics Data System (ADS)

Chaerani, D.; Lesmana, E.; Tressiana, N.

2018-03-01

In this paper, an application of Robust Optimization in agricultural water resource management problem under gross margin and water demand uncertainty is presented. Water resource management is a series of activities that includes planning, developing, distributing and managing the use of water resource optimally. Water resource management for agriculture can be one of the efforts to optimize the benefits of agricultural output. The objective function of agricultural water resource management problem is to maximizing total benefits by water allocation to agricultural areas covered by the irrigation network in planning horizon. Due to gross margin and water demand uncertainty, we assume that the uncertain data lies within ellipsoidal uncertainty set. We employ robust counterpart methodology to get the robust optimal solution.

Vehicle System Integration, Optimization, and Robustness

Science.gov Websites

Operations Technology Exchange Initiating Partnerships University Partners Government Partners Industry Contacts Researchers Thrust Area 5: Vehicle System Integration, Optimization, and Robustness Thrust Area only optimal design of the vehicle components, but also an optimization of the interactions between

Robust fuel- and time-optimal control of uncertain flexible space structures

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi; Sunkel, John; Cox, Ken

1993-01-01

The problem of computing open-loop, fuel- and time-optimal control inputs for flexible space structures in the face of modeling uncertainty is investigated. Robustified, fuel- and time-optimal pulse sequences are obtained by solving a constrained optimization problem subject to robustness constraints. It is shown that 'bang-off-bang' pulse sequences with a finite number of switchings provide a practical tradeoff among the maneuvering time, fuel consumption, and performance robustness of uncertain flexible space structures.

Automatic multi-organ segmentation using learning-based segmentation and level set optimization.

PubMed

Kohlberger, Timo; Sofka, Michal; Zhang, Jingdan; Birkbeck, Neil; Wetzl, Jens; Kaftan, Jens; Declerck, Jérôme; Zhou, S Kevin

2011-01-01

We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89 mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.

Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.

PubMed

Hering, Jan; Wolf, Ivo; Maier-Hein, Klaus H

2016-10-01

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm -2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

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

High Temperature Joining and Characterization of Joint Properties in Silicon Carbide-Based Composite Materials

NASA Technical Reports Server (NTRS)

Halbig, Michael C.; Singh, Mrityunjay

2015-01-01

Advanced silicon carbide-based ceramics and composites are being developed for a wide variety of high temperature extreme environment applications. Robust high temperature joining and integration technologies are enabling for the fabrication and manufacturing of large and complex shaped components. The development of a new joining approach called SET (Single-step Elevated Temperature) joining will be described along with the overview of previously developed joining approaches including high temperature brazing, ARCJoinT (Affordable, Robust Ceramic Joining Technology), diffusion bonding, and REABOND (Refractory Eutectic Assisted Bonding). Unlike other approaches, SET joining does not have any lower temperature phases and will therefore have a use temperature above 1315C. Optimization of the composition for full conversion to silicon carbide will be discussed. The goal is to find a composition with no remaining carbon or free silicon. Green tape interlayers were developed for joining. Microstructural analysis and preliminary mechanical tests of the joints will be presented.

Info-gap robust-satisficing model of foraging behavior: do foragers optimize or satisfice?

PubMed

Carmel, Yohay; Ben-Haim, Yakov

2005-11-01

In this note we compare two mathematical models of foraging that reflect two competing theories of animal behavior: optimizing and robust satisficing. The optimal-foraging model is based on the marginal value theorem (MVT). The robust-satisficing model developed here is an application of info-gap decision theory. The info-gap robust-satisficing model relates to the same circumstances described by the MVT. We show how these two alternatives translate into specific predictions that at some points are quite disparate. We test these alternative predictions against available data collected in numerous field studies with a large number of species from diverse taxonomic groups. We show that a large majority of studies appear to support the robust-satisficing model and reject the optimal-foraging model.

A practical globalization of one-shot optimization for optimal design of tokamak divertors

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

Blommaert, Maarten, E-mail: maarten.blommaert@kuleuven.be; Dekeyser, Wouter; Baelmans, Martine

In past studies, nested optimization methods were successfully applied to design of the magnetic divertor configuration in nuclear fusion reactors. In this paper, so-called one-shot optimization methods are pursued. Due to convergence issues, a globalization strategy for the one-shot solver is sought. Whereas Griewank introduced a globalization strategy using a doubly augmented Lagrangian function that includes primal and adjoint residuals, its practical usability is limited by the necessity of second order derivatives and expensive line search iterations. In this paper, a practical alternative is offered that avoids these drawbacks by using a regular augmented Lagrangian merit function that penalizes onlymore » state residuals. Additionally, robust rank-two Hessian estimation is achieved by adaptation of Powell's damped BFGS update rule. The application of the novel one-shot approach to magnetic divertor design is considered in detail. For this purpose, the approach is adapted to be complementary with practical in parts adjoint sensitivities. Using the globalization strategy, stable convergence of the one-shot approach is achieved.« less

Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

PubMed Central

Moghri, Mehdi; Omidi, Mostafa; Farahnakian, Masoud

2014-01-01

During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636

Wing Shaping and Gust Load Controls of Flexible Aircraft: An LPV Approach

NASA Technical Reports Server (NTRS)

Hammerton, Jared R.; Su, Weihua; Zhu, Guoming; Swei, Sean Shan-Min

2018-01-01

In the proposed paper, the optimum wing shape of a highly flexible aircraft under varying flight conditions will be controlled by a linear parameter-varying approach. The optimum shape determined under multiple objectives, including flight performance, ride quality, and control effort, will be determined as well. This work is an extension of work done previously by the authors, and updates the existing optimization and utilizes the results to generate a robust flight controller.

Robust intelligent flight control for hypersonic vehicles. Ph.D. Thesis - Massachusetts Inst. of Technology

NASA Technical Reports Server (NTRS)

Chamitoff, Gregory Errol

1992-01-01

Intelligent optimization methods are applied to the problem of real-time flight control for a class of airbreathing hypersonic vehicles (AHSV). The extreme flight conditions that will be encountered by single-stage-to-orbit vehicles, such as the National Aerospace Plane, present a tremendous challenge to the entire spectrum of aerospace technologies. Flight control for these vehicles is particularly difficult due to the combination of nonlinear dynamics, complex constraints, and parametric uncertainty. An approach that utilizes all available a priori and in-flight information to perform robust, real time, short-term trajectory planning is presented.

Towards Robust Designs Via Multiple-Objective Optimization Methods

NASA Technical Reports Server (NTRS)

Man Mohan, Rai

2006-01-01

Fabricating and operating complex systems involves dealing with uncertainty in the relevant variables. In the case of aircraft, flow conditions are subject to change during operation. Efficiency and engine noise may be different from the expected values because of manufacturing tolerances and normal wear and tear. Engine components may have a shorter life than expected because of manufacturing tolerances. In spite of the important effect of operating- and manufacturing-uncertainty on the performance and expected life of the component or system, traditional aerodynamic shape optimization has focused on obtaining the best design given a set of deterministic flow conditions. Clearly it is important to both maintain near-optimal performance levels at off-design operating conditions, and, ensure that performance does not degrade appreciably when the component shape differs from the optimal shape due to manufacturing tolerances and normal wear and tear. These requirements naturally lead to the idea of robust optimal design wherein the concept of robustness to various perturbations is built into the design optimization procedure. The basic ideas involved in robust optimal design will be included in this lecture. The imposition of the additional requirement of robustness results in a multiple-objective optimization problem requiring appropriate solution procedures. Typically the costs associated with multiple-objective optimization are substantial. Therefore efficient multiple-objective optimization procedures are crucial to the rapid deployment of the principles of robust design in industry. Hence the companion set of lecture notes (Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks ) deals with methodology for solving multiple-objective Optimization problems efficiently, reliably and with little user intervention. Applications of the methodologies presented in the companion lecture to robust design will be included here. The evolutionary method (DE) is first used to solve a relatively difficult problem in extended surface heat transfer wherein optimal fin geometries are obtained for different safe operating base temperatures. The objective of maximizing the safe operating base temperature range is in direct conflict with the objective of maximizing fin heat transfer. This problem is a good example of achieving robustness in the context of changing operating conditions. The evolutionary method is then used to design a turbine airfoil; the two objectives being reduced sensitivity of the pressure distribution to small changes in the airfoil shape and the maximization of the trailing edge wedge angle with the consequent increase in airfoil thickness and strength. This is a relevant example of achieving robustness to manufacturing tolerances and wear and tear in the presence of other objectives.

Two Reconfigurable Flight-Control Design Methods: Robust Servomechanism and Control Allocation

NASA Technical Reports Server (NTRS)

Burken, John J.; Lu, Ping; Wu, Zheng-Lu; Bahm, Cathy

2001-01-01

Two methods for control system reconfiguration have been investigated. The first method is a robust servomechanism control approach (optimal tracking problem) that is a generalization of the classical proportional-plus-integral control to multiple input-multiple output systems. The second method is a control-allocation approach based on a quadratic programming formulation. A globally convergent fixed-point iteration algorithm has been developed to make onboard implementation of this method feasible. These methods have been applied to reconfigurable entry flight control design for the X-33 vehicle. Examples presented demonstrate simultaneous tracking of angle-of-attack and roll angle commands during failures of the fight body flap actuator. Although simulations demonstrate success of the first method in most cases, the control-allocation method appears to provide uniformly better performance in all cases.

A Genetic Algorithm for the Generation of Packetization Masks for Robust Image Communication

PubMed Central

Zapata-Quiñones, Katherine; Duran-Faundez, Cristian; Gutiérrez, Gilberto; Lecuire, Vincent; Arredondo-Flores, Christopher; Jara-Lipán, Hugo

2017-01-01

Image interleaving has proven to be an effective solution to provide the robustness of image communication systems when resource limitations make reliable protocols unsuitable (e.g., in wireless camera sensor networks); however, the search for optimal interleaving patterns is scarcely tackled in the literature. In 2008, Rombaut et al. presented an interesting approach introducing a packetization mask generator based in Simulated Annealing (SA), including a cost function, which allows assessing the suitability of a packetization pattern, avoiding extensive simulations. In this work, we present a complementary study about the non-trivial problem of generating optimal packetization patterns. We propose a genetic algorithm, as an alternative to the cited work, adopting the mentioned cost function, then comparing it to the SA approach and a torus automorphism interleaver. In addition, we engage the validation of the cost function and provide results attempting to conclude about its implication in the quality of reconstructed images. Several scenarios based on visual sensor networks applications were tested in a computer application. Results in terms of the selected cost function and image quality metric PSNR show that our algorithm presents similar results to the other approaches. Finally, we discuss the obtained results and comment about open research challenges. PMID:28452934

Tabu Search enhances network robustness under targeted attacks

NASA Astrophysics Data System (ADS)

Sun, Shi-wen; Ma, Yi-lin; Li, Rui-qi; Wang, Li; Xia, Cheng-yi

2016-03-01

We focus on the optimization of network robustness with respect to intentional attacks on high-degree nodes. Given an existing network, this problem can be considered as a typical single-objective combinatorial optimization problem. Based on the heuristic Tabu Search optimization algorithm, a link-rewiring method is applied to reconstruct the network while keeping the degree of every node unchanged. Through numerical simulations, BA scale-free network and two real-world networks are investigated to verify the effectiveness of the proposed optimization method. Meanwhile, we analyze how the optimization affects other topological properties of the networks, including natural connectivity, clustering coefficient and degree-degree correlation. The current results can help to improve the robustness of existing complex real-world systems, as well as to provide some insights into the design of robust networks.

Uncertainty-Based Multi-Objective Optimization of Groundwater Remediation Design

NASA Astrophysics Data System (ADS)

Singh, A.; Minsker, B.

2003-12-01

Management of groundwater contamination is a cost-intensive undertaking filled with conflicting objectives and substantial uncertainty. A critical source of this uncertainty in groundwater remediation design problems comes from the hydraulic conductivity values for the aquifer, upon which the prediction of flow and transport of contaminants are dependent. For a remediation solution to be reliable in practice it is important that it is robust over the potential error in the model predictions. This work focuses on incorporating such uncertainty within a multi-objective optimization framework, to get reliable as well as Pareto optimal solutions. Previous research has shown that small amounts of sampling within a single-objective genetic algorithm can produce highly reliable solutions. However with multiple objectives the noise can interfere with the basic operations of a multi-objective solver, such as determining non-domination of individuals, diversity preservation, and elitism. This work proposes several approaches to improve the performance of noisy multi-objective solvers. These include a simple averaging approach, taking samples across the population (which we call extended averaging), and a stochastic optimization approach. All the approaches are tested on standard multi-objective benchmark problems and a hypothetical groundwater remediation case-study; the best-performing approach is then tested on a field-scale case at Umatilla Army Depot.

Non-Convex Sparse and Low-Rank Based Robust Subspace Segmentation for Data Mining.

PubMed

Cheng, Wenlong; Zhao, Mingbo; Xiong, Naixue; Chui, Kwok Tai

2017-07-15

Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l ₁-norm or nuclear norm constraints. However, the obtained results by convex optimization are usually suboptimal to solutions of original sparse or low-rank problems. In this paper, a novel robust subspace segmentation algorithm has been proposed by integrating l p -norm and Schatten p -norm constraints. Our so-obtained affinity graph can better capture local geometrical structure and the global information of the data. As a consequence, our algorithm is more generative, discriminative and robust. An efficient linearized alternating direction method is derived to realize our model. Extensive segmentation experiments are conducted on public datasets. The proposed algorithm is revealed to be more effective and robust compared to five existing algorithms.

Using multi-objective robust decision making to support seasonal water management in the Chao Phraya River basin, Thailand

NASA Astrophysics Data System (ADS)

Riegels, Niels; Jessen, Oluf; Madsen, Henrik

2016-04-01

A multi-objective robust decision making approach is demonstrated that supports seasonal water management in the Chao Phraya River basin in Thailand. The approach uses multi-objective optimization to identify a Pareto-optimal set of management alternatives. Ensemble simulation is used to evaluate how each member of the Pareto set performs under a range of uncertain future conditions, and a robustness criterion is used to select a preferred alternative. Data mining tools are then used to identify ranges of uncertain factor values that lead to unacceptable performance for the preferred alternative. The approach is compared to a multi-criteria scenario analysis approach to estimate whether the introduction of additional complexity has the potential to improve decision making. Dry season irrigation in Thailand is managed through non-binding recommendations about the maximum extent of rice cultivation along with incentives for less water-intensive crops. Management authorities lack authority to prevent river withdrawals for irrigation when rice cultivation exceeds recommendations. In practice, this means that water must be provided to irrigate the actual planted area because of downstream municipal water supply requirements and water quality constraints. This results in dry season reservoir withdrawals that exceed planned withdrawals, reducing carryover storage to hedge against insufficient wet season runoff. The dry season planning problem in Thailand can therefore be framed in terms of decisions, objectives, constraints, and uncertainties. Decisions include recommendations about the maximum extent of rice cultivation and incentives for growing less water-intensive crops. Objectives are to maximize benefits to farmers, minimize the risk of inadequate carryover storage, and minimize incentives. Constraints include downstream municipal demands and water quality requirements. Uncertainties include the actual extent of rice cultivation, dry season precipitation, and precipitation in the following wet season. The multi-objective robust decision making approach is implemented as follows. First, three baseline simulation models are developed, including a crop water demand model, a river basin simulation model, and model of the impact of incentives on cropping patterns. The crop water demand model estimates irrigation water demands; the river basin simulation model estimates reservoir drawdown required to meet demands given forecasts of precipitation, evaporation, and runoff; the model of incentive impacts estimates the cost of incentives as function of marginal changes in rice yields. Optimization is used to find a set of non-dominated alternatives as a function of rice area and incentive decisions. An ensemble of uncertain model inputs is generated to represent uncertain hydrological and crop area forecasts. An ensemble of indicator values is then generated for each of the decision objectives: farmer benefits, end-of-wet-season reservoir storage, and the cost of incentives. A single alternative is selected from the Pareto set using a robustness criterion. Threshold values are defined for each of the objectives to identify ensemble members for which objective values are unacceptable, and the PRIM data mining algorithm is then used to identify input values associated with unacceptable model outcomes.

Definition of a Robust Supervisory Control Scheme for Sodium-Cooled Fast Reactors

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

Ponciroli, R.; Passerini, S.; Vilim, R. B.

In this work, an innovative control approach for metal-fueled Sodium-cooled Fast Reactors is proposed. With respect to the classical approach adopted for base-load Nuclear Power Plants, an alternative control strategy for operating the reactor at different power levels by respecting the system physical constraints is presented. In order to achieve a higher operational flexibility along with ensuring that the implemented control loops do not influence the system inherent passive safety features, a dedicated supervisory control scheme for the dynamic definition of the corresponding set-points to be supplied to the PID controllers is designed. In particular, the traditional approach based onmore » the adoption of tabulated lookup tables for the set-point definition is found not to be robust enough when failures of the implemented SISO (Single Input Single Output) actuators occur. Therefore, a feedback algorithm based on the Reference Governor approach, which allows for the optimization of reference signals according to the system operating conditions, is proposed.« less

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

PubMed Central

Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

PubMed

Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Unified control/structure design and modeling research

NASA Technical Reports Server (NTRS)

Mingori, D. L.; Gibson, J. S.; Blelloch, P. A.; Adamian, A.

1986-01-01

To demonstrate the applicability of the control theory for distributed systems to large flexible space structures, research was focused on a model of a space antenna which consists of a rigid hub, flexible ribs, and a mesh reflecting surface. The space antenna model used is discussed along with the finite element approximation of the distributed model. The basic control problem is to design an optimal or near-optimal compensator to suppress the linear vibrations and rigid-body displacements of the structure. The application of an infinite dimensional Linear Quadratic Gaussian (LQG) control theory to flexible structure is discussed. Two basic approaches for robustness enhancement were investigated: loop transfer recovery and sensitivity optimization. A third approach synthesized from elements of these two basic approaches is currently under development. The control driven finite element approximation of flexible structures is discussed. Three sets of finite element basic vectors for computing functional control gains are compared. The possibility of constructing a finite element scheme to approximate the infinite dimensional Hamiltonian system directly, instead of indirectly is discussed.

Robust stability of bidirectional associative memory neural networks with time delays

NASA Astrophysics Data System (ADS)

Park, Ju H.

2006-01-01

Based on the Lyapunov Krasovskii functionals combined with linear matrix inequality approach, a novel stability criterion is proposed for asymptotic stability of bidirectional associative memory neural networks with time delays. A novel delay-dependent stability criterion is given in terms of linear matrix inequalities, which can be solved easily by various optimization algorithms.

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

PubMed

Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

Optimization of controllability and robustness of complex networks by edge directionality

NASA Astrophysics Data System (ADS)

Liang, Man; Jin, Suoqin; Wang, Dingjie; Zou, Xiufen

2016-09-01

Recently, controllability of complex networks has attracted enormous attention in various fields of science and engineering. How to optimize structural controllability has also become a significant issue. Previous studies have shown that an appropriate directional assignment can improve structural controllability; however, the evolution of the structural controllability of complex networks under attacks and cascading has always been ignored. To address this problem, this study proposes a new edge orientation method (NEOM) based on residual degree that changes the link direction while conserving topology and directionality. By comparing the results with those of previous methods in two random graph models and several realistic networks, our proposed approach is demonstrated to be an effective and competitive method for improving the structural controllability of complex networks. Moreover, numerical simulations show that our method is near-optimal in optimizing structural controllability. Strikingly, compared to the original network, our method maintains the structural controllability of the network under attacks and cascading, indicating that the NEOM can also enhance the robustness of controllability of networks. These results alter the view of the nature of controllability in complex networks, change the understanding of structural controllability and affect the design of network models to control such networks.

Comparison of global optimization approaches for robust calibration of hydrologic model parameters

NASA Astrophysics Data System (ADS)

Jung, I. W.

2015-12-01

Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

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

2011-12-01

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

Ordinal optimization and its application to complex deterministic problems

NASA Astrophysics Data System (ADS)

Yang, Mike Shang-Yu

1998-10-01

We present in this thesis a new perspective to approach a general class of optimization problems characterized by large deterministic complexities. Many problems of real-world concerns today lack analyzable structures and almost always involve high level of difficulties and complexities in the evaluation process. Advances in computer technology allow us to build computer models to simulate the evaluation process through numerical means, but the burden of high complexities remains to tax the simulation with an exorbitant computing cost for each evaluation. Such a resource requirement makes local fine-tuning of a known design difficult under most circumstances, let alone global optimization. Kolmogorov equivalence of complexity and randomness in computation theory is introduced to resolve this difficulty by converting the complex deterministic model to a stochastic pseudo-model composed of a simple deterministic component and a white-noise like stochastic term. The resulting randomness is then dealt with by a noise-robust approach called Ordinal Optimization. Ordinal Optimization utilizes Goal Softening and Ordinal Comparison to achieve an efficient and quantifiable selection of designs in the initial search process. The approach is substantiated by a case study in the turbine blade manufacturing process. The problem involves the optimization of the manufacturing process of the integrally bladed rotor in the turbine engines of U.S. Air Force fighter jets. The intertwining interactions among the material, thermomechanical, and geometrical changes makes the current FEM approach prohibitively uneconomical in the optimization process. The generalized OO approach to complex deterministic problems is applied here with great success. Empirical results indicate a saving of nearly 95% in the computing cost.

Topology Optimization of Lightweight Lattice Structural Composites Inspired by Cuttlefish Bone

NASA Astrophysics Data System (ADS)

Hu, Zhong; Gadipudi, Varun Kumar; Salem, David R.

2018-03-01

Lattice structural composites are of great interest to various industries where lightweight multifunctionality is important, especially aerospace. However, strong coupling among the composition, microstructure, porous topology, and fabrication of such materials impedes conventional trial-and-error experimental development. In this work, a discontinuous carbon fiber reinforced polymer matrix composite was adopted for structural design. A reliable and robust design approach for developing lightweight multifunctional lattice structural composites was proposed, inspired by biomimetics and based on topology optimization. Three-dimensional periodic lattice blocks were initially designed, inspired by the cuttlefish bone microstructure. The topologies of the three-dimensional periodic blocks were further optimized by computer modeling, and the mechanical properties of the topology optimized lightweight lattice structures were characterized by computer modeling. The lattice structures with optimal performance were identified.

Evolutionary algorithm based optimization of hydraulic machines utilizing a state-of-the-art block coupled CFD solver and parametric geometry and mesh generation tools

NASA Astrophysics Data System (ADS)

S, Kyriacou; E, Kontoleontos; S, Weissenberger; L, Mangani; E, Casartelli; I, Skouteropoulou; M, Gattringer; A, Gehrer; M, Buchmayr

2014-03-01

An efficient hydraulic optimization procedure, suitable for industrial use, requires an advanced optimization tool (EASY software), a fast solver (block coupled CFD) and a flexible geometry generation tool. EASY optimization software is a PCA-driven metamodel-assisted Evolutionary Algorithm (MAEA (PCA)) that can be used in both single- (SOO) and multiobjective optimization (MOO) problems. In MAEAs, low cost surrogate evaluation models are used to screen out non-promising individuals during the evolution and exclude them from the expensive, problem specific evaluation, here the solution of Navier-Stokes equations. For additional reduction of the optimization CPU cost, the PCA technique is used to identify dependences among the design variables and to exploit them in order to efficiently drive the application of the evolution operators. To further enhance the hydraulic optimization procedure, a very robust and fast Navier-Stokes solver has been developed. This incompressible CFD solver employs a pressure-based block-coupled approach, solving the governing equations simultaneously. This method, apart from being robust and fast, also provides a big gain in terms of computational cost. In order to optimize the geometry of hydraulic machines, an automatic geometry and mesh generation tool is necessary. The geometry generation tool used in this work is entirely based on b-spline curves and surfaces. In what follows, the components of the tool chain are outlined in some detail and the optimization results of hydraulic machine components are shown in order to demonstrate the performance of the presented optimization procedure.

Robust optimization of the billet for isothermal local loading transitional region of a Ti-alloy rib-web component based on dual-response surface method

NASA Astrophysics Data System (ADS)

Wei, Ke; Fan, Xiaoguang; Zhan, Mei; Meng, Miao

2018-03-01

Billet optimization can greatly improve the forming quality of the transitional region in the isothermal local loading forming (ILLF) of large-scale Ti-alloy ribweb components. However, the final quality of the transitional region may be deteriorated by uncontrollable factors, such as the manufacturing tolerance of the preforming billet, fluctuation of the stroke length, and friction factor. Thus, a dual-response surface method (RSM)-based robust optimization of the billet was proposed to address the uncontrollable factors in transitional region of the ILLF. Given that the die underfilling and folding defect are two key factors that influence the forming quality of the transitional region, minimizing the mean and standard deviation of the die underfilling rate and avoiding folding defect were defined as the objective function and constraint condition in robust optimization. Then, the cross array design was constructed, a dual-RSM model was established for the mean and standard deviation of the die underfilling rate by considering the size parameters of the billet and uncontrollable factors. Subsequently, an optimum solution was derived to achieve the robust optimization of the billet. A case study on robust optimization was conducted. Good results were attained for improving the die filling and avoiding folding defect, suggesting that the robust optimization of the billet in the transitional region of the ILLF was efficient and reliable.

Rigorous force field optimization principles based on statistical distance minimization

DOE PAGES

Vlcek, Lukas; Chialvo, Ariel A.

2015-10-12

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model’s static measurable properties to those of the target. Here we exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of themore » approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.« less

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

Robust plan optimization for electromagnetic transponder guided hypo-fractionated prostate treatment using volumetric modulated arc therapy

NASA Astrophysics Data System (ADS)

Zhang, Pengpeng; Hunt, Margie; Happersett, Laura; Yang, Jie; Zelefsky, Michael; Mageras, Gig

2013-11-01

To develop an optimization algorithm for volumetric modulated arc therapy which incorporates an electromagnetic tracking (EMT) guided gating strategy and is robust to residual intra-fractional motion uncertainties. In a computer simulation, intra-fractional motion traces from prior treatments with EMT were converted to a probability distribution function (PDF), truncated using a patient specific action volume that encloses allowed deviations from the planned position, and renormalized to yield a new PDF with EMT-gated interventions. In lieu of a conventional planning target volume (PTV), multiple instances of clinical target volume (CTV) and organs at risk (OARs) were replicated and displaced to extreme positions inside the action volume representing possible delivery scenarios. When optimizing the volumetric modulated arc therapy plan, doses to the CTV and OARs were calculated as a sum of doses to the replicas weighted by the PDF to account for motion. A treatment plan meeting the clinical constraints was produced and compared to the counterpart conventional margin (PTV) plan. EMT traces from a separate testing database served to simulate motion during gated delivery. Dosimetric end points extracted from dose accumulations for each motion trace were utilized to evaluate potential clinical benefit. Five prostate cases from a hypofractionated protocol (42.5 Gy in 5 fractions) were retrospectively investigated. The patient specific gating window resulted in tight anterior and inferior action levels (∼1 mm) to protect rectal wall and bladder wall, and resulted in an average of four beam interruptions per fraction in the simulation. The robust-optimized plans achieved the same average CTV D95 coverage of 40.5 Gy as the PTV-optimized plans, but with reduced patient-averaged rectum wall D1cc by 2.2 Gy (range 0.7 to 4.7 Gy) and bladder wall mean dose by 2.9 Gy (range 2.0 to 3.4 Gy). Integration of an intra-fractional motion management strategy into the robust optimization process is feasible and may yield improved OAR sparing compared to the standard margin approach.

Robust plan optimization for electromagnetic transponder guided hypo-fractionated prostate treatment using volumetric modulated arc therapy.

PubMed

Zhang, Pengpeng; Hunt, Margie; Happersett, Laura; Yang, Jie; Zelefsky, Michael; Mageras, Gig

2013-11-07

To develop an optimization algorithm for volumetric modulated arc therapy which incorporates an electromagnetic tracking (EMT) guided gating strategy and is robust to residual intra-fractional motion uncertainties. In a computer simulation, intra-fractional motion traces from prior treatments with EMT were converted to a probability distribution function (PDF), truncated using a patient specific action volume that encloses allowed deviations from the planned position, and renormalized to yield a new PDF with EMT-gated interventions. In lieu of a conventional planning target volume (PTV), multiple instances of clinical target volume (CTV) and organs at risk (OARs) were replicated and displaced to extreme positions inside the action volume representing possible delivery scenarios. When optimizing the volumetric modulated arc therapy plan, doses to the CTV and OARs were calculated as a sum of doses to the replicas weighted by the PDF to account for motion. A treatment plan meeting the clinical constraints was produced and compared to the counterpart conventional margin (PTV) plan. EMT traces from a separate testing database served to simulate motion during gated delivery. Dosimetric end points extracted from dose accumulations for each motion trace were utilized to evaluate potential clinical benefit. Five prostate cases from a hypofractionated protocol (42.5 Gy in 5 fractions) were retrospectively investigated. The patient specific gating window resulted in tight anterior and inferior action levels (~1 mm) to protect rectal wall and bladder wall, and resulted in an average of four beam interruptions per fraction in the simulation. The robust-optimized plans achieved the same average CTV D95 coverage of 40.5 Gy as the PTV-optimized plans, but with reduced patient-averaged rectum wall D1cc by 2.2 Gy (range 0.7 to 4.7 Gy) and bladder wall mean dose by 2.9 Gy (range 2.0 to 3.4 Gy). Integration of an intra-fractional motion management strategy into the robust optimization process is feasible and may yield improved OAR sparing compared to the standard margin approach.

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

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

A Robust Method to Integrate End-to-End Mission Architecture Optimization Tools

NASA Technical Reports Server (NTRS)

Lugo, Rafael; Litton, Daniel; Qu, Min; Shidner, Jeremy; Powell, Richard

2016-01-01

End-to-end mission simulations include multiple phases of flight. For example, an end-to-end Mars mission simulation may include launch from Earth, interplanetary transit to Mars and entry, descent and landing. Each phase of flight is optimized to meet specified constraints and often depend on and impact subsequent phases. The design and optimization tools and methodologies used to combine different aspects of end-to-end framework and their impact on mission planning are presented. This work focuses on a robust implementation of a Multidisciplinary Design Analysis and Optimization (MDAO) method that offers the flexibility to quickly adapt to changing mission design requirements. Different simulations tailored to the liftoff, ascent, and atmospheric entry phases of a trajectory are integrated and optimized in the MDAO program Isight, which provides the user a graphical interface to link simulation inputs and outputs. This approach provides many advantages to mission planners, as it is easily adapted to different mission scenarios and can improve the understanding of the integrated system performance within a particular mission configuration. A Mars direct entry mission using the Space Launch System (SLS) is presented as a generic end-to-end case study. For the given launch period, the SLS launch performance is traded for improved orbit geometry alignment, resulting in an optimized a net payload that is comparable to that in the SLS Mission Planner's Guide.

Topology optimization of 3D shell structures with porous infill

NASA Astrophysics Data System (ADS)

Clausen, Anders; Andreassen, Erik; Sigmund, Ole

2017-08-01

This paper presents a 3D topology optimization approach for designing shell structures with a porous or void interior. It is shown that the resulting structures are significantly more robust towards load perturbations than completely solid structures optimized under the same conditions. The study indicates that the potential benefit of using porous structures is higher for lower total volume fractions. Compared to earlier work dealing with 2D topology optimization, we found several new effects in 3D problems. Most notably, the opportunity for designing closed shells significantly improves the performance of porous structures due to the sandwich effect. Furthermore, the paper introduces improved filter boundary conditions to ensure a completely uniform coating thickness at the design domain boundary.

Enriched Imperialist Competitive Algorithm for system identification of magneto-rheological dampers

NASA Astrophysics Data System (ADS)

Talatahari, Siamak; Rahbari, Nima Mohajer

2015-10-01

In the current research, the imperialist competitive algorithm is dramatically enhanced and a new optimization method dubbed as Enriched Imperialist Competitive Algorithm (EICA) is effectively introduced to deal with high non-linear optimization problems. To conduct a close examination of its functionality and efficacy, the proposed metaheuristic optimization approach is actively employed to sort out the parameter identification of two different types of hysteretic Bouc-Wen models which are simulating the non-linear behavior of MR dampers. Two types of experimental data are used for the optimization problems to minutely examine the robustness of the proposed EICA. The obtained results self-evidently demonstrate the high adaptability of EICA to suitably get to the bottom of such non-linear and hysteretic problems.

A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

NASA Astrophysics Data System (ADS)

Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi

2017-10-01

This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

Novel strategies in immunotherapy for allergic diseases.

PubMed

Rajakulendran, Mohana; Tham, Elizabeth Huiwen; Soh, Jian Yi; Van Bever, H P

2018-04-01

Conventional immunotherapy (IT) for optimal control of respiratory and food allergies has been fraught with concerns of efficacy, safety, and tolerability. The development of adjuvants to conventional IT has potentially increased the effectiveness and safety of allergen IT, which may translate into improved clinical outcomes and sustained unresponsiveness even after cessation of therapy. Novel strategies incorporating the successful use of adjuvants such as allergoids, immunostimulatory DNA sequences, monoclonal antibodies, carriers, recombinant proteins, and probiotics have now been described in clinical and murine studies. Future approaches may include fungal compounds, parasitic molecules, vitamin D, and traditional Chinese herbs. More robust comparative clinical trials are needed to evaluate the safety, clinical efficacy, and cost effectiveness of various adjuvants in order to determine ideal candidates in disease-specific and allergen-specific models. Other suggested approaches to further optimize outcomes of IT include early introduction of IT during an optimal window period. Alternative routes of administration of IT to optimize delivery and yet minimize potential side effects require further evaluation for safety and efficacy before they can be recommended.

Robust source and mask optimization compensating for mask topography effects in computational lithography.

PubMed

Li, Jia; Lam, Edmund Y

2014-04-21

Mask topography effects need to be taken into consideration for a more accurate solution of source mask optimization (SMO) in advanced optical lithography. However, rigorous 3D mask models generally involve intensive computation and conventional SMO fails to manipulate the mask-induced undesired phase errors that degrade the usable depth of focus (uDOF) and process yield. In this work, an optimization approach incorporating pupil wavefront aberrations into SMO procedure is developed as an alternative to maximize the uDOF. We first design the pupil wavefront function by adding primary and secondary spherical aberrations through the coefficients of the Zernike polynomials, and then apply the conjugate gradient method to achieve an optimal source-mask pair under the condition of aberrated pupil. We also use a statistical model to determine the Zernike coefficients for the phase control and adjustment. Rigorous simulations of thick masks show that this approach provides compensation for mask topography effects by improving the pattern fidelity and increasing uDOF.

Optimizing Water Use and Hydropower Production in Operational Reservoir System Scheduling with RiverWare

NASA Astrophysics Data System (ADS)

Magee, T. M.; Zagona, E. A.

2017-12-01

Practical operational optimization of multipurpose reservoir systems is challenging for several reasons. Each purpose has its own constraints which may conflict with those of other purposes. While hydropower generation typically provides the bulk of the revenue, it is also among the lowest priority purposes. Each river system has important details that are specific to the location such as hydrology, reservoir storage capacity, physical limitations, bottlenecks, and the continuing evolution of operational policy. In addition, reservoir operations models include discrete, nonlinear, and nonconvex physical processes and if-then operating policies. Typically, the forecast horizon for scheduling needs to be extended far into the future to avoid near term (e.g., a few hours or a day) scheduling decisions that result in undesirable future states; this makes the computational effort much larger than may be expected. Put together, these challenges lead to large and customized mathematical optimization problems which must be solved efficiently to be of practical use. In addition, the solution process must be robust in an operational setting. We discuss a unique modeling approach in RiverWare that meets these challenges in an operational setting. The approach combines a Preemptive Linear Goal Programming optimization model to handle prioritized policies complimented by preprocessing and postprocessing with Rulebased Simulation to improve the solution with regard to nonlinearities, discrete issues, and if-then logic. An interactive policy language with a graphical user interface allows modelers to customize both the optimization and simulation based on the unique aspects of the policy for their system while the routine physical aspect of operations are modeled automatically. The modeler is aided by a set of compiled predefined functions and functions shared by other modelers. We illustrate the success of the approach with examples from daily use at the Tennessee Valley Authority, the Bonneville Power Administration, and public utility districts on the Mid-Columbia River. We discuss recent innovations to improve solution quality, robustness, and performance for these systems. We conclude with new modeling challenges to extend the modeling approach to other uses.

Inter-slice Leakage Artifact Reduction Technique for Simultaneous Multi-Slice Acquisitions

PubMed Central

Cauley, Stephen F.; Polimeni, Jonathan R.; Bhat, Himanshu; Wang, Dingxin; Wald, Lawrence L.; Setsompop, Kawin

2015-01-01

Purpose Controlled aliasing techniques for simultaneously acquired EPI slices have been shown to significantly increase the temporal efficiency for both diffusion-weighted imaging (DWI) and fMRI studies. The “slice-GRAPPA” (SG) method has been widely used to reconstruct such data. We investigate robust optimization techniques for SG to ensure image reconstruction accuracy through a reduction of leakage artifacts. Methods Split slice-GRAPPA (SP-SG) is proposed as an alternative kernel optimization method. The performance of SP-SG is compared to standard SG using data collected on a spherical phantom and in-vivo on two subjects at 3T. Slice accelerated and non-accelerated data were collected for a spin-echo diffusion weighted acquisition. Signal leakage metrics and time-series SNR were used to quantify the performance of the kernel fitting approaches. Results The SP-SG optimization strategy significantly reduces leakage artifacts for both phantom and in-vivo acquisitions. In addition, a significant boost in time-series SNR for in-vivo diffusion weighted acquisitions with in-plane 2× and slice 3× accelerations was observed with the SP-SG approach. Conclusion By minimizing the influence of leakage artifacts during the training of slice-GRAPPA kernels, we have significantly improved reconstruction accuracy. Our robust kernel fitting strategy should enable better reconstruction accuracy and higher slice-acceleration across many applications. PMID:23963964

Design of a broadband active silencer using μ-synthesis

NASA Astrophysics Data System (ADS)

Bai, Mingsian R.; Zeung, Pingshun

2004-01-01

A robust spatially feedforward controller is developed for broadband attenuation of noise in ducts. To meet the requirements of robust performance and robust stability in the presence of plant uncertainties, a μ-synthesis procedure via D- K iteration is exploited to obtain the optimal controller. This approach considers uncertainties as modelling errors of the nominal plant in high frequency and is implemented using a floating point digital signal processor (DSP). Experimental investigation was undertaken on a finite-length duct to justify the proposed controller. The μ- controller is compared to other control algorithms such as the H2 method, the H∞ method and the filtered-U least mean square (FULMS) algorithm. Experimental results indicate that the proposed system has attained 25.8 dB maximal attenuation in the band 250-650 Hz.

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties.

PubMed

Wu, Sheng; Jin, Qibing; Zhang, Ridong; Zhang, Junfeng; Gao, Furong

2017-07-01

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal auxiliary-covariate-based two-phase sampling design for semiparametric efficient estimation of a mean or mean difference, with application to clinical trials.

PubMed

Gilbert, Peter B; Yu, Xuesong; Rotnitzky, Andrea

2014-03-15

To address the objective in a clinical trial to estimate the mean or mean difference of an expensive endpoint Y, one approach employs a two-phase sampling design, wherein inexpensive auxiliary variables W predictive of Y are measured in everyone, Y is measured in a random sample, and the semiparametric efficient estimator is applied. This approach is made efficient by specifying the phase two selection probabilities as optimal functions of the auxiliary variables and measurement costs. While this approach is familiar to survey samplers, it apparently has seldom been used in clinical trials, and several novel results practicable for clinical trials are developed. We perform simulations to identify settings where the optimal approach significantly improves efficiency compared to approaches in current practice. We provide proofs and R code. The optimality results are developed to design an HIV vaccine trial, with objective to compare the mean 'importance-weighted' breadth (Y) of the T-cell response between randomized vaccine groups. The trial collects an auxiliary response (W) highly predictive of Y and measures Y in the optimal subset. We show that the optimal design-estimation approach can confer anywhere between absent and large efficiency gain (up to 24 % in the examples) compared to the approach with the same efficient estimator but simple random sampling, where greater variability in the cost-standardized conditional variance of Y given W yields greater efficiency gains. Accurate estimation of E[Y | W] is important for realizing the efficiency gain, which is aided by an ample phase two sample and by using a robust fitting method. Copyright © 2013 John Wiley & Sons, Ltd.

Optimal Auxiliary-Covariate Based Two-Phase Sampling Design for Semiparametric Efficient Estimation of a Mean or Mean Difference, with Application to Clinical Trials

PubMed Central

Gilbert, Peter B.; Yu, Xuesong; Rotnitzky, Andrea

2014-01-01

To address the objective in a clinical trial to estimate the mean or mean difference of an expensive endpoint Y, one approach employs a two-phase sampling design, wherein inexpensive auxiliary variables W predictive of Y are measured in everyone, Y is measured in a random sample, and the semi-parametric efficient estimator is applied. This approach is made efficient by specifying the phase-two selection probabilities as optimal functions of the auxiliary variables and measurement costs. While this approach is familiar to survey samplers, it apparently has seldom been used in clinical trials, and several novel results practicable for clinical trials are developed. Simulations are performed to identify settings where the optimal approach significantly improves efficiency compared to approaches in current practice. Proofs and R code are provided. The optimality results are developed to design an HIV vaccine trial, with objective to compare the mean “importance-weighted” breadth (Y) of the T cell response between randomized vaccine groups. The trial collects an auxiliary response (W) highly predictive of Y, and measures Y in the optimal subset. We show that the optimal design-estimation approach can confer anywhere between absent and large efficiency gain (up to 24% in the examples) compared to the approach with the same efficient estimator but simple random sampling, where greater variability in the cost-standardized conditional variance of Y given W yields greater efficiency gains. Accurate estimation of E[Y∣W] is important for realizing the efficiency gain, which is aided by an ample phase-two sample and by using a robust fitting method. PMID:24123289

Real time PI-backstepping induction machine drive with efficiency optimization.

PubMed

Farhani, Fethi; Ben Regaya, Chiheb; Zaafouri, Abderrahmen; Chaari, Abdelkader

2017-09-01

This paper describes a robust and efficient speed control of a three phase induction machine (IM) subjected to load disturbances. First, a Multiple-Input Multiple-Output (MIMO) PI-Backstepping controller is proposed for a robust and highly accurate tracking of the mechanical speed and rotor flux. Asymptotic stability of the control scheme is proven by Lyapunov Stability Theory. Second, an active online optimization algorithm is used to optimize the efficiency of the drive system. The efficiency improvement approach consists of adjusting the rotor flux with respect to the load torque in order to minimize total losses in the IM. A dSPACE DS1104 R&D board is used to implement the proposed solution. The experimental results released on 3kW squirrel cage IM, show that the reference speed as well as the rotor flux are rapidly achieved with a fast transient response and without overshoot. A good load disturbances rejection response and IM parameters variation are fairly handled. The improvement of drive system efficiency reaches up to 180% at light load. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive optics stochastic optical reconstruction microscopy (AO-STORM) by particle swarm optimization

PubMed Central

Tehrani, Kayvan F.; Zhang, Yiwen; Shen, Ping; Kner, Peter

2017-01-01

Stochastic optical reconstruction microscopy (STORM) can achieve resolutions of better than 20nm imaging single fluorescently labeled cells. However, when optical aberrations induced by larger biological samples degrade the point spread function (PSF), the localization accuracy and number of localizations are both reduced, destroying the resolution of STORM. Adaptive optics (AO) can be used to correct the wavefront, restoring the high resolution of STORM. A challenge for AO-STORM microscopy is the development of robust optimization algorithms which can efficiently correct the wavefront from stochastic raw STORM images. Here we present the implementation of a particle swarm optimization (PSO) approach with a Fourier metric for real-time correction of wavefront aberrations during STORM acquisition. We apply our approach to imaging boutons 100 μm deep inside the central nervous system (CNS) of Drosophila melanogaster larvae achieving a resolution of 146 nm. PMID:29188105

Adaptive optics stochastic optical reconstruction microscopy (AO-STORM) by particle swarm optimization.

PubMed

Tehrani, Kayvan F; Zhang, Yiwen; Shen, Ping; Kner, Peter

2017-11-01

Stochastic optical reconstruction microscopy (STORM) can achieve resolutions of better than 20nm imaging single fluorescently labeled cells. However, when optical aberrations induced by larger biological samples degrade the point spread function (PSF), the localization accuracy and number of localizations are both reduced, destroying the resolution of STORM. Adaptive optics (AO) can be used to correct the wavefront, restoring the high resolution of STORM. A challenge for AO-STORM microscopy is the development of robust optimization algorithms which can efficiently correct the wavefront from stochastic raw STORM images. Here we present the implementation of a particle swarm optimization (PSO) approach with a Fourier metric for real-time correction of wavefront aberrations during STORM acquisition. We apply our approach to imaging boutons 100 μm deep inside the central nervous system (CNS) of Drosophila melanogaster larvae achieving a resolution of 146 nm.

Microgravity vibration isolation: An optimal control law for the one-dimensional case

NASA Technical Reports Server (NTRS)

Hampton, Richard D.; Grodsinsky, Carlos M.; Allaire, Paul E.; Lewis, David W.; Knospe, Carl R.

1991-01-01

Certain experiments contemplated for space platforms must be isolated from the accelerations of the platform. An optimal active control is developed for microgravity vibration isolation, using constant state feedback gains (identical to those obtained from the Linear Quadratic Regulator (LQR) approach) along with constant feedforward gains. The quadratic cost function for this control algorithm effectively weights external accelerations of the platform disturbances by a factor proportional to (1/omega) exp 4. Low frequency accelerations are attenuated by greater than two orders of magnitude. The control relies on the absolute position and velocity feedback of the experiment and the absolute position and velocity feedforward of the platform, and generally derives the stability robustness characteristics guaranteed by the LQR approach to optimality. The method as derived is extendable to the case in which only the relative positions and velocities and the absolute accelerations of the experiment and space platform are available.

Nonreciprocal signal routing in an active quantum network

NASA Astrophysics Data System (ADS)

Metelmann, A.; Türeci, H. E.

2018-04-01

As superconductor quantum technologies are moving towards large-scale integrated circuits, a robust and flexible approach to routing photons at the quantum level becomes a critical problem. Active circuits, which contain parametrically driven elements selectively embedded in the circuit, offer a viable solution. Here, we present a general strategy for routing nonreciprocally quantum signals between two sites of a given lattice of oscillators, implementable with existing superconducting circuit components. Our approach makes use of a dual lattice of overdamped oscillators linking the nodes of the main lattice. Solutions for spatially selective driving of the lattice elements can be found, which optimally balance coherent and dissipative hopping of microwave photons to nonreciprocally route signals between two given nodes. In certain lattices these optimal solutions are obtained at the exceptional point of the dynamical matrix of the network. We also demonstrate that signal and noise transmission characteristics can be separately optimized.

Microgravity vibration isolation: An optimal control law for the one-dimensional case

NASA Technical Reports Server (NTRS)

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

1991-01-01

Certain experiments contemplated for space platforms must be isolated from the accelerations of the platforms. An optimal active control is developed for microgravity vibration isolation, using constant state feedback gains (identical to those obtained from the Linear Quadratic Regulator (LQR) approach) along with constant feedforward (preview) gains. The quadratic cost function for this control algorithm effectively weights external accelerations of the platform disturbances by a factor proportional to (1/omega)(exp 4). Low frequency accelerations (less than 50 Hz) are attenuated by greater than two orders of magnitude. The control relies on the absolute position and velocity feedback of the experiment and the absolute position and velocity feedforward of the platform, and generally derives the stability robustness characteristics guaranteed by the LQR approach to optimality. The method as derived is extendable to the case in which only the relative positions and velocities and the absolute accelerations of the experiment and space platform are available.

How robust is a robust policy? A comparative analysis of alternative robustness metrics for supporting robust decision analysis.

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2015-04-01

In response to climate and socio-economic change, in various policy domains there is increasingly a call for robust plans or policies. That is, plans or policies that performs well in a very large range of plausible futures. In the literature, a wide range of alternative robustness metrics can be found. The relative merit of these alternative conceptualizations of robustness has, however, received less attention. Evidently, different robustness metrics can result in different plans or policies being adopted. This paper investigates the consequences of several robustness metrics on decision making, illustrated here by the design of a flood risk management plan. A fictitious case, inspired by a river reach in the Netherlands is used. The performance of this system in terms of casualties, damages, and costs for flood and damage mitigation actions is explored using a time horizon of 100 years, and accounting for uncertainties pertaining to climate change and land use change. A set of candidate policy options is specified up front. This set of options includes dike raising, dike strengthening, creating more space for the river, and flood proof building and evacuation options. The overarching aim is to design an effective flood risk mitigation strategy that is designed from the outset to be adapted over time in response to how the future actually unfolds. To this end, the plan will be based on the dynamic adaptive policy pathway approach (Haasnoot, Kwakkel et al. 2013) being used in the Dutch Delta Program. The policy problem is formulated as a multi-objective robust optimization problem (Kwakkel, Haasnoot et al. 2014). We solve the multi-objective robust optimization problem using several alternative robustness metrics, including both satisficing robustness metrics and regret based robustness metrics. Satisficing robustness metrics focus on the performance of candidate plans across a large ensemble of plausible futures. Regret based robustness metrics compare the performance of a candidate plan with the performance of other candidate plans across a large ensemble of plausible futures. Initial results suggest that the simplest satisficing metric, inspired by the signal to noise ratio, results in very risk averse solutions. Other satisficing metrics, which handle the average performance and the dispersion around the average separately, provide substantial additional insights into the trade off between the average performance, and the dispersion around this average. In contrast, the regret-based metrics enhance insight into the relative merits of candidate plans, while being less clear on the average performance or the dispersion around this performance. These results suggest that it is beneficial to use multiple robustness metrics when doing a robust decision analysis study. Haasnoot, M., J. H. Kwakkel, W. E. Walker and J. Ter Maat (2013). "Dynamic Adaptive Policy Pathways: A New Method for Crafting Robust Decisions for a Deeply Uncertain World." Global Environmental Change 23(2): 485-498. Kwakkel, J. H., M. Haasnoot and W. E. Walker (2014). "Developing Dynamic Adaptive Policy Pathways: A computer-assisted approach for developing adaptive strategies for a deeply uncertain world." Climatic Change.

Design and analysis of tilt integral derivative controller with filter for load frequency control of multi-area interconnected power systems.

PubMed

Kumar Sahu, Rabindra; Panda, Sidhartha; Biswal, Ashutosh; Chandra Sekhar, G T

2016-03-01

In this paper, a novel Tilt Integral Derivative controller with Filter (TIDF) is proposed for Load Frequency Control (LFC) of multi-area power systems. Initially, a two-area power system is considered and the parameters of the TIDF controller are optimized using Differential Evolution (DE) algorithm employing an Integral of Time multiplied Absolute Error (ITAE) criterion. The superiority of the proposed approach is demonstrated by comparing the results with some recently published heuristic approaches such as Firefly Algorithm (FA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) optimized PID controllers for the same interconnected power system. Investigations reveal that proposed TIDF controllers provide better dynamic response compared to PID controller in terms of minimum undershoots and settling times of frequency as well as tie-line power deviations following a disturbance. The proposed approach is also extended to two widely used three area test systems considering nonlinearities such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB). To improve the performance of the system, a Thyristor Controlled Series Compensator (TCSC) is also considered and the performance of TIDF controller in presence of TCSC is investigated. It is observed that system performance improves with the inclusion of TCSC. Finally, sensitivity analysis is carried out to test the robustness of the proposed controller by varying the system parameters, operating condition and load pattern. It is observed that the proposed controllers are robust and perform satisfactorily with variations in operating condition, system parameters and load pattern. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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

NASA Technical Reports Server (NTRS)

Schmidt, Phillip; Garg, Sanjay; Holowecky, Brian

1992-01-01

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

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

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Multi-strategy coevolving aging particle optimization.

PubMed

Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante

2014-02-01

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

Flexible Demand Management under Time-Varying Prices

NASA Astrophysics Data System (ADS)

Liang, Yong

In this dissertation, the problem of flexible demand management under time-varying prices is studied. This generic problem has many applications, which usually have multiple periods in which decisions on satisfying demand need to be made, and prices in these periods are time-varying. Examples of such applications include multi-period procurement problem, operating room scheduling, and user-end demand scheduling in the Smart Grid, where the last application is used as the main motivating story throughout the dissertation. The current grid is experiencing an upgrade with lots of new designs. What is of particular interest is the idea of passing time-varying prices that reflect electricity market conditions to end users as incentives for load shifting. One key component, consequently, is the demand management system at the user-end. The objective of the system is to find the optimal trade-off between cost saving and discomfort increment resulted from load shifting. In this dissertation, we approach this problem from the following aspects: (1) construct a generic model, solve for Pareto optimal solutions, and analyze the robust solution that optimizes the worst-case payoffs, (2) extend to a distribution-free model for multiple types of demand (appliances), for which an approximate dynamic programming (ADP) approach is developed, and (3) design other efficient algorithms for practical purposes of the flexible demand management system. We first construct a novel multi-objective flexible demand management model, in which there are a finite number of periods with time-varying prices, and demand arrives in each period. In each period, the decision maker chooses to either satisfy or defer outstanding demand to minimize costs and discomfort over a certain number of periods. We consider both the deterministic model, models with stochastic demand or prices, and when only partial information about the stochastic demand or prices is known. We first analyze the stochastic optimization problem when the objective is to minimize the expected total cost and discomfort, then since the decision maker is likely to be risk-averse, and she wants to protect herself from price spikes, we study the robust optimization problem to address the risk-aversion of the decision maker. We conduct numerical studies to evaluate the price of robustness. Next, we present a detailed model that manages multiple types of flexible demand in the absence of knowledge regarding the distributions of related stochastic processes. Specifically, we consider the case in which time-varying prices with general structures are offered to users, and an energy management system for each household makes optimal energy usage, storage, and trading decisions according to the preferences of users. Because of the uncertainties associated with electricity prices, local generation, and the arrival processes of demand, we formulate a stochastic dynamic programming model, and outline a novel and tractable ADP approach to overcome the curses of dimensionality. Then, we perform numerical studies, whose results demonstrate the effectiveness of the ADP approach. At last, we propose another approximation approach based on Q-learning. In addition, we also develop another decentralization-based heuristic. Both the Q-learning approach and the heuristic make necessary assumptions on the knowledge of information, and each of them has unique advantages. We conduct numerical studies on a testing problem. The simulation results show that both the Q-learning and the decentralization based heuristic approaches work well. Lastly, we conclude the paper with some discussions on future extension directions.

Efficacy of robust optimization plan with partial-arc VMAT for photon volumetric-modulated arc therapy: A phantom study.

PubMed

Miura, Hideharu; Ozawa, Shuichi; Nagata, Yasushi

2017-09-01

This study investigated position dependence in planning target volume (PTV)-based and robust optimization plans using full-arc and partial-arc volumetric modulated arc therapy (VMAT). The gantry angles at the periphery, intermediate, and center CTV positions were 181°-180° (full-arc VMAT) and 181°-360° (partial-arc VMAT). A PTV-based optimization plan was defined by 5 mm margin expansion of the CTV to a PTV volume, on which the dose constraints were applied. The robust optimization plan consisted of a directly optimized dose to the CTV under a maximum-uncertainties setup of 5 mm. The prescription dose was normalized to the CTV D 99% (the minimum relative dose that covers 99% of the volume of the CTV) as an original plan. The isocenter was rigidly shifted at 1 mm intervals in the anterior-posterior (A-P), superior-inferior (S-I), and right-left (R-L) directions from the original position to the maximum-uncertainties setup of 5 mm in the original plan, yielding recalculated dose distributions. It was found that for the intermediate and center positions, the uncertainties in the D 99% doses to the CTV for all directions did not significantly differ when comparing the PTV-based and robust optimization plans (P > 0.05). For the periphery position, uncertainties in the D 99% doses to the CTV in the R-L direction for the robust optimization plan were found to be lower than those in the PTV-based optimization plan (P < 0.05). Our study demonstrated that a robust optimization plan's efficacy using partial-arc VMAT depends on the periphery CTV position. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Designing Robust and Resilient Tactical MANETs

DTIC Science & Technology

2014-09-25

Bounds on the Throughput Efficiency of Greedy Maximal Scheduling in Wireless Networks , IEEE/ACM Transactions on Networking , (06 2011): 0. doi: N... Wireless Sensor Networks and Effects of Long Range Dependant Data, Special IWSM Issue of Sequential Analysis, (11 2012): 0. doi: A. D. Dominguez...Bushnell, R. Poovendran. A Convex Optimization Approach for Clone Detection in Wireless Sensor Networks , Pervasive and Mobile Computing, (01 2012

An Alternative Optimization Model and Robust Experimental Design for the Assignment Scheduling Capability for Unmanned Aerial Vehicles (ASC-U) Simulation

DTIC Science & Technology

2007-06-01

introduces ASC-U’s approach for solving the dynamic UAV allocation problem. 26 Christopher J...18 Figure 6. Assignments Dynamics Example (after) .........................................................20 Figure 7. ASC-U Dynamic Cueing...decisions in order to respond to the dynamic environment they face. Thus, to succeed, the Army’s transformation cannot rely

Universal Influenza B Virus Genomic Amplification Facilitates Sequencing, Diagnostics, and Reverse Genetics

PubMed Central

Zhou, Bin; Lin, Xudong; Wang, Wei; Halpin, Rebecca A.; Bera, Jayati; Stockwell, Timothy B.; Barr, Ian G.

2014-01-01

Although human influenza B virus (IBV) is a significant human pathogen, its great genetic diversity has limited our ability to universally amplify the entire genome for subsequent sequencing or vaccine production. The generation of sequence data via next-generation approaches and the rapid cloning of viral genes are critical for basic research, diagnostics, antiviral drugs, and vaccines to combat IBV. To overcome the difficulty of amplifying the diverse and ever-changing IBV genome, we developed and optimized techniques that amplify the complete segmented negative-sense RNA genome from any IBV strain in a single tube/well (IBV genomic amplification [IBV-GA]). Amplicons for >1,000 diverse IBV genomes from different sample types (e.g., clinical specimens) were generated and sequenced using this robust technology. These approaches are sensitive, robust, and sequence independent (i.e., universally amplify past, present, and future IBVs), which facilitates next-generation sequencing and advanced genomic diagnostics. Importantly, special terminal sequences engineered into the optimized IBV-GA2 products also enable ligation-free cloning to rapidly generate reverse-genetics plasmids, which can be used for the rescue of recombinant viruses and/or the creation of vaccine seed stock. PMID:24501036

Enhancing Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Abbaszadeh, P.; Yan, H.

2016-12-01

Particle Filters (PFs) have received increasing attention by the researchers from different disciplines in hydro-geosciences as an effective method to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation by means of data assimilation in hydrology and geoscience has evolved since 2005 from SIR-PF to PF-MCMC and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC. In this framework, the posterior distribution undergoes an evolutionary process to update an ensemble of prior states that more closely resemble realistic posterior probability distribution. The premise of this approach is that the particles move to optimal position using the GA optimization coupled with MCMC increasing the number of effective particles, hence the particle degeneracy is avoided while the particle diversity is improved. The proposed algorithm is applied on a conceptual and highly nonlinear hydrologic model and the effectiveness, robustness and reliability of the method in jointly estimating the states and parameters and also reducing the uncertainty is demonstrated for few river basins across the United States.

Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry

NASA Astrophysics Data System (ADS)

Kilgour, David P. A.; Hughes, Sam; Kilgour, Samantha L.; Mackay, C. Logan; Palmblad, Magnus; Tran, Bao Quoc; Goo, Young Ah; Ernst, Robert K.; Clarke, David J.; Goodlett, David R.

2017-02-01

We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks.

Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry.

PubMed

Kilgour, David P A; Hughes, Sam; Kilgour, Samantha L; Mackay, C Logan; Palmblad, Magnus; Tran, Bao Quoc; Goo, Young Ah; Ernst, Robert K; Clarke, David J; Goodlett, David R

2017-02-01

We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks. Graphical Abstract ᅟ.

Quantitative segmentation of fluorescence microscopy images of heterogeneous tissue: Approach for tuning algorithm parameters

NASA Astrophysics Data System (ADS)

Mueller, Jenna L.; Harmany, Zachary T.; Mito, Jeffrey K.; Kennedy, Stephanie A.; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G.; Willett, Rebecca M.; Brown, J. Quincy; Ramanujam, Nimmi

2013-02-01

The combination of fluorescent contrast agents with microscopy is a powerful technique to obtain real time images of tissue histology without the need for fixing, sectioning, and staining. The potential of this technology lies in the identification of robust methods for image segmentation and quantitation, particularly in heterogeneous tissues. Our solution is to apply sparse decomposition (SD) to monochrome images of fluorescently-stained microanatomy to segment and quantify distinct tissue types. The clinical utility of our approach is demonstrated by imaging excised margins in a cohort of mice after surgical resection of a sarcoma. Representative images of excised margins were used to optimize the formulation of SD and tune parameters associated with the algorithm. Our results demonstrate that SD is a robust solution that can advance vital fluorescence microscopy as a clinically significant technology.

Formulation and demonstration of a robust mean variance optimization approach for concurrent airline network and aircraft design

NASA Astrophysics Data System (ADS)

Davendralingam, Navindran

Conceptual design of aircraft and the airline network (routes) on which aircraft fly on are inextricably linked to passenger driven demand. Many factors influence passenger demand for various Origin-Destination (O-D) city pairs including demographics, geographic location, seasonality, socio-economic factors and naturally, the operations of directly competing airlines. The expansion of airline operations involves the identificaion of appropriate aircraft to meet projected future demand. The decisions made in incorporating and subsequently allocating these new aircraft to serve air travel demand affects the inherent risk and profit potential as predicted through the airline revenue management systems. Competition between airlines then translates to latent passenger observations of the routes served between OD pairs and ticket pricing---this in effect reflexively drives future states of demand. This thesis addresses the integrated nature of aircraft design, airline operations and passenger demand, in order to maximize future expected profits as new aircraft are brought into service. The goal of this research is to develop an approach that utilizes aircraft design, airline network design and passenger demand as a unified framework to provide better integrated design solutions in order to maximize expexted profits of an airline. This is investigated through two approaches. The first is a static model that poses the concurrent engineering paradigm above as an investment portfolio problem. Modern financial portfolio optimization techniques are used to leverage risk of serving future projected demand using a 'yet to be introduced' aircraft against potentially generated future profits. Robust optimization methodologies are incorporated to mitigate model sensitivity and address estimation risks associated with such optimization techniques. The second extends the portfolio approach to include dynamic effects of an airline's operations. A dynamic programming approach is employed to simulate the reflexive nature of airline supply-demand interactions by modeling the aggregate changes in demand that would result from tactical allocations of aircraft to maximize profit. The best yet-to-be-introduced aircraft maximizes profit by minimizing the long term fleetwide direct operating costs.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Multiobjective robust design of the double wishbone suspension system based on particle swarm optimization.

PubMed

Cheng, Xianfu; Lin, Yuqun

2014-01-01

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

Optimization of rotor shaft shrink fit method for motor using "Robust design"

NASA Astrophysics Data System (ADS)

Toma, Eiji

2018-01-01

This research is collaborative investigation with the general-purpose motor manufacturer. To review construction method in production process, we applied the parameter design method of quality engineering and tried to approach the optimization of construction method. Conventionally, press-fitting method has been adopted in process of fitting rotor core and shaft which is main component of motor, but quality defects such as core shaft deflection occurred at the time of press fitting. In this research, as a result of optimization design of "shrink fitting method by high-frequency induction heating" devised as a new construction method, its construction method was feasible, and it was possible to extract the optimum processing condition.

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels

PubMed Central

Hyun, Seung Won; Wong, Weng Kee

2016-01-01

We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem. We propose a method for finding multiple-objective optimal designs that estimate the three features with user-specified higher efficiencies for the more important objectives. We use the flexible 4-parameter logistic model to illustrate the methodology but our approach is applicable to find multiple-objective optimal designs for other types of objectives and models. We also investigate robustness properties of multiple-objective optimal designs to mis-specification in the nominal parameter values and to a variation in the optimality criterion. We also provide computer code for generating tailor made multiple-objective optimal designs. PMID:26565557

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels.

PubMed

Hyun, Seung Won; Wong, Weng Kee

2015-11-01

We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem. We propose a method for finding multiple-objective optimal designs that estimate the three features with user-specified higher efficiencies for the more important objectives. We use the flexible 4-parameter logistic model to illustrate the methodology but our approach is applicable to find multiple-objective optimal designs for other types of objectives and models. We also investigate robustness properties of multiple-objective optimal designs to mis-specification in the nominal parameter values and to a variation in the optimality criterion. We also provide computer code for generating tailor made multiple-objective optimal designs.

Optimization of cascading failure on complex network based on NNIA

NASA Astrophysics Data System (ADS)

Zhu, Qian; Zhu, Zhiliang; Qi, Yi; Yu, Hai; Xu, Yanjie

2018-07-01

Recently, the robustness of networks under cascading failure has attracted extensive attention. Different from previous studies, we concentrate on how to improve the robustness of the networks from the perspective of intelligent optimization. We establish two multi-objective optimization models that comprehensively consider the operational cost of the edges in the networks and the robustness of the networks. The NNIA (Non-dominated Neighbor Immune Algorithm) is applied to solve the optimization models. We finished simulations of the Barabási-Albert (BA) network and Erdös-Rényi (ER) network. In the solutions, we find the edges that can facilitate the propagation of cascading failure and the edges that can suppress the propagation of cascading failure. From the conclusions, we take optimal protection measures to weaken the damage caused by cascading failures. We also consider actual situations of operational cost feasibility of the edges. People can make a more practical choice based on the operational cost. Our work will be helpful in the design of highly robust networks or improvement of the robustness of networks in the future.

A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

PubMed Central

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

3D gravity inversion and uncertainty assessment of basement relief via Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Pallero, J. L. G.; Fernández-Martínez, J. L.; Bonvalot, S.; Fudym, O.

2017-04-01

Nonlinear gravity inversion in sedimentary basins is a classical problem in applied geophysics. Although a 2D approximation is widely used, 3D models have been also proposed to better take into account the basin geometry. A common nonlinear approach to this 3D problem consists in modeling the basin as a set of right rectangular prisms with prescribed density contrast, whose depths are the unknowns. Then, the problem is iteratively solved via local optimization techniques from an initial model computed using some simplifications or being estimated using prior geophysical models. Nevertheless, this kind of approach is highly dependent on the prior information that is used, and lacks from a correct solution appraisal (nonlinear uncertainty analysis). In this paper, we use the family of global Particle Swarm Optimization (PSO) optimizers for the 3D gravity inversion and model appraisal of the solution that is adopted for basement relief estimation in sedimentary basins. Synthetic and real cases are illustrated, showing that robust results are obtained. Therefore, PSO seems to be a very good alternative for 3D gravity inversion and uncertainty assessment of basement relief when used in a sampling while optimizing approach. That way important geological questions can be answered probabilistically in order to perform risk assessment in the decisions that are made.

A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.

PubMed

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Optimizing the response to surveillance alerts in automated surveillance systems.

PubMed

Izadi, Masoumeh; Buckeridge, David L

2011-02-28

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Effect of interaction strength on robustness of controlling edge dynamics in complex networks

NASA Astrophysics Data System (ADS)

Pang, Shao-Peng; Hao, Fei

2018-05-01

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses.

PubMed

Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun

2016-07-08

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses

PubMed Central

Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun

2016-01-01

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses. PMID:27387525

A Hierarchical Mechanism of RIG-I Ubiquitination Provides Sensitivity, Robustness and Synergy in Antiviral Immune Responses

NASA Astrophysics Data System (ADS)

Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun

2016-07-01

RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.

Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate.

PubMed

Song, Qi; Wu, Xiaodong; Liu, Yunlong; Smith, Mark; Buatti, John; Sonka, Milan

2009-01-01

We present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-D graph-theoretic approach. Hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. The soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. The globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. To the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of multiple partially interacting objects, which admits a globally optimal solution in a low-order polynomial time. Our new approach was applied to the simultaneous surface detection of bladder and prostate. The result was quite encouraging in spite of the low saliency of the bladder and prostate in CT images.

Robust allocation of a defensive budget considering an attacker's private information.

PubMed

Nikoofal, Mohammad E; Zhuang, Jun

2012-05-01

Attackers' private information is one of the main issues in defensive resource allocation games in homeland security. The outcome of a defense resource allocation decision critically depends on the accuracy of estimations about the attacker's attributes. However, terrorists' goals may be unknown to the defender, necessitating robust decisions by the defender. This article develops a robust-optimization game-theoretical model for identifying optimal defense resource allocation strategies for a rational defender facing a strategic attacker while the attacker's valuation of targets, being the most critical attribute of the attacker, is unknown but belongs to bounded distribution-free intervals. To our best knowledge, no previous research has applied robust optimization in homeland security resource allocation when uncertainty is defined in bounded distribution-free intervals. The key features of our model include (1) modeling uncertainty in attackers' attributes, where uncertainty is characterized by bounded intervals; (2) finding the robust-optimization equilibrium for the defender using concepts dealing with budget of uncertainty and price of robustness; and (3) applying the proposed model to real data. © 2011 Society for Risk Analysis.

Active model-based balancing strategy for self-reconfigurable batteries

NASA Astrophysics Data System (ADS)

Bouchhima, Nejmeddine; Schnierle, Marc; Schulte, Sascha; Birke, Kai Peter

2016-08-01

This paper describes a novel balancing strategy for self-reconfigurable batteries where the discharge and charge rates of each cell can be controlled. While much effort has been focused on improving the hardware architecture of self-reconfigurable batteries, energy equalization algorithms have not been systematically optimized in terms of maximizing the efficiency of the balancing system. Our approach includes aspects of such optimization theory. We develop a balancing strategy for optimal control of the discharge rate of battery cells. We first formulate the cell balancing as a nonlinear optimal control problem, which is modeled afterward as a network program. Using dynamic programming techniques and MATLAB's vectorization feature, we solve the optimal control problem by generating the optimal battery operation policy for a given drive cycle. The simulation results show that the proposed strategy efficiently balances the cells over the life of the battery, an obvious advantage that is absent in the other conventional approaches. Our algorithm is shown to be robust when tested against different influencing parameters varying over wide spectrum on different drive cycles. Furthermore, due to the little computation time and the proved low sensitivity to the inaccurate power predictions, our strategy can be integrated in a real-time system.

Stochastic Robust Mathematical Programming Model for Power System Optimization

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

Liu, Cong; Changhyeok, Lee; Haoyong, Chen

2016-01-01

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

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

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

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

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

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

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

Strict Constraint Feasibility in Analysis and Design of Uncertain Systems

NASA Technical Reports Server (NTRS)

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

2006-01-01

This paper proposes a methodology for the analysis and design optimization of models subject to parametric uncertainty, where hard inequality constraints are present. Hard constraints are those that must be satisfied for all parameter realizations prescribed by the uncertainty model. Emphasis is given to uncertainty models prescribed by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles. These models make it possible to consider sets of parameters having comparable as well as dissimilar levels of uncertainty. Two alternative formulations for hyper-rectangular sets are proposed, one based on a transformation of variables and another based on an infinity norm approach. The suite of tools developed enable us to determine if the satisfaction of hard constraints is feasible by identifying critical combinations of uncertain parameters. Since this practice is performed without sampling or partitioning the parameter space, the resulting assessments of robustness are analytically verifiable. Strategies that enable the comparison of the robustness of competing design alternatives, the approximation of the robust design space, and the systematic search for designs with improved robustness characteristics are also proposed. Since the problem formulation is generic and the solution methods only require standard optimization algorithms for their implementation, the tools developed are applicable to a broad range of problems in several disciplines.

Reservoir adaptive operating rules based on both of historical streamflow and future projections

NASA Astrophysics Data System (ADS)

Zhang, Wei; Liu, Pan; Wang, Hao; Chen, Jie; Lei, Xiaohui; Feng, Maoyuan

2017-10-01

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately.

Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

NASA Astrophysics Data System (ADS)

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

2018-04-01

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images.

Robust and fast nonlinear optimization of diffusion MRI microstructure models.

PubMed

Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A

2017-07-15

Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Development of an RNA-based kit for easy generation of TCR-engineered lymphocytes to control T-cell assay performance.

PubMed

Bidmon, Nicole; Kind, Sonja; Welters, Marij J P; Joseph-Pietras, Deborah; Laske, Karoline; Maurer, Dominik; Hadrup, Sine Reker; Schreibelt, Gerty; Rae, Richard; Sahin, Ugur; Gouttefangeas, Cécile; Britten, Cedrik M; van der Burg, Sjoerd H

2018-07-01

Cell-based assays to monitor antigen-specific T-cell responses are characterized by their high complexity and should be conducted under controlled conditions to lower multiple possible sources of assay variation. However, the lack of standard reagents makes it difficult to directly compare results generated in one lab over time and across institutions. Therefore TCR-engineered reference samples (TERS) that contain a defined number of antigen-specific T cells and continuously deliver stable results are urgently needed. We successfully established a simple and robust TERS technology that constitutes a useful tool to overcome this issue for commonly used T-cell immuno-assays. To enable users to generate large-scale TERS, on-site using the most commonly used electroporation (EP) devices, an RNA-based kit approach, providing stable TCR mRNA and an optimized manufacturing protocol were established. In preparation for the release of this immuno-control kit, we established optimal EP conditions on six devices and initiated an extended RNA stability study. Furthermore, we coordinated on-site production of TERS with 4 participants. Finally, a proficiency panel was organized to test the unsupervised production of TERS at different laboratories using the kit approach. The results obtained show the feasibility and robustness of the kit approach for versatile in-house production of cellular control samples. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Stabilization Approaches for Linear and Nonlinear Reduced Order Models

NASA Astrophysics Data System (ADS)

Rezaian, Elnaz; Wei, Mingjun

2017-11-01

It has been a major concern to establish reduced order models (ROMs) as reliable representatives of the dynamics inherent in high fidelity simulations, while fast computation is achieved. In practice it comes to stability and accuracy of ROMs. Given the inviscid nature of Euler equations it becomes more challenging to achieve stability, especially where moving discontinuities exist. Originally unstable linear and nonlinear ROMs are stabilized here by two approaches. First, a hybrid method is developed by integrating two different stabilization algorithms. At the same time, symmetry inner product is introduced in the generation of ROMs for its known robust behavior for compressible flows. Results have shown a notable improvement in computational efficiency and robustness compared to similar approaches. Second, a new stabilization algorithm is developed specifically for nonlinear ROMs. This method adopts Particle Swarm Optimization to enforce a bounded ROM response for minimum discrepancy between the high fidelity simulation and the ROM outputs. Promising results are obtained in its application on the nonlinear ROM of an inviscid fluid flow with discontinuities. Supported by ARL.

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

PubMed Central

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-01-01

Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems. PMID:17081289

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

PubMed

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-11-02

We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.

Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.

PubMed

Dietzel, Matthias; Baltzer, Pascal A T; Dietzel, Andreas; Zoubi, Ramy; Gröschel, Tobias; Burmeister, Hartmut P; Bogdan, Martin; Kaiser, Werner A

2012-07-01

Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database. For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of "Training Epochs" (TE), "Hidden Layers" (HL), "Learning Rate" (LR) and "Neurons" (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC). Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885-0.892; range: 0.880-0.898). The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A complete methodology towards accuracy and lot-to-lot robustness in on-product overlay metrology using flexible wavelength selection

NASA Astrophysics Data System (ADS)

Bhattacharyya, Kaustuve; den Boef, Arie; Noot, Marc; Adam, Omer; Grzela, Grzegorz; Fuchs, Andreas; Jak, Martin; Liao, Sax; Chang, Ken; Couraudon, Vincent; Su, Eason; Tzeng, Wilson; Wang, Cathy; Fouquet, Christophe; Huang, Guo-Tsai; Chen, Kai-Hsiung; Wang, Y. C.; Cheng, Kevin; Ke, Chih-Ming; Terng, L. G.

2017-03-01

The optical coupling between gratings in diffraction-based overlay triggers a swing-curve1,6 like response of the target's signal contrast and overlay sensitivity through measurement wavelengths and polarizations. This means there are distinct measurement recipes (wavelength and polarization combinations) for a given target where signal contrast and overlay sensitivity are located at the optimal parts of the swing-curve that can provide accurate and robust measurements. Some of these optimal recipes can be the ideal choices of settings for production. The user has to stay away from the non-optimal recipe choices (that are located on the undesirable parts of the swing-curve) to avoid possibilities to make overlay measurement error that can be sometimes (depending on the amount of asymmetry and stack) in the order of several "nm". To accurately identify these optimum operating areas of the swing-curve during an experimental setup, one needs to have full-flexibility in wavelength and polarization choices. In this technical publication, a diffraction-based overlay (DBO) measurement tool with many choices of wavelengths and polarizations is utilized on advanced production stacks to study swing-curves. Results show that depending on the stack and the presence of asymmetry, the swing behavior can significantly vary and a solid procedure is needed to identify a recipe during setup that is robust against variations in stack and grating asymmetry. An approach is discussed on how to use this knowledge of swing-curve to identify recipe that is not only accurate at setup, but also robust over the wafer, and wafer-to-wafer. KPIs are reported in run-time to ensure the quality / accuracy of the reading (basically acting as an error bar to overlay measurement).

Sparse distributed memory: understanding the speed and robustness of expert memory

PubMed Central

Brogliato, Marcelo S.; Chada, Daniel M.; Linhares, Alexandre

2014-01-01

How can experts, sometimes in exacting detail, almost immediately and very precisely recall memory items from a vast repertoire? The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of ways. A crucial characteristic concerns the limits of human recollection, the “tip-of-tongue” memory event—which is found at a non-linearity in the model. We expand the theoretical framework, deriving an optimization formula to solve this non-linearity. Numerical results demonstrate how the higher frequency of rehearsal, through work or study, immediately increases the robustness and speed associated with expert memory. PMID:24808842

Low-Thrust Trajectory Optimization with Simplified SQP Algorithm

NASA Technical Reports Server (NTRS)

Parrish, Nathan L.; Scheeres, Daniel J.

2017-01-01

The problem of low-thrust trajectory optimization in highly perturbed dynamics is a stressing case for many optimization tools. Highly nonlinear dynamics and continuous thrust are each, separately, non-trivial problems in the field of optimal control, and when combined, the problem is even more difficult. This paper de-scribes a fast, robust method to design a trajectory in the CRTBP (circular restricted three body problem), beginning with no or very little knowledge of the system. The approach is inspired by the SQP (sequential quadratic programming) algorithm, in which a general nonlinear programming problem is solved via a sequence of quadratic problems. A few key simplifications make the algorithm presented fast and robust to initial guess: a quadratic cost function, neglecting the line search step when the solution is known to be far away, judicious use of end-point constraints, and mesh refinement on multiple shooting with fixed-step integration.In comparison to the traditional approach of plugging the problem into a “black-box” NLP solver, the methods shown converge even when given no knowledge of the solution at all. It was found that the only piece of information that the user needs to provide is a rough guess for the time of flight, as the transfer time guess will dictate which set of local solutions the algorithm could converge on. This robustness to initial guess is a compelling feature, as three-body orbit transfers are challenging to design with intuition alone. Of course, if a high-quality initial guess is available, the methods shown are still valid.We have shown that endpoints can be efficiently constrained to lie on 3-body repeating orbits, and that time of flight can be optimized as well. When optimizing the endpoints, we must make a trade between converging quickly on sub-optimal endpoints or converging more slowly on end-points that are arbitrarily close to optimal. It is easy for the mission design engineer to adjust this trade based on the problem at hand.The biggest limitation to the algorithm at this point is that multi-revolution transfers (greater than 2 revolutions) do not work nearly as well. This restriction comes in because the relationship between node 1 and node N becomes increasingly nonlinear as the angular distance grows. Trans-fers with more than about 1.5 complete revolutions generally require the line search to improve convergence. Future work includes: Comparison of this algorithm with other established tools; improvements to how multiple-revolution transfers are handled; parallelization of the Jacobian computation; in-creased efficiency for the line search; and optimization of many more trajectories between a variety of 3-body orbits.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

PubMed

Gumaei, Abdu; Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-05-15

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang's method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Estimating 3D positions and velocities of projectiles from monocular views.

PubMed

Ribnick, Evan; Atev, Stefan; Papanikolopoulos, Nikolaos P

2009-05-01

In this paper, we consider the problem of localizing a projectile in 3D based on its apparent motion in a stationary monocular view. A thorough theoretical analysis is developed, from which we establish the minimum conditions for the existence of a unique solution. The theoretical results obtained have important implications for applications involving projectile motion. A robust, nonlinear optimization-based formulation is proposed, and the use of a local optimization method is justified by detailed examination of the local convexity structure of the cost function. The potential of this approach is validated by experimental results.

Synchronic interval Gaussian mixed-integer programming for air quality management.

PubMed

Cheng, Guanhui; Huang, Guohe Gordon; Dong, Cong

2015-12-15

To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can help decision makers mitigate potential risks, e.g. insufficiency of pollutant treatment capabilities, exceedance of air quality standards, deficiency of pollution control fund, or imbalance of economic or environmental stress, in the process of guiding AQM. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-point objective-oriented sequential sampling strategy for constrained robust design

NASA Astrophysics Data System (ADS)

Zhu, Ping; Zhang, Siliang; Chen, Wei

2015-03-01

Metamodelling techniques are widely used to approximate system responses of expensive simulation models. In association with the use of metamodels, objective-oriented sequential sampling methods have been demonstrated to be effective in balancing the need for searching an optimal solution versus reducing the metamodelling uncertainty. However, existing infilling criteria are developed for deterministic problems and restricted to one sampling point in one iteration. To exploit the use of multiple samples and identify the true robust solution in fewer iterations, a multi-point objective-oriented sequential sampling strategy is proposed for constrained robust design problems. In this article, earlier development of objective-oriented sequential sampling strategy for unconstrained robust design is first extended to constrained problems. Next, a double-loop multi-point sequential sampling strategy is developed. The proposed methods are validated using two mathematical examples followed by a highly nonlinear automotive crashworthiness design example. The results show that the proposed method can mitigate the effect of both metamodelling uncertainty and design uncertainty, and identify the robust design solution more efficiently than the single-point sequential sampling approach.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Robust, Optimal Subsonic Airfoil Shapes

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2014-01-01

A method has been developed to create an airfoil robust enough to operate satisfactorily in different environments. This method determines a robust, optimal, subsonic airfoil shape, beginning with an arbitrary initial airfoil shape, and imposes the necessary constraints on the design. Also, this method is flexible and extendible to a larger class of requirements and changes in constraints imposed.

Optimal estimation for the satellite attitude using star tracker measurements

NASA Technical Reports Server (NTRS)

Lo, J. T.-H.

1986-01-01

An optimal estimation scheme is presented, which determines the satellite attitude using the gyro readings and the star tracker measurements of a commonly used satellite attitude measuring unit. The scheme is mainly based on the exponential Fourier densities that have the desirable closure property under conditioning. By updating a finite and fixed number of parameters, the conditional probability density, which is an exponential Fourier density, is recursively determined. Simulation results indicate that the scheme is more accurate and robust than extended Kalman filtering. It is believed that this approach is applicable to many other attitude measuring units. As no linearization and approximation are necessary in the approach, it is ideal for systems involving high levels of randomness and/or low levels of observability and systems for which accuracy is of overriding importance.

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.

Optimal reinforcement of training datasets in semi-supervised landmark-based segmentation

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2015-03-01

During the last couple of decades, the development of computerized image segmentation shifted from unsupervised to supervised methods, which made segmentation results more accurate and robust. However, the main disadvantage of supervised segmentation is a need for manual image annotation that is time-consuming and subjected to human error. To reduce the need for manual annotation, we propose a novel learning approach for training dataset reinforcement in the area of landmark-based segmentation, where newly detected landmarks are optimally combined with reference landmarks from the training dataset and therefore enriches the training process. The approach is formulated as a nonlinear optimization problem, where the solution is a vector of weighting factors that measures how reliable are the detected landmarks. The detected landmarks that are found to be more reliable are included into the training procedure with higher weighting factors, whereas the detected landmarks that are found to be less reliable are included with lower weighting factors. The approach is integrated into the landmark-based game-theoretic segmentation framework and validated against the problem of lung field segmentation from chest radiographs.

A bat algorithm with mutation for UCAV path planning.

PubMed

Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi

2012-01-01

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.

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

PubMed Central

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

2016-01-01

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

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

PubMed Central

Barish, Syndi; Ochs, Michael F.; Sontag, Eduardo D.; Gevertz, Jana L.

2017-01-01

Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists. PMID:28716945

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

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

Robust Airfoil Optimization in High Resolution Design Space

NASA Technical Reports Server (NTRS)

Li, Wu; Padula, Sharon L.

2003-01-01

The robust airfoil shape optimization is a direct method for drag reduction over a given range of operating conditions and has three advantages: (1) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (2) it uses a large number of B-spline control points as design variables yet the resulting airfoil shape is fairly smooth, and (3) it allows the user to make a trade-off between the level of optimization and the amount of computing time consumed. The robust optimization method is demonstrated by solving a lift-constrained drag minimization problem for a two-dimensional airfoil in viscous flow with a large number of geometric design variables. Our experience with robust optimization indicates that our strategy produces reasonable airfoil shapes that are similar to the original airfoils, but these new shapes provide drag reduction over the specified range of Mach numbers. We have tested this strategy on a number of advanced airfoil models produced by knowledgeable aerodynamic design team members and found that our strategy produces airfoils better or equal to any designs produced by traditional design methods.

GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

PubMed

Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

2012-09-24

Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

Reduction of product-related species during the fermentation and purification of a recombinant IL-1 receptor antagonist at the laboratory and pilot scale.

PubMed

Schirmer, Emily B; Golden, Kathryn; Xu, Jin; Milling, Jesse; Murillo, Alec; Lowden, Patricia; Mulagapati, Srihariraju; Hou, Jinzhao; Kovalchin, Joseph T; Masci, Allyson; Collins, Kathryn; Zarbis-Papastoitsis, Gregory

2013-08-01

Through a parallel approach of tracking product quality through fermentation and purification development, a robust process was designed to reduce the levels of product-related species. Three biochemically similar product-related species were identified as byproducts of host-cell enzymatic activity. To modulate intracellular proteolytic activity, key fermentation parameters (temperature, pH, trace metals, EDTA levels, and carbon source) were evaluated through bioreactor optimization, while balancing negative effects on growth, productivity, and oxygen demand. The purification process was based on three non-affinity steps and resolved product-related species by exploiting small charge differences. Using statistical design of experiments for elution conditions, a high-resolution cation exchange capture column was optimized for resolution and recovery. Further reduction of product-related species was achieved by evaluating a matrix of conditions for a ceramic hydroxyapatite column. The optimized fermentation process was transferred from the 2-L laboratory scale to the 100-L pilot scale and the purification process was scaled accordingly to process the fermentation harvest. The laboratory- and pilot-scale processes resulted in similar process recoveries of 60 and 65%, respectively, and in a product that was of equal quality and purity to that of small-scale development preparations. The parallel approach for up- and downstream development was paramount in achieving a robust and scalable clinical process. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

NASA Astrophysics Data System (ADS)

Paasche, H.; Tronicke, J.

2012-04-01

In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto optimality of the found solutions can be made. Identification of the leading particle traditionally requires a costly combination of ranking and niching techniques. In our approach, we use a decision rule under uncertainty to identify the currently leading particle of the swarm. In doing so, we consider the different objectives of our optimization problem as competing agents with partially conflicting interests. Analysis of the maximin fitness function allows for robust and cheap identification of the currently leading particle. The final optimization result comprises a set of possible models spread along the Pareto front. For convex Pareto fronts, solution density is expected to be maximal in the region ideally compromising all objectives, i.e. the region of highest curvature.

Robustness and cognition in stabilization problem of dynamical systems based on asymptotic methods

NASA Astrophysics Data System (ADS)

Dubovik, S. A.; Kabanov, A. A.

2017-01-01

The problem of synthesis of stabilizing systems based on principles of cognitive (logical-dynamic) control for mobile objects used under uncertain conditions is considered. This direction in control theory is based on the principles of guaranteeing robust synthesis focused on worst-case scenarios of the controlled process. The guaranteeing approach is able to provide functioning of the system with the required quality and reliability only at sufficiently low disturbances and in the absence of large deviations from some regular features of the controlled process. The main tool for the analysis of large deviations and prediction of critical states here is the action functional. After the forecast is built, the choice of anti-crisis control is the supervisory control problem that optimizes the control system in a normal mode and prevents escape of the controlled process in critical states. An essential aspect of the approach presented here is the presence of a two-level (logical-dynamic) control: the input data are used not only for generating of synthesized feedback (local robust synthesis) in advance (off-line), but also to make decisions about the current (on-line) quality of stabilization in the global sense. An example of using the presented approach for the problem of development of the ship tilting prediction system is considered.

Robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming.

PubMed

Baran, Richard; Northen, Trent R

2013-10-15

Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

Fast and robust online adaptive planning in stereotactic MR-guided adaptive radiation therapy (SMART) for pancreatic cancer.

PubMed

Bohoudi, O; Bruynzeel, A M E; Senan, S; Cuijpers, J P; Slotman, B J; Lagerwaard, F J; Palacios, M A

2017-12-01

To implement a robust and fast stereotactic MR-guided adaptive radiation therapy (SMART) online strategy in locally advanced pancreatic cancer (LAPC). SMART strategy for plan adaptation was implemented with the MRIdian system (ViewRay Inc.). At each fraction, OAR (re-)contouring is done within a distance of 3cm from the PTV surface. Online plan re-optimization is based on robust prediction of OAR dose and optimization objectives, obtained by building an artificial neural network (ANN). Proposed limited re-contouring strategy for plan adaptation (SMART 3CM ) is evaluated by comparing 50 previously delivered fractions against a standard (re-)planning method using full-scale OAR (re-)contouring (FULLOAR). Plan quality was assessed using PTV coverage (V 95% , D mean , D 1cc ) and institutional OAR constraints (e.g. V 33Gy ). SMART 3CM required a significant lower number of optimizations than FULLOAR (4 vs 18 on average) to generate a plan meeting all objectives and institutional OAR constraints. PTV coverage with both strategies was identical (mean V 95% =89%). Adaptive plans with SMART 3CM exhibited significant lower intermediate and high doses to all OARs than FULLOAR, which also failed in 36% of the cases to adhere to the V 33Gy dose constraint. SMART 3CM approach for LAPC allows good OAR sparing and adequate target coverage while requiring only limited online (re-)contouring from clinicians. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimization-Based Robust Nonlinear Control

DTIC Science & Technology

2006-08-01

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

Experimental design for the optimization and robustness testing of a liquid chromatography tandem mass spectrometry method for the trace analysis of the potentially genotoxic 1,3-diisopropylurea.

PubMed

Székely, György; Henriques, Bruno; Gil, Marco; Alvarez, Carlos

2014-09-01

This paper discusses a design of experiments (DoE) assisted optimization and robustness testing of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method development for the trace analysis of the potentially genotoxic 1,3-diisopropylurea (IPU) impurity in mometasone furoate glucocorticosteroid. Compared to the conventional trial-and-error method development, DoE is a cost-effective and systematic approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response are considered. The LC and MS factors were studied simultaneously: flow (F), gradient (G), injection volume (Vinj), cone voltage (E(con)), and collision energy (E(col)). The optimization was carried out with respect to four responses: separation of peaks (Sep), peak area (A(p)), length of the analysis (T), and the signal-to-noise ratio (S/N). An optimization central composite face (CCF) DoE was conducted leading to the early discovery of carry-over effect which was further investigated in order to establish the maximum injectable sample load. A second DoE was conducted in order to obtain the optimal LC-MS/MS method. As part of the validation of the obtained method, its robustness was determined by conducting a fractional factorial of resolution III DoE, wherein column temperature and quadrupole resolution were considered as additional factors. The method utilizes a common Phenomenex Gemini NX C-18 HPLC analytical column with electrospray ionization and a triple quadrupole mass detector in multiple reaction monitoring (MRM) mode, resulting in short analyses with a 10-min runtime. The high sensitivity and low limit of quantification (LOQ) was achieved by (1) MRM mode (instead of single ion monitoring) and (2) avoiding the drawbacks of derivatization (incomplete reaction and time-consuming sample preparation). Quantitatively, the DoE method development strategy resulted in the robust trace analysis of IPU at 1.25 ng/mL absolute concentration corresponding to 0.25 ppm LOQ in 5 g/l mometasone furoate glucocorticosteroid. Validation was carried out in a linear range of 0.25-10 ppm and presented a relative standard deviation (RSD) of 1.08% for system precision. Regarding IPU recovery in mometasone furoate, spiked samples produced recoveries between 96 and 109 % in the range of 0.25 to 2 ppm. Copyright © 2013 John Wiley & Sons, Ltd.

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

2005-01-01

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Novel strategies in immunotherapy for allergic diseases

PubMed Central

Tham, Elizabeth Huiwen; Soh, Jian Yi; Van Bever, HP

2018-01-01

Conventional immunotherapy (IT) for optimal control of respiratory and food allergies has been fraught with concerns of efficacy, safety, and tolerability. The development of adjuvants to conventional IT has potentially increased the effectiveness and safety of allergen IT, which may translate into improved clinical outcomes and sustained unresponsiveness even after cessation of therapy. Novel strategies incorporating the successful use of adjuvants such as allergoids, immunostimulatory DNA sequences, monoclonal antibodies, carriers, recombinant proteins, and probiotics have now been described in clinical and murine studies. Future approaches may include fungal compounds, parasitic molecules, vitamin D, and traditional Chinese herbs. More robust comparative clinical trials are needed to evaluate the safety, clinical efficacy, and cost effectiveness of various adjuvants in order to determine ideal candidates in disease-specific and allergen-specific models. Other suggested approaches to further optimize outcomes of IT include early introduction of IT during an optimal window period. Alternative routes of administration of IT to optimize delivery and yet minimize potential side effects require further evaluation for safety and efficacy before they can be recommended. PMID:29732290

Image gathering, coding, and processing: End-to-end optimization for efficient and robust acquisition of visual information

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.; Fales, Carl L.

1990-01-01

Researchers are concerned with the end-to-end performance of image gathering, coding, and processing. The applications range from high-resolution television to vision-based robotics, wherever the resolution, efficiency and robustness of visual information acquisition and processing are critical. For the presentation at this workshop, it is convenient to divide research activities into the following two overlapping areas: The first is the development of focal-plane processing techniques and technology to effectively combine image gathering with coding, with an emphasis on low-level vision processing akin to the retinal processing in human vision. The approach includes the familiar Laplacian pyramid, the new intensity-dependent spatial summation, and parallel sensing/processing networks. Three-dimensional image gathering is attained by combining laser ranging with sensor-array imaging. The second is the rigorous extension of information theory and optimal filtering to visual information acquisition and processing. The goal is to provide a comprehensive methodology for quantitatively assessing the end-to-end performance of image gathering, coding, and processing.

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

Research reactor loading pattern optimization using estimation of distribution algorithms

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

Jiang, S.; Ziver, K.; AMCG Group, RM Consultants, Abingdon

2006-07-01

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K{sub eff}) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K{sub eff} with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristicmore » Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)« less

A practical approach to automate randomized design of experiments for ligand-binding assays.

PubMed

Tsoi, Jennifer; Patel, Vimal; Shih, Judy

2014-03-01

Design of experiments (DOE) is utilized in optimizing ligand-binding assay by modeling factor effects. To reduce the analyst's workload and error inherent with DOE, we propose the integration of automated liquid handlers to perform the randomized designs. A randomized design created from statistical software was imported into custom macro converting the design into a liquid-handler worklist to automate reagent delivery. An optimized assay was transferred to a contract research organization resulting in a successful validation. We developed a practical solution for assay optimization by integrating DOE and automation to increase assay robustness and enable successful method transfer. The flexibility of this process allows it to be applied to a variety of assay designs.

A variable-gain output feedback control design approach

NASA Technical Reports Server (NTRS)

Haylo, Nesim

1989-01-01

A multi-model design technique to find a variable-gain control law defined over the whole operating range is proposed. The design is formulated as an optimal control problem which minimizes a cost function weighing the performance at many operating points. The solution is obtained by embedding into the Multi-Configuration Control (MCC) problem, a multi-model robust control design technique. In contrast to conventional gain scheduling which uses a curve fit of single model designs, the optimal variable-gain control law stabilizes the plant at every operating point included in the design. An iterative algorithm to compute the optimal control gains is presented. The methodology has been successfully applied to reconfigurable aircraft flight control and to nonlinear flight control systems.

Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

NASA Astrophysics Data System (ADS)

He, Yaoyao; Yang, Shanlin; Xu, Qifa

2013-07-01

In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

Robust Optimization and Sensitivity Analysis with Multi-Objective Genetic Algorithms: Single- and Multi-Disciplinary Applications

DTIC Science & Technology

2007-01-01

multi-disciplinary optimization with uncertainty. Robust optimization and sensitivity analysis is usually used when an optimization model has...formulation is introduced in Section 2.3. We briefly discuss several definitions used in the sensitivity analysis in Section 2.4. Following in...2.5. 2.4 SENSITIVITY ANALYSIS In this section, we discuss several definitions used in Chapter 5 for Multi-Objective Sensitivity Analysis . Inner

Mission planning for on-orbit servicing through multiple servicing satellites: A new approach

NASA Astrophysics Data System (ADS)

Daneshjou, K.; Mohammadi-Dehabadi, A. A.; Bakhtiari, M.

2017-09-01

In this paper, a novel approach is proposed for the mission planning of on-orbit servicing such as visual inspection, active debris removal and refueling through multiple servicing satellites (SSs). The scheduling has been done with the aim of minimization of fuel consumption and mission duration. So a multi-objective optimization problem is dealt with here which is solved by employing particle swarm optimization algorithm. Also, Taguchi technique is employed for robust design of effective parameters of optimization problem. The day that the SSs have to leave parking orbit, transfer duration from parking orbit to final orbit, transfer duration between one target to another, and time spent for the SS on each target are the decision parameters which are obtained from the optimization problem. The raised idea is that in addition to the aforementioned decision parameters, eccentricity and inclination related to the initial orbit and also phase difference between the SSs on the initial orbit are identified by means of optimization problem, so that the designer has not much role on determining them. Furthermore, it is considered that the SS and the target rendezvous at the servicing point and the SS does not perform any phasing maneuver to reach the target. It should be noted that Lambert theorem is used for determination of the transfer orbit. The results show that the proposed approach reduces the fuel consumption and the mission duration significantly in comparison with the conventional approaches.

Designing robust control laws using genetic algorithms

NASA Technical Reports Server (NTRS)

Marrison, Chris

1994-01-01

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

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

PubMed Central

Zeng, Xiaozheng; McGough, Robert J.

2009-01-01

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

An improved 3D MoF method based on analytical partial derivatives

NASA Astrophysics Data System (ADS)

Chen, Xiang; Zhang, Xiong

2016-12-01

MoF (Moment of Fluid) method is one of the most accurate approaches among various surface reconstruction algorithms. As other second order methods, MoF method needs to solve an implicit optimization problem to obtain the optimal approximate surface. Therefore, the partial derivatives of the objective function have to be involved during the iteration for efficiency and accuracy. However, to the best of our knowledge, the derivatives are currently estimated numerically by finite difference approximation because it is very difficult to obtain the analytical derivatives of the object function for an implicit optimization problem. Employing numerical derivatives in an iteration not only increase the computational cost, but also deteriorate the convergence rate and robustness of the iteration due to their numerical error. In this paper, the analytical first order partial derivatives of the objective function are deduced for 3D problems. The analytical derivatives can be calculated accurately, so they are incorporated into the MoF method to improve its accuracy, efficiency and robustness. Numerical studies show that by using the analytical derivatives the iterations are converged in all mixed cells with the efficiency improvement of 3 to 4 times.

A quality-by-design approach to risk reduction and optimization for human embryonic stem cell cryopreservation processes.

PubMed

Mitchell, Peter D; Ratcliffe, Elizabeth; Hourd, Paul; Williams, David J; Thomas, Robert J

2014-12-01

It is well documented that cryopreservation and resuscitation of human embryonic stem cells (hESCs) is complex and ill-defined, and often suffers poor cell recovery and increased levels of undesirable cell differentiation. In this study we have applied Quality-by-Design (QbD) concepts to the critical processes of slow-freeze cryopreservation and resuscitation of hESC colony cultures. Optimized subprocesses were linked together to deliver a controlled complete process. We have demonstrated a rapid, high-throughput, and stable system for measurement of cell adherence and viability as robust markers of in-process and postrecovery cell state. We observed that measurement of adherence and viability of adhered cells at 1 h postseeding was predictive of cell proliferative ability up to 96 h in this system. Application of factorial design defined the operating spaces for cryopreservation and resuscitation, critically linking the performance of these two processes. Optimization of both processes resulted in enhanced reattachment and post-thaw viability, resulting in substantially greater recovery of cryopreserved, pluripotent cell colonies. This study demonstrates the importance of QbD concepts and tools for rapid, robust, and low-risk process design that can inform manufacturing controls and logistics.

A global optimization approach to multi-polarity sentiment analysis.

PubMed

Li, Xinmiao; Li, Jing; Wu, Yukeng

2015-01-01

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From the results of this comparison, we found that PSOGO-Senti is more suitable for improving a difficult multi-polarity sentiment analysis problem.

Real-Time Robust Adaptive Modeling and Scheduling for an Electronic Commerce Server

NASA Astrophysics Data System (ADS)

Du, Bing; Ruan, Chun

With the increasing importance and pervasiveness of Internet services, it is becoming a challenge for the proliferation of electronic commerce services to provide performance guarantees under extreme overload. This paper describes a real-time optimization modeling and scheduling approach for performance guarantee of electronic commerce servers. We show that an electronic commerce server may be simulated as a multi-tank system. A robust adaptive server model is subject to unknown additive load disturbances and uncertain model matching. Overload control techniques are based on adaptive admission control to achieve timing guarantees. We evaluate the performance of the model using a complex simulation that is subjected to varying model parameters and massive overload.

Light field reconstruction robust to signal dependent noise

NASA Astrophysics Data System (ADS)

Ren, Kun; Bian, Liheng; Suo, Jinli; Dai, Qionghai

2014-11-01

Capturing four dimensional light field data sequentially using a coded aperture camera is an effective approach but suffers from low signal noise ratio. Although multiplexing can help raise the acquisition quality, noise is still a big issue especially for fast acquisition. To address this problem, this paper proposes a noise robust light field reconstruction method. Firstly, scene dependent noise model is studied and incorporated into the light field reconstruction framework. Then, we derive an optimization algorithm for the final reconstruction. We build a prototype by hacking an off-the-shelf camera for data capturing and prove the concept. The effectiveness of this method is validated with experiments on the real captured data.

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Optimal design of loudspeaker arrays for robust cross-talk cancellation using the Taguchi method and the genetic algorithm.

PubMed

Bai, Mingsian R; Tung, Chih-Wei; Lee, Chih-Chung

2005-05-01

An optimal design technique of loudspeaker arrays for cross-talk cancellation with application in three-dimensional audio is presented. An array focusing scheme is presented on the basis of the inverse propagation that relates the transducers to a set of chosen control points. Tikhonov regularization is employed in designing the inverse cancellation filters. An extensive analysis is conducted to explore the cancellation performance and robustness issues. To best compromise the performance and robustness of the cross-talk cancellation system, optimal configurations are obtained with the aid of the Taguchi method and the genetic algorithm (GA). The proposed systems are further justified by physical as well as subjective experiments. The results reveal that large number of loudspeakers, closely spaced configuration, and optimal control point design all contribute to the robustness of cross-talk cancellation systems (CCS) against head misalignment.

Network planning under uncertainties

NASA Astrophysics Data System (ADS)

Ho, Kwok Shing; Cheung, Kwok Wai

2008-11-01

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

A Multi-Band Uncertainty Set Based Robust SCUC With Spatial and Temporal Budget Constraints

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

Dai, Chenxi; Wu, Lei; Wu, Hongyu

2016-11-01

The dramatic increase of renewable energy resources in recent years, together with the long-existing load forecast errors and increasingly involved price sensitive demands, has introduced significant uncertainties into power systems operation. In order to guarantee the operational security of power systems with such uncertainties, robust optimization has been extensively studied in security-constrained unit commitment (SCUC) problems, for immunizing the system against worst uncertainty realizations. However, traditional robust SCUC models with single-band uncertainty sets may yield over-conservative solutions in most cases. This paper proposes a multi-band robust model to accurately formulate various uncertainties with higher resolution. By properly tuning band intervalsmore » and weight coefficients of individual bands, the proposed multi-band robust model can rigorously and realistically reflect spatial/temporal relationships and asymmetric characteristics of various uncertainties, and in turn could effectively leverage the tradeoff between robustness and economics of robust SCUC solutions. The proposed multi-band robust SCUC model is solved by Benders decomposition (BD) and outer approximation (OA), while taking the advantage of integral property of the proposed multi-band uncertainty set. In addition, several accelerating techniques are developed for enhancing the computational performance and the convergence speed. Numerical studies on a 6-bus system and the modified IEEE 118-bus system verify the effectiveness of the proposed robust SCUC approach for enhancing uncertainty modeling capabilities and mitigating conservativeness of the robust SCUC solution.« less

Redundant interferometric calibration as a complex optimization problem

NASA Astrophysics Data System (ADS)

Grobler, T. L.; Bernardi, G.; Kenyon, J. S.; Parsons, A. R.; Smirnov, O. M.

2018-05-01

Observations of the redshifted 21 cm line from the epoch of reionization have recently motivated the construction of low-frequency radio arrays with highly redundant configurations. These configurations provide an alternative calibration strategy - `redundant calibration' - and boost sensitivity on specific spatial scales. In this paper, we formulate calibration of redundant interferometric arrays as a complex optimization problem. We solve this optimization problem via the Levenberg-Marquardt algorithm. This calibration approach is more robust to initial conditions than current algorithms and, by leveraging an approximate matrix inversion, allows for further optimization and an efficient implementation (`redundant STEFCAL'). We also investigated using the preconditioned conjugate gradient method as an alternative to the approximate matrix inverse, but found that its computational performance is not competitive with respect to `redundant STEFCAL'. The efficient implementation of this new algorithm is made publicly available.

Optimal decision making modeling for copper-matte Peirce-Smith converting process by means of data mining

NASA Astrophysics Data System (ADS)

Song, Yanpo; Peng, Xiaoqi; Tang, Ying; Hu, Zhikun

2013-07-01

To improve the operation level of copper converter, the approach to optimal decision making modeling for coppermatte converting process based on data mining is studied: in view of the characteristics of the process data, such as containing noise, small sample size and so on, a new robust improved ANN (artificial neural network) modeling method is proposed; taking into account the application purpose of decision making model, three new evaluation indexes named support, confidence and relative confidence are proposed; using real production data and the methods mentioned above, optimal decision making model for blowing time of S1 period (the 1st slag producing period) are developed. Simulation results show that this model can significantly improve the converting quality of S1 period, increase the optimal probability from about 70% to about 85%.

Development of a chromatographic method with multi-criteria decision making design for simultaneous determination of nifedipine and atenolol in content uniformity testing.

PubMed

Ahmed, Sameh; Alqurshi, Abdulmalik; Mohamed, Abdel-Maaboud Ismail

2018-07-01

A new robust and reliable high-performance liquid chromatography (HPLC) method with multi-criteria decision making (MCDM) approach was developed to allow simultaneous quantification of atenolol (ATN) and nifedipine (NFD) in content uniformity testing. Felodipine (FLD) was used as an internal standard (I.S.) in this study. A novel marriage between a new interactive response optimizer and a HPLC method was suggested for multiple response optimizations of target responses. An interactive response optimizer was used as a decision and prediction tool for the optimal settings of target responses, according to specified criteria, based on Derringer's desirability. Four independent variables were considered in this study: Acetonitrile%, buffer pH and concentration along with column temperature. Eight responses were optimized: retention times of ATN, NFD, and FLD, resolutions between ATN/NFD and NFD/FLD, and plate numbers for ATN, NFD, and FLD. Multiple regression analysis was applied in order to scan the influences of the most significant variables for the regression models. The experimental design was set to give minimum retention times, maximum resolution and plate numbers. The interactive response optimizer allowed prediction of optimum conditions according to these criteria with a good composite desirability value of 0.98156. The developed method was validated according to the International Conference on Harmonization (ICH) guidelines with the aid of the experimental design. The developed MCDM-HPLC method showed superior robustness and resolution in short analysis time allowing successful simultaneous content uniformity testing of ATN and NFD in marketed capsules. The current work presents an interactive response optimizer as an efficient platform to optimize, predict responses, and validate HPLC methodology with tolerable design space for assay in quality control laboratories. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of Differential Evolutionary Optimization Methodology for Parameter Structure Identification in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.

2013-12-01

With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Impact of Spot Size and Spacing on the Quality of Robustly Optimized Intensity Modulated Proton Therapy Plans for Lung Cancer.

PubMed

Liu, Chenbin; Schild, Steven E; Chang, Joe Y; Liao, Zhongxing; Korte, Shawn; Shen, Jiajian; Ding, Xiaoning; Hu, Yanle; Kang, Yixiu; Keole, Sameer R; Sio, Terence T; Wong, William W; Sahoo, Narayan; Bues, Martin; Liu, Wei

2018-06-01

To investigate how spot size and spacing affect plan quality, robustness, and interplay effects of robustly optimized intensity modulated proton therapy (IMPT) for lung cancer. Two robustly optimized IMPT plans were created for 10 lung cancer patients: first by a large-spot machine with in-air energy-dependent large spot size at isocenter (σ: 6-15 mm) and spacing (1.3 σ), and second by a small-spot machine with in-air energy-dependent small spot size (σ: 2-6 mm) and spacing (5 mm). Both plans were generated by optimizing radiation dose to internal target volume on averaged 4-dimensional computed tomography scans using an in-house-developed IMPT planning system. The dose-volume histograms band method was used to evaluate plan robustness. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effects with randomized starting phases for each field per fraction. Patient anatomy voxels were mapped phase-to-phase via deformable image registration, and doses were scored using in-house-developed software. Dose-volume histogram indices, including internal target volume dose coverage, homogeneity, and organs at risk (OARs) sparing, were compared using the Wilcoxon signed-rank test. Compared with the large-spot machine, the small-spot machine resulted in significantly lower heart and esophagus mean doses, with comparable target dose coverage, homogeneity, and protection of other OARs. Plan robustness was comparable for targets and most OARs. With interplay effects considered, significantly lower heart and esophagus mean doses with comparable target dose coverage and homogeneity were observed using smaller spots. Robust optimization with a small spot-machine significantly improves heart and esophagus sparing, with comparable plan robustness and interplay effects compared with robust optimization with a large-spot machine. A small-spot machine uses a larger number of spots to cover the same tumors compared with a large-spot machine, which gives the planning system more freedom to compensate for the higher sensitivity to uncertainties and interplay effects for lung cancer treatments. Copyright © 2018 Elsevier Inc. All rights reserved.

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

PubMed

Xu, Shidong; Sun, Guanghui; Sun, Weichao

2017-01-01

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

Wisdom of crowds for robust gene network inference

PubMed Central

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

2012-01-01

Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

Practical, Real-Time, and Robust Watermarking on the Spatial Domain for High-Definition Video Contents

NASA Astrophysics Data System (ADS)

Kim, Kyung-Su; Lee, Hae-Yeoun; Im, Dong-Hyuck; Lee, Heung-Kyu

Commercial markets employ digital right management (DRM) systems to protect valuable high-definition (HD) quality videos. DRM system uses watermarking to provide copyright protection and ownership authentication of multimedia contents. We propose a real-time video watermarking scheme for HD video in the uncompressed domain. Especially, our approach is in aspect of practical perspectives to satisfy perceptual quality, real-time processing, and robustness requirements. We simplify and optimize human visual system mask for real-time performance and also apply dithering technique for invisibility. Extensive experiments are performed to prove that the proposed scheme satisfies the invisibility, real-time processing, and robustness requirements against video processing attacks. We concentrate upon video processing attacks that commonly occur in HD quality videos to display on portable devices. These attacks include not only scaling and low bit-rate encoding, but also malicious attacks such as format conversion and frame rate change.

Design of control laws for flutter suppression based on the aerodynamic energy concept and comparisons with other design methods

NASA Technical Reports Server (NTRS)

Nissim, E.

1989-01-01

The aerodynamic energy method is used in this paper to synthesize control laws for NASA's Drone for Aerodynamic and Structural Testing-Aerodynamic Research Wing 1 (DAST-ARW1) mathematical model. The performance of these control laws in terms of closed-loop flutter dynamic pressure, control surface activity, and robustness is compared against other control laws that appear in the literature and relate to the same model. A control law synthesis technique that makes use of the return difference singular values is developed in this paper. it is based on the aerodynamic energy approach and is shown to yield results superior to those given in the literature and based on optimal control theory. Nyquist plots are presented together with a short discussion regarding the relative merits of the minimum singular value as a measure of robustness, compared with the more traditional measure of robustness involving phase and gain margins.

Novel Scalable 3-D MT Inverse Solver

NASA Astrophysics Data System (ADS)

Kuvshinov, A. V.; Kruglyakov, M.; Geraskin, A.

2016-12-01

We present a new, robust and fast, three-dimensional (3-D) magnetotelluric (MT) inverse solver. As a forward modelling engine a highly-scalable solver extrEMe [1] is used. The (regularized) inversion is based on an iterative gradient-type optimization (quasi-Newton method) and exploits adjoint sources approach for fast calculation of the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT (single-site and/or inter-site) responses, and supports massive parallelization. Different parallelization strategies implemented in the code allow for optimal usage of available computational resources for a given problem set up. To parameterize an inverse domain a mask approach is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to high-performance clusters demonstrate practically linear scalability of the code up to thousands of nodes. 1. Kruglyakov, M., A. Geraskin, A. Kuvshinov, 2016. Novel accurate and scalable 3-D MT forward solver based on a contracting integral equation method, Computers and Geosciences, in press.

A nonlinear optimal control approach for chaotic finance dynamics

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

2017-11-01

A new nonlinear optimal control approach is proposed for stabilization of the dynamics of a chaotic finance model. The dynamic model of the financial system, which expresses interaction between the interest rate, the investment demand, the price exponent and the profit margin, undergoes approximate linearization round local operating points. These local equilibria are defined at each iteration of the control algorithm and consist of the present value of the systems state vector and the last value of the control inputs vector that was exerted on it. The approximate linearization makes use of Taylor series expansion and of the computation of the associated Jacobian matrices. The truncation of higher order terms in the Taylor series expansion is considered to be a modelling error that is compensated by the robustness of the control loop. As the control algorithm runs, the temporary equilibrium is shifted towards the reference trajectory and finally converges to it. The control method needs to compute an H-infinity feedback control law at each iteration, and requires the repetitive solution of an algebraic Riccati equation. Through Lyapunov stability analysis it is shown that an H-infinity tracking performance criterion holds for the control loop. This implies elevated robustness against model approximations and external perturbations. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven.

Robust quantum optimizer with full connectivity.

PubMed

Nigg, Simon E; Lörch, Niels; Tiwari, Rakesh P

2017-04-01

Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example, quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size than classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. By leveraging the phenomenon of flux quantization, all-to-all connectivity with sufficient tunability to implement many relevant optimization problems is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the nondeterministic polynomial-time hard (NP-hard) and fully connected number partitioning problem in the presence of dissipation.

Robust optimization model and algorithm for railway freight center location problem in uncertain environment.

PubMed

Liu, Xing-Cai; He, Shi-Wei; Song, Rui; Sun, Yang; Li, Hao-Dong

2014-01-01

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.

Terahertz thickness measurements for real industrial applications: from automotive paints to aerospace industry (Conference Presentation)

NASA Astrophysics Data System (ADS)

Krimi, Soufiene; Beigang, René

2017-02-01

In this contribution, we present a highly accurate approach for real-time thickness measurements of multilayered coatings using terahertz time domain spectroscopy in reflection geometry. The proposed approach combines the benefits of a model-based material parameters extraction method to calibrate the specimen under test, a generalized modeling method to simulate the terahertz radiation behavior within arbitrary thin films, and the robustness of a powerful evolutionary optimization algorithm to increase the sensitivity and the precision of the minimum thickness measurement limit. Furthermore, a novel self-calibration model is introduced, which takes into consideration the real industrial challenges such as the effect of wet-on-wet spray in the car painting process and the influence of the spraying conditions and the sintering process on ceramic thermal barrier coatings (TBCs) in aircraft industry. In addition, the developed approach enables for some applications the simultaneous determination of the complex refractive index and the coating thickness. Hence, a pre-calibration of the specimen under test is not required for such cases. Due to the high robustness of the self-calibration method and the genetic optimization algorithms, the approach has been successfully applied to resolve individual layer thicknesses within multi-layered coated samples down to less than 10 µm. The regression method can be applied in time-domain, frequency-domain or in both the time and frequency-domain simultaneously. The data evaluation uses general-purpose computing on graphics processing units and thanks to the developed highly parallelized algorithm lasts less than 300 ms. Thus, industrial requirements for fast thickness measurements with an "every-second-cycle" can be fulfilled.

Multiresolution strategies for the numerical solution of optimal control problems

NASA Astrophysics Data System (ADS)

Jain, Sachin

There exist many numerical techniques for solving optimal control problems but less work has been done in the field of making these algorithms run faster and more robustly. The main motivation of this work is to solve optimal control problems accurately in a fast and efficient way. Optimal control problems are often characterized by discontinuities or switchings in the control variables. One way of accurately capturing the irregularities in the solution is to use a high resolution (dense) uniform grid. This requires a large amount of computational resources both in terms of CPU time and memory. Hence, in order to accurately capture any irregularities in the solution using a few computational resources, one can refine the mesh locally in the region close to an irregularity instead of refining the mesh uniformly over the whole domain. Therefore, a novel multiresolution scheme for data compression has been designed which is shown to outperform similar data compression schemes. Specifically, we have shown that the proposed approach results in fewer grid points in the grid compared to a common multiresolution data compression scheme. The validity of the proposed mesh refinement algorithm has been verified by solving several challenging initial-boundary value problems for evolution equations in 1D. The examples have demonstrated the stability and robustness of the proposed algorithm. The algorithm adapted dynamically to any existing or emerging irregularities in the solution by automatically allocating more grid points to the region where the solution exhibited sharp features and fewer points to the region where the solution was smooth. Thereby, the computational time and memory usage has been reduced significantly, while maintaining an accuracy equivalent to the one obtained using a fine uniform mesh. Next, a direct multiresolution-based approach for solving trajectory optimization problems is developed. The original optimal control problem is transcribed into a nonlinear programming (NLP) problem that is solved using standard NLP codes. The novelty of the proposed approach hinges on the automatic calculation of a suitable, nonuniform grid over which the NLP problem is solved, which tends to increase numerical efficiency and robustness. Control and/or state constraints are handled with ease, and without any additional computational complexity. The proposed algorithm is based on a simple and intuitive method to balance several conflicting objectives, such as accuracy of the solution, convergence, and speed of the computations. The benefits of the proposed algorithm over uniform grid implementations are demonstrated with the help of several nontrivial examples. Furthermore, two sequential multiresolution trajectory optimization algorithms for solving problems with moving targets and/or dynamically changing environments have been developed. For such problems, high accuracy is desirable only in the immediate future, yet the ultimate mission objectives should be accommodated as well. An intelligent trajectory generation for such situations is thus enabled by introducing the idea of multigrid temporal resolution to solve the associated trajectory optimization problem on a non-uniform grid across time that is adapted to: (i) immediate future, and (ii) potential discontinuities in the state and control variables.

Robust detection-isolation-accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Weiss, J. L.; Pattipati, K. R.; Willsky, A. S.; Eterno, J. S.; Crawford, J. T.

1985-01-01

The results of a one year study to: (1) develop a theory for Robust Failure Detection and Identification (FDI) in the presence of model uncertainty, (2) develop a design methodology which utilizes the robust FDI ththeory, (3) apply the methodology to a sensor FDI problem for the F-100 jet engine, and (4) demonstrate the application of the theory to the evaluation of alternative FDI schemes are presented. Theoretical results in statistical discrimination are used to evaluate the robustness of residual signals (or parity relations) in terms of their usefulness for FDI. Furthermore, optimally robust parity relations are derived through the optimization of robustness metrics. The result is viewed as decentralization of the FDI process. A general structure for decentralized FDI is proposed and robustness metrics are used for determining various parameters of the algorithm.

Non-linear Multidimensional Optimization for use in Wire Scanner Fitting

NASA Astrophysics Data System (ADS)

Henderson, Alyssa; Terzic, Balsa; Hofler, Alicia; Center Advanced Studies of Accelerators Collaboration

2014-03-01

To ensure experiment efficiency and quality from the Continuous Electron Beam Accelerator at Jefferson Lab, beam energy, size, and position must be measured. Wire scanners are devices inserted into the beamline to produce measurements which are used to obtain beam properties. Extracting physical information from the wire scanner measurements begins by fitting Gaussian curves to the data. This study focuses on optimizing and automating this curve-fitting procedure. We use a hybrid approach combining the efficiency of Newton Conjugate Gradient (NCG) method with the global convergence of three nature-inspired (NI) optimization approaches: genetic algorithm, differential evolution, and particle-swarm. In this Python-implemented approach, augmenting the locally-convergent NCG with one of the globally-convergent methods ensures the quality, robustness, and automation of curve-fitting. After comparing the methods, we establish that given an initial data-derived guess, each finds a solution with the same chi-square- a measurement of the agreement of the fit to the data. NCG is the fastest method, so it is the first to attempt data-fitting. The curve-fitting procedure escalates to one of the globally-convergent NI methods only if NCG fails, thereby ensuring a successful fit. This method allows for the most optimal signal fit and can be easily applied to similar problems.

Full-Duplex Bidirectional Secure Communications Under Perfect and Distributionally Ambiguous Eavesdropper's CSI

NASA Astrophysics Data System (ADS)

Li, Qiang; Zhang, Ying; Lin, Jingran; Wu, Sissi Xiaoxiao

2017-09-01

Consider a full-duplex (FD) bidirectional secure communication system, where two communication nodes, named Alice and Bob, simultaneously transmit and receive confidential information from each other, and an eavesdropper, named Eve, overhears the transmissions. Our goal is to maximize the sum secrecy rate (SSR) of the bidirectional transmissions by optimizing the transmit covariance matrices at Alice and Bob. To tackle this SSR maximization (SSRM) problem, we develop an alternating difference-of-concave (ADC) programming approach to alternately optimize the transmit covariance matrices at Alice and Bob. We show that the ADC iteration has a semi-closed-form beamforming solution, and is guaranteed to converge to a stationary solution of the SSRM problem. Besides the SSRM design, this paper also deals with a robust SSRM transmit design under a moment-based random channel state information (CSI) model, where only some roughly estimated first and second-order statistics of Eve's CSI are available, but the exact distribution or other high-order statistics is not known. This moment-based error model is new and different from the widely used bounded-sphere error model and the Gaussian random error model. Under the consider CSI error model, the robust SSRM is formulated as an outage probability-constrained SSRM problem. By leveraging the Lagrangian duality theory and DC programming, a tractable safe solution to the robust SSRM problem is derived. The effectiveness and the robustness of the proposed designs are demonstrated through simulations.

A Robust Adaptive Autonomous Approach to Optimal Experimental Design

NASA Astrophysics Data System (ADS)

Gu, Hairong

Experimentation is the fundamental tool of scientific inquiries to understand the laws governing the nature and human behaviors. Many complex real-world experimental scenarios, particularly in quest of prediction accuracy, often encounter difficulties to conduct experiments using an existing experimental procedure for the following two reasons. First, the existing experimental procedures require a parametric model to serve as the proxy of the latent data structure or data-generating mechanism at the beginning of an experiment. However, for those experimental scenarios of concern, a sound model is often unavailable before an experiment. Second, those experimental scenarios usually contain a large number of design variables, which potentially leads to a lengthy and costly data collection cycle. Incompetently, the existing experimental procedures are unable to optimize large-scale experiments so as to minimize the experimental length and cost. Facing the two challenges in those experimental scenarios, the aim of the present study is to develop a new experimental procedure that allows an experiment to be conducted without the assumption of a parametric model while still achieving satisfactory prediction, and performs optimization of experimental designs to improve the efficiency of an experiment. The new experimental procedure developed in the present study is named robust adaptive autonomous system (RAAS). RAAS is a procedure for sequential experiments composed of multiple experimental trials, which performs function estimation, variable selection, reverse prediction and design optimization on each trial. Directly addressing the challenges in those experimental scenarios of concern, function estimation and variable selection are performed by data-driven modeling methods to generate a predictive model from data collected during the course of an experiment, thus exempting the requirement of a parametric model at the beginning of an experiment; design optimization is performed to select experimental designs on the fly of an experiment based on their usefulness so that fewest designs are needed to reach useful inferential conclusions. Technically, function estimation is realized by Bayesian P-splines, variable selection is realized by Bayesian spike-and-slab prior, reverse prediction is realized by grid-search and design optimization is realized by the concepts of active learning. The present study demonstrated that RAAS achieves statistical robustness by making accurate predictions without the assumption of a parametric model serving as the proxy of latent data structure while the existing procedures can draw poor statistical inferences if a misspecified model is assumed; RAAS also achieves inferential efficiency by taking fewer designs to acquire useful statistical inferences than non-optimal procedures. Thus, RAAS is expected to be a principled solution to real-world experimental scenarios pursuing robust prediction and efficient experimentation.

Global dynamic optimization approach to predict activation in metabolic pathways.

PubMed

de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

2014-01-06

During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

Robust Bayesian Experimental Design for Conceptual Model Discrimination

NASA Astrophysics Data System (ADS)

Pham, H. V.; Tsai, F. T. C.

2015-12-01

A robust Bayesian optimal experimental design under uncertainty is presented to provide firm information for model discrimination, given the least number of pumping wells and observation wells. Firm information is the maximum information of a system can be guaranteed from an experimental design. The design is based on the Box-Hill expected entropy decrease (EED) before and after the experiment design and the Bayesian model averaging (BMA) framework. A max-min programming is introduced to choose the robust design that maximizes the minimal Box-Hill EED subject to that the highest expected posterior model probability satisfies a desired probability threshold. The EED is calculated by the Gauss-Hermite quadrature. The BMA method is used to predict future observations and to quantify future observation uncertainty arising from conceptual and parametric uncertainties in calculating EED. Monte Carlo approach is adopted to quantify the uncertainty in the posterior model probabilities. The optimal experimental design is tested by a synthetic 5-layer anisotropic confined aquifer. Nine conceptual groundwater models are constructed due to uncertain geological architecture and boundary condition. High-performance computing is used to enumerate all possible design solutions in order to identify the most plausible groundwater model. Results highlight the impacts of scedasticity in future observation data as well as uncertainty sources on potential pumping and observation locations.

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

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

Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity providedmore » by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.« less

Event-triggered resilient filtering with stochastic uncertainties and successive packet dropouts via variance-constrained approach

NASA Astrophysics Data System (ADS)

Jia, Chaoqing; Hu, Jun; Chen, Dongyan; Liu, Yurong; Alsaadi, Fuad E.

2018-07-01

In this paper, we discuss the event-triggered resilient filtering problem for a class of time-varying systems subject to stochastic uncertainties and successive packet dropouts. The event-triggered mechanism is employed with hope to reduce the communication burden and save network resources. The stochastic uncertainties are considered to describe the modelling errors and the phenomenon of successive packet dropouts is characterized by a random variable obeying the Bernoulli distribution. The aim of the paper is to provide a resilient event-based filtering approach for addressed time-varying systems such that, for all stochastic uncertainties, successive packet dropouts and filter gain perturbation, an optimized upper bound of the filtering error covariance is obtained by designing the filter gain. Finally, simulations are provided to demonstrate the effectiveness of the proposed robust optimal filtering strategy.

Discovery of highly selective brain-penetrant vasopressin 1a antagonists for the potential treatment of autism via a chemogenomic and scaffold hopping approach.

PubMed

Ratni, Hasane; Rogers-Evans, Mark; Bissantz, Caterina; Grundschober, Christophe; Moreau, Jean-Luc; Schuler, Franz; Fischer, Holger; Alvarez Sanchez, Ruben; Schnider, Patrick

2015-03-12

From a micromolar high throughput screening hit 7, the successful complementary application of a chemogenomic approach and of a scaffold hopping exercise rapidly led to a low single digit nanomolar human vasopressin 1a (hV1a) receptor antagonist 38. Initial optimization of the mouse V1a activities delivered suitable tool compounds which demonstrated a V1a mediated central in vivo effect. This novel series was further optimized through parallel synthesis with a focus on balancing lipophilicity to achieve robust aqueous solubility while avoiding P-gp mediated efflux. These efforts led to the discovery of the highly potent and selective brain-penetrant hV1a antagonist RO5028442 (8) suitable for human clinical studies in people with autism.

Design of order statistics filters using feedforward neural networks

NASA Astrophysics Data System (ADS)

Maslennikova, Yu. S.; Bochkarev, V. V.

2016-08-01

In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.

The HDAC Inhibitor TSA Ameliorates a Zebrafish Model of Duchenne Muscular Dystrophy.

PubMed

Johnson, Nathan M; Farr, Gist H; Maves, Lisa

2013-09-17

Zebrafish are an excellent model for Duchenne muscular dystrophy. In particular, zebrafish provide a system for rapid, easy, and low-cost screening of small molecules that can ameliorate muscle damage in dystrophic larvae. Here we identify an optimal anti-sense morpholino cocktail that robustly knocks down zebrafish Dystrophin (dmd-MO). We use two approaches, muscle birefringence and muscle actin expression, to quantify muscle damage and show that the dmd-MO dystrophic phenotype closely resembles the zebrafish dmd mutant phenotype. We then show that the histone deacetylase (HDAC) inhibitor TSA, which has been shown to ameliorate the mdx mouse Duchenne model, can rescue muscle fiber damage in both dmd-MO and dmd mutant larvae. Our study identifies optimal morpholino and phenotypic scoring approaches for dystrophic zebrafish, further enhancing the zebrafish dmd model for rapid and cost-effective small molecule screening.

A Hybrid Interval-Robust Optimization Model for Water Quality Management.

PubMed

Xu, Jieyu; Li, Yongping; Huang, Guohe

2013-05-01

In water quality management problems, uncertainties may exist in many system components and pollution-related processes ( i.e. , random nature of hydrodynamic conditions, variability in physicochemical processes, dynamic interactions between pollutant loading and receiving water bodies, and indeterminacy of available water and treated wastewater). These complexities lead to difficulties in formulating and solving the resulting nonlinear optimization problems. In this study, a hybrid interval-robust optimization (HIRO) method was developed through coupling stochastic robust optimization and interval linear programming. HIRO can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original chemical oxygen demand (COD) discharge constraints, HIRO enhances the robustness of the optimization processes and resulting solutions. This method was applied to planning of industry development in association with river-water pollution concern in New Binhai District of Tianjin, China. Results demonstrated that the proposed optimization model can effectively communicate uncertainties into the optimization process and generate a spectrum of potential inexact solutions supporting local decision makers in managing benefit-effective water quality management schemes. HIRO is helpful for analysis of policy scenarios related to different levels of economic penalties, while also providing insight into the tradeoff between system benefits and environmental requirements.

Experimental demonstration of a quantum annealing algorithm for the traveling salesman problem in a nuclear-magnetic-resonance quantum simulator

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

Chen Hongwei; High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031; Kong Xi

The method of quantum annealing (QA) is a promising way for solving many optimization problems in both classical and quantum information theory. The main advantage of this approach, compared with the gate model, is the robustness of the operations against errors originated from both external controls and the environment. In this work, we succeed in demonstrating experimentally an application of the method of QA to a simplified version of the traveling salesman problem by simulating the corresponding Schroedinger evolution with a NMR quantum simulator. The experimental results unambiguously yielded the optimal traveling route, in good agreement with the theoretical prediction.

A nonlinear bi-level programming approach for product portfolio management.

PubMed

Ma, Shuang

2016-01-01

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today's competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors' actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader-follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader-follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader-follower bi-level optimization model is robust and can empower product portfolio optimization.

Robust Control of Uncertain Systems via Dissipative LQG-Type Controllers

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2000-01-01

Optimal controller design is addressed for a class of linear, time-invariant systems which are dissipative with respect to a quadratic power function. The system matrices are assumed to be affine functions of uncertain parameters confined to a convex polytopic region in the parameter space. For such systems, a method is developed for designing a controller which is dissipative with respect to a given power function, and is simultaneously optimal in the linear-quadratic-Gaussian (LQG) sense. The resulting controller provides robust stability as well as optimal performance. Three important special cases, namely, passive, norm-bounded, and sector-bounded controllers, which are also LQG-optimal, are presented. The results give new methods for robust controller design in the presence of parametric uncertainties.

Robust DEA under discrete uncertain data: a case study of Iranian electricity distribution companies

NASA Astrophysics Data System (ADS)

Hafezalkotob, Ashkan; Haji-Sami, Elham; Omrani, Hashem

2015-06-01

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there are discrete uncertain input and output data. The method is based upon the discrete robust optimization approaches proposed by Mulvey et al. (1995) that utilizes probable scenarios to capture the effect of ambiguous data in the case study. Our primary concern in this research is evaluating electricity distribution companies under uncertainty about input/output data. To illustrate the ability of proposed model, a numerical example of 38 Iranian electricity distribution companies is investigated. There are a large amount ambiguous data about these companies. Some electricity distribution companies may not report clear and real statistics to the government. Thus, it is needed to utilize a prominent approach to deal with this uncertainty. The results reveal that the RDEA model is suitable and reliable for target setting based on decision makers (DM's) preferences when there are uncertain input/output data.

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

PubMed

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

2017-01-30

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

PI controller design of a wind turbine: evaluation of the pole-placement method and tuning using constrained optimization

NASA Astrophysics Data System (ADS)

Mirzaei, Mahmood; Tibaldi, Carlo; Hansen, Morten H.

2016-09-01

PI/PID controllers are the most common wind turbine controllers. Normally a first tuning is obtained using methods such as pole-placement or Ziegler-Nichols and then extensive aeroelastic simulations are used to obtain the best tuning in terms of regulation of the outputs and reduction of the loads. In the traditional tuning approaches, the properties of different open loop and closed loop transfer functions of the system are not normally considered. In this paper, an assessment of the pole-placement tuning method is presented based on robustness measures. Then a constrained optimization setup is suggested to automatically tune the wind turbine controller subject to robustness constraints. The properties of the system such as the maximum sensitivity and complementary sensitivity functions (Ms and Mt ), along with some of the responses of the system, are used to investigate the controller performance and formulate the optimization problem. The cost function is the integral absolute error (IAE) of the rotational speed from a disturbance modeled as a step in wind speed. Linearized model of the DTU 10-MW reference wind turbine is obtained using HAWCStab2. Thereafter, the model is reduced with model order reduction. The trade-off curves are given to assess the tunings of the poles- placement method and a constrained optimization problem is solved to find the best tuning.

Efficient robust conditional random fields.

PubMed

Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A

2015-10-01

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

Direct SQP-methods for solving optimal control problems with delays

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

Goellmann, L.; Bueskens, C.; Maurer, H.

The maximum principle for optimal control problems with delays leads to a boundary value problem (BVP) which is retarded in the state and advanced in the costate function. Based on shooting techniques, solution methods for this type of BVP have been proposed. In recent years, direct optimization methods have been favored for solving control problems without delays. Direct methods approximate the control and the state over a fixed mesh and solve the resulting NLP-problem with SQP-methods. These methods dispense with the costate function and have shown to be robust and efficient. In this paper, we propose a direct SQP-method formore » retarded control problems. In contrast to conventional direct methods, only the control variable is approximated by e.g. spline-functions. The state is computed via a high order Runge-Kutta type algorithm and does not enter explicitly the NLP-problem through an equation. This approach reduces the number of optimization variables considerably and is implementable even on a PC. Our method is illustrated by the numerical solution of retarded control problems with constraints. In particular, we consider the control of a continuous stirred tank reactor which has been solved by dynamic programming. This example illustrates the robustness and efficiency of the proposed method. Open questions concerning sufficient conditions and convergence of discretized NLP-problems are discussed.« less

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

PubMed Central

Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-01-01

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. PMID:29762519

Robust design optimization method for centrifugal impellers under surface roughness uncertainties due to blade fouling

NASA Astrophysics Data System (ADS)

Ju, Yaping; Zhang, Chuhua

2016-03-01

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression (SVR) metamodel is combined with the Monte Carlo simulation (MCS) method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.

Multiscale approach to contour fitting for MR images

NASA Astrophysics Data System (ADS)

Rueckert, Daniel; Burger, Peter

1996-04-01

We present a new multiscale contour fitting process which combines information about the image and the contour of the object at different levels of scale. The algorithm is based on energy minimizing deformable models but avoids some of the problems associated with these models. The segmentation algorithm starts by constructing a linear scale-space of an image through convolution of the original image with a Gaussian kernel at different levels of scale, where the scale corresponds to the standard deviation of the Gaussian kernel. At high levels of scale large scale features of the objects are preserved while small scale features, like object details as well as noise, are suppressed. In order to maximize the accuracy of the segmentation, the contour of the object of interest is then tracked in scale-space from coarse to fine scales. We propose a hybrid multi-temperature simulated annealing optimization to minimize the energy of the deformable model. At high levels of scale the SA optimization is started at high temperatures, enabling the SA optimization to find a global optimal solution. At lower levels of scale the SA optimization is started at lower temperatures (at the lowest level the temperature is close to 0). This enforces a more deterministic behavior of the SA optimization at lower scales and leads to an increasingly local optimization as high energy barriers cannot be crossed. The performance and robustness of the algorithm have been tested on spin-echo MR images of the cardiovascular system. The task was to segment the ascending and descending aorta in 15 datasets of different individuals in order to measure regional aortic compliance. The results show that the algorithm is able to provide more accurate segmentation results than the classic contour fitting process and is at the same time very robust to noise and initialization.

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.

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

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

Zhou, Ping; Lv, Youbin; Wang, Hong

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation basedmore » robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.« less

Optimal design of a microgripper-type actuator based on AlN/Si heterogeneous bimorph

NASA Astrophysics Data System (ADS)

Ruiz, D.; Díaz-Molina, A.; Sigmund, O.; Donoso, A.; Bellido, J. C.; Sánchez-Rojas, J. L.

2017-05-01

This work presents a systematic procedure to design piezoelectrically actuated microgrippers. Topology optimization combined with optimal design of electrodes is used to maximize the displacement at the output port of the gripper. The fabrication at the microscale leads us to overcome an important issue: the difficulty of placing a piezoelectric film on both top and bottom of the host layer. Due to the non-symmetric lamination of the structure, an out-of-plane bending spoils the behaviour of the gripper. Suppression of this out-of-plane deformation is the main novelty introduced. In addition, a robust formulation approach is used in order to control the length scale in the whole domain and to reduce sensitivity of the designs to small manufacturing errors.

Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

2015-05-15

The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves. Copyright © 2015 Elsevier B.V. All rights reserved.

Rethinking antithyroid drugs in pregnancy.

PubMed

Napier, C; Pearce, S H S

2015-04-01

Uncontrolled hyperthyroidism in pregnancy poses a risk to both mother and foetus, and the optimal treatment strategy in this setting remains elusive. Instigation of pharmacological therapy or an alternative intervention during pregnancy requires careful consideration, and the evidence that has underpinned our choice of antithyroid drug has not been robust. Recent research developments have prompted us to question our practice, and reconsider our approach to managing this patient group. © 2014 John Wiley & Sons Ltd.

Functional differentiation of human pluripotent stem cells on a chip.

PubMed

Giobbe, Giovanni G; Michielin, Federica; Luni, Camilla; Giulitti, Stefano; Martewicz, Sebastian; Dupont, Sirio; Floreani, Annarosa; Elvassore, Nicola

2015-07-01

Microengineering human "organs-on-chips" remains an open challenge. Here, we describe a robust microfluidics-based approach for the differentiation of human pluripotent stem cells directly on a chip. Extrinsic signal modulation, achieved through optimal frequency of medium delivery, can be used as a parameter for improved germ layer specification and cell differentiation. Human cardiomyocytes and hepatocytes derived on chips showed functional phenotypes and responses to temporally defined drug treatments.

Design of a candidate flutter suppression control law for DAST ARW-2. [Drones for Aerodynamic and Structural Testing Aeroelastic Research Wing

NASA Technical Reports Server (NTRS)

Adams, W. M., Jr.; Tiffany, S. H.

1983-01-01

A control law is developed to suppress symmetric flutter for a mathematical model of an aeroelastic research vehicle. An implementable control law is attained by including modified LQG (linear quadratic Gaussian) design techniques, controller order reduction, and gain scheduling. An alternate (complementary) design approach is illustrated for one flight condition wherein nongradient-based constrained optimization techniques are applied to maximize controller robustness.

SIFT optimization and automation for matching images from multiple temporal sources

NASA Astrophysics Data System (ADS)

Castillo-Carrión, Sebastián; Guerrero-Ginel, José-Emilio

2017-05-01

Scale Invariant Feature Transformation (SIFT) was applied to extract tie-points from multiple source images. Although SIFT is reported to perform reliably under widely different radiometric and geometric conditions, using the default input parameters resulted in too few points being found. We found that the best solution was to focus on large features as these are more robust and not prone to scene changes over time, which constitutes a first approach to the automation of processes using mapping applications such as geometric correction, creation of orthophotos and 3D models generation. The optimization of five key SIFT parameters is proposed as a way of increasing the number of correct matches; the performance of SIFT is explored in different images and parameter values, finding optimization values which are corroborated using different validation imagery. The results show that the optimization model improves the performance of SIFT in correlating multitemporal images captured from different sources.

Designing a Pediatric Study for an Antimalarial Drug by Using Information from Adults

PubMed Central

Jullien, Vincent; Samson, Adeline; Guedj, Jérémie; Kiechel, Jean-René; Zohar, Sarah; Comets, Emmanuelle

2015-01-01

The objectives of this study were to design a pharmacokinetic (PK) study by using information about adults and evaluate the robustness of the recommended design through a case study of mefloquine. PK data about adults and children were available from two different randomized studies of the treatment of malaria with the same artesunate-mefloquine combination regimen. A recommended design for pediatric studies of mefloquine was optimized on the basis of an extrapolated model built from adult data through the following approach. (i) An adult PK model was built, and parameters were estimated by using the stochastic approximation expectation-maximization algorithm. (ii) Pediatric PK parameters were then obtained by adding allometry and maturation to the adult model. (iii) A D-optimal design for children was obtained with PFIM by assuming the extrapolated design. Finally, the robustness of the recommended design was evaluated in terms of the relative bias and relative standard errors (RSE) of the parameters in a simulation study with four different models and was compared to the empirical design used for the pediatric study. Combining PK modeling, extrapolation, and design optimization led to a design for children with five sampling times. PK parameters were well estimated by this design with few RSE. Although the extrapolated model did not predict the observed mefloquine concentrations in children very accurately, it allowed precise and unbiased estimates across various model assumptions, contrary to the empirical design. Using information from adult studies combined with allometry and maturation can help provide robust designs for pediatric studies. PMID:26711749

Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach.

PubMed

Stroet, Martin; Koziara, Katarzyna B; Malde, Alpeshkumar K; Mark, Alan E

2017-12-12

A general method for parametrizing atomic interaction functions is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters (parameter space mapping). The consideration of surfaces in parameter space as opposed to local values or gradients leads to a better understanding of the relationships between the parameters being optimized and a given set of target data. This in turn enables for a range of target data from multiple molecules to be combined in a robust manner and for the optimal region of parameter space to be trivially identified. The effectiveness of the approach is illustrated by using the method to refine the chlorine 6-12 Lennard-Jones parameters against experimental solvation free enthalpies in water and hexane as well as the density and heat of vaporization of the liquid at atmospheric pressure for a set of 10 aromatic-chloro compounds simultaneously. Single-step perturbation is used to efficiently calculate solvation free enthalpies for a wide range of parameter combinations. The capacity of this approach to parametrize accurate and transferrable force fields is discussed.

A Bat Algorithm with Mutation for UCAV Path Planning

PubMed Central

Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi

2012-01-01

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models. PMID:23365518

Unified anomaly suppression and boundary extraction in laser radar range imagery based on a joint curve-evolution and expectation-maximization algorithm.

PubMed

Feng, Haihua; Karl, William Clem; Castañon, David A

2008-05-01

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data. This formulation allows direct incorporation of prior information concerning the target boundary, target ranges, and background ranges into an optimal reconstruction process. Curve evolution techniques and a generalized expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy, resulting in a coupled pair of object and intensity optimization tasks. The method directly and optimally extracts the target boundary, avoiding a suboptimal two-step process involving image smoothing followed by boundary extraction. Experiments are presented demonstrating that the proposed approach is robust to anomalous pixels (missing data) and capable of producing accurate estimation of the target boundary and range values from noisy data.

Phase retrieval from intensity-only data by relative entropy minimization.

PubMed

Deming, Ross W

2007-11-01

A recursive algorithm, which appears to be new, is presented for estimating the amplitude and phase of a wave field from intensity-only measurements on two or more scan planes at different axial positions. The problem is framed as a nonlinear optimization, in which the angular spectrum of the complex field model is adjusted in order to minimize the relative entropy, or Kullback-Leibler divergence, between the measured and reconstructed intensities. The most common approach to this so-called phase retrieval problem is a variation of the well-known Gerchberg-Saxton algorithm devised by Misell (J. Phys. D6, L6, 1973), which is efficient and extremely simple to implement. The new algorithm has a computational structure that is very similar to Misell's approach, despite the fundamental difference in the optimization criteria used for each. Based upon results from noisy simulated data, the new algorithm appears to be more robust than Misell's approach and to produce better results from low signal-to-noise ratio data. The convergence of the new algorithm is examined.

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

PubMed

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

2016-04-01

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

On Reducing Delay in Mesh-Based P2P Streaming: A Mesh-Push Approach

NASA Astrophysics Data System (ADS)

Liu, Zheng; Xue, Kaiping; Hong, Peilin

The peer-assisted streaming paradigm has been widely employed to distribute live video data on the internet recently. In general, the mesh-based pull approach is more robust and efficient than the tree-based push approach. However, pull protocol brings about longer streaming delay, which is caused by the handshaking process of advertising buffer map message, sending request message and scheduling of the data block. In this paper, we propose a new approach, mesh-push, to address this issue. Different from the traditional pull approach, mesh-push implements block scheduling algorithm at sender side, where the block transmission is initiated by the sender rather than by the receiver. We first formulate the optimal upload bandwidth utilization problem, then present the mesh-push approach, in which a token protocol is designed to avoid block redundancy; a min-cost flow model is employed to derive the optimal scheduling for the push peer; and a push peer selection algorithm is introduced to reduce control overhead. Finally, we evaluate mesh-push through simulation, the results of which show mesh-push outperforms the pull scheduling in streaming delay, and achieves comparable delivery ratio at the same time.

Design of optimally normal minimum gain controllers by continuation method

NASA Technical Reports Server (NTRS)

Lim, K. B.; Juang, J.-N.; Kim, Z. C.

1989-01-01

A measure of the departure from normality is investigated for system robustness. An attractive feature of the normality index is its simplicity for pole placement designs. To allow a tradeoff between system robustness and control effort, a cost function consisting of the sum of a norm of weighted gain matrix and a normality index is minimized. First- and second-order necessary conditions for the constrained optimization problem are derived and solved by a Newton-Raphson algorithm imbedded into a one-parameter family of neighboring zero problems. The method presented allows the direct computation of optimal gains in terms of robustness and control effort for pole placement problems.

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures.

PubMed

Bonmati, Ester; Hu, Yipeng; Gibson, Eli; Uribarri, Laura; Keane, Geri; Gurusami, Kurinchi; Davidson, Brian; Pereira, Stephen P; Clarkson, Matthew J; Barratt, Dean C

2018-06-01

Navigation of endoscopic ultrasound (EUS)-guided procedures of the upper gastrointestinal (GI) system can be technically challenging due to the small fields-of-view of ultrasound and optical devices, as well as the anatomical variability and limited number of orienting landmarks during navigation. Co-registration of an EUS device and a pre-procedure 3D image can enhance the ability to navigate. However, the fidelity of this contextual information depends on the accuracy of registration. The purpose of this study was to develop and test the feasibility of a simulation-based planning method for pre-selecting patient-specific EUS-visible anatomical landmark locations to maximise the accuracy and robustness of a feature-based multimodality registration method. A registration approach was adopted in which landmarks are registered to anatomical structures segmented from the pre-procedure volume. The predicted target registration errors (TREs) of EUS-CT registration were estimated using simulated visible anatomical landmarks and a Monte Carlo simulation of landmark localisation error. The optimal planes were selected based on the 90th percentile of TREs, which provide a robust and more accurate EUS-CT registration initialisation. The method was evaluated by comparing the accuracy and robustness of registrations initialised using optimised planes versus non-optimised planes using manually segmented CT images and simulated ([Formula: see text]) or retrospective clinical ([Formula: see text]) EUS landmarks. The results show a lower 90th percentile TRE when registration is initialised using the optimised planes compared with a non-optimised initialisation approach (p value [Formula: see text]). The proposed simulation-based method to find optimised EUS planes and landmarks for EUS-guided procedures may have the potential to improve registration accuracy. Further work will investigate applying the technique in a clinical setting.

Robust quantum optimizer with full connectivity

PubMed Central

Nigg, Simon E.; Lörch, Niels; Tiwari, Rakesh P.

2017-01-01

Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example, quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size than classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. By leveraging the phenomenon of flux quantization, all-to-all connectivity with sufficient tunability to implement many relevant optimization problems is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the nondeterministic polynomial-time hard (NP-hard) and fully connected number partitioning problem in the presence of dissipation. PMID:28435880

Study of the fractional order proportional integral controller for the permanent magnet synchronous motor based on the differential evolution algorithm.

PubMed

Zheng, Weijia; Pi, Youguo

2016-07-01

A tuning method of the fractional order proportional integral speed controller for a permanent magnet synchronous motor is proposed in this paper. Taking the combination of the integral of time and absolute error and the phase margin as the optimization index, the robustness specification as the constraint condition, the differential evolution algorithm is applied to search the optimal controller parameters. The dynamic response performance and robustness of the obtained optimal controller are verified by motor speed-tracking experiments on the motor speed control platform. Experimental results show that the proposed tuning method can enable the obtained control system to achieve both the optimal dynamic response performance and the robustness to gain variations. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Hybrid Interval–Robust Optimization Model for Water Quality Management

PubMed Central

Xu, Jieyu; Li, Yongping; Huang, Guohe

2013-01-01

Abstract In water quality management problems, uncertainties may exist in many system components and pollution-related processes (i.e., random nature of hydrodynamic conditions, variability in physicochemical processes, dynamic interactions between pollutant loading and receiving water bodies, and indeterminacy of available water and treated wastewater). These complexities lead to difficulties in formulating and solving the resulting nonlinear optimization problems. In this study, a hybrid interval–robust optimization (HIRO) method was developed through coupling stochastic robust optimization and interval linear programming. HIRO can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original chemical oxygen demand (COD) discharge constraints, HIRO enhances the robustness of the optimization processes and resulting solutions. This method was applied to planning of industry development in association with river-water pollution concern in New Binhai District of Tianjin, China. Results demonstrated that the proposed optimization model can effectively communicate uncertainties into the optimization process and generate a spectrum of potential inexact solutions supporting local decision makers in managing benefit-effective water quality management schemes. HIRO is helpful for analysis of policy scenarios related to different levels of economic penalties, while also providing insight into the tradeoff between system benefits and environmental requirements. PMID:23922495

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints.

PubMed

Tchamna, Rodrigue; Lee, Moonyong

2018-01-01

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

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

Liu, Guodong; Xu, Yan; Tomsovic, Kevin

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total costmore » of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.« less

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

DOE PAGES

Liu, Guodong; Xu, Yan; Tomsovic, Kevin

2016-01-01

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total costmore » of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.« less

Purification optimization for a recombinant single-chain variable fragment against type 1 insulin-like growth factor receptor (IGF-1R) by using design of experiment (DoE).

PubMed

Song, Yong-Hong; Sun, Xue-Wen; Jiang, Bo; Liu, Ji-En; Su, Xian-Hui

2015-12-01

Design of experiment (DoE) is a statistics-based technique for experimental design that could overcome the shortcomings of traditional one-factor-at-a-time (OFAT) approach for protein purification optimization. In this study, a DoE approach was applied for optimizing purification of a recombinant single-chain variable fragment (scFv) against type 1 insulin-like growth factor receptor (IGF-1R) expressed in Escherichia coli. In first capture step using Capto L, a 2-level fractional factorial analysis and successively a central composite circumscribed (CCC) design were used to identify the optimal elution conditions. Two main effects, pH and trehalose, were identified, and high recovery (above 95%) and low aggregates ratio (below 10%) were achieved at the pH range from 2.9 to 3.0 with 32-35% (w/v) trehalose added. In the second step using cation exchange chromatography, an initial screening of media and elution pH and a following CCC design were performed, whereby the optimal selectivity of the scFv was obtained on Capto S at pH near 6.0, and the optimal conditions for fulfilling high DBC and purity were identified as pH range of 5.9-6.1 and loading conductivity range of 5-12.5 mS/cm. Upon a further gel filtration, the final purified scFv with a purity of 98% was obtained. Finally, the optimized conditions were verified by a 20-fold scale-up experiment. The purities and yields of intermediate and final products all fell within the regions predicted by DoE approach, suggesting the robustness of the optimized conditions. We proposed that the DoE approach described here is also applicable in production of other recombinant antibody constructs. Copyright © 2015 Elsevier Inc. All rights reserved.

Deformable 3D-2D registration for CT and its application to low dose tomographic fluoroscopy

NASA Astrophysics Data System (ADS)

Flach, Barbara; Brehm, Marcus; Sawall, Stefan; Kachelrieß, Marc

2014-12-01

Many applications in medical imaging include image registration for matching of images from the same or different modalities. In the case of full data sampling, the respective reconstructed images are usually of such a good image quality that standard deformable volume-to-volume (3D-3D) registration approaches can be applied. But research in temporal-correlated image reconstruction and dose reductions increases the number of cases where rawdata are available from only few projection angles. Here, deteriorated image quality leads to non-acceptable deformable volume-to-volume registration results. Therefore a registration approach is required that is robust against a decreasing number of projections defining the target position. We propose a deformable volume-to-rawdata (3D-2D) registration method that aims at finding a displacement vector field maximizing the alignment of a CT volume and the acquired rawdata based on the sum of squared differences in rawdata domain. The registration is constrained by a regularization term in accordance with a fluid-based diffusion. Both cost function components, the rawdata fidelity and the regularization term, are optimized in an alternating manner. The matching criterion is optimized by a conjugate gradient descent for nonlinear functions, while the regularization is realized by convolution of the vector fields with Gaussian kernels. We validate the proposed method and compare it to the demons algorithm, a well-known 3D-3D registration method. The comparison is done for a range of 4-60 target projections using datasets from low dose tomographic fluoroscopy as an application example. The results show a high correlation to the ground truth target position without introducing artifacts even in the case of very few projections. In particular the matching in the rawdata domain is improved compared to the 3D-3D registration for the investigated range. The proposed volume-to-rawdata registration increases the robustness regarding sparse rawdata and provides more stable results than volume-to-volume approaches. By applying the proposed registration approach to low dose tomographic fluoroscopy it is possible to improve the temporal resolution and thus to increase the robustness of low dose tomographic fluoroscopy.

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

PubMed

Wang, Mingming; Sun, Yuanxiang; Sweetapple, Chris

2017-12-15

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

A global optimization algorithm inspired in the behavior of selfish herds.

PubMed

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimizing ELISAs for precision and robustness using laboratory automation and statistical design of experiments.

PubMed

Joelsson, Daniel; Moravec, Phil; Troutman, Matthew; Pigeon, Joseph; DePhillips, Pete

2008-08-20

Transferring manual ELISAs to automated platforms requires optimizing the assays for each particular robotic platform. These optimization experiments are often time consuming and difficult to perform using a traditional one-factor-at-a-time strategy. In this manuscript we describe the development of an automated process using statistical design of experiments (DOE) to quickly optimize immunoassays for precision and robustness on the Tecan EVO liquid handler. By using fractional factorials and a split-plot design, five incubation time variables and four reagent concentration variables can be optimized in a short period of time.

Optimization of monopiles for offshore wind turbines.

PubMed

Kallehave, Dan; Byrne, Byron W; LeBlanc Thilsted, Christian; Mikkelsen, Kristian Kousgaard

2015-02-28

The offshore wind industry currently relies on subsidy schemes to be competitive with fossil-fuel-based energy sources. For the wind industry to survive, it is vital that costs are significantly reduced for future projects. This can be partly achieved by introducing new technologies and partly through optimization of existing technologies and design methods. One of the areas where costs can be reduced is in the support structure, where better designs, cheaper fabrication and quicker installation might all be possible. The prevailing support structure design is the monopile structure, where the simple design is well suited to mass-fabrication, and the installation approach, based on conventional impact driving, is relatively low-risk and robust for most soil conditions. The range of application of the monopile for future wind farms can be extended by using more accurate engineering design methods, specifically tailored to offshore wind industry design. This paper describes how state-of-the-art optimization approaches are applied to the design of current wind farms and monopile support structures and identifies the main drivers where more accurate engineering methods could impact on a next generation of highly optimized monopiles. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

Model-based identification of optimal operating conditions for amino acid simulated moving bed enantioseparation using a macrocyclic glycopeptide stationary phase.

PubMed

Fuereder, Markus; Majeed, Imthiyas N; Panke, Sven; Bechtold, Matthias

2014-06-13

Teicoplanin aglycone columns allow efficient separation of amino acid enantiomers in aqueous mobile phases and enable robust and predictable simulated moving bed (SMB) separation of racemic methionine despite a dependency of the adsorption behavior on the column history (memory effect). In this work we systematically investigated the influence of the mobile phase (methanol content) and temperature on SMB performance using a model-based optimization approach that accounts for methionine solubility, adsorption behavior and back pressure. Adsorption isotherms became more favorable with increasing methanol content but methionine solubility was decreased and back pressure increased. Numerical optimization suggested a moderate methanol content (25-35%) for most efficient operation. Higher temperature had a positive effect on specific productivity and desorbent requirement due to higher methionine solubility, lower back pressure and virtually invariant selectivity at high loadings of racemic methionine. However, process robustness (defined as a difference in flow rate ratios) decreased strongly with increasing temperature to the extent that any significant increase in temperature over 32°C will likely result in operating points that cannot be realized technically even with the lab-scale piston pump SMB system employed in this study. Copyright © 2014. Published by Elsevier B.V.

Block-diagonalization as a tool for the robust diabatization of high-dimensional potential energy surfaces

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

Venghaus, Florian; Eisfeld, Wolfgang, E-mail: wolfgang.eisfeld@uni-bielefeld.de

2016-03-21

Robust diabatization techniques are key for the development of high-dimensional coupled potential energy surfaces (PESs) to be used in multi-state quantum dynamics simulations. In the present study we demonstrate that, besides the actual diabatization technique, common problems with the underlying electronic structure calculations can be the reason why a diabatization fails. After giving a short review of the theoretical background of diabatization, we propose a method based on the block-diagonalization to analyse the electronic structure data. This analysis tool can be used in three different ways: First, it allows to detect issues with the ab initio reference data and ismore » used to optimize the setup of the electronic structure calculations. Second, the data from the block-diagonalization are utilized for the development of optimal parametrized diabatic model matrices by identifying the most significant couplings. Third, the block-diagonalization data are used to fit the parameters of the diabatic model, which yields an optimal initial guess for the non-linear fitting required by standard or more advanced energy based diabatization methods. The new approach is demonstrated by the diabatization of 9 electronic states of the propargyl radical, yielding fully coupled full-dimensional (12D) PESs in closed form.« less

TRANSP-based Trajectory Optimization of the Current Profile Evolution to Facilitate Robust Non-inductive Ramp-up in NSTX-U

NASA Astrophysics Data System (ADS)

Wehner, William; Schuster, Eugenio; Poli, Francesca

2016-10-01

Initial progress towards the design of non-inductive current ramp-up scenarios in the National Spherical Torus Experiment Upgrade (NSTX-U) has been made through the use of TRANSP predictive simulations. The strategy involves, first, ramping the plasma current with high harmonic fast waves (HHFW) to about 400 kA, and then further ramping to 900 kA with neutral beam injection (NBI). However, the early ramping of neutral beams and application of HHFW leads to an undesirably peaked current profile making the plasma unstable to ballooning modes. We present an optimization-based control approach to improve on the non-inductive ramp-up strategy. We combine the TRANSP code with an optimization algorithm based on sequential quadratic programming to search for time evolutions of the NBI powers, the HHFW powers, and the line averaged density that define an open-loop actuator strategy that maximizes the non-inductive current while satisfying constraints associated with the current profile evolution for MHD stable plasmas. This technique has the potential of playing a critical role in achieving robustly stable non-inductive ramp-up, which will ultimately be necessary to demonstrate applicability of the spherical torus concept to larger devices without sufficient room for a central coil. Supported by the US DOE under the SCGSR Program.

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

PubMed

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

A robust and high precision optimal explicit guidance scheme for solid motor propelled launch vehicles with thrust and drag uncertainty

NASA Astrophysics Data System (ADS)

Maity, Arnab; Padhi, Radhakant; Mallaram, Sanjeev; Mallikarjuna Rao, G.; Manickavasagam, M.

2016-10-01

A new nonlinear optimal and explicit guidance law is presented in this paper for launch vehicles propelled by solid motors. It can ensure very high terminal precision despite not having the exact knowledge of the thrust-time curve apriori. This was motivated from using it for a carrier launch vehicle in a hypersonic mission, which demands an extremely narrow terminal accuracy window for the launch vehicle for successful initiation of operation of the hypersonic vehicle. The proposed explicit guidance scheme, which computes the optimal guidance command online, ensures the required stringent final conditions with high precision at the injection point. A key feature of the proposed guidance law is an innovative extension of the recently developed model predictive static programming guidance with flexible final time. A penalty function approach is also followed to meet the input and output inequality constraints throughout the vehicle trajectory. In this paper, the guidance law has been successfully validated from nonlinear six degree-of-freedom simulation studies by designing an inner-loop autopilot as well, which enhances confidence of its usefulness significantly. In addition to excellent nominal results, the proposed guidance has been found to have good robustness for perturbed cases as well.

An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

Multi-objective optimization of piezoelectric circuitry network for mode delocalization and suppression of bladed disk

NASA Astrophysics Data System (ADS)

Yoo, David; Tang, J.

2017-04-01

Since weakly-coupled bladed disks are highly sensitive to the presence of uncertainties, they can easily undergo vibration localization. When vibration localization occurs, vibration modes of bladed disk become dramatically different from those under the perfectly periodic condition, and the dynamic response under engine-order excitation is drastically amplified. In previous studies, it is investigated that amplified vibration response can be suppressed by connecting piezoelectric circuitry into individual blades to induce the damped absorber effect, and localized vibration modes can be alleviated by integrating piezoelectric circuitry network. Delocalization of vibration modes and vibration suppression of bladed disk, however, require different optimal set of circuit parameters. In this research, multi-objective optimization approach is developed to enable finding the best circuit parameters, simultaneously achieving both objectives. In this way, the robustness and reliability in bladed disk can be ensured. Gradient-based optimizations are individually developed for mode delocalization and vibration suppression, which are then integrated into multi-objective optimization framework.

Cascade Optimization Strategy with Neural Network and Regression Approximations Demonstrated on a Preliminary Aircraft Engine Design

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Patnaik, Surya N.

2000-01-01

A preliminary aircraft engine design methodology is being developed that utilizes a cascade optimization strategy together with neural network and regression approximation methods. The cascade strategy employs different optimization algorithms in a specified sequence. The neural network and regression methods are used to approximate solutions obtained from the NASA Engine Performance Program (NEPP), which implements engine thermodynamic cycle and performance analysis models. The new methodology is proving to be more robust and computationally efficient than the conventional optimization approach of using a single optimization algorithm with direct reanalysis. The methodology has been demonstrated on a preliminary design problem for a novel subsonic turbofan engine concept that incorporates a wave rotor as a cycle-topping device. Computations of maximum thrust were obtained for a specific design point in the engine mission profile. The results (depicted in the figure) show a significant improvement in the maximum thrust obtained using the new methodology in comparison to benchmark solutions obtained using NEPP in a manual design mode.

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.

Modeling Physarum space exploration using memristors

NASA Astrophysics Data System (ADS)

Ntinas, V.; Vourkas, I.; Sirakoulis, G. Ch; Adamatzky, A. I.

2017-05-01

Slime mold Physarum polycephalum optimizes its foraging behaviour by minimizing the distances between the sources of nutrients it spans. When two sources of nutrients are present, the slime mold connects the sources, with its protoplasmic tubes, along the shortest path. We present a two-dimensional mesh grid memristor based model as an approach to emulate Physarum’s foraging strategy, which includes space exploration and reinforcement of the optimally formed interconnection network in the presence of multiple aliment sources. The proposed algorithmic approach utilizes memristors and LC contours and is tested in two of the most popular computational challenges for Physarum, namely maze and transportation networks. Furthermore, the presented model is enriched with the notion of noise presence, which positively contributes to a collective behavior and enables us to move from deterministic to robust results. Consequently, the corresponding simulation results manage to reproduce, in a much better qualitative way, the expected transportation networks.

Development of a combined feed forward-feedback system for an electron Linac

NASA Astrophysics Data System (ADS)

Meier, E.; Biedron, S. G.; LeBlanc, G.; Morgan, M. J.; Wu, J.

2009-10-01

This paper describes the results of an advanced control algorithm for the stabilization of electron beam energy in a Linac. The approach combines a conventional Proportional-Integral (PI) controller with a neural network (NNET) feed forward algorithm; it utilizes the robustness of PI control and the ability of a feed forward system in order to exert control over a wider range of frequencies. The NNET is trained to recognize jitter occurring in the phase and voltage of one of the klystrons, based on a record of these parameters, and predicts future energy deviations. A systematic approach is developed to determine the optimal NNET parameters that are then applied to the Australian Synchrotron Linac. The system's capability to fully cancel multi-frequency jitter is demonstrated. The NNET system is then augmented with the PI algorithm, and further jitter attenuation is achieved when the NNET is not operating optimally.

A 3D Freehand Ultrasound System for Multi-view Reconstructions from Sparse 2D Scanning Planes

PubMed Central

2011-01-01

Background A significant limitation of existing 3D ultrasound systems comes from the fact that the majority of them work with fixed acquisition geometries. As a result, the users have very limited control over the geometry of the 2D scanning planes. Methods We present a low-cost and flexible ultrasound imaging system that integrates several image processing components to allow for 3D reconstructions from limited numbers of 2D image planes and multiple acoustic views. Our approach is based on a 3D freehand ultrasound system that allows users to control the 2D acquisition imaging using conventional 2D probes. For reliable performance, we develop new methods for image segmentation and robust multi-view registration. We first present a new hybrid geometric level-set approach that provides reliable segmentation performance with relatively simple initializations and minimum edge leakage. Optimization of the segmentation model parameters and its effect on performance is carefully discussed. Second, using the segmented images, a new coarse to fine automatic multi-view registration method is introduced. The approach uses a 3D Hotelling transform to initialize an optimization search. Then, the fine scale feature-based registration is performed using a robust, non-linear least squares algorithm. The robustness of the multi-view registration system allows for accurate 3D reconstructions from sparse 2D image planes. Results Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions. The volume error of multi-view reconstruction is measured to be less than 5% of the true volume. We show that volume reconstruction accuracy is a function of the total number of 2D image planes and the number of views for calibrated phantom. In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions. Conclusions Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems. The flexibility and low-cost of the proposed system allow for fine control of the image acquisition planes for optimal 3D reconstructions from multiple views. PMID:21251284

A 3D freehand ultrasound system for multi-view reconstructions from sparse 2D scanning planes.

PubMed

Yu, Honggang; Pattichis, Marios S; Agurto, Carla; Beth Goens, M

2011-01-20

A significant limitation of existing 3D ultrasound systems comes from the fact that the majority of them work with fixed acquisition geometries. As a result, the users have very limited control over the geometry of the 2D scanning planes. We present a low-cost and flexible ultrasound imaging system that integrates several image processing components to allow for 3D reconstructions from limited numbers of 2D image planes and multiple acoustic views. Our approach is based on a 3D freehand ultrasound system that allows users to control the 2D acquisition imaging using conventional 2D probes.For reliable performance, we develop new methods for image segmentation and robust multi-view registration. We first present a new hybrid geometric level-set approach that provides reliable segmentation performance with relatively simple initializations and minimum edge leakage. Optimization of the segmentation model parameters and its effect on performance is carefully discussed. Second, using the segmented images, a new coarse to fine automatic multi-view registration method is introduced. The approach uses a 3D Hotelling transform to initialize an optimization search. Then, the fine scale feature-based registration is performed using a robust, non-linear least squares algorithm. The robustness of the multi-view registration system allows for accurate 3D reconstructions from sparse 2D image planes. Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions. The volume error of multi-view reconstruction is measured to be less than 5% of the true volume. We show that volume reconstruction accuracy is a function of the total number of 2D image planes and the number of views for calibrated phantom. In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions. Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems. The flexibility and low-cost of the proposed system allow for fine control of the image acquisition planes for optimal 3D reconstructions from multiple views.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

Gradient descent for robust kernel-based regression

NASA Astrophysics Data System (ADS)

Guo, Zheng-Chu; Hu, Ting; Shi, Lei

2018-06-01

In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.

Topology Optimization using the Level Set and eXtended Finite Element Methods: Theory and Applications

NASA Astrophysics Data System (ADS)

Villanueva Perez, Carlos Hernan

Computational design optimization provides designers with automated techniques to develop novel and non-intuitive optimal designs. Topology optimization is a design optimization technique that allows for the evolution of a broad variety of geometries in the optimization process. Traditional density-based topology optimization methods often lack a sufficient resolution of the geometry and physical response, which prevents direct use of the optimized design in manufacturing and the accurate modeling of the physical response of boundary conditions. The goal of this thesis is to introduce a unified topology optimization framework that uses the Level Set Method (LSM) to describe the design geometry and the eXtended Finite Element Method (XFEM) to solve the governing equations and measure the performance of the design. The methodology is presented as an alternative to density-based optimization approaches, and is able to accommodate a broad range of engineering design problems. The framework presents state-of-the-art methods for immersed boundary techniques to stabilize the systems of equations and enforce the boundary conditions, and is studied with applications in 2D and 3D linear elastic structures, incompressible flow, and energy and species transport problems to test the robustness and the characteristics of the method. A comparison of the framework against density-based topology optimization approaches is studied with regards to convergence, performance, and the capability to manufacture the designs. Furthermore, the ability to control the shape of the design to operate within manufacturing constraints is developed and studied. The analysis capability of the framework is validated quantitatively through comparison against previous benchmark studies, and qualitatively through its application to topology optimization problems. The design optimization problems converge to intuitive designs and resembled well the results from previous 2D or density-based studies.

A Robust Kalman Framework with Resampling and Optimal Smoothing

PubMed Central

Kautz, Thomas; Eskofier, Bjoern M.

2015-01-01

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies. PMID:25734647

Fixed-Order Mixed Norm Designs for Building Vibration Control

NASA Technical Reports Server (NTRS)

Whorton, Mark S.; Calise, Anthony J.

2000-01-01

This study investigates the use of H2, mu-synthesis, and mixed H2/mu methods to construct full order controllers and optimized controllers of fixed dimensions. The benchmark problem definition is first extended to include uncertainty within the controller bandwidth in the form of parametric uncertainty representative of uncertainty in the natural frequencies of the design model. The sensitivity of H2 design to unmodeled dynamics and parametric uncertainty is evaluated for a range of controller levels of authority. Next, mu-synthesis methods are applied to design full order compensators that are robust to both unmodeled dynamics and to parametric uncertainty. Finally, a set of mixed H2/mu compensators are designed which are optimized for a fixed compensator dimension. These mixed norm designs recover the H2 design performance levels while providing the same levels of robust stability as the mu designs. It is shown that designing with the mixed norm approach permits higher levels of controller authority for which the H2 designs are destabilizing. The benchmark problem is that of an active tendon system. The controller designs are all based on the use of acceleration feedback.

Robust fiber clustering of cerebral fiber bundles in white matter

NASA Astrophysics Data System (ADS)

Yao, Xufeng; Wang, Yongxiong; Zhuang, Songlin

2014-11-01

Diffusion tensor imaging fiber tracking (DTI-FT) has been widely accepted in the diagnosis and treatment of brain diseases. During the rendering pipeline of specific fiber tracts, the image noise and low resolution of DTI would lead to false propagations. In this paper, we propose a robust fiber clustering (FC) approach to diminish false fibers from one fiber tract. Our algorithm consists of three steps. Firstly, the optimized fiber assignment continuous tracking (FACT) is implemented to reconstruct one fiber tract; and then each curved fiber in the fiber tract is mapped to a point by kernel principal component analysis (KPCA); finally, the point clouds of fiber tract are clustered by hierarchical clustering which could distinguish false fibers from true fibers in one tract. In our experiment, the corticospinal tract (CST) in one case of human data in vivo was used to validate our method. Our method showed reliable capability in decreasing the false fibers in one tract. In conclusion, our method could effectively optimize the visualization of fiber bundles and would help a lot in the field of fiber evaluation.

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.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

On the convergence of a linesearch based proximal-gradient method for nonconvex optimization

NASA Astrophysics Data System (ADS)

Bonettini, S.; Loris, I.; Porta, F.; Prato, M.; Rebegoldi, S.

2017-05-01

We consider a variable metric linesearch based proximal gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth term. We prove convergence of this iterative algorithm to a critical point if the objective function satisfies the Kurdyka-Łojasiewicz property at each point of its domain, under the assumption that a limit point exists. The proposed method is applied to a wide collection of image processing problems and our numerical tests show that our algorithm results to be flexible, robust and competitive when compared to recently proposed approaches able to address the optimization problems arising in the considered applications.

Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.

2016-01-01

The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.

Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models

USGS Publications Warehouse

Welter, David E.; White, Jeremy T.; Hunt, Randall J.; Doherty, John E.

2015-09-18

The PEST++ Version 3 software suite can be compiled for Microsoft Windows®4 and Linux®5 operating systems; the source code is available in a Microsoft Visual Studio®6 2013 solution; Linux Makefiles are also provided. PEST++ Version 3 continues to build a foundation for an open-source framework capable of producing robust and efficient parameter estimation tools for large environmental models.

The novel high-performance 3-D MT inverse solver

NASA Astrophysics Data System (ADS)

Kruglyakov, Mikhail; Geraskin, Alexey; Kuvshinov, Alexey

2016-04-01

We present novel, robust, scalable, and fast 3-D magnetotelluric (MT) inverse solver. The solver is written in multi-language paradigm to make it as efficient, readable and maintainable as possible. Separation of concerns and single responsibility concepts go through implementation of the solver. As a forward modelling engine a modern scalable solver extrEMe, based on contracting integral equation approach, is used. Iterative gradient-type (quasi-Newton) optimization scheme is invoked to search for (regularized) inverse problem solution, and adjoint source approach is used to calculate efficiently the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT responses, and supports massive parallelization. Moreover, different parallelization strategies implemented in the code allow optimal usage of available computational resources for a given problem statement. To parameterize an inverse domain the so-called mask parameterization is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to HPC Piz Daint (6th supercomputer in the world) demonstrate practically linear scalability of the code up to thousands of nodes.

YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features.

PubMed

Kleftogiannis, Dimitrios; Theofilatos, Konstantinos; Likothanassis, Spiros; Mavroudi, Seferina

2015-01-01

MicroRNAs (miRNAs) are small non-coding RNAs, which play a significant role in gene regulation. Predicting miRNA genes is a challenging bioinformatics problem and existing experimental and computational methods fail to deal with it effectively. We developed YamiPred, an embedded classification method that combines the efficiency and robustness of support vector machines (SVM) with genetic algorithms (GA) for feature selection and parameters optimization. YamiPred was tested in a new and realistic human dataset and was compared with state-of-the-art computational intelligence approaches and the prevalent SVM-based tools for miRNA prediction. Experimental results indicate that YamiPred outperforms existing approaches in terms of accuracy and of geometric mean of sensitivity and specificity. The embedded feature selection component selects a compact feature subset that contributes to the performance optimization. Further experimentation with this minimal feature subset has achieved very high classification performance and revealed the minimum number of samples required for developing a robust predictor. YamiPred also confirmed the important role of commonly used features such as entropy and enthalpy, and uncovered the significance of newly introduced features, such as %A-U aggregate nucleotide frequency and positional entropy. The best model trained on human data has successfully predicted pre-miRNAs to other organisms including the category of viruses.

Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods

NASA Astrophysics Data System (ADS)

Maximov, Ivan I.; Vinding, Mads S.; Tse, Desmond H. Y.; Nielsen, Niels Chr.; Shah, N. Jon

2015-05-01

There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community.

Optimal sampling with prior information of the image geometry in microfluidic MRI.

PubMed

Han, S H; Cho, H; Paulsen, J L

2015-03-01

Recent advances in MRI acquisition for microscopic flows enable unprecedented sensitivity and speed in a portable NMR/MRI microfluidic analysis platform. However, the application of MRI to microfluidics usually suffers from prolonged acquisition times owing to the combination of the required high resolution and wide field of view necessary to resolve details within microfluidic channels. When prior knowledge of the image geometry is available as a binarized image, such as for microfluidic MRI, it is possible to reduce sampling requirements by incorporating this information into the reconstruction algorithm. The current approach to the design of the partial weighted random sampling schemes is to bias toward the high signal energy portions of the binarized image geometry after Fourier transformation (i.e. in its k-space representation). Although this sampling prescription is frequently effective, it can be far from optimal in certain limiting cases, such as for a 1D channel, or more generally yield inefficient sampling schemes at low degrees of sub-sampling. This work explores the tradeoff between signal acquisition and incoherent sampling on image reconstruction quality given prior knowledge of the image geometry for weighted random sampling schemes, finding that optimal distribution is not robustly determined by maximizing the acquired signal but from interpreting its marginal change with respect to the sub-sampling rate. We develop a corresponding sampling design methodology that deterministically yields a near optimal sampling distribution for image reconstructions incorporating knowledge of the image geometry. The technique robustly identifies optimal weighted random sampling schemes and provides improved reconstruction fidelity for multiple 1D and 2D images, when compared to prior techniques for sampling optimization given knowledge of the image geometry. Copyright © 2015 Elsevier Inc. All rights reserved.

Non-linear Multidimensional Optimization for use in Wire Scanner Fitting

NASA Astrophysics Data System (ADS)

Henderson, Alyssa; Terzic, Balsa; Hofler, Alicia; CASA and Accelerator Ops Collaboration

2013-10-01

To ensure experiment efficiency and quality from the Continuous Electron Beam Accelerator at Jefferson Lab, beam energy, size, and position must be measured. Wire scanners are devices inserted into the beamline to produce measurements which are used to obtain beam properties. Extracting physical information from the wire scanner measurements begins by fitting Gaussian curves to the data. This study focuses on optimizing and automating this curve-fitting procedure. We use a hybrid approach combining the efficiency of Newton Conjugate Gradient (NCG) method with the global convergence of three nature-inspired (NI) optimization approaches: genetic algorithm, differential evolution, and particle-swarm. In this Python-implemented approach, augmenting the locally-convergent NCG with one of the globally-convergent methods ensures the quality, robustness, and automation of curve-fitting. After comparing the methods, we establish that given an initial data-derived guess, each finds a solution with the same chi-square- a measurement of the agreement of the fit to the data. NCG is the fastest method, so it is the first to attempt data-fitting. The curve-fitting procedure escalates to one of the globally-convergent NI methods only if NCG fails, thereby ensuring a successful fit. This method allows for the most optimal signal fit and can be easily applied to similar problems. Financial support from DoE, NSF, ODU, DoD, and Jefferson Lab.

Optimum design of bolted composite lap joints under mechanical and thermal loading

NASA Astrophysics Data System (ADS)

Kradinov, Vladimir Yurievich

A new approach is developed for the analysis and design of mechanically fastened composite lap joints under mechanical and thermal loading. Based on the combined complex potential and variational formulation, the solution method satisfies the equilibrium equations exactly while the boundary conditions are satisfied by minimizing the total potential. This approach is capable of modeling finite laminate planform dimensions, uniform and variable laminate thickness, laminate lay-up, interaction among bolts, bolt torque, bolt flexibility, bolt size, bolt-hole clearance and interference, insert dimensions and insert material properties. Comparing to the finite element analysis, the robustness of the method does not decrease when modeling the interaction of many bolts; also, the method is more suitable for parametric study and design optimization. The Genetic Algorithm (GA), a powerful optimization technique for multiple extrema functions in multiple dimensions search spaces, is applied in conjunction with the complex potential and variational formulation to achieve optimum designs of bolted composite lap joints. The objective of the optimization is to acquire such a design that ensures the highest strength of the joint. The fitness function for the GA optimization is based on the average stress failure criterion predicting net-section, shear-out, and bearing failure modes in bolted lap joints. The criterion accounts for the stress distribution in the thickness direction at the bolt location by applying an approach utilizing a beam on an elastic foundation formulation.

Stochastic simulation and robust design optimization of integrated photonic filters

NASA Astrophysics Data System (ADS)

Weng, Tsui-Wei; Melati, Daniele; Melloni, Andrea; Daniel, Luca

2017-01-01

Manufacturing variations are becoming an unavoidable issue in modern fabrication processes; therefore, it is crucial to be able to include stochastic uncertainties in the design phase. In this paper, integrated photonic coupled ring resonator filters are considered as an example of significant interest. The sparsity structure in photonic circuits is exploited to construct a sparse combined generalized polynomial chaos model, which is then used to analyze related statistics and perform robust design optimization. Simulation results show that the optimized circuits are more robust to fabrication process variations and achieve a reduction of 11%-35% in the mean square errors of the 3 dB bandwidth compared to unoptimized nominal designs.

Ridge filter design and optimization for the broad-beam three-dimensional irradiation system for heavy-ion radiotherapy.

PubMed

Schaffner, B; Kanai, T; Futami, Y; Shimbo, M; Urakabe, E

2000-04-01

The broad-beam three-dimensional irradiation system under development at National Institute of Radiological Sciences (NIRS) requires a small ridge filter to spread the initially monoenergetic heavy-ion beam to a small spread-out Bragg peak (SOBP). A large SOBP covering the target volume is then achieved by a superposition of differently weighted and displaced small SOBPs. Two approaches were studied for the definition of a suitable ridge filter and experimental verifications were performed. Both approaches show a good agreement between the calculated and measured dose and lead to a good homogeneity of the biological dose in the target. However, the ridge filter design that produces a Gaussian-shaped spectrum of the particle ranges was found to be more robust to small errors and uncertainties in the beam application. Furthermore, an optimization procedure for two fields was applied to compensate for the missing dose from the fragmentation tail for the case of a simple-geometry target. The optimized biological dose distributions show that a very good homogeneity is achievable in the target.

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

PubMed Central

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

2017-01-01

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

Finding Optimal Apertures in Kepler Data

NASA Astrophysics Data System (ADS)

Smith, Jeffrey C.; Morris, Robert L.; Jenkins, Jon M.; Bryson, Stephen T.; Caldwell, Douglas A.; Girouard, Forrest R.

2016-12-01

With the loss of two spacecraft reaction wheels precluding further data collection for the Kepler primary mission, even greater pressure is placed on the processing pipeline to eke out every last transit signal in the data. To that end, we have developed a new method to optimize the Kepler Simple Aperture Photometry (SAP) photometric apertures for both planet detection and minimization of systematic effects. The approach uses a per cadence modeling of the raw pixel data and then performs an aperture optimization based on signal-to-noise ratio and the Kepler Combined Differential Photometric Precision (CDPP), which is a measure of the noise over the duration of a reference transit signal. We have found the new apertures to be superior to the previous Kepler apertures. We can now also find a per cadence flux fraction in aperture and crowding metric. The new approach has also been proven to be robust at finding apertures in K2 data that help mitigate the larger motion-induced systematics in the photometry. The method further allows us to identify errors in the Kepler and K2 input catalogs.

Ant system: optimization by a colony of cooperating agents.

PubMed

Dorigo, M; Maniezzo, V; Colorni, A

1996-01-01

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

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

PubMed

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

2017-05-19

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

TH-CD-209-05: Impact of Spot Size and Spacing On the Quality of Robustly-Optimized Intensity-Modulated Proton Therapy Plans for Lung Cancer

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

Liu, W; Ding, X; Hu, Y

Purpose: To investigate how spot size and spacing affect plan quality, especially, plan robustness and the impact of interplay effect, of robustly-optimized intensity-modulated proton therapy (IMPT) plans for lung cancer. Methods: Two robustly-optimized IMPT plans were created for 10 lung cancer patients: (1) one for a proton beam with in-air energy dependent large spot size at isocenter (σ: 5–15 mm) and spacing (1.53σ); (2) the other for a proton beam with small spot size (σ: 2–6 mm) and spacing (5 mm). Both plans were generated on the average CTs with internal-gross-tumor-volume density overridden to irradiate internal target volume (ITV). Themore » root-mean-square-dose volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under RVH curves were used to evaluate plan robustness. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Patient anatomy voxels were mapped from phase to phase via deformable image registration to score doses. Dose-volume-histogram indices including ITV coverage, homogeneity, and organs-at-risk (OAR) sparing were compared using Student-t test. Results: Compared to large spots, small spots resulted in significantly better OAR sparing with comparable ITV coverage and homogeneity in the nominal plan. Plan robustness was comparable for ITV and most OARs. With interplay effect considered, significantly better OAR sparing with comparable ITV coverage and homogeneity is observed using smaller spots. Conclusion: Robust optimization with smaller spots significantly improves OAR sparing with comparable plan robustness and similar impact of interplay effect compare to larger spots. Small spot size requires the use of larger number of spots, which gives optimizer more freedom to render a plan more robust. The ratio between spot size and spacing was found to be more relevant to determine plan robustness and the impact of interplay effect than spot size alone. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation.« less

A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram

NASA Astrophysics Data System (ADS)

Tian, Xiange; Xi Gu, James; Rehab, Ibrahim; Abdalla, Gaballa M.; Gu, Fengshou; Ball, A. D.

2018-02-01

Envelope analysis is a widely used method for rolling element bearing fault detection. To obtain high detection accuracy, it is critical to determine an optimal frequency narrowband for the envelope demodulation. However, many of the schemes which are used for the narrowband selection, such as the Kurtogram, can produce poor detection results because they are sensitive to random noise and aperiodic impulses which normally occur in practical applications. To achieve the purposes of denoising and frequency band optimisation, this paper proposes a novel modulation signal bispectrum (MSB) based robust detector for bearing fault detection. Because of its inherent noise suppression capability, the MSB allows effective suppression of both stationary random noise and discrete aperiodic noise. The high magnitude features that result from the use of the MSB also enhance the modulation effects of a bearing fault and can be used to provide optimal frequency bands for fault detection. The Kurtogram is generally accepted as a powerful means of selecting the most appropriate frequency band for envelope analysis, and as such it has been used as the benchmark comparator for performance evaluation in this paper. Both simulated and experimental data analysis results show that the proposed method produces more accurate and robust detection results than Kurtogram based approaches for common bearing faults under a range of representative scenarios.

Geometry-based ensembles: toward a structural characterization of the classification boundary.

PubMed

Pujol, Oriol; Masip, David

2009-06-01

This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.