WorldWideScience

Sample records for robust optimization submissions

  1. Robust Portfolio Optimization Using Pseudodistances.

    Science.gov (United States)

    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.

  2. Robust Portfolio Optimization Using Pseudodistances

    Science.gov (United States)

    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

  3. Robust boosting via convex optimization

    Science.gov (United States)

    Rätsch, Gunnar

    2001-12-01

    In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules - also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues: o The statistical learning theory framework for analyzing boosting methods. We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution. o How can boosting methods be related to mathematical optimization techniques? To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms. o How to make Boosting noise robust? One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness. o How to adapt boosting to regression problems

  4. Evolution strategies for robust optimization

    NARCIS (Netherlands)

    Kruisselbrink, Johannes Willem

    2012-01-01

    Real-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for, optimization may fail or may yield solutions that are optimal in the classical strict notion of optimality, but

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

    Science.gov (United States)

    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

  6. Primal and dual approaches to adjustable robust optimization

    NARCIS (Netherlands)

    de Ruiter, Frans

    2018-01-01

    Robust optimization has become an important paradigm to deal with optimization under uncertainty. Adjustable robust optimization is an extension that deals with multistage problems. This thesis starts with a short but comprehensive introduction to adjustable robust optimization. Then the two

  7. Robust Optimization of Database Queries

    Indian Academy of Sciences (India)

    JAYANT

    2011-07-06

    Jul 6, 2011 ... Based on first-order logic. ○ Edgar ... Cost-based Query Optimizer s choice of execution plan ... Determines the values of goods shipped between nations in a time period select ..... Born: 1881 Elected: 1934 Section: Medicine.

  8. Efficient reanalysis techniques for robust topology optimization

    DEFF Research Database (Denmark)

    Amir, Oded; Sigmund, Ole; Lazarov, Boyan Stefanov

    2012-01-01

    efficient robust topology optimization procedures based on reanalysis techniques. The approach is demonstrated on two compliant mechanism design problems where robust design is achieved by employing either a worst case formulation or a stochastic formulation. It is shown that the time spent on finite...

  9. Robust quantum optimizer with full connectivity.

    Science.gov (United States)

    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.

  10. Robust Structured Control Design via LMI Optimization

    DEFF Research Database (Denmark)

    Adegas, Fabiano Daher; Stoustrup, Jakob

    2011-01-01

    This paper presents a new procedure for discrete-time robust structured control design. Parameter-dependent nonconvex conditions for stabilizable and induced L2-norm performance controllers are solved by an iterative linear matrix inequalities (LMI) optimization. A wide class of controller...... structures including decentralized of any order, fixed-order dynamic output feedback, static output feedback can be designed robust to polytopic uncertainties. Stability is proven by a parameter-dependent Lyapunov function. Numerical examples on robust stability margins shows that the proposed procedure can...

  11. Robust Portfolio Optimization using CAPM Approach

    Directory of Open Access Journals (Sweden)

    mohsen gharakhani

    2013-08-01

    Full Text Available In this paper, a new robust model of multi-period portfolio problem has been developed. One of the key concerns in any asset allocation problem is how to cope with uncertainty about future returns. There are some approaches in the literature for this purpose including stochastic programming and robust optimization. Applying these techniques to multi-period portfolio problem may increase the problem size in a way that the resulting model is intractable. In this paper, a novel approach has been proposed to formulate multi-period portfolio problem as an uncertain linear program assuming that asset return follows the single-index factor model. Robust optimization technique has been also used to solve the problem. In order to evaluate the performance of the proposed model, a numerical example has been applied using simulated data.

  12. Robustizing Circuit Optimization using Huber Functions

    DEFF Research Database (Denmark)

    Bandler, John W.; Biernacki, Radek M.; Chen, Steve H.

    1993-01-01

    The authors introduce a novel approach to 'robustizing' microwave circuit optimization using Huber functions, both two-sided and one-sided. They compare Huber optimization with l/sub 1/, l/sub 2/, and minimax methods in the presence of faults, large and small measurement errors, bad starting poin......, a preliminary optimization by selecting a small number of dominant variables. It is demonstrated, through multiplexer optimization, that the one-sided Huber function can be more effective and efficient than minimax in overcoming a bad starting point.......The authors introduce a novel approach to 'robustizing' microwave circuit optimization using Huber functions, both two-sided and one-sided. They compare Huber optimization with l/sub 1/, l/sub 2/, and minimax methods in the presence of faults, large and small measurement errors, bad starting points......, and statistical uncertainties. They demonstrate FET statistical modeling, multiplexer optimization, analog fault location, and data fitting. They extend the Huber concept by introducing a 'one-sided' Huber function for large-scale optimization. For large-scale problems, the designer often attempts, by intuition...

  13. Robust design optimization using the price of robustness, robust least squares and regularization methods

    Science.gov (United States)

    Bukhari, Hassan J.

    2017-12-01

    In this paper a framework for robust optimization of mechanical design problems and process systems that have parametric uncertainty is presented using three different approaches. Robust optimization problems are formulated so that the optimal solution is robust which means it is minimally sensitive to any perturbations in parameters. The first method uses the price of robustness approach which assumes the uncertain parameters to be symmetric and bounded. The robustness for the design can be controlled by limiting the parameters that can perturb.The second method uses the robust least squares method to determine the optimal parameters when data itself is subjected to perturbations instead of the parameters. The last method manages uncertainty by restricting the perturbation on parameters to improve sensitivity similar to Tikhonov regularization. The methods are implemented on two sets of problems; one linear and the other non-linear. This methodology will be compared with a prior method using multiple Monte Carlo simulation runs which shows that the approach being presented in this paper results in better performance.

  14. Optimal interdependence enhances robustness of complex systems

    OpenAIRE

    Singh, R. K.; Sinha, Sitabhra

    2017-01-01

    While interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more robust. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the g...

  15. Robust topology optimization accounting for geometric imperfections

    DEFF Research Database (Denmark)

    Schevenels, M.; Jansen, M.; Lombaert, Geert

    2013-01-01

    performance. As a consequence, the actual structure may be far from optimal. In this paper, a robust approach to topology optimization is presented, taking into account two types of geometric imperfections: variations of (1) the crosssections and (2) the locations of structural elements. The first type...... is modeled by means of a scalar non-Gaussian random field, which is represented as a translation process. The underlying Gaussian field is simulated by means of the EOLE method. The second type of imperfections is modeled as a Gaussian vector-valued random field, which is simulated directly by means...... of the EOLE method. In each iteration of the optimization process, the relevant statistics of the structural response are evaluated by means of a Monte Carlo simulation. The proposed methodology is successfully applied to a test problem involving the design of a compliant mechanism (for the first type...

  16. Automatic Synthesis of Robust and Optimal Controllers

    DEFF Research Database (Denmark)

    Cassez, Franck; Jessen, Jan Jacob; Larsen, Kim Guldstrand

    2009-01-01

    In this paper, we show how to apply recent tools for the automatic synthesis of robust and near-optimal controllers for a real industrial case study. We show how to use three different classes of models and their supporting existing tools, Uppaal-TiGA for synthesis, phaver for verification......, and Simulink for simulation, in a complementary way. We believe that this case study shows that our tools have reached a level of maturity that allows us to tackle interesting and relevant industrial control problems....

  17. Robust Optimal Design of Quantum Electronic Devices

    Directory of Open Access Journals (Sweden)

    Ociel Morales

    2018-01-01

    Full Text Available We consider the optimal design of a sequence of quantum barriers, in order to manufacture an electronic device at the nanoscale such that the dependence of its transmission coefficient on the bias voltage is linear. The technique presented here is easily adaptable to other response characteristics. There are two distinguishing features of our approach. First, the transmission coefficient is determined using a semiclassical approximation, so we can explicitly compute the gradient of the objective function. Second, in contrast with earlier treatments, manufacturing uncertainties are incorporated in the model through random variables; the optimal design problem is formulated in a probabilistic setting and then solved using a stochastic collocation method. As a measure of robustness, a weighted sum of the expectation and the variance of a least-squares performance metric is considered. Several simulations illustrate the proposed technique, which shows an improvement in accuracy over 69% with respect to brute-force, Monte-Carlo-based methods.

  18. Kinematically Optimal Robust Control of Redundant Manipulators

    Science.gov (United States)

    Galicki, M.

    2017-12-01

    This work deals with the problem of the robust optimal task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the endeffector. Furthermore, the movement is to be accomplished in such a way as to minimize both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we derive a class of chattering-free robust kinematically optimal controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.

  19. Distributed Robust Optimization in Networked System.

    Science.gov (United States)

    Wang, Shengnan; Li, Chunguang

    2016-10-11

    In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.

  20. Robust optimization based upon statistical theory.

    Science.gov (United States)

    Sobotta, B; Söhn, M; Alber, M

    2010-08-01

    Organ movement is still the biggest challenge in cancer treatment despite advances in online imaging. Due to the resulting geometric uncertainties, the delivered dose cannot be predicted precisely at treatment planning time. Consequently, all associated dose metrics (e.g., EUD and maxDose) are random variables with a patient-specific probability distribution. The method that the authors propose makes these distributions the basis of the optimization and evaluation process. The authors start from a model of motion derived from patient-specific imaging. On a multitude of geometry instances sampled from this model, a dose metric is evaluated. The resulting pdf of this dose metric is termed outcome distribution. The approach optimizes the shape of the outcome distribution based on its mean and variance. This is in contrast to the conventional optimization of a nominal value (e.g., PTV EUD) computed on a single geometry instance. The mean and variance allow for an estimate of the expected treatment outcome along with the residual uncertainty. Besides being applicable to the target, the proposed method also seamlessly includes the organs at risk (OARs). The likelihood that a given value of a metric is reached in the treatment is predicted quantitatively. This information reveals potential hazards that may occur during the course of the treatment, thus helping the expert to find the right balance between the risk of insufficient normal tissue sparing and the risk of insufficient tumor control. By feeding this information to the optimizer, outcome distributions can be obtained where the probability of exceeding a given OAR maximum and that of falling short of a given target goal can be minimized simultaneously. The method is applicable to any source of residual motion uncertainty in treatment delivery. Any model that quantifies organ movement and deformation in terms of probability distributions can be used as basis for the algorithm. Thus, it can generate dose

  1. Oracle-based online robust optimization via online learning

    NARCIS (Netherlands)

    Ben-Tal, A.; Hazan, E.; Koren, T.; Shie, M.

    2015-01-01

    Robust optimization is a common optimization framework under uncertainty when problem parameters are unknown, but it is known that they belong to some given uncertainty set. In the robust optimization framework, a min-max problem is solved wherein a solution is evaluated according to its performance

  2. PARAMETER COORDINATION AND ROBUST OPTIMIZATION FOR MULTIDISCIPLINARY DESIGN

    Institute of Scientific and Technical Information of China (English)

    HU Jie; PENG Yinghong; XIONG Guangleng

    2006-01-01

    A new parameter coordination and robust optimization approach for multidisciplinary design is presented. Firstly, the constraints network model is established to support engineering change, coordination and optimization. In this model, interval boxes are adopted to describe the uncertainty of design parameters quantitatively to enhance the design robustness. Secondly, the parameter coordination method is presented to solve the constraints network model, monitor the potential conflicts due to engineering changes, and obtain the consistency solution space corresponding to the given product specifications. Finally, the robust parameter optimization model is established, and genetic arithmetic is used to obtain the robust optimization parameter. An example of bogie design is analyzed to show the scheme to be effective.

  3. Where should I send it? Optimizing the submission decision process.

    Directory of Open Access Journals (Sweden)

    Santiago Salinas

    Full Text Available How do scientists decide where to submit manuscripts? Many factors influence this decision, including prestige, acceptance probability, turnaround time, target audience, fit, and impact factor. Here, we present a framework for evaluating where to submit a manuscript based on the theory of Markov decision processes. We derive two models, one in which an author is trying to optimally maximize citations and another in which that goal is balanced by either minimizing the number of resubmissions or the total time in review. We parameterize the models with data on acceptance probability, submission-to-decision times, and impact factors for 61 ecology journals. We find that submission sequences beginning with Ecology Letters, Ecological Monographs, or PLOS ONE could be optimal depending on the importance given to time to acceptance or number of resubmissions. This analysis provides some guidance on where to submit a manuscript given the individual-specific values assigned to these disparate objectives.

  4. Optimal Robust Fault Detection for Linear Discrete Time Systems

    Directory of Open Access Journals (Sweden)

    Nike Liu

    2008-01-01

    Full Text Available This paper considers robust fault-detection problems for linear discrete time systems. It is shown that the optimal robust detection filters for several well-recognized robust fault-detection problems, such as ℋ−/ℋ∞, ℋ2/ℋ∞, and ℋ∞/ℋ∞ problems, are the same and can be obtained by solving a standard algebraic Riccati equation. Optimal filters are also derived for many other optimization criteria and it is shown that some well-studied and seeming-sensible optimization criteria for fault-detection filter design could lead to (optimal but useless fault-detection filters.

  5. Extending the Scope of Robust Quadratic Optimization

    NARCIS (Netherlands)

    Marandi, Ahmadreza; Ben-Tal, A.; den Hertog, Dick; Melenberg, Bertrand

    In this paper, we derive tractable reformulations of the robust counterparts of convex quadratic and conic quadratic constraints with concave uncertainties for a broad range of uncertainty sets. For quadratic constraints with convex uncertainty, it is well-known that the robust counterpart is, in

  6. Self-optimizing robust nonlinear model predictive control

    NARCIS (Netherlands)

    Lazar, M.; Heemels, W.P.M.H.; Jokic, A.; Thoma, M.; Allgöwer, F.; Morari, M.

    2009-01-01

    This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique

  7. Robust balance shift control with posture optimization

    NARCIS (Netherlands)

    Kavafoglu, Z.; Kavafoglu, Ersan; Egges, J.

    2015-01-01

    In this paper we present a control framework which creates robust and natural balance shifting behaviours during standing. Given high-level features such as the position of the center of mass projection and the foot configurations, a kinematic posture satisfying these features is synthesized using

  8. Chance constrained uncertain classification via robust optimization

    NARCIS (Netherlands)

    Ben-Tal, A.; Bhadra, S.; Bhattacharayya, C.; Saketha Nat, J.

    2011-01-01

    This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out

  9. Robust structural optimization using Gauss-type quadrature formula

    International Nuclear Information System (INIS)

    Lee, Sang Hoon; Seo, Ki Seog; Chen, Shikui; Chen, Wei

    2009-01-01

    In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the Tensor Product Quadrature (TPQ) formula and the Univariate Dimension Reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

  10. Robust structural optimization using Gauss-type quadrature formula

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Hoon; Seo, Ki Seog [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Chen, Shikui; Chen, Wei [Northwestern University, Illinois (United States)

    2009-07-01

    In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the Tensor Product Quadrature (TPQ) formula and the Univariate Dimension Reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

  11. Robust Structural Optimization Using Gauss-type Quadrature Formula

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Hoon; Seo, Ki Seog; Chen, Shikui; Chen, Wei [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2009-08-15

    In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the tensor product quadrature (TPQ) formula and the univariate dimension reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

  12. Robust Structural Optimization Using Gauss-type Quadrature Formula

    International Nuclear Information System (INIS)

    Lee, Sang Hoon; Seo, Ki Seog; Chen, Shikui; Chen, Wei

    2009-01-01

    In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the tensor product quadrature (TPQ) formula and the univariate dimension reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty

  13. TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization

    Science.gov (United States)

    2016-11-28

    methods is presented in the book chapter [CG16d]. 4.4 Robust Timetable Information Problems. Timetable information is the process of determining a...Princeton and Oxford, 2009. [BTN98] A. Ben-Tal and A. Nemirovski. Robust convex optimization. Math - ematics of Operations Research, 23(4):769–805...Goerigk. A note on upper bounds to the robust knapsack problem with discrete scenarios. Annals of Operations Research, 223(1):461–469, 2014. [GS16] M

  14. Robust Optimization in Simulation : Taguchi and Response Surface Methodology

    NARCIS (Netherlands)

    Dellino, G.; Kleijnen, J.P.C.; Meloni, C.

    2008-01-01

    Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by

  15. Topology optimization of robust superhydrophobic surfaces

    DEFF Research Database (Denmark)

    Cavalli, Andrea; Bøggild, Peter; Okkels, Fridolin

    2013-01-01

    In this paper we apply topology optimization to micro-structured superhydrophobic surfaces for the first time. It has been experimentally observed that a droplet suspended on a brush of micrometric posts shows a high static contact angle and low roll-off angle. To keep the fluid from penetrating...

  16. Optimal design of robust piezoelectric unimorph microgrippers

    DEFF Research Database (Denmark)

    Ruiz, David; Díaz-Molina, Alex; Sigmund, Ole

    2018-01-01

    Topology optimization can be used to design piezoelectric actuators by simultaneous design of host structure and polarization profile. Subsequent micro-scale fabrication leads us to overcome important manufacturing limitations: difficulties in placing a piezoelectric layer on both top and bottom...

  17. Geometrical framework for robust portfolio optimization

    OpenAIRE

    Bazovkin, Pavel

    2014-01-01

    We consider a vector-valued multivariate risk measure that depends on the user's profile given by the user's utility. It is constructed on the basis of weighted-mean trimmed regions and represents the solution of an optimization problem. The key feature of this measure is convexity. We apply the measure to the portfolio selection problem, employing different measures of performance as objective functions in a common geometrical framework.

  18. Stochastic Robust Mathematical Programming Model for Power System Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Cong; Changhyeok, Lee; Haoyong, Chen; Mehrotra, Sanjay

    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.

  19. Linear systems optimal and robust control

    CERN Document Server

    Sinha, Alok

    2007-01-01

    Introduction Overview Contents of the Book State Space Description of a Linear System Transfer Function of a Single Input/Single Output (SISO) System State Space Realizations of a SISO System SISO Transfer Function from a State Space Realization Solution of State Space Equations Observability and Controllability of a SISO System Some Important Similarity Transformations Simultaneous Controllability and Observability Multiinput/Multioutput (MIMO) Systems State Space Realizations of a Transfer Function Matrix Controllability and Observability of a MIMO System Matrix-Fraction Description (MFD) MFD of a Transfer Function Matrix for the Minimal Order of a State Space Realization Controller Form Realization from a Right MFD Poles and Zeros of a MIMO Transfer Function Matrix Stability Analysis State Feedback Control and Optimization State Variable Feedback for a Single Input System Computation of State Feedback Gain Matrix for a Multiinput System State Feedback Gain Matrix for a Multi...

  20. Robust optimization methods for cardiac sparing in tangential breast IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Mahmoudzadeh, Houra, E-mail: houra@mie.utoronto.ca [Mechanical and Industrial Engineering Department, University of Toronto, Toronto, Ontario M5S 3G8 (Canada); Lee, Jenny [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Chan, Timothy C. Y. [Mechanical and Industrial Engineering Department, University of Toronto, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, Toronto, Ontario M5G 1P5 (Canada); Purdie, Thomas G. [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3S2 (Canada); Techna Institute for the Advancement of Technology for Health, Toronto, Ontario M5G 1P5 (Canada)

    2015-05-15

    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 constructed 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

  1. Optimal Robust Self-Testing by Binary Nonlocal XOR Games

    OpenAIRE

    Miller, Carl A.; Shi, Yaoyun

    2013-01-01

    Self-testing a quantum apparatus means verifying the existence of a certain quantum state as well as the effect of the associated measuring devices based only on the statistics of the measurement outcomes. Robust (i.e., error-tolerant) self-testing quantum apparatuses are critical building blocks for quantum cryptographic protocols that rely on imperfect or untrusted devices. We devise a general scheme for proving optimal robust self-testing properties for tests based on nonlocal binary XOR g...

  2. Robust and optimal control a two-port framework approach

    CERN Document Server

    Tsai, Mi-Ching

    2014-01-01

    A Two-port Framework for Robust and Optimal Control introduces an alternative approach to robust and optimal controller synthesis procedures for linear, time-invariant systems, based on the two-port system widespread in electrical engineering. The novel use of the two-port system in this context allows straightforward engineering-oriented solution-finding procedures to be developed, requiring no mathematics beyond linear algebra. A chain-scattering description provides a unified framework for constructing the stabilizing controller set and for synthesizing H2 optimal and H∞ sub-optimal controllers. Simple yet illustrative examples explain each step. A Two-port Framework for Robust and Optimal Control  features: ·         a hands-on, tutorial-style presentation giving the reader the opportunity to repeat the designs presented and easily to modify them for their own programs; ·         an abundance of examples illustrating the most important steps in robust and optimal design; and ·   �...

  3. Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

    Science.gov (United States)

    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.

  4. Design optimization for cost and quality: The robust design approach

    Science.gov (United States)

    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.

  5. Robust topology optimization accounting for spatially varying manufacturing errors

    DEFF Research Database (Denmark)

    Schevenels, M.; Lazarov, Boyan Stefanov; Sigmund, Ole

    2011-01-01

    This paper presents a robust approach for the design of macro-, micro-, or nano-structures by means of topology optimization, accounting for spatially varying manufacturing errors. The focus is on structures produced by milling or etching; in this case over- or under-etching may cause parts...... optimization problem is formulated in a probabilistic way: the objective function is defined as a weighted sum of the mean value and the standard deviation of the structural performance. The optimization problem is solved by means of a Monte Carlo method: in each iteration of the optimization scheme, a Monte...

  6. Robust Design Optimization of an Aerospace Vehicle Prolusion System

    Directory of Open Access Journals (Sweden)

    Muhammad Aamir Raza

    2011-01-01

    Full Text Available This paper proposes a robust design optimization methodology under design uncertainties of an aerospace vehicle propulsion system. The approach consists of 3D geometric design coupled with complex internal ballistics, hybrid optimization, worst-case deviation, and efficient statistical approach. The uncertainties are propagated through worst-case deviation using first-order orthogonal design matrices. The robustness assessment is measured using the framework of mean-variance and percentile difference approach. A parametric sensitivity analysis is carried out to analyze the effects of design variables variation on performance parameters. A hybrid simulated annealing and pattern search approach is used as an optimizer. The results show the objective function of optimizing the mean performance and minimizing the variation of performance parameters in terms of thrust ratio and total impulse could be achieved while adhering to the system constraints.

  7. Robust Optimization of Fourth Party Logistics Network Design under Disruptions

    Directory of Open Access Journals (Sweden)

    Jia Li

    2015-01-01

    Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.

  8. Application of Six Sigma Robust Optimization in Sheet Metal Forming

    International Nuclear Information System (INIS)

    Li, Y.Q.; Cui, Z.S.; Ruan, X.Y.; Zhang, D.J.

    2005-01-01

    Numerical simulation technology and optimization method have been applied in sheet metal forming process to improve design quality and shorten design cycle. While the existence of fluctuation in design variables or operation condition has great influence on the quality. In addition to that, iterative solution in numerical simulation and optimization usually take huge computational time or endure expensive experiment cost In order to eliminate effect of perturbations in design and improve design efficiency, a CAE-based six sigma robust design method is developed in this paper. In the six sigma procedure for sheet metal forming, statistical technology and dual response surface approximate model as well as algorithm of 'Design for Six Sigma (DFSS)' are integrated together to perform reliability optimization and robust improvement. A deep drawing process of a rectangular cup is taken as an example to illustrate the method. The optimization solutions show that the proposed optimization procedure not only improves significantly the reliability and robustness of the forming quality, but also increases optimization efficiency with approximate model

  9. Robust Optimization for Household Load Scheduling with Uncertain Parameters

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2018-04-01

    Full Text Available Home energy management systems (HEMS face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers’ comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

  10. Robust Pitch Estimation Using an Optimal Filter on Frequency Estimates

    DEFF Research Database (Denmark)

    Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll

    2014-01-01

    of such signals from unconstrained frequency estimates (UFEs). A minimum variance distortionless response (MVDR) method is proposed as an optimal solution to minimize the variance of UFEs considering the constraint of integer harmonics. The MVDR filter is designed based on noise statistics making it robust...

  11. Robust Optimization in Simulation : Taguchi and Krige Combined

    NARCIS (Netherlands)

    Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.

    2009-01-01

    Optimization of simulated systems is the goal of many methods, but most methods as- sume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by

  12. Metamodel-based robust simulation-optimization : An overview

    NARCIS (Netherlands)

    Dellino, G.; Meloni, C.; Kleijnen, J.P.C.; Dellino, Gabriella; Meloni, Carlo

    2015-01-01

    Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by

  13. Towards robust optimal design of storm water systems

    Science.gov (United States)

    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

  14. Intelligent and robust optimization frameworks for smart grids

    Science.gov (United States)

    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

  15. Robust Optimization Model for Production Planning Problem under Uncertainty

    Directory of Open Access Journals (Sweden)

    Pembe GÜÇLÜ

    2017-01-01

    Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

  16. Robust output LQ optimal control via integral sliding modes

    CERN Document Server

    Fridman, Leonid; Bejarano, Francisco Javier

    2014-01-01

    Featuring original research from well-known experts in the field of sliding mode control, this monograph presents new design schemes for implementing LQ control solutions in situations where the output system is the only information provided about the state of the plant. This new design works under the restrictions of matched disturbances without losing its desirable features. On the cutting-edge of optimal control research, Robust Output LQ Optimal Control via Integral Sliding Modes is an excellent resource for both graduate students and professionals involved in linear systems, optimal control, observation of systems with unknown inputs, and automatization. In the theory of optimal control, the linear quadratic (LQ) optimal problem plays an important role due to its physical meaning, and its solution is easily given by an algebraic Riccati equation. This solution turns out to be restrictive, however, because of two assumptions: the system must be free from disturbances and the entire state vector must be kn...

  17. Robustness Recipes for Minimax Robust Optimization in Intensity Modulated Proton Therapy for Oropharyngeal Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Voort, Sebastian van der [Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam (Netherlands); Section of Nuclear Energy and Radiation Applications, Department of Radiation, Science and Technology, Delft University of Technology, Delft (Netherlands); Water, Steven van de [Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam (Netherlands); Perkó, Zoltán [Section of Nuclear Energy and Radiation Applications, Department of Radiation, Science and Technology, Delft University of Technology, Delft (Netherlands); Heijmen, Ben [Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam (Netherlands); Lathouwers, Danny [Section of Nuclear Energy and Radiation Applications, Department of Radiation, Science and Technology, Delft University of Technology, Delft (Netherlands); Hoogeman, Mischa, E-mail: m.hoogeman@erasmusmc.nl [Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam (Netherlands)

    2016-05-01

    Purpose: We aimed to derive a “robustness recipe” giving the range robustness (RR) and setup robustness (SR) settings (ie, the error values) that ensure adequate clinical target volume (CTV) coverage in oropharyngeal cancer patients for given gaussian distributions of systematic setup, random setup, and range errors (characterized by standard deviations of Σ, σ, and ρ, respectively) when used in minimax worst-case robust intensity modulated proton therapy (IMPT) optimization. Methods and Materials: For the analysis, contoured computed tomography (CT) scans of 9 unilateral and 9 bilateral patients were used. An IMPT plan was considered robust if, for at least 98% of the simulated fractionated treatments, 98% of the CTV received 95% or more of the prescribed dose. For fast assessment of the CTV coverage for given error distributions (ie, different values of Σ, σ, and ρ), polynomial chaos methods were used. Separate recipes were derived for the unilateral and bilateral cases using one patient from each group, and all 18 patients were included in the validation of the recipes. Results: Treatment plans for bilateral cases are intrinsically more robust than those for unilateral cases. The required RR only depends on the ρ, and SR can be fitted by second-order polynomials in Σ and σ. The formulas for the derived robustness recipes are as follows: Unilateral patients need SR = −0.15Σ{sup 2} + 0.27σ{sup 2} + 1.85Σ − 0.06σ + 1.22 and RR=3% for ρ = 1% and ρ = 2%; bilateral patients need SR = −0.07Σ{sup 2} + 0.19σ{sup 2} + 1.34Σ − 0.07σ + 1.17 and RR=3% and 4% for ρ = 1% and 2%, respectively. For the recipe validation, 2 plans were generated for each of the 18 patients corresponding to Σ = σ = 1.5 mm and ρ = 0% and 2%. Thirty-four plans had adequate CTV coverage in 98% or more of the simulated fractionated treatments; the remaining 2 had adequate coverage in 97.8% and 97.9%. Conclusions: Robustness recipes were derived that can

  18. Robust Optimal Adaptive Control Method with Large Adaptive Gain

    Science.gov (United States)

    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.

  19. Reactive Robustness and Integrated Approaches for Railway Optimization Problems

    DEFF Research Database (Denmark)

    Haahr, Jørgen Thorlund

    journeys helps the driver to drive efficiently and enhances robustness in a realistic (dynamic) environment. Four international scientific prizes have been awarded for distinct parts of the research during the course of this PhD project. The first prize was awarded for work during the \\2014 RAS Problem...... to absorb or withstand unexpected events such as delays. Making robust plans is central in order to maintain a safe and timely railway operation. This thesis focuses on reactive robustness, i.e., the ability to react once a plan is rendered infeasible in operation due to disruptions. In such time...... Solving Competition", where a freight yard optimization problem was considered. The second junior (PhD) prize was awared for the work performed in the \\ROADEF/EURO Challenge 2014: Trains don't vanish!", where the planning of rolling stock movements at a large station was considered. An honorable mention...

  20. Integrating robust timetabling in line plan optimization for railway systems

    DEFF Research Database (Denmark)

    Burggraeve, Sofie; Bull, Simon Henry; Vansteenwegen, Pieter

    2017-01-01

    We propose a heuristic algorithm to build a railway line plan from scratch that minimizes passenger travel time and operator cost and for which a feasible and robust timetable exists. A line planning module and a timetabling module work iteratively and interactively. The line planning module......, but is constrained by limited shunt capacity. While the operator and passenger cost remain close to those of the initially and (for these costs) optimally built line plan, the timetable corresponding to the finally developed robust line plan significantly improves the minimum buffer time, and thus the robustness...... creates an initial line plan. The timetabling module evaluates the line plan and identifies a critical line based on minimum buffer times between train pairs. The line planning module proposes a new line plan in which the time length of the critical line is modified in order to provide more flexibility...

  1. Optimization of robustness of interdependent network controllability by redundant design.

    Directory of Open Access Journals (Sweden)

    Zenghu Zhang

    Full Text Available Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy or DBS (degree based strategy for node backup and HDF(high degree first for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability.

  2. Stochastic simulation and robust design optimization of integrated photonic filters

    Directory of Open Access Journals (Sweden)

    Weng Tsui-Wei

    2016-07-01

    Full Text Available 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.

  3. On projection methods, convergence and robust formulations in topology optimization

    DEFF Research Database (Denmark)

    Wang, Fengwen; Lazarov, Boyan Stefanov; Sigmund, Ole

    2011-01-01

    alleviated using various projection methods. In this paper we show that simple projection methods do not ensure local mesh-convergence and propose a modified robust topology optimization formulation based on erosion, intermediate and dilation projections that ensures both global and local mesh-convergence.......Mesh convergence and manufacturability of topology optimized designs have previously mainly been assured using density or sensitivity based filtering techniques. The drawback of these techniques has been gray transition regions between solid and void parts, but this problem has recently been...

  4. Robust optimal self tuning regulator of nuclear reactors

    International Nuclear Information System (INIS)

    Nouri Khajavi, M.; Menhaj, M.B.; Ghofrani, M.B.

    2000-01-01

    Nuclear power reactors are, in nature nonlinear and time varying. These characteristics must be considered, if large power variations occur in their working regime. In this paper a robust optimal self-tuning regulator for regulating the power of a nuclear reactor has been designed and simulated. The proposed controller is capable of regulating power levels in a wide power range (10% to 100% power levels). The controller achieves a fast and good transient response. The simulation results show that the proposed controller outperforms the fixed optimal control recently cited in the literature for nuclear power plants

  5. Cascade-robustness optimization of coupling preference in interconnected networks

    International Nuclear Information System (INIS)

    Zhang, Xue-Jun; Xu, Guo-Qiang; Zhu, Yan-Bo; Xia, Yong-Xiang

    2016-01-01

    Highlights: • A specific memetic algorithm was proposed to optimize coupling links. • A small toy model was investigated to examine the underlying mechanism. • The MA optimized strategy exhibits a moderate assortative pattern. • A novel coupling coefficient index was proposed to quantify coupling preference. - Abstract: Recently, the robustness of interconnected networks has attracted extensive attentions, one of which is to investigate the influence of coupling preference. In this paper, the memetic algorithm (MA) is employed to optimize the coupling links of interconnected networks. Afterwards, a comparison is made between MA optimized coupling strategy and traditional assortative, disassortative and random coupling preferences. It is found that the MA optimized coupling strategy with a moderate assortative value shows an outstanding performance against cascading failures on both synthetic scale-free interconnected networks and real-world networks. We then provide an explanation for this phenomenon from a micro-scope point of view and propose a coupling coefficient index to quantify the coupling preference. Our work is helpful for the design of robust interconnected networks.

  6. Robust optimization of a tandem grating solar thermal absorber

    Science.gov (United States)

    Choi, Jongin; Kim, Mingeon; Kang, Kyeonghwan; Lee, Ikjin; Lee, Bong Jae

    2018-04-01

    Ideal solar thermal absorbers need to have a high value of the spectral absorptance in the broad solar spectrum to utilize the solar radiation effectively. Majority of recent studies about solar thermal absorbers focus on achieving nearly perfect absorption using nanostructures, whose characteristic dimension is smaller than the wavelength of sunlight. However, precise fabrication of such nanostructures is not easy in reality; that is, unavoidable errors always occur to some extent in the dimension of fabricated nanostructures, causing an undesirable deviation of the absorption performance between the designed structure and the actually fabricated one. In order to minimize the variation in the solar absorptance due to the fabrication error, the robust optimization can be performed during the design process. However, the optimization of solar thermal absorber considering all design variables often requires tremendous computational costs to find an optimum combination of design variables with the robustness as well as the high performance. To achieve this goal, we apply the robust optimization using the Kriging method and the genetic algorithm for designing a tandem grating solar absorber. By constructing a surrogate model through the Kriging method, computational cost can be substantially reduced because exact calculation of the performance for every combination of variables is not necessary. Using the surrogate model and the genetic algorithm, we successfully design an effective solar thermal absorber exhibiting a low-level of performance degradation due to the fabrication uncertainty of design variables.

  7. On the Optimization of GLite-Based Job Submission

    International Nuclear Information System (INIS)

    Misurelli, Giuseppe; Veronesi, Paolo; Palmieri, Francesco; Pardi, Silvio

    2011-01-01

    A Grid is a very dynamic, complex and heterogeneous system, whose reliability can be adversely conditioned by several different factors such as communications and hardware faults, middleware bugs or wrong configurations due to human errors. As the infrastructure scales, spanning a large number of sites, each hosting hundreds or thousands of hosts/resources, the occurrence of runtime faults following job submission becomes a very frequent and phenomenon. Therefore, fault avoidance becomes a fundamental aim in modern Grids since the dependability of individual resources spread upon widely distributed computing infrastructures and often used outside of their native organizational boundaries, cannot be guaranteed in any systematic way. Accordingly, we propose a simple job optimization solution based on a user-driven fault avoidance strategy. Such strategy starts from the introduction within the grid information system of several on-line service-monitoring metrics that can be used as specific hints to the workload management system for driving resource discovery operations according to a fault-free resource-scheduling plan. This solution, whose main goal is to minimize the execution time by avoiding execution failures, demonstrated to be very effective in incrementing both the user perceivable quality and the overall grid performance.

  8. Group Elevator Peak Scheduling Based on Robust Optimization Model

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  9. On the robust optimization to the uncertain vaccination strategy problem

    International Nuclear Information System (INIS)

    Chaerani, D.; Anggriani, N.; Firdaniza

    2014-01-01

    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 vaccination 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

  10. On the robust optimization to the uncertain vaccination strategy problem

    Energy Technology Data Exchange (ETDEWEB)

    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 [Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Padjadjaran Indonesia, Jalan Raya Bandung Sumedang KM 21 Jatinangor Sumedang 45363 (Indonesia)

    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 vaccination 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.

  11. Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

    Directory of Open Access Journals (Sweden)

    M. Navabi

    Full Text Available Abstract This paper focuses on robust optimal adaptive control strategy to deal with tracking problem of a quadrotor unmanned aerial vehicle (UAV in presence of parametric uncertainties, actuator amplitude constraints, and unknown time-varying external disturbances. First, Lyapunov-based indirect adaptive controller optimized by particle swarm optimization (PSO is developed for multi-input multi-output (MIMO nonlinear quadrotor to prevent input constraints violation, and then disturbance observer-based control (DOBC technique is aggregated with the control system to attenuate the effects of disturbance generated by an exogenous system. The performance of synthesis control method is evaluated by a new performance index function in time-domain, and the stability analysis is carried out using Lyapunov theory. Finally, illustrative numerical simulations are conducted to demonstrate the effectiveness of the presented approach in altitude and attitude tracking under several conditions, including large time-varying uncertainty, exogenous disturbance, and control input constraints.

  12. Robust sawtooth period control based on adaptive online optimization

    International Nuclear Information System (INIS)

    Bolder, J.J.; Witvoet, G.; De Baar, M.R.; Steinbuch, M.; Van de Wouw, N.; Haring, M.A.M.; Westerhof, E.; Doelman, N.J.

    2012-01-01

    The systematic design of a robust adaptive control strategy for the sawtooth period using electron cyclotron current drive (ECCD) is presented. Recent developments in extremum seeking control (ESC) are employed to derive an optimized controller structure and offer practical tuning guidelines for its parameters. In this technique a cost function in terms of the desired sawtooth period is optimized online by changing the ECCD deposition location based on online estimations of the gradient of the cost function. The controller design does not require a detailed model of the sawtooth instability. Therefore, the proposed ESC is widely applicable to any sawtoothing plasma or plasma simulation and is inherently robust against uncertainties or plasma variations. Moreover, it can handle a broad class of disturbances. This is demonstrated by time-domain simulations, which show successful tracking of time-varying sawtooth period references throughout the whole operating space, even in the presence of variations in plasma parameters, disturbances and slow launcher mirror dynamics. Due to its simplicity and robustness the proposed ESC is a valuable sawtooth control candidate for any experimental tokamak plasma, and may even be applicable to other fusion-related control problems. (paper)

  13. Doubly Robust Estimation of Optimal Dynamic Treatment Regimes

    DEFF Research Database (Denmark)

    Barrett, Jessica K; Henderson, Robin; Rosthøj, Susanne

    2014-01-01

    We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression appro......We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret......-regression approach of Almirall et al. (in Biometrics 66:131-139, 2010) and Henderson et al. (in Biometrics 66:1192-1201, 2010) and demonstrate that it is equivalent to a reduced form of Robins' efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp....... 189-326, 2004). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex...

  14. Optimizing the robustness of electrical power systems against cascading failures.

    Science.gov (United States)

    Zhang, Yingrui; Yağan, Osman

    2016-06-21

    Electrical power systems are one of the most important infrastructures that support our society. However, their vulnerabilities have raised great concern recently due to several large-scale blackouts around the world. In this paper, we investigate the robustness of power systems against cascading failures initiated by a random attack. This is done under a simple yet useful model based on global and equal redistribution of load upon failures. We provide a comprehensive understanding of system robustness under this model by (i) deriving an expression for the final system size as a function of the size of initial attacks; (ii) deriving the critical attack size after which system breaks down completely; (iii) showing that complete system breakdown takes place through a first-order (i.e., discontinuous) transition in terms of the attack size; and (iv) establishing the optimal load-capacity distribution that maximizes robustness. In particular, we show that robustness is maximized when the difference between the capacity and initial load is the same for all lines; i.e., when all lines have the same redundant space regardless of their initial load. This is in contrast with the intuitive and commonly used setting where capacity of a line is a fixed factor of its initial load.

  15. Enhancing product robustness in reliability-based design optimization

    International Nuclear Information System (INIS)

    Zhuang, Xiaotian; Pan, Rong; Du, Xiaoping

    2015-01-01

    Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example. - Highlights: • A unifying framework for integrating RBDO and RPD is proposed. • Implicit product performance function is considered. • The design problem is solved by sequential optimization and reliability assessment. • A sequential sampling technique is developed for improving design optimization. • The comparison with traditional RBDO is provided

  16. Distributionally Robust Return-Risk Optimization Models and Their Applications

    Directory of Open Access Journals (Sweden)

    Li Yang

    2014-01-01

    Full Text Available Based on the risk control of conditional value-at-risk, distributionally robust return-risk optimization models with box constraints of random vector are proposed. They describe uncertainty in both the distribution form and moments (mean and covariance matrix of random vector. It is difficult to solve them directly. Using the conic duality theory and the minimax theorem, the models are reformulated as semidefinite programming problems, which can be solved by interior point algorithms in polynomial time. An important theoretical basis is therefore provided for applications of the models. Moreover, an application of the models to a practical example of portfolio selection is considered, and the example is evaluated using a historical data set of four stocks. Numerical results show that proposed methods are robust and the investment strategy is safe.

  17. Robust C subroutines for non-linear optimization

    DEFF Research Database (Denmark)

    Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun

    2004-01-01

    This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems. The intention is that the routines should use the currently best algorithms available. All routines have standardized calls, and the user does not have...... by changing 1 to 0. The present report is a new and updated version of a previous report NI-91-03 with the same title, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated from Fortran to C. The reason for writing the present report is that some...... of the C subroutines have been replaced by more effective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modi ed to some extent. For a description of the original Fortran subroutines see the report [17]. The software...

  18. An adaptive robust optimization scheme for water-flooding optimization in oil reservoirs using residual analysis

    NARCIS (Netherlands)

    Siraj, M.M.; Van den Hof, P.M.J.; Jansen, J.D.

    2017-01-01

    Model-based dynamic optimization of the water-flooding process in oil reservoirs is a computationally complex problem and suffers from high levels of uncertainty. A traditional way of quantifying uncertainty in robust water-flooding optimization is by considering an ensemble of uncertain model

  19. A robust optimization approach for energy generation scheduling in microgrids

    International Nuclear Information System (INIS)

    Wang, Ran; Wang, Ping; Xiao, Gaoxi

    2015-01-01

    Highlights: • A new uncertainty model is proposed for better describing unstable energy demands. • An optimization problem is formulated to minimize the cost of microgrid operations. • Robust optimization algorithms are developed to transform and solve the problem. • The proposed scheme can prominently reduce energy expenses. • Numerical results provide useful insights for future investment policy making. - Abstract: In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and combined heat and power (CHP) generators. In such systems, the fluctuant net demands (i.e., the electricity demands not balanced by renewable energies) and heat demands impose unprecedented challenges. To cope with the uncertainty nature of net demand and heat demand, a new flexible uncertainty model is developed. Specifically, we introduce reference distributions according to predictions and field measurements and then define uncertainty sets to confine net and heat demands. The model allows the net demand and heat demand distributions to fluctuate around their reference distributions. Another difficulty existing in this problem is the indeterminate electricity market prices. We develop chance constraint approximations and robust optimization approaches to firstly transform and then solve the prime problem. Numerical results based on real-world data evaluate the impacts of different parameters. It is shown that our energy generation scheduling strategy performs well and the integration of combined heat and power (CHP) generators effectively reduces the system expenditure. Our research also helps shed some illuminations on the investment policy making for microgrids.

  20. Robust optimization of supersonic ORC nozzle guide vanes

    Science.gov (United States)

    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.

  1. Robust buckling optimization of laminated composite structures using discrete material optimization considering “worst” shape imperfections

    DEFF Research Database (Denmark)

    Henrichsen, Søren Randrup; Lindgaard, Esben; Lund, Erik

    2015-01-01

    Robust buckling optimal design of laminated composite structures is conducted in this work. Optimal designs are obtained by considering geometric imperfections in the optimization procedure. Discrete Material Optimization is applied to obtain optimal laminate designs. The optimal geometric...... imperfection is represented by the “worst” shape imperfection. The two optimization problems are combined through the recurrence optimization. Hereby the imperfection sensitivity of the considered structures can be studied. The recurrence optimization is demonstrated through a U-profile and a cylindrical panel...... example. The imperfection sensitivity of the optimized structure decreases during the recurrence optimization for both examples, hence robust buckling optimal structures are designed....

  2. Robust and fast nonlinear optimization of diffusion MRI microstructure models.

    Science.gov (United States)

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

    2017-07-15

    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.

  3. A Robust Statistics Approach to Minimum Variance Portfolio Optimization

    Science.gov (United States)

    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.

  4. Robust approximate optimal guidance strategies for aeroassisted orbital transfer missions

    Science.gov (United States)

    Ilgen, Marc R.

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

  5. Multidisciplinary Design Optimization for High Reliability and Robustness

    National Research Council Canada - National Science Library

    Grandhi, Ramana

    2005-01-01

    .... Over the last 3 years Wright State University has been applying analysis tools to predict the behavior of critical disciplines to produce highly robust torpedo designs using robust multi-disciplinary...

  6. A kriging metamodel-assisted robust optimization method based on a reverse model

    Science.gov (United States)

    Zhou, Hui; Zhou, Qi; Liu, Congwei; Zhou, Taotao

    2018-02-01

    The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer-inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.

  7. On the relation between flexibility analysis and robust optimization for linear systems

    KAUST Repository

    Zhang, Qi; Grossmann, Ignacio E.; Lima, Ricardo

    2016-01-01

    Flexibility analysis and robust optimization are two approaches to solving optimization problems under uncertainty that share some fundamental concepts, such as the use of polyhedral uncertainty sets and the worst-case approach to guarantee

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  10. Systematic and robust design of photonic crystal waveguides by topology optimization

    DEFF Research Database (Denmark)

    Wang, Fengwen; Jensen, Jakob Søndergaard; Sigmund, Ole

    2010-01-01

    on a threshold projection. The objective is formulated to minimize the maximum error between actual group indices and a prescribed group index among these three designs. Novel photonic crystal waveguide facilitating slow light with a group index of n(g) = 40 is achieved by the robust optimization approach......A robust topology optimization method is presented to consider manufacturing uncertainties in tailoring dispersion properties of photonic crystal waveguides. The under, normal and over-etching scenarios in manufacturing process are represented by dilated, intermediate and eroded designs based....... The numerical result illustrates that the robust topology optimization provides a systematic and robust design methodology for photonic crystal waveguide design....

  11. The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

    International Nuclear Information System (INIS)

    Guo, Chunxiang; Liu, Xiaoli; Jin, Maozhu; Lv, Zhihan

    2016-01-01

    Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.

  12. Robust Optimization Using Supremum of the Objective Function for Nonlinear Programming Problems

    International Nuclear Information System (INIS)

    Lee, Se Jung; Park, Gyung Jin

    2014-01-01

    In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency

  13. Robust fluence map optimization via alternating direction method of multipliers with empirical parameter optimization

    International Nuclear Information System (INIS)

    Gao, Hao

    2016-01-01

    For the treatment planning during intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), beam fluence maps can be first optimized via fluence map optimization (FMO) under the given dose prescriptions and constraints to conformally deliver the radiation dose to the targets while sparing the organs-at-risk, and then segmented into deliverable MLC apertures via leaf or arc sequencing algorithms. This work is to develop an efficient algorithm for FMO based on alternating direction method of multipliers (ADMM). Here we consider FMO with the least-square cost function and non-negative fluence constraints, and its solution algorithm is based on ADMM, which is efficient and simple-to-implement. In addition, an empirical method for optimizing the ADMM parameter is developed to improve the robustness of the ADMM algorithm. The ADMM based FMO solver was benchmarked with the quadratic programming method based on the interior-point (IP) method using the CORT dataset. The comparison results suggested the ADMM solver had a similar plan quality with slightly smaller total objective function value than IP. A simple-to-implement ADMM based FMO solver with empirical parameter optimization is proposed for IMRT or VMAT. (paper)

  14. A Two-Stage Robust Optimization for Centralized-Optimal Dispatch of Photovoltaic Inverters in Active Distribution Networks

    DEFF Research Database (Denmark)

    Ding, Tao; Li, Cheng; Yang, Yongheng

    2017-01-01

    Optimally dispatching Photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most...... nature of solar PV energy may affect the selection of the critical PV inverters and also the final optimal objective value. In order to address this issue, a two-stage robust optimization model is proposed in this paper to achieve a robust optimal solution to the PV inverter dispatch, which can hedge...... against any possible realization within the uncertain PV outputs. In addition, the conic relaxation-based branch flow formulation and second-order cone programming based column-and-constraint generation algorithm are employed to deal with the proposed robust optimization model. Case studies on a 33-bus...

  15. Robust

    DEFF Research Database (Denmark)

    2017-01-01

    Robust – Reflections on Resilient Architecture’, is a scientific publication following the conference of the same name in November of 2017. Researches and PhD-Fellows, associated with the Masters programme: Cultural Heritage, Transformation and Restoration (Transformation), at The Royal Danish...

  16. Optimal design of robust piezoelectric microgrippers undergoing large displacements

    DEFF Research Database (Denmark)

    Ruiz, D.; Sigmund, Ole

    2018-01-01

    Topology optimization combined with optimal design of electrodes is used to design piezoelectric microgrippers. Fabrication at micro-scale presents an important challenge: due to non-symmetrical lamination of the structures, out-of-plane bending spoils the behaviour of the grippers. Suppression...

  17. Assuring robustness to noise in optimal quantum control experiments

    International Nuclear Information System (INIS)

    Bartelt, A.F.; Roth, M.; Mehendale, M.; Rabitz, H.

    2005-01-01

    Closed-loop optimal quantum control experiments operate in the inherent presence of laser noise. In many applications, attaining high quality results [i.e., a high signal-to-noise (S/N) ratio for the optimized objective] is as important as producing a high control yield. Enhancement of the S/N ratio will typically be in competition with the mean signal, however, the latter competition can be balanced by biasing the optimization experiments towards higher mean yields while retaining a good S/N ratio. Other strategies can also direct the optimization to reduce the standard deviation of the statistical signal distribution. The ability to enhance the S/N ratio through an optimized choice of the control is demonstrated for two condensed phase model systems: second harmonic generation in a nonlinear optical crystal and stimulated emission pumping in a dye solution

  18. Approximating the Pareto set of multiobjective linear programs via robust optimization

    NARCIS (Netherlands)

    Gorissen, B.L.; den Hertog, D.

    2012-01-01

    We consider problems with multiple linear objectives and linear constraints and use adjustable robust optimization and polynomial optimization as tools to approximate the Pareto set with polynomials of arbitrarily large degree. The main difference with existing techniques is that we optimize a

  19. Design and Validation of Optimized Feedforward with Robust Feedback Control of a Nuclear Reactor

    International Nuclear Information System (INIS)

    Shaffer, Roman; He Weidong; Edwards, Robert M.

    2004-01-01

    Design applications for robust feedback and optimized feedforward control, with confirming results from experiments conducted on the Pennsylvania State University TRIGA reactor, are presented. The combination of feedforward and feedback control techniques complement each other in that robust control offers guaranteed closed-loop stability in the presence of uncertainties, and optimized feedforward offers an approach to achieving performance that is sometimes limited by overly conservative robust feedback control. The design approach taken in this work combines these techniques by first designing robust feedback control. Alternative methods for specifying a low-order linear model and uncertainty specifications, while seeking as much performance as possible, are discussed and evaluated. To achieve desired performance characteristics, the optimized feedforward control is then computed by using the nominal nonlinear plant model that incorporates the robust feedback control

  20. Simulation-based robust optimization for signal timing and setting.

    Science.gov (United States)

    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 ...

  1. Three Essays on Robust Optimization of Efficient Portfolios

    OpenAIRE

    Liu, Hao

    2013-01-01

    The mean-variance approach was first proposed by Markowitz (1952), and laid the foundation of the modern portfolio theory. Despite its theoretical appeal, the practical implementation of optimized portfolios is strongly restricted by the fact that the two inputs, the means and the covariance matrix of asset returns, are unknown and have to be estimated by available historical information. Due to the estimation risk inherited from inputs, desired properties of estimated optimal portfolios are ...

  2. APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    N. Belavendram

    2010-12-01

    Full Text Available Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.

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

    Science.gov (United States)

    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.

  4. Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties

    Directory of Open Access Journals (Sweden)

    Qinghai Zhao

    2015-01-01

    Full Text Available A robust topology optimization (RTO approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach.

  5. Hybrid Robust Optimization for the Design of a Smartphone Metal Frame Antenna

    Directory of Open Access Journals (Sweden)

    Sungwoo Lee

    2018-01-01

    Full Text Available Hybrid robust optimization that combines a genetical swarm optimization (GSO scheme with an orthogonal array (OA is proposed to design an antenna robust to the tolerances arising during the fabrication process of the antenna in this paper. An inverted-F antenna with a metal frame serves as an example to explain the procedure of the proposed method. GSO is adapted to determine the design variables of the antenna, which operates on the GSM850 band (824–894 MHz. The robustness of the antenna is evaluated through a noise test using the OA. The robustness of the optimized antenna is improved by approximately 61.3% relative to that of a conventional antenna. Conventional and optimized antennas are fabricated and measured to validate the experimental results.

  6. LMI–based robust controller design approach in aircraft multidisciplinary design optimization problem

    Directory of Open Access Journals (Sweden)

    Qinghua Zeng

    2015-07-01

    Full Text Available This article proposes a linear matrix inequality–based robust controller design approach to implement the synchronous design of aircraft control discipline and other disciplines, in which the variation in design parameters is treated as equivalent perturbations. Considering the complicated mapping relationships between the coefficient arrays of aircraft motion model and the aircraft design parameters, the robust controller designed is directly based on the variation in these coefficient arrays so conservative that the multidisciplinary design optimization problem would be too difficult to solve, or even if there is a solution, the robustness of design result is generally poor. Therefore, this article derives the uncertainty model of disciplinary design parameters based on response surface approximation, converts the design problem of the robust controller into a problem of solving a standard linear matrix inequality, and theoretically gives a less conservative design method of the robust controller which is based on the variation in design parameters. Furthermore, the concurrent subspace approach is applied to the multidisciplinary system with this kind of robust controller in the design loop. A multidisciplinary design optimization of a tailless aircraft as example is shown that control discipline can be synchronous optimal design with other discipline, especially this method will greatly reduce the calculated amount of multidisciplinary design optimization and make multidisciplinary design optimization results more robustness of flight performance.

  7. Benefits and Challenges when Performing Robust Topology Optimization for Interior Acoustic Problems

    DEFF Research Database (Denmark)

    Christiansen, Rasmus Ellebæk; Jensen, Jakob Søndergaard; Lazarov, Boyan Stefanov

    The objective of this work is to present benets and challenges of using robust topology optimization techniques for minimizing the sound pressure in interior acoustic problems. The focus is on creating designs which maintain high performance under uniform spatial variations. This work takes offset...... in previous work considering topology optimization for interior acoustic problems, [1]. However in the previous work the robustness of the designs was not considered....

  8. Benefits and Challenges when Performing Robust Topology Optimization for Interior Acoustic Problems

    OpenAIRE

    Christiansen, Rasmus Ellebæk; Jensen, Jakob Søndergaard; Lazarov, Boyan Stefanov; Sigmund, Ole

    2014-01-01

    The objective of this work is to present benets and challenges of using robust topology optimization techniques for minimizing the sound pressure in interior acoustic problems. The focus is on creating designs which maintain high performance under uniform spatial variations. This work takes offset in previous work considering topology optimization for interior acoustic problems, [1]. However in the previous work the robustness of the designs was not considered.

  9. Robust Optimization for Time-Cost Tradeoff Problem in Construction Projects

    OpenAIRE

    Li, Ming; Wu, Guangdong

    2014-01-01

    Construction projects are generally subject to uncertainty, which influences the realization of time-cost tradeoff in project management. This paper addresses a time-cost tradeoff problem under uncertainty, in which activities in projects can be executed in different construction modes corresponding to specified time and cost with interval uncertainty. Based on multiobjective robust optimization method, a robust optimization model for time-cost tradeoff problem is developed. In order to illus...

  10. Nickel-Cadmium Battery Operation Management Optimization Using Robust Design

    Science.gov (United States)

    Blosiu, Julian O.; Deligiannis, Frank; DiStefano, Salvador

    1996-01-01

    In recent years following several spacecraft battery anomalies, it was determined that managing the operational factors of NASA flight NiCd rechargeable battery was very important in order to maintain space flight battery nominal performance. The optimization of existing flight battery operational performance was viewed as something new for a Taguchi Methods application.

  11. Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

    Science.gov (United States)

    2009-03-10

    xfar by xint. Else, generate a new individual, using the Sobol pseudo- random sequence generator within the upper and lower bounds of the variables...12. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons. 2002. 13. Sobol , I. M., "Uniformly Distributed Sequences

  12. Robust optimization in simulation : Taguchi and Krige combined

    NARCIS (Netherlands)

    Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.

    2012-01-01

    Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by

  13. Robust Bayesian decision theory applied to optimal dosage.

    Science.gov (United States)

    Abraham, Christophe; Daurès, Jean-Pierre

    2004-04-15

    We give a model for constructing an utility function u(theta,d) in a dose prescription problem. theta and d denote respectively the patient state of health and the dose. The construction of u is based on the conditional probabilities of several variables. These probabilities are described by logistic models. Obviously, u is only an approximation of the true utility function and that is why we investigate the sensitivity of the final decision with respect to the utility function. We construct a class of utility functions from u and approximate the set of all Bayes actions associated to that class. Then, we measure the sensitivity as the greatest difference between the expected utilities of two Bayes actions. Finally, we apply these results to weighing up a chemotherapy treatment of lung cancer. This application emphasizes the importance of measuring robustness through the utility of decisions rather than the decisions themselves. Copyright 2004 John Wiley & Sons, Ltd.

  14. Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

    Directory of Open Access Journals (Sweden)

    Shujuan Wang

    2015-01-01

    Full Text Available This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

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

    Science.gov (United States)

    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.

  16. Creating geometrically robust designs for highly sensitive problems using topology optimization: Acoustic cavity design

    DEFF Research Database (Denmark)

    Christiansen, Rasmus E.; Lazarov, Boyan S.; Jensen, Jakob S.

    2015-01-01

    Resonance and wave-propagation problems are known to be highly sensitive towards parameter variations. This paper discusses topology optimization formulations for creating designs that perform robustly under spatial variations for acoustic cavity problems. For several structural problems, robust...... and limitations are discussed. In addition, a known explicit penalization approach is considered for comparison. For near-uniform spatial variations it is shown that highly robust designs can be obtained using the double filter approach. It is finally demonstrated that taking non-uniform variations into account...... further improves the robustness of the designs....

  17. Optimal robust control strategy of a solid oxide fuel cell system

    Science.gov (United States)

    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.

  18. Hybrid robust predictive optimization method of power system dispatch

    Science.gov (United States)

    Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

    2011-08-02

    A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

  19. Robust Optimization for Time-Cost Tradeoff Problem in Construction Projects

    Directory of Open Access Journals (Sweden)

    Ming Li

    2014-01-01

    Full Text Available Construction projects are generally subject to uncertainty, which influences the realization of time-cost tradeoff in project management. This paper addresses a time-cost tradeoff problem under uncertainty, in which activities in projects can be executed in different construction modes corresponding to specified time and cost with interval uncertainty. Based on multiobjective robust optimization method, a robust optimization model for time-cost tradeoff problem is developed. In order to illustrate the robust model, nondominated sorting genetic algorithm-II (NSGA-II is modified to solve the project example. The results show that, by means of adjusting the time and cost robust coefficients, the robust Pareto sets for time-cost tradeoff can be obtained according to different acceptable risk level, from which the decision maker could choose the preferred construction alternative.

  20. Optimal interdependence enhances the dynamical robustness of complex systems

    Science.gov (United States)

    Singh, Rishu Kumar; Sinha, Sitabhra

    2017-08-01

    Although interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more likely to survive over long times. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the global dynamics comprising disjoint sets ("islands") of stable activity.

  1. Robust topology optimization accounting for misplacement of material

    DEFF Research Database (Denmark)

    Jansen, Miche; Lombaert, Geert; Diehl, Moritz

    2013-01-01

    into account this type of geometric imperfections. A density filter based approach is followed, and translations of material are obtained by adding a small perturbation to the center of the filter kernel. The spatial variation of the geometric imperfections is modeled by means of a vector valued random field....... A sampling method is used to estimate these statistics during the optimization process. The proposed method is successfully applied to three example problems: the minimum compliance design of a slender column-like structure and a cantilever beam and a compliant mechanism design. An extensive Monte Carlo...

  2. Robust Estimation of Diffusion-Optimized Ensembles for Enhanced Sampling

    DEFF Research Database (Denmark)

    Tian, Pengfei; Jónsson, Sigurdur Æ.; Ferkinghoff-Borg, Jesper

    2014-01-01

    The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely......, and subsequently reweight the obtained statistics to recover the Boltzmann distribution at the temperature of interest. While this method has been successful in practice, the choice of a flat distribution is not necessarily optimal. Recently, it was proposed that additional performance gains could be obtained...

  3. Robust optimization methods for chance constrained, simulation-based, and bilevel problems

    NARCIS (Netherlands)

    Yanikoglu, I.

    2014-01-01

    The objective of robust optimization is to find solutions that are immune to the uncertainty of the parameters in a mathematical optimization problem. It requires that the constraints of a given problem should be satisfied for all realizations of the uncertain parameters in a so-called uncertainty

  4. Robust Optimization of Thermal Aspects of Friction Stir Welding Using Manifold Mapping Techniques

    DEFF Research Database (Denmark)

    Larsen, Anders Astrup; Lahaye, Domenico; Schmidt, Henrik Nikolaj Blicher

    2008-01-01

    The aim of this paper is to optimize a friction stir welding process taking robustness into account. The optimization problems are formulated with the goal of obtaining desired mean responses while reducing the variance of the response. We restrict ourselves to a thermal model of the process...

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

    Science.gov (United States)

    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.

  6. Robust Homography Estimation Based on Nonlinear Least Squares Optimization

    Directory of Open Access Journals (Sweden)

    Wei Mou

    2014-01-01

    Full Text Available The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints’ geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers.

  7. Including robustness in multi-criteria optimization for intensity-modulated proton therapy

    Science.gov (United States)

    Chen, Wei; Unkelbach, Jan; Trofimov, Alexei; Madden, Thomas; Kooy, Hanne; Bortfeld, Thomas; Craft, David

    2012-02-01

    We present a method to include robustness in a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives as well as the trade-off between robustness and nominal plan quality. In MCO, a database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. In our approach, robustness is integrated into MCO by adding robustified objectives and constraints to the MCO problem. Uncertainties (or errors) of the robust problem are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios (shifted patient positions, proton beam undershoot and overshoot). Objectives and constraints can be defined for the nominal scenario, thus characterizing nominal plan quality. A robustified objective represents the worst objective function value that can be realized for any of the error scenarios and thus provides a measure of plan robustness. The optimization method is based on a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. A base-of-skull case is used to demonstrate the robust optimization method. It is demonstrated that the robust optimization method reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved trade-offs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The latter illustrates the advantage of MCO in the context of robust planning. For all cases examined, the robust optimization for

  8. Robust Multivariable Optimization and Performance Simulation for ASIC Design

    Science.gov (United States)

    DuMonthier, Jeffrey; Suarez, George

    2013-01-01

    Application-specific-integrated-circuit (ASIC) design for space applications involves multiple challenges of maximizing performance, minimizing power, and ensuring reliable operation in extreme environments. This is a complex multidimensional optimization problem, which must be solved early in the development cycle of a system due to the time required for testing and qualification severely limiting opportunities to modify and iterate. Manual design techniques, which generally involve simulation at one or a small number of corners with a very limited set of simultaneously variable parameters in order to make the problem tractable, are inefficient and not guaranteed to achieve the best possible results within the performance envelope defined by the process and environmental requirements. What is required is a means to automate design parameter variation, allow the designer to specify operational constraints and performance goals, and to analyze the results in a way that facilitates identifying the tradeoffs defining the performance envelope over the full set of process and environmental corner cases. The system developed by the Mixed Signal ASIC Group (MSAG) at the Goddard Space Flight Center is implemented as a framework of software modules, templates, and function libraries. It integrates CAD tools and a mathematical computing environment, and can be customized for new circuit designs with only a modest amount of effort as most common tasks are already encapsulated. Customization is required for simulation test benches to determine performance metrics and for cost function computation.

  9. Stochastic analysis and robust optimization for a deck lid inner panel stamping

    International Nuclear Information System (INIS)

    Hou, Bo; Wang, Wurong; Li, Shuhui; Lin, Zhongqin; Xia, Z. Cedric

    2010-01-01

    FE-simulation and optimization are widely used in the stamping process to improve design quality and shorten development cycle. However, the current simulation and optimization may lead to non-robust results due to not considering the variation of material and process parameters. In this study, a novel stochastic analysis and robust optimization approach is proposed to improve the stamping robustness, where the uncertainties are involved to reflect manufacturing reality. A meta-model based stochastic analysis method is developed, where FE-simulation, uniform design and response surface methodology (RSM) are used to construct meta-model, based on which Monte-Carlo simulation is performed to predict the influence of input parameters variation on the final product quality. By applying the stochastic analysis, uniform design and RSM, the mean and the standard deviation (SD) of product quality are calculated as functions of the controllable process parameters. The robust optimization model composed of mean and SD is constructed and solved, the result of which is compared with the deterministic one to show its advantages. It is demonstrated that the product quality variations are reduced significantly, and quality targets (reject rate) are achieved under the robust optimal solution. The developed approach offers rapid and reliable results for engineers to deal with potential stamping problems during the early phase of product and tooling design, saving more time and resources.

  10. Robust optimization model and algorithm for railway freight center location problem in uncertain environment.

    Science.gov (United States)

    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.

  11. Robust Optimization Model and Algorithm for Railway Freight Center Location Problem in Uncertain Environment

    Directory of Open Access Journals (Sweden)

    Xing-cai Liu

    2014-01-01

    Full Text Available 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.

  12. Optimal control of quantum systems: Origins of inherent robustness to control field fluctuations

    International Nuclear Information System (INIS)

    Rabitz, Herschel

    2002-01-01

    The impact of control field fluctuations on the optimal manipulation of quantum dynamics phenomena is investigated. The quantum system is driven by an optimal control field, with the physical focus on the evolving expectation value of an observable operator. A relationship is shown to exist between the system dynamics and the control field fluctuations, wherein the process of seeking optimal performance assures an inherent degree of system robustness to such fluctuations. The presence of significant field fluctuations breaks down the evolution of the observable expectation value into a sequence of partially coherent robust steps. Robustness occurs because the optimization process reduces sensitivity to noise-driven quantum system fluctuations by taking advantage of the observable expectation value being bilinear in the evolution operator and its adjoint. The consequences of this inherent robustness are discussed in the light of recent experiments and numerical simulations on the optimal control of quantum phenomena. The analysis in this paper bodes well for the future success of closed-loop quantum optimal control experiments, even in the presence of reasonable levels of field fluctuations

  13. Robust design optimization method for centrifugal impellers under surface roughness uncertainties due to blade fouling

    Science.gov (United States)

    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.

  14. Data-adaptive Robust Optimization Method for the Economic Dispatch of Active Distribution Networks

    DEFF Research Database (Denmark)

    Zhang, Yipu; Ai, Xiaomeng; Fang, Jiakun

    2018-01-01

    Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from the power system operators. This paper proposes a novel data-adaptive robust optimization method for the economic dispatch...... of active distribution network with renewables. The scenario-generation method and the two-stage robust optimization are combined in the proposed method. To reduce the conservativeness, a few extreme scenarios selected from the historical data are used to replace the conventional uncertainty set....... The proposed extreme-scenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all the possible scenarios if they hold in the selected extreme scenarios, which...

  15. Robust D-optimal designs under correlated error, applicable invariantly for some lifetime distributions

    International Nuclear Information System (INIS)

    Das, Rabindra Nath; Kim, Jinseog; Park, Jeong-Soo

    2015-01-01

    In quality engineering, the most commonly used lifetime distributions are log-normal, exponential, gamma and Weibull. Experimental designs are useful for predicting the optimal operating conditions of the process in lifetime improvement experiments. In the present article, invariant robust first-order D-optimal designs are derived for correlated lifetime responses having the above four distributions. Robust designs are developed for some correlated error structures. It is shown that robust first-order D-optimal designs for these lifetime distributions are always robust rotatable but the converse is not true. Moreover, it is observed that these designs depend on the respective error covariance structure but are invariant to the above four lifetime distributions. This article generalizes the results of Das and Lin [7] for the above four lifetime distributions with general (intra-class, inter-class, compound symmetry, and tri-diagonal) correlated error structures. - Highlights: • This paper presents invariant robust first-order D-optimal designs under correlated lifetime responses. • The results of Das and Lin [7] are extended for the four lifetime (log-normal, exponential, gamma and Weibull) distributions. • This paper also generalizes the results of Das and Lin [7] to more general correlated error structures

  16. A robust optimization model for agile and build-to-order supply chain planning under uncertainties

    DEFF Research Database (Denmark)

    Lalmazloumian, Morteza; Wong, Kuan Yew; Govindan, Kannan

    2016-01-01

    Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms' success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various....... The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio...

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

    International Nuclear Information System (INIS)

    Bender, Edward T.

    2012-01-01

    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 compared 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.

  18. A robust optimization model for distribution and evacuation in the disaster response phase

    Science.gov (United States)

    Fereiduni, Meysam; Shahanaghi, Kamran

    2017-03-01

    Natural disasters, such as earthquakes, affect thousands of people and can cause enormous financial loss. Therefore, an efficient response immediately following a natural disaster is vital to minimize the aforementioned negative effects. This research paper presents a network design model for humanitarian logistics which will assist in location and allocation decisions for multiple disaster periods. At first, a single-objective optimization model is presented that addresses the response phase of disaster management. This model will help the decision makers to make the most optimal choices in regard to location, allocation, and evacuation simultaneously. The proposed model also considers emergency tents as temporary medical centers. To cope with the uncertainty and dynamic nature of disasters, and their consequences, our multi-period robust model considers the values of critical input data in a set of various scenarios. Second, because of probable disruption in the distribution infrastructure (such as bridges), the Monte Carlo simulation is used for generating related random numbers and different scenarios; the p-robust approach is utilized to formulate the new network. The p-robust approach can predict possible damages along pathways and among relief bases. We render a case study of our robust optimization approach for Tehran's plausible earthquake in region 1. Sensitivity analysis' experiments are proposed to explore the effects of various problem parameters. These experiments will give managerial insights and can guide DMs under a variety of conditions. Then, the performances of the "robust optimization" approach and the "p-robust optimization" approach are evaluated. Intriguing results and practical insights are demonstrated by our analysis on this comparison.

  19. Design optimization of a robust sleeve antenna for hepatic microwave ablation

    International Nuclear Information System (INIS)

    Prakash, Punit; Webster, John G; Deng Geng; Converse, Mark C; Mahvi, David M; Ferris, Michael C

    2008-01-01

    We describe the application of a Bayesian variable-number sample-path (VNSP) optimization algorithm to yield a robust design for a floating sleeve antenna for hepatic microwave ablation. Finite element models are used to generate the electromagnetic (EM) field and thermal distribution in liver given a particular design. Dielectric properties of the tissue are assumed to vary within ± 10% of average properties to simulate the variation among individuals. The Bayesian VNSP algorithm yields an optimal design that is a 14.3% improvement over the original design and is more robust in terms of lesion size, shape and efficiency. Moreover, the Bayesian VNSP algorithm finds an optimal solution saving 68.2% simulation of the evaluations compared to the standard sample-path optimization method

  20. Robust optimization of psychotropic drug mixture separation in hydrophilic interaction liquid chromatography.

    Science.gov (United States)

    Rakić, Tijana; Jovanović, Marko; Dumić, Aleksandra; Pekić, Marina; Ribić, Sanja; Stojanović, Biljana Jancić

    2013-01-01

    This paper presents multiobjective optimization of complex mixtures separation in hydrophilic interaction liquid chromatography (HILIC). The selected model mixture consisted of five psychotropic drugs: clozapine, thioridazine, sulpiride, pheniramine and lamotrigine. Three factors related to the mobile phase composition (acetonitrile content, pH of the water phase and concentration of ammonium acetate) were optimized in order to achieve the following goals: maximal separation quality, minimal total analysis duration and robustness of an optimum. The consideration of robustness in early phases of the method development provides reliable methods with low risk for failure in validation phase. The simultaneous optimization of all goals was achieved by multiple threshold approach combined with grid point search. The identified optimal separation conditions (acetonitrile content 83%, pH of the water phase 3.5 and ammonium acetate content in water phase 14 mM) were experimentally verified.

  1. Dynamic optimization and robust explicit model predictive control of hydrogen storage tank

    KAUST Repository

    Panos, C.

    2010-09-01

    We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.

  2. Robust design of broadband EUV multilayer beam splitters based on particle swarm optimization

    International Nuclear Information System (INIS)

    Jiang, Hui; Michette, Alan G.

    2013-01-01

    A robust design idea for broadband EUV multilayer beam splitters is introduced that achieves the aim of decreasing the influence of layer thickness errors on optical performances. Such beam splitters can be used in interferometry to determine the quality of EUVL masks by comparing with a reference multilayer. In the optimization, particle swarm techniques were used for the first time in such designs. Compared to conventional genetic algorithms, particle swarm optimization has stronger ergodicity, simpler processing and faster convergence

  3. Commitment and dispatch of heat and power units via affinely adjustable robust optimization

    DEFF Research Database (Denmark)

    Zugno, Marco; Morales González, Juan Miguel; Madsen, Henrik

    2016-01-01

    compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization...... and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming. (C) 2016 Elsevier Ltd. All rights reserved....

  4. Dynamic optimization and robust explicit model predictive control of hydrogen storage tank

    KAUST Repository

    Panos, C.; Kouramas, K.I.; Georgiadis, M.C.; Pistikopoulos, E.N.

    2010-01-01

    We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.

  5. Machine learning meliorates computing and robustness in discrete combinatorial optimization problems.

    Directory of Open Access Journals (Sweden)

    Fushing Hsieh

    2016-11-01

    Full Text Available Discrete combinatorial optimization problems in real world are typically defined via an ensemble of potentially high dimensional measurements pertaining to all subjects of a system under study. We point out that such a data ensemble in fact embeds with system's information content that is not directly used in defining the combinatorial optimization problems. Can machine learning algorithms extract such information content and make combinatorial optimizing tasks more efficient? Would such algorithmic computations bring new perspectives into this classic topic of Applied Mathematics and Theoretical Computer Science? We show that answers to both questions are positive. One key reason is due to permutation invariance. That is, the data ensemble of subjects' measurement vectors is permutation invariant when it is represented through a subject-vs-measurement matrix. An unsupervised machine learning algorithm, called Data Mechanics (DM, is applied to find optimal permutations on row and column axes such that the permuted matrix reveals coupled deterministic and stochastic structures as the system's information content. The deterministic structures are shown to facilitate geometry-based divide-and-conquer scheme that helps optimizing task, while stochastic structures are used to generate an ensemble of mimicries retaining the deterministic structures, and then reveal the robustness pertaining to the original version of optimal solution. Two simulated systems, Assignment problem and Traveling Salesman problem, are considered. Beyond demonstrating computational advantages and intrinsic robustness in the two systems, we propose brand new robust optimal solutions. We believe such robust versions of optimal solutions are potentially more realistic and practical in real world settings.

  6. A Hybrid Interval-Robust Optimization Model for Water Quality Management.

    Science.gov (United States)

    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.

  7. A Hybrid Interval–Robust Optimization Model for Water Quality Management

    Science.gov (United States)

    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

  8. A novel non-probabilistic approach using interval analysis for robust design optimization

    International Nuclear Information System (INIS)

    Sun, Wei; Dong, Rongmei; Xu, Huanwei

    2009-01-01

    A technique for formulation of the objective and constraint functions with uncertainty plays a crucial role in robust design optimization. This paper presents the first application of interval methods for reformulating the robust optimization problem. Based on interval mathematics, the original real-valued objective and constraint functions are replaced with the interval-valued functions, which directly represent the upper and lower bounds of the new functions under uncertainty. The single objective function is converted into two objective functions for minimizing the mean value and the variation, and the constraint functions are reformulated with the acceptable robustness level, resulting in a bi-level mathematical model. Compared with other methods, this method is efficient and does not require presumed probability distribution of uncertain factors or gradient or continuous information of constraints. Two numerical examples are used to illustrate the validity and feasibility of the presented method

  9. Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Bingyong Yan

    2015-01-01

    Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

  10. An Effective, Robust And Parallel Implementation Of An Interior Point Algorithm For Limit State Optimization

    DEFF Research Database (Denmark)

    Dollerup, Niels; Jepsen, Michael S.; Damkilde, Lars

    2013-01-01

    The artide describes a robust and effective implementation of the interior point optimization algorithm. The adopted method includes a precalculation step, which reduces the number of variables by fulfilling the equilibrium equations a priori. This work presents an improved implementation of the ...

  11. An effective, robust and parallel implementation of an interior point algorithm for limit state optimization

    DEFF Research Database (Denmark)

    Dollerup, Niels; Jepsen, Michael S.; Frier, Christian

    2014-01-01

    A robust and effective finite element based implementation of lower bound limit state analysis applying an interior point formulation is presented in this paper. The lower bound formulation results in a convex optimization problem consisting of a number of linear constraints from the equilibrium...

  12. Optimizing clinical performance and geometrical robustness of a new electrode device for intracranial tumor electroporation

    DEFF Research Database (Denmark)

    Mahmood, Faisal; Gehl, Julie

    2011-01-01

    and genes to intracranial tumors in humans, and demonstrate a method to optimize the design (i.e. geometry) of the electrode device prototype to improve both clinical performance and geometrical tolerance (robustness). We have employed a semiempirical objective function based on constraints similar to those...... sensitive to random geometrical deviations. The method is readily applicable to other electrode configurations....

  13. Towards a Robuster Interpretive Parsing: learning from overt forms in Optimality Theory

    NARCIS (Netherlands)

    Biró, T.

    2013-01-01

    The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this

  14. Avoiding Optimal Mean ℓ2,1-Norm Maximization-Based Robust PCA for Reconstruction.

    Science.gov (United States)

    Luo, Minnan; Nie, Feiping; Chang, Xiaojun; Yang, Yi; Hauptmann, Alexander G; Zheng, Qinghua

    2017-04-01

    Robust principal component analysis (PCA) is one of the most important dimension-reduction techniques for handling high-dimensional data with outliers. However, most of the existing robust PCA presupposes that the mean of the data is zero and incorrectly utilizes the average of data as the optimal mean of robust PCA. In fact, this assumption holds only for the squared [Formula: see text]-norm-based traditional PCA. In this letter, we equivalently reformulate the objective of conventional PCA and learn the optimal projection directions by maximizing the sum of projected difference between each pair of instances based on [Formula: see text]-norm. The proposed method is robust to outliers and also invariant to rotation. More important, the reformulated objective not only automatically avoids the calculation of optimal mean and makes the assumption of centered data unnecessary, but also theoretically connects to the minimization of reconstruction error. To solve the proposed nonsmooth problem, we exploit an efficient optimization algorithm to soften the contributions from outliers by reweighting each data point iteratively. We theoretically analyze the convergence and computational complexity of the proposed algorithm. Extensive experimental results on several benchmark data sets illustrate the effectiveness and superiority of the proposed method.

  15. Robust optimization of robotic pick and place operations for deformable objects through simulation

    DEFF Research Database (Denmark)

    Bo Jorgensen, Troels; Debrabant, Kristian; Kruger, Norbert

    2016-01-01

    for the task. The solutions are parameterized in terms of the robot motion and the gripper configuration, and after each simulation various objective scores are determined and combined. This enables the use of various optimization strategies. Based on visual inspection of the most robust solution found...

  16. Approximating the Pareto Set of Multiobjective Linear Programs via Robust Optimization

    NARCIS (Netherlands)

    Gorissen, B.L.; den Hertog, D.

    2012-01-01

    Abstract: The Pareto set of a multiobjective optimization problem consists of the solutions for which one or more objectives can not be improved without deteriorating one or more other objectives. We consider problems with linear objectives and linear constraints and use Adjustable Robust

  17. Optimal and Robust Switching Control Strategies : Theory, and Applications in Traffic Management

    NARCIS (Netherlands)

    Hajiahmadi, M.

    2015-01-01

    Macroscopic modeling, predictive and robust control and route guidance for large-scale freeway and urban traffic networks are the main focus of this thesis. In order to increase the efficiency of our control strategies, we propose several mathematical and optimization techniques. Moreover, in the

  18. Quantifying the robustness of optimal reservoir operation for the Xinanjiang-Fuchunjiang Reservoir Cascade

    NARCIS (Netherlands)

    Vonk, E.; Xu, YuePing; Booij, Martijn J.; Augustijn, Dionysius C.M.

    2016-01-01

    In this research we investigate the robustness of the common implicit stochastic optimization (ISO) method for dam reoperation. As a case study, we focus on the Xinanjiang-Fuchunjiang reservoir cascade in eastern China, for which adapted operating rules were proposed as a means to reduce the impact

  19. Optimization Versus Robustness in Simulation : A Practical Methodology, With a Production-Management Case-Study

    NARCIS (Netherlands)

    Kleijnen, J.P.C.; Gaury, E.G.A.

    2001-01-01

    Whereas Operations Research has always paid much attention to optimization, practitioners judge the robustness of the 'optimum' solution to be of greater importance.Therefore this paper proposes a practical methodology that is a stagewise combination of the following four proven techniques: (1)

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

    Energy Technology Data Exchange (ETDEWEB)

    Liao, L [Proton Therapy Center, UT MD Anderson Cancer Center, Houston, TX (United States); Department of Industrial Engineering, University of Houston, Houston, TX (United States); Yu, J; Zhu, X; Li, H; Zhang, X [Proton Therapy Center, UT MD Anderson Cancer Center, Houston, TX (United States); Li, Y [Proton Therapy Center, UT MD Anderson Cancer Center, Houston, TX (United States); Varian Medical Systems, Houston, TX (United States); Lim, G [Department of Industrial Engineering, University of Houston, Houston, TX (United States)

    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 to 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.

  1. On robust control of uncertain chaotic systems: a sliding-mode synthesis via chaotic optimization

    International Nuclear Information System (INIS)

    Lu Zhao; Shieh Leangsan; Chen GuanRong

    2003-01-01

    This paper presents a novel Lyapunov-based control approach which utilizes a Lyapunov function of the nominal plant for robust tracking control of general multi-input uncertain nonlinear systems. The difficulty of constructing a control Lyapunov function is alleviated by means of predefining an optimal sliding mode. The conventional schemes for constructing sliding modes of nonlinear systems stipulate that the system of interest is canonical-transformable or feedback-linearizable. An innovative approach that exploits a chaotic optimizing algorithm is developed thereby obtaining the optimal sliding manifold for the control purpose. Simulations on the uncertain chaotic Chen's system illustrate the effectiveness of the proposed approach

  2. A mean–variance objective for robust production optimization in uncertain geological scenarios

    DEFF Research Database (Denmark)

    Capolei, Andrea; Suwartadi, Eka; Foss, Bjarne

    2014-01-01

    directly. In the mean–variance bi-criterion objective function risk appears directly, it also considers an ensemble of reservoir models, and has robust optimization as a special extreme case. The mean–variance objective is common for portfolio optimization problems in finance. The Markowitz portfolio...... optimization problem is the original and simplest example of a mean–variance criterion for mitigating risk. Risk is mitigated in oil production by including both the expected NPV (mean of NPV) and the risk (variance of NPV) for the ensemble of possible reservoir models. With the inclusion of the risk...

  3. Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xianfu Cheng

    2014-01-01

    Full Text Available 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.

  4. Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

    Science.gov (United States)

    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. PMID:24683334

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

    Science.gov (United States)

    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.

  6. Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

    Directory of Open Access Journals (Sweden)

    Sengupta Raghu Nandan

    2017-02-01

    Full Text Available We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO method wherein financial script/asset loss return distributions are considered as extreme valued. The objective function is a convex combination of portfolio’s CVaR and expected value of loss return, subject to a set of randomly perturbed chance constraints with specified probability values. The robust deterministic counterpart of the model takes the form of Second Order Cone Programming (SOCP problem. Results from extensive simulation runs show the efficacy of our proposed models, as it helps the investor to (i utilize extensive simulation studies to draw insights into the effect of randomness in portfolio decision making process, (ii incorporate different risk appetite scenarios to find the optimal solutions for the financial portfolio allocation problem and (iii compare the risk and return profiles of the investments made in both deterministic as well as in uncertain and highly volatile financial markets.

  7. Robust Inventory System Optimization Based on Simulation and Multiple Criteria Decision Making

    Directory of Open Access Journals (Sweden)

    Ahmad Mortazavi

    2014-01-01

    Full Text Available Inventory management in retailers is difficult and complex decision making process which is related to the conflict criteria, also existence of cyclic changes and trend in demand is inevitable in many industries. In this paper, simulation modeling is considered as efficient tool for modeling of retailer multiproduct inventory system. For simulation model optimization, a novel multicriteria and robust surrogate model is designed based on multiple attribute decision making (MADM method, design of experiments (DOE, and principal component analysis (PCA. This approach as a main contribution of this paper, provides a framework for robust multiple criteria decision making under uncertainty.

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

    Science.gov (United States)

    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.

  9. On the relation between flexibility analysis and robust optimization for linear systems

    KAUST Repository

    Zhang, Qi

    2016-03-05

    Flexibility analysis and robust optimization are two approaches to solving optimization problems under uncertainty that share some fundamental concepts, such as the use of polyhedral uncertainty sets and the worst-case approach to guarantee feasibility. The connection between these two approaches has not been sufficiently acknowledged and examined in the literature. In this context, the contributions of this work are fourfold: (1) a comparison between flexibility analysis and robust optimization from a historical perspective is presented; (2) for linear systems, new formulations for the three classical flexibility analysis problems—flexibility test, flexibility index, and design under uncertainty—based on duality theory and the affinely adjustable robust optimization (AARO) approach are proposed; (3) the AARO approach is shown to be generally more restrictive such that it may lead to overly conservative solutions; (4) numerical examples show the improved computational performance from the proposed formulations compared to the traditional flexibility analysis models. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3109–3123, 2016

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

    Science.gov (United States)

    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

  11. A robust optimization model for blood supply chain in emergency situations

    Directory of Open Access Journals (Sweden)

    Meysam Fereiduni

    2016-09-01

    Full Text Available In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed.

  12. RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks

    Energy Technology Data Exchange (ETDEWEB)

    Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)

    2013-12-25

    The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need

  13. Robustness of IPSA optimized high-dose-rate prostate brachytherapy treatment plans to catheter displacements.

    Science.gov (United States)

    Poder, Joel; Whitaker, May

    2016-06-01

    Inverse planning simulated annealing (IPSA) optimized brachytherapy treatment plans are characterized with large isolated dwell times at the first or last dwell position of each catheter. The potential of catheter shifts relative to the target and organs at risk in these plans may lead to a more significant change in delivered dose to the volumes of interest relative to plans with more uniform dwell times. This study aims to determine if the Nucletron Oncentra dwell time deviation constraint (DTDC) parameter can be optimized to improve the robustness of high-dose-rate (HDR) prostate brachytherapy plans to catheter displacements. A set of 10 clinically acceptable prostate plans were re-optimized with a DTDC parameter of 0 and 0.4. For each plan, catheter displacements of 3, 7, and 14 mm were retrospectively applied and the change in dose volume histogram (DVH) indices and conformity indices analyzed. The robustness of clinically acceptable prostate plans to catheter displacements in the caudal direction was found to be dependent on the DTDC parameter. A DTDC value of 0 improves the robustness of planning target volume (PTV) coverage to catheter displacements, whereas a DTDC value of 0.4 improves the robustness of the plans to changes in hotspots. The results indicate that if used in conjunction with a pre-treatment catheter displacement correction protocol and a tolerance of 3 mm, a DTDC value of 0.4 may produce clinically superior plans. However, the effect of the DTDC parameter in plan robustness was not observed to be as strong as initially suspected.

  14. Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

    Directory of Open Access Journals (Sweden)

    Semi Jeon

    2017-02-01

    Full Text Available Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i robust feature detection using particle keypoints between adjacent frames; (ii camera path estimation and smoothing; and (iii rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV. The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.

  15. Robust state feedback controller design of STATCOM using chaotic optimization algorithm

    Directory of Open Access Journals (Sweden)

    Safari Amin

    2010-01-01

    Full Text Available In this paper, a new design technique for the design of robust state feedback controller for static synchronous compensator (STATCOM using Chaotic Optimization Algorithm (COA is presented. The design is formulated as an optimization problem which is solved by the COA. Since chaotic planning enjoys reliability, ergodicity and stochastic feature, the proposed technique presents chaos mapping using Lozi map chaotic sequences which increases its convergence rate. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results reveal that the proposed controller has an excellent capability in damping power system low frequency oscillations and enhances greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions shows that the phase based controller is superior compare to the magnitude based controller.

  16. Robust non-gradient C subroutines for non-linear optimization

    DEFF Research Database (Denmark)

    Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun

    2004-01-01

    This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems, where gradient information is not required. The intention is that the routines should use the currently best algorithms available. All routines have...... subroutines are obtained by changing 0 to 1. The present report is a new and updated version of a previous report NI-91-04 with the title Non-gradient c Subroutines for Non- Linear Optimization, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated...... from Fortran to C. The reason for writing the present report is that some of the C subroutines have been replaced by more e ective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modified to some extent...

  17. The French biofuels mandates under cost uncertainty - an assessment based on robust optimization

    International Nuclear Information System (INIS)

    Lorne, Daphne; Tchung-Ming, Stephane

    2012-01-01

    This paper investigates the impact of primary energy and technology cost uncertainty on the achievement of renewable and especially biofuel policies - mandates and norms - in France by 2030. A robust optimization technique that allows to deal with uncertainty sets of high dimensionality is implemented in a TIMES-based long-term planning model of the French energy transport and electricity sectors. The energy system costs and potential benefits (GHG emissions abatements, diversification) of the French renewable mandates are assessed within this framework. The results of this systemic analysis highlight how setting norms and mandates allows to reduce the variability of CO 2 emissions reductions and supply mix diversification when the costs of technological progress and prices are uncertain. Beyond that, we discuss the usefulness of robust optimization in complement of other techniques to integrate uncertainty in large-scale energy models. (authors)

  18. Primal-dual convex optimization in large deformation diffeomorphic metric mapping: LDDMM meets robust regularizers

    Science.gov (United States)

    Hernandez, Monica

    2017-12-01

    This paper proposes a method for primal-dual convex optimization in variational large deformation diffeomorphic metric mapping problems formulated with robust regularizers and robust image similarity metrics. The method is based on Chambolle and Pock primal-dual algorithm for solving general convex optimization problems. Diagonal preconditioning is used to ensure the convergence of the algorithm to the global minimum. We consider three robust regularizers liable to provide acceptable results in diffeomorphic registration: Huber, V-Huber and total generalized variation. The Huber norm is used in the image similarity term. The primal-dual equations are derived for the stationary and the non-stationary parameterizations of diffeomorphisms. The resulting algorithms have been implemented for running in the GPU using Cuda. For the most memory consuming methods, we have developed a multi-GPU implementation. The GPU implementations allowed us to perform an exhaustive evaluation study in NIREP and LPBA40 databases. The experiments showed that, for all the considered regularizers, the proposed method converges to diffeomorphic solutions while better preserving discontinuities at the boundaries of the objects compared to baseline diffeomorphic registration methods. In most cases, the evaluation showed a competitive performance for the robust regularizers, close to the performance of the baseline diffeomorphic registration methods.

  19. SOCP relaxation bounds for the optimal subset selection problem applied to robust linear regression

    OpenAIRE

    Flores, Salvador

    2015-01-01

    This paper deals with the problem of finding the globally optimal subset of h elements from a larger set of n elements in d space dimensions so as to minimize a quadratic criterion, with an special emphasis on applications to computing the Least Trimmed Squares Estimator (LTSE) for robust regression. The computation of the LTSE is a challenging subset selection problem involving a nonlinear program with continuous and binary variables, linked in a highly nonlinear fashion. The selection of a ...

  20. Robust subspace estimation using low-rank optimization theory and applications

    CERN Document Server

    Oreifej, Omar

    2014-01-01

    Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame

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

    Science.gov (United States)

    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.

  2. Optimal Control for Fast and Robust Generation of Entangled States in Anisotropic Heisenberg Chains

    Science.gov (United States)

    Zhang, Xiong-Peng; Shao, Bin; Zou, Jian

    2017-05-01

    Motivated by some recent results of the optimal control (OC) theory, we study anisotropic XXZ Heisenberg spin-1/2 chains with control fields acting on a single spin, with the aim of exploring how maximally entangled state can be prepared. To achieve the goal, we use a numerical optimization algorithm (e.g., the Krotov algorithm, which was shown to be capable of reaching the quantum speed limit) to search an optimal set of control parameters, and then obtain OC pulses corresponding to the target fidelity. We find that the minimum time for implementing our target state depending on the anisotropy parameter Δ of the model. Finally, we analyze the robustness of the obtained results for the optimal fidelities and the effectiveness of the Krotov method under some realistic conditions.

  3. EABOT - Energetic analysis as a basis for robust optimization of trigeneration systems by linear programming

    International Nuclear Information System (INIS)

    Piacentino, A.; Cardona, F.

    2008-01-01

    The optimization of synthesis, design and operation in trigeneration systems for building applications is a quite complex task, due to the high number of decision variables, the presence of irregular heat, cooling and electric load profiles and the variable electricity price. Consequently, computer-aided techniques are usually adopted to achieve the optimal solution, based either on iterative techniques, linear or non-linear programming or evolutionary search. Large efforts have been made in improving algorithm efficiency, which have resulted in an increasingly rapid convergence to the optimal solution and in reduced calculation time; robust algorithm have also been formulated, assuming stochastic behaviour for energy loads and prices. This paper is based on the assumption that margins for improvements in the optimization of trigeneration systems still exist, which require an in-depth understanding of plant's energetic behaviour. Robustness in the optimization of trigeneration systems has more to do with a 'correct and comprehensive' than with an 'efficient' modelling, being larger efforts required to energy specialists rather than to experts in efficient algorithms. With reference to a mixed integer linear programming model implemented in MatLab for a trigeneration system including a pressurized (medium temperature) heat storage, the relevant contribute of thermoeconomics and energo-environmental analysis in the phase of mathematical modelling and code testing are shown

  4. A robust optimization based approach for microgrid operation in deregulated environment

    International Nuclear Information System (INIS)

    Gupta, R.A.; Gupta, Nand Kishor

    2015-01-01

    Highlights: • RO based approach developed for optimal MG operation in deregulated environment. • Wind uncertainty modeled by interval forecasting through ARIMA model. • Proposed approach evaluated using two realistic case studies. • Proposed approach evaluated the impact of degree of robustness. • Proposed approach gives a significant reduction in operation cost of microgrid. - Abstract: Micro Grids (MGs) are clusters of Distributed Energy Resource (DER) units and loads. MGs are self-sustainable and generally operated in two modes: (1) grid connected and (2) grid isolated. In deregulated environment, the operation of MG is managed by the Microgrid Operator (MO) with an objective to minimize the total cost of operation. The MG management is crucial in the deregulated power system due to (i) integration of intermittent renewable sources such as wind and Photo Voltaic (PV) generation, and (ii) volatile grid prices. This paper presents robust optimization based approach for optimal MG management considering wind power uncertainty. Time series based Autoregressive Integrated Moving Average (ARIMA) model is used to characterize the wind power uncertainty through interval forecasting. The proposed approach is illustrated through a case study having both dispatchable and non-dispatchable generators through different modes of operation. Further the impact of degree of robustness is analyzed in both cases on the total cost of operation of the MG. A comparative analysis between obtained results using proposed approach and other existing approach shows the strength of proposed approach in cost minimization in MG management

  5. Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty

    Science.gov (United States)

    Herman, Jonathan D.; Zeff, Harrison B.; Reed, Patrick M.; Characklis, Gregory W.

    2014-10-01

    While optimality is a foundational mathematical concept in water resources planning and management, "optimal" solutions may be vulnerable to failure if deeply uncertain future conditions deviate from those assumed during optimization. These vulnerabilities may produce severely asymmetric impacts across a region, making it vital to evaluate the robustness of management strategies as well as their impacts for regional stakeholders. In this study, we contribute a multistakeholder many-objective robust decision making (MORDM) framework that blends many-objective search and uncertainty analysis tools to discover key tradeoffs between water supply alternatives and their robustness to deep uncertainties (e.g., population pressures, climate change, and financial risks). The proposed framework is demonstrated for four interconnected water utilities representing major stakeholders in the "Research Triangle" region of North Carolina, U.S. The utilities supply well over one million customers and have the ability to collectively manage drought via transfer agreements and shared infrastructure. We show that water portfolios for this region that compose optimal tradeoffs (i.e., Pareto-approximate solutions) under expected future conditions may suffer significantly degraded performance with only modest changes in deeply uncertain hydrologic and economic factors. We then use the Patient Rule Induction Method (PRIM) to identify which uncertain factors drive the individual and collective vulnerabilities for the four cooperating utilities. Our framework identifies key stakeholder dependencies and robustness tradeoffs associated with cooperative regional planning, which are critical to understanding the tensions between individual versus regional water supply goals. Cooperative demand management was found to be the key factor controlling the robustness of regional water supply planning, dominating other hydroclimatic and economic uncertainties through the 2025 planning horizon. Results

  6. Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms.

    Science.gov (United States)

    Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali

    2018-05-11

    The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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

  8. An integrated framework of agent-based modelling and robust optimization for microgrid energy management

    International Nuclear Information System (INIS)

    Kuznetsova, Elizaveta; Li, Yan-Fu; Ruiz, Carlos; Zio, Enrico

    2014-01-01

    Highlights: • Microgrid composed of a train station, wind power plant and district is investigated. • Each player is modeled as an individual agent aiming at a particular goal. • Prediction Intervals quantify the uncertain operational and environmental parameters. • Optimal goal-directed actions planning is achieved with robust optimization. • Optimization framework improves system reliability and decreases power imbalances. - Abstract: A microgrid energy management framework for the optimization of individual objectives of microgrid stakeholders is proposed. The framework is exemplified by way of a microgrid that is connected to an external grid via a transformer and includes the following players: a middle-size train station with integrated photovoltaic power production system, a small energy production plant composed of urban wind turbines, and a surrounding district including residences and small businesses. The system is described by Agent-Based Modelling (ABM), in which each player is modelled as an individual agent aiming at a particular goal, (i) decreasing its expenses for power purchase or (ii) increasing its revenues from power selling. The context in which the agents operate is uncertain due to the stochasticity of operational and environmental parameters, and the technical failures of the renewable power generators. The uncertain operational and environmental parameters of the microgrid are quantified in terms of Prediction Intervals (PIs) by a Non-dominated Sorting Genetic Algorithm (NSGA-II) – trained Neural Network (NN). Under these uncertainties, each agent is seeking for optimal goal-directed actions planning by Robust Optimization (RO). The developed framework is shown to lead to an increase in system performance, evaluated in terms of typical reliability (adequacy) indicators for energy systems, such as Loss of Load Expectation (LOLE) and Loss of Expected Energy (LOEE), in comparison with optimal planning based on expected values of

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

    Science.gov (United States)

    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.

  10. An optimization methodology for identifying robust process integration investments under uncertainty

    International Nuclear Information System (INIS)

    Svensson, Elin; Berntsson, Thore; Stroemberg, Ann-Brith; Patriksson, Michael

    2009-01-01

    Uncertainties in future energy prices and policies strongly affect decisions on investments in process integration measures in industry. In this paper, we present a five-step methodology for the identification of robust investment alternatives incorporating explicitly such uncertainties in the optimization model. Methods for optimization under uncertainty (or, stochastic programming) are thus combined with a deep understanding of process integration and process technology in order to achieve a framework for decision-making concerning the investment planning of process integration measures under uncertainty. The proposed methodology enables the optimization of investments in energy efficiency with respect to their net present value or an environmental objective. In particular, as a result of the optimization approach, complex investment alternatives, allowing for combinations of energy efficiency measures, can be analyzed. Uncertainties as well as time-dependent parameters, such as energy prices and policies, are modelled using a scenario-based approach, enabling the identification of robust investment solutions. The methodology is primarily an aid for decision-makers in industry, but it will also provide insight for policy-makers into how uncertainties regarding future price levels and policy instruments affect the decisions on investments in energy efficiency measures. (author)

  11. Estimation and robust control of microalgae culture for optimization of biological fixation of CO2

    International Nuclear Information System (INIS)

    Filali, R.

    2012-01-01

    This thesis deals with the optimization of carbon dioxide consumption by microalgae. Indeed, following several current environmental issues primarily related to large emissions of CO 2 , it is shown that microalgae represent a very promising solution for CO 2 mitigation. From this perspective, we are interested in the optimization strategy of CO 2 consumption through the development of a robust control law. The main aim is to ensure optimal operating conditions for a Chlorella vulgaris culture in an instrumented photo-bioreactor. The thesis is based on three major axes. The first one concerns growth modeling of the selected species based on a mathematical model reflecting the influence of light and total inorganic carbon concentration. For the control context, the second axis is related to biomass estimation from the real-time measurement of dissolved carbon dioxide. This step is necessary for the control part due to the lack of affordable real-time sensors for this kind of measurement. Three observers structures have been studied and compared: an extended Kalman filter, an asymptotic observer and an interval observer. The last axis deals with the implementation of a non-linear predictive control law coupled to the estimation strategy for the regulation of the cellular concentration around a value which maximizes the CO 2 consumption. Performance and robustness of this control law have been validated in simulation and experimentally on a laboratory-scale instrumented photo-bioreactor. This thesis represents a preliminary study for the optimization of CO 2 mitigation strategy by microalgae. (author)

  12. An optimization methodology for identifying robust process integration investments under uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, Elin; Berntsson, Thore [Department of Energy and Environment, Division of Heat and Power Technology, Chalmers University of Technology, SE-412 96 Goeteborg (Sweden); Stroemberg, Ann-Brith [Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg (Sweden); Patriksson, Michael [Department of Mathematical Sciences, Chalmers University of Technology and Department of Mathematical Sciences, University of Gothenburg, SE-412 96 Goeteborg (Sweden)

    2009-02-15

    Uncertainties in future energy prices and policies strongly affect decisions on investments in process integration measures in industry. In this paper, we present a five-step methodology for the identification of robust investment alternatives incorporating explicitly such uncertainties in the optimization model. Methods for optimization under uncertainty (or, stochastic programming) are thus combined with a deep understanding of process integration and process technology in order to achieve a framework for decision-making concerning the investment planning of process integration measures under uncertainty. The proposed methodology enables the optimization of investments in energy efficiency with respect to their net present value or an environmental objective. In particular, as a result of the optimization approach, complex investment alternatives, allowing for combinations of energy efficiency measures, can be analyzed. Uncertainties as well as time-dependent parameters, such as energy prices and policies, are modelled using a scenario-based approach, enabling the identification of robust investment solutions. The methodology is primarily an aid for decision-makers in industry, but it will also provide insight for policy-makers into how uncertainties regarding future price levels and policy instruments affect the decisions on investments in energy efficiency measures. (author)

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

    Science.gov (United States)

    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.

  14. Robust Nearfield Wideband Beamforming Design Based on Adaptive-Weighted Convex Optimization

    Directory of Open Access Journals (Sweden)

    Guo Ye-Cai

    2017-01-01

    Full Text Available Nearfield wideband beamformers for microphone arrays have wide applications in multichannel speech enhancement. The nearfield wideband beamformer design based on convex optimization is one of the typical representatives of robust approaches. However, in this approach, the coefficient of convex optimization is a constant, which has not used all the freedom provided by the weighting coefficient efficiently. Therefore, it is still necessary to further improve the performance. To solve this problem, we developed a robust nearfield wideband beamformer design approach based on adaptive-weighted convex optimization. The proposed approach defines an adaptive-weighted function by the adaptive array signal processing theory and adjusts its value flexibly, which has improved the beamforming performance. During each process of the adaptive updating of the weighting function, the convex optimization problem can be formulated as a SOCP (Second-Order Cone Program problem, which could be solved efficiently using the well-established interior-point methods. This method is suitable for the case where the sound source is in the nearfield range, can work well in the presence of microphone mismatches, and is applicable to arbitrary array geometries. Several design examples are presented to verify the effectiveness of the proposed approach and the correctness of the theoretical analysis.

  15. Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method

    International Nuclear Information System (INIS)

    Alavi, Seyed Arash; Ahmadian, Ali; Aliakbar-Golkar, Masoud

    2015-01-01

    Highlights: • Energy management is necessary in the active distribution network to reduce operation costs. • Uncertainty modeling is essential in energy management studies in active distribution networks. • Point estimate method is a suitable method for uncertainty modeling due to its lower computation time and acceptable accuracy. • In the absence of Probability Distribution Function (PDF) robust optimization has a good ability for uncertainty modeling. - Abstract: Uncertainty can be defined as the probability of difference between the forecasted value and the real value. As this probability is small, the operation cost of the power system will be less. This purpose necessitates modeling of system random variables (such as the output power of renewable resources and the load demand) with appropriate and practicable methods. In this paper, an adequate procedure is proposed in order to do an optimal energy management on a typical micro-grid with regard to the relevant uncertainties. The point estimate method is applied for modeling the wind power and solar power uncertainties, and robust optimization technique is utilized to model load demand uncertainty. Finally, a comparison is done between deterministic and probabilistic management in different scenarios and their results are analyzed and evaluated

  16. Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration.

    Science.gov (United States)

    Young Kim, Eun; Johnson, Hans J

    2013-01-01

    A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.

  17. System optimization for HVAC energy management using the robust evolutionary algorithm

    International Nuclear Information System (INIS)

    Fong, K.F.; Hanby, V.I.; Chow, T.T.

    2009-01-01

    For an installed centralized heating, ventilating and air conditioning (HVAC) system, appropriate energy management measures would achieve energy conservation targets through the optimal control and operation. The performance optimization of conventional HVAC systems may be handled by operation experience, but it may not cover different optimization scenarios and parameters in response to a variety of load and weather conditions. In this regard, it is common to apply the suitable simulation-optimization technique to model the system then determine the required operation parameters. The particular plant simulation models can be built up by either using the available simulation programs or a system of mathematical expressions. To handle the simulation models, iterations would be involved in the numerical solution methods. Since the gradient information is not easily available due to the complex nature of equations, the traditional gradient-based optimization methods are not applicable for this kind of system models. For the heuristic optimization methods, the continual search is commonly necessary, and the system function call is required for each search. The frequency of simulation function calls would then be a time-determining step, and an efficient optimization method is crucial, in order to find the solution through a number of function calls in a reasonable computational period. In this paper, the robust evolutionary algorithm (REA) is presented to tackle this nature of the HVAC simulation models. REA is based on one of the paradigms of evolutionary algorithm, evolution strategy, which is a stochastic population-based searching technique emphasized on mutation. The REA, which incorporates the Cauchy deterministic mutation, tournament selection and arithmetic recombination, would provide a synergetic effect for optimal search. The REA is effective to cope with the complex simulation models, as well as those represented by explicit mathematical expressions of

  18. Robust video watermarking via optimization algorithm for quantization of pseudo-random semi-global statistics

    Science.gov (United States)

    Kucukgoz, Mehmet; Harmanci, Oztan; Mihcak, Mehmet K.; Venkatesan, Ramarathnam

    2005-03-01

    In this paper, we propose a novel semi-blind video watermarking scheme, where we use pseudo-random robust semi-global features of video in the three dimensional wavelet transform domain. We design the watermark sequence via solving an optimization problem, such that the features of the mark-embedded video are the quantized versions of the features of the original video. The exact realizations of the algorithmic parameters are chosen pseudo-randomly via a secure pseudo-random number generator, whose seed is the secret key, that is known (resp. unknown) by the embedder and the receiver (resp. by the public). We experimentally show the robustness of our algorithm against several attacks, such as conventional signal processing modifications and adversarial estimation attacks.

  19. A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

    CERN Document Server

    AUTHOR|(CDS)2092751; Martino, Michele

    The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. Albeit a model may be available; however, such a model may be too complex to consider for an appropriate controller design. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs...

  20. A case study on robust optimal experimental design for model calibration of ω-Transaminase

    DEFF Research Database (Denmark)

    Daele, Timothy, Van; Van Hauwermeiren, Daan; Ringborg, Rolf Hoffmeyer

    the experimental space. However, it is expected that more informative experiments can be designed to increase the confidence of the parameter estimates. Therefore, we apply Optimal Experimental Design (OED) to the calibrated model of Shin and Kim (1998). The total number of samples was retained to allow fair......” parameter values are not known before finishing the model calibration. However, it is important that the chosen parameter values are close to the real parameter values, otherwise the OED can possibly yield non-informative experiments. To counter this problem, one can use robust OED. The idea of robust OED......Proper calibration of models describing enzyme kinetics can be quite challenging. This is especially the case for more complex models like transaminase models (Shin and Kim, 1998). The latter fitted model parameters, but the confidence on the parameter estimation was not derived. Hence...

  1. QFT Based Robust Positioning Control of the PMSM Using Automatic Loop Shaping with Teaching Learning Optimization

    Directory of Open Access Journals (Sweden)

    Nitish Katal

    2016-01-01

    Full Text Available Automation of the robust control system synthesis for uncertain systems is of great practical interest. In this paper, the loop shaping step for synthesizing quantitative feedback theory (QFT based controller for a two-phase permanent magnet stepper motor (PMSM has been automated using teaching learning-based optimization (TLBO algorithm. The QFT controller design problem has been posed as an optimization problem and TLBO algorithm has been used to minimize the proposed cost function. This facilitates designing low-order fixed-structure controller, eliminates the need of manual loop shaping step on the Nichols charts, and prevents the overdesign of the controller. A performance comparison of the designed controller has been made with the classical PID tuning method of Ziegler-Nichols and QFT controller tuned using other optimization algorithms. The simulation results show that the designed QFT controller using TLBO offers robust stability, disturbance rejection, and proper reference tracking over a range of PMSM’s parametric uncertainties as compared to the classical design techniques.

  2. A critical evaluation of worst case optimization methods for robust intensity-modulated proton therapy planning

    International Nuclear Information System (INIS)

    Fredriksson, Albin; Bokrantz, Rasmus

    2014-01-01

    Purpose: To critically evaluate and compare three worst case optimization methods that have been previously employed to generate intensity-modulated proton therapy treatment plans that are robust against systematic errors. The goal of the evaluation is to identify circumstances when the methods behave differently and to describe the mechanism behind the differences when they occur. Methods: The worst case methods optimize plans to perform as well as possible under the worst case scenario that can physically occur (composite worst case), the combination of the worst case scenarios for each objective constituent considered independently (objectivewise worst case), and the combination of the worst case scenarios for each voxel considered independently (voxelwise worst case). These three methods were assessed with respect to treatment planning for prostate under systematic setup uncertainty. An equivalence with probabilistic optimization was used to identify the scenarios that determine the outcome of the optimization. Results: If the conflict between target coverage and normal tissue sparing is small and no dose-volume histogram (DVH) constraints are present, then all three methods yield robust plans. Otherwise, they all have their shortcomings: Composite worst case led to unnecessarily low plan quality in boundary scenarios that were less difficult than the worst case ones. Objectivewise worst case generally led to nonrobust plans. Voxelwise worst case led to overly conservative plans with respect to DVH constraints, which resulted in excessive dose to normal tissue, and less sharp dose fall-off than the other two methods. Conclusions: The three worst case methods have clearly different behaviors. These behaviors can be understood from which scenarios that are active in the optimization. No particular method is superior to the others under all circumstances: composite worst case is suitable if the conflicts are not very severe or there are DVH constraints whereas

  3. An intrinsic robust rank-one-approximation approach for currencyportfolio optimization

    Directory of Open Access Journals (Sweden)

    Hongxuan Huang

    2018-03-01

    Full Text Available A currency portfolio is a special kind of wealth whose value fluctuates with foreignexchange rates over time, which possesses 3Vs (volume, variety and velocity properties of big datain the currency market. In this paper, an intrinsic robust rank one approximation (ROA approachis proposed to maximize the value of currency portfolios over time. The main results of the paperinclude four parts: Firstly, under the assumptions about the currency market, the currency portfoliooptimization problem is formulated as the basic model, in which there are two types of variablesdescribing currency amounts in portfolios and the amount of each currency exchanged into another,respectively. Secondly, the rank one approximation problem and its variants are also formulated toapproximate a foreign exchange rate matrix, whose performance is measured by the Frobenius normor the 2-norm of a residual matrix. The intrinsic robustness of the rank one approximation is provedtogether with summarizing properties of the basic ROA problem and designing a modified powermethod to search for the virtual exchange rates hidden in a foreign exchange rate matrix. Thirdly,a technique for decision variables reduction is presented to attack the currency portfolio optimization.The reduced formulation is referred to as the ROA model, which keeps only variables describingcurrency amounts in portfolios. The optimal solution to the ROA model also induces a feasible solutionto the basic model of the currency portfolio problem by integrating forex operations from the ROAmodel with practical forex rates. Finally, numerical examples are presented to verify the feasibility ande ciency of the intrinsic robust rank one approximation approach. They also indicate that there existsan objective measure for evaluating and optimizing currency portfolios over time, which is related tothe virtual standard currency and independent of any real currency selected specially for measurement.

  4. Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures

    DEFF Research Database (Denmark)

    Codas, Andrés; Hanssen, Kristian G.; Foss, Bjarne

    2017-01-01

    The production life of oil reservoirs starts under significant uncertainty regarding the actual economical return of the recovery process due to the lack of oil field data. Consequently, investors and operators make management decisions based on a limited and uncertain description of the reservoir....... In this work, we propose a new formulation for robust optimization of reservoir well controls. It is inspired by the multiple shooting (MS) method which permits a broad range of parallelization opportunities and output constraint handling. This formulation exploits coherent risk measures, a concept...

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

    Science.gov (United States)

    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.

  6. Robust optimal control design using a differential game approach for open-loop linear quadratic descriptor systems

    NARCIS (Netherlands)

    Musthofa, M.W.; Salmah, S.; Engwerda, Jacob; Suparwanto, A.

    This paper studies the robust optimal control problem for descriptor systems. We applied differential game theory to solve the disturbance attenuation problem. The robust control problem was converted into a reduced ordinary zero-sum game. Within a linear quadratic setting, we solved the problem for

  7. Optimal design of modular cogeneration plants for hospital facilities and robustness evaluation of the results

    International Nuclear Information System (INIS)

    Gimelli, A.; Muccillo, M.; Sannino, R.

    2017-01-01

    Highlights: • A specific methodology has been set up based on genetic optimization algorithm. • Results highlight a tradeoff between primary energy savings (TPES) and simple payback (SPB). • Optimized plant configurations show TPES exceeding 18% and SPB of approximately three years. • The study aims to identify the most stable plant solutions through the robust design optimization. • The research shows how a deterministic definition of the decision variables could lead to an overestimation of the results. - Abstract: The widespread adoption of combined heat and power generation is widely recognized as a strategic goal to achieve significant primary energy savings and lower carbon dioxide emissions. In this context, the purpose of this research is to evaluate the potential of cogeneration based on reciprocating gas engines for some Italian hospital buildings. Comparative analyses have been conducted based on the load profiles of two specific hospital facilities and through the study of the cogeneration system-user interaction. To this end, a specific methodology has been set up by coupling a specifically developed calculation algorithm to a genetic optimization algorithm, and a multi-objective approach has been adopted. The results from the optimization problem highlight a clear trade-off between total primary energy savings (TPES) and simple payback period (SPB). Optimized plant configurations and management strategies show TPES exceeding 18% for the reference hospital facilities and multi–gas engine solutions along with a minimum SPB of approximately three years, thereby justifying the European regulation promoting cogeneration. However, designing a CHP plant for a specific energetic, legislative or market scenario does not guarantee good performance when these scenarios change. For this reason, the proposed methodology has been enhanced in order to focus on some innovative aspects. In particular, this study proposes an uncommon and effective approach

  8. Optimal robustness of supervised learning from a noniterative point of view

    Science.gov (United States)

    Hu, Chia-Lun J.

    1995-08-01

    In most artificial neural network applications, (e.g. pattern recognition) if the dimension of the input vectors is much larger than the number of patterns to be recognized, generally, a one- layered, hard-limited perceptron is sufficient to do the recognition job. As long as the training input-output mapping set is numerically given, and as long as this given set satisfies a special linear-independency relation, the connection matrix to meet the supervised learning requirements can be solved by a noniterative, one-step, algebra method. The learning of this noniterative scheme is very fast (close to real-time learning) because the learning is one-step and noniterative. The recognition of the untrained patterns is very robust because a universal geometrical optimization process of selecting the solution can be applied to the learning process. This paper reports the theoretical foundation of this noniterative learning scheme and focuses the result at the optimal robustness analysis. A real-time character recognition scheme is then designed along this line. This character recognition scheme will be used (in a movie presentation) to demonstrate the experimental results of some theoretical parts reported in this paper.

  9. Experimental Optimization In Polymer BLEND Composite Preparation Based On Mix Level of Taguchi Robust Design

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed; Jaafar Abdullah; Dahlan Mohd; Rozaidi Rasid; Megat Harun AlRashid Megat Ahmad; Mahathir Mohamad; Mohd Hamzah Harun

    2012-01-01

    L 18 orthogonal array in mix level of Taguchi robust design method was carried out to optimize experimental conditions for the preparation of polymer blend composite. Tensile strength and neutron absorption of the composite were the properties of interest. Filler size, filler loading, ball mixing time and dispersion agent concentration were selected as parameters or factors which are expected to affect the composite properties. As a result of Taguchi analysis, filler loading was the most influencing parameter on the tensile strength and neutron absorption. The least influencing was ball-mixing time. The optimal conditions were determined by using mix-level Taguchi robust design method and a polymer composite with tensile strength of 6.33 MPa was successfully prepared. The composite was found to fully absorb thermal neutron flux of 1.04 x 10 5 n/ cm 2 / s with only 2 mm in thickness. In addition, the filler was also characterized by scanning electron microscopy (SEM) and elemental analysis (EDX). (Author)

  10. Robust Optimization-Based Generation Self-Scheduling under Uncertain Price

    Directory of Open Access Journals (Sweden)

    Xiao Luo

    2011-01-01

    Full Text Available This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.

  11. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

    International Nuclear Information System (INIS)

    Luo, Xiongbiao; Wan, Ying; He, Xiangjian

    2015-01-01

    Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was

  12. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

    Science.gov (United States)

    Luo, Xiongbiao; Wan, Ying; He, Xiangjian

    2015-04-01

    Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm

  13. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au [Robarts Research Institute, Western University, London, Ontario N6A 5K8 (Canada); Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian [School of Computing and Communications, University of Technology, Sydney, New South Wales 2007 (Australia)

    2015-04-15

    Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was

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

    Energy Technology Data Exchange (ETDEWEB)

    Newpower, M; Ge, S; Mohan, R [UT MD Anderson Cancer Center, Houston, TX (United States)

    2016-06-15

    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 EUD 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.

  15. Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller

    Directory of Open Access Journals (Sweden)

    Xiaozhang Qu

    2016-07-01

    Full Text Available A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction,the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

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

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Wei, E-mail: Liu.Wei@mayo.edu [Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona (United States); Schild, Steven E. [Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona (United States); Chang, Joe Y.; Liao, Zhongxing [Department of Radiation Oncology, the University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Chang, Yu-Hui [Division of Health Sciences Research, Mayo Clinic Arizona, Phoenix, Arizona (United States); Wen, Zhifei [Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Shen, Jiajian; Stoker, Joshua B.; Ding, Xiaoning; Hu, Yanle [Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona (United States); Sahoo, Narayan [Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Herman, Michael G. [Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota (United States); Vargas, Carlos; Keole, Sameer; Wong, William; Bues, Martin [Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona (United States)

    2016-05-01

    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. 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

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

    Science.gov (United States)

    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.

  18. Optimization of controllability and robustness of complex networks by edge directionality

    Science.gov (United States)

    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.

  19. L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

    Science.gov (United States)

    Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

    2014-04-01

    A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

  20. The Study of an Optimal Robust Design and Adjustable Ordering Strategies in the HSCM.

    Science.gov (United States)

    Liao, Hung-Chang; Chen, Yan-Kwang; Wang, Ya-huei

    2015-01-01

    The purpose of this study was to establish a hospital supply chain management (HSCM) model in which three kinds of drugs in the same class and with the same indications were used in creating an optimal robust design and adjustable ordering strategies to deal with a drug shortage. The main assumption was that although each doctor has his/her own prescription pattern, when there is a shortage of a particular drug, the doctor may choose a similar drug with the same indications as a replacement. Four steps were used to construct and analyze the HSCM model. The computation technology used included a simulation, a neural network (NN), and a genetic algorithm (GA). The mathematical methods of the simulation and the NN were used to construct a relationship between the factor levels and performance, while the GA was used to obtain the optimal combination of factor levels from the NN. A sensitivity analysis was also used to assess the change in the optimal factor levels. Adjustable ordering strategies were also developed to prevent drug shortages.

  1. APPLYING ROBUST RANKING METHOD IN TWO PHASE FUZZY OPTIMIZATION LINEAR PROGRAMMING PROBLEMS (FOLPP

    Directory of Open Access Journals (Sweden)

    Monalisha Pattnaik

    2014-12-01

    Full Text Available Background: This paper explores the solutions to the fuzzy optimization linear program problems (FOLPP where some parameters are fuzzy numbers. In practice, there are many problems in which all decision parameters are fuzzy numbers, and such problems are usually solved by either probabilistic programming or multi-objective programming methods. Methods: In this paper, using the concept of comparison of fuzzy numbers, a very effective method is introduced for solving these problems. This paper extends linear programming based problem in fuzzy environment. With the problem assumptions, the optimal solution can still be theoretically solved using the two phase simplex based method in fuzzy environment. To handle the fuzzy decision variables can be initially generated and then solved and improved sequentially using the fuzzy decision approach by introducing robust ranking technique. Results and conclusions: The model is illustrated with an application and a post optimal analysis approach is obtained. The proposed procedure was programmed with MATLAB (R2009a version software for plotting the four dimensional slice diagram to the application. Finally, numerical example is presented to illustrate the effectiveness of the theoretical results, and to gain additional managerial insights. 

  2. Aerodynamic design applying automatic differentiation and using robust variable fidelity optimization

    Science.gov (United States)

    Takemiya, Tetsushi

    , and that (2) the AMF terminates optimization erroneously when the optimization problems have constraints. The first problem is due to inaccuracy in computing derivatives in the AMF, and the second problem is due to erroneous treatment of the trust region ratio, which sets the size of the domain for an optimization in the AMF. In order to solve the first problem of the AMF, automatic differentiation (AD) technique, which reads the codes of analysis models and automatically generates new derivative codes based on some mathematical rules, is applied. If derivatives are computed with the generated derivative code, they are analytical, and the required computational time is independent of the number of design variables, which is very advantageous for realistic aerospace engineering problems. However, if analysis models implement iterative computations such as computational fluid dynamics (CFD), which solves system partial differential equations iteratively, computing derivatives through the AD requires a massive memory size. The author solved this deficiency by modifying the AD approach and developing a more efficient implementation with CFD, and successfully applied the AD to general CFD software. In order to solve the second problem of the AMF, the governing equation of the trust region ratio, which is very strict against the violation of constraints, is modified so that it can accept the violation of constraints within some tolerance. By accepting violations of constraints during the optimization process, the AMF can continue optimization without terminating immaturely and eventually find the true optimum design point. With these modifications, the AMF is referred to as "Robust AMF," and it is applied to airfoil and wing aerodynamic design problems using Euler CFD software. The former problem has 21 design variables, and the latter 64. In both problems, derivatives computed with the proposed AD method are first compared with those computed with the finite

  3. Optimal robust stabilizer design based on UPFC for interconnected power systems considering time delay

    Directory of Open Access Journals (Sweden)

    Koofigar Hamid Reza

    2017-09-01

    Full Text Available A robust auxiliary wide area damping controller is proposed for a unified power flow controller (UPFC. The mixed H2 / H∞ problem with regional pole placement, resolved by linear matrix inequality (LMI, is applied for controller design. Based on modal analysis, the optimal wide area input signals for the controller are selected. The time delay of input signals, due to electrical distance from the UPFC location is taken into account in the design procedure. The proposed controller is applied to a multi-machine interconnected power system from the IRAN power grid. It is shown that the both transient and dynamic stability are significantly improved despite different disturbances and loading conditions.

  4. Application of Iterative Robust Model-based Optimal Experimental Design for the Calibration of Biocatalytic Models

    DEFF Research Database (Denmark)

    Van Daele, Timothy; Gernaey, Krist V.; Ringborg, Rolf Hoffmeyer

    2017-01-01

    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 optimise 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 catalysed 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 a more accurate, but also a computationally more expensive method. As a result, an important deviation between both approaches...

  5. Stochastic algorithm for channel optimized vector quantization: application to robust narrow-band speech coding

    International Nuclear Information System (INIS)

    Bouzid, M.; Benkherouf, H.; Benzadi, K.

    2011-01-01

    In this paper, we propose a stochastic joint source-channel scheme developed for efficient and robust encoding of spectral speech LSF parameters. The encoding system, named LSF-SSCOVQ-RC, is an LSF encoding scheme based on a reduced complexity stochastic split vector quantizer optimized for noisy channel. For transmissions over noisy channel, we will show first that our LSF-SSCOVQ-RC encoder outperforms the conventional LSF encoder designed by the split vector quantizer. After that, we applied the LSF-SSCOVQ-RC encoder (with weighted distance) for the robust encoding of LSF parameters of the 2.4 Kbits/s MELP speech coder operating over a noisy/noiseless channel. The simulation results will show that the proposed LSF encoder, incorporated in the MELP, ensure better performances than the original MELP MSVQ of 25 bits/frame; especially when the transmission channel is highly disturbed. Indeed, we will show that the LSF-SSCOVQ-RC yields significant improvement to the LSFs encoding performances by ensuring reliable transmissions over noisy channel.

  6. Energy-scales convergence for optimal and robust quantum transport in photosynthetic complexes

    Energy Technology Data Exchange (ETDEWEB)

    Mohseni, M. [Google Research, Venice, California 90291 (United States); Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Shabani, A. [Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States); Department of Chemistry, University of California at Berkeley, Berkeley, California 94720 (United States); Lloyd, S. [Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Rabitz, H. [Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)

    2014-01-21

    Underlying physical principles for the high efficiency of excitation energy transfer in light-harvesting complexes are not fully understood. Notably, the degree of robustness of these systems for transporting energy is not known considering their realistic interactions with vibrational and radiative environments within the surrounding solvent and scaffold proteins. In this work, we employ an efficient technique to estimate energy transfer efficiency of such complex excitonic systems. We observe that the dynamics of the Fenna-Matthews-Olson (FMO) complex leads to optimal and robust energy transport due to a convergence of energy scales among all important internal and external parameters. In particular, we show that the FMO energy transfer efficiency is optimum and stable with respect to important parameters of environmental interactions including reorganization energy λ, bath frequency cutoff γ, temperature T, and bath spatial correlations. We identify the ratio of k{sub B}λT/ℏγ⁢g as a single key parameter governing quantum transport efficiency, where g is the average excitonic energy gap.

  7. Energy-scales convergence for optimal and robust quantum transport in photosynthetic complexes

    International Nuclear Information System (INIS)

    Mohseni, M.; Shabani, A.; Lloyd, S.; Rabitz, H.

    2014-01-01

    Underlying physical principles for the high efficiency of excitation energy transfer in light-harvesting complexes are not fully understood. Notably, the degree of robustness of these systems for transporting energy is not known considering their realistic interactions with vibrational and radiative environments within the surrounding solvent and scaffold proteins. In this work, we employ an efficient technique to estimate energy transfer efficiency of such complex excitonic systems. We observe that the dynamics of the Fenna-Matthews-Olson (FMO) complex leads to optimal and robust energy transport due to a convergence of energy scales among all important internal and external parameters. In particular, we show that the FMO energy transfer efficiency is optimum and stable with respect to important parameters of environmental interactions including reorganization energy λ, bath frequency cutoff γ, temperature T, and bath spatial correlations. We identify the ratio of k B λT/ℏγ⁢g as a single key parameter governing quantum transport efficiency, where g is the average excitonic energy gap

  8. Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

    Directory of Open Access Journals (Sweden)

    Saket Navlakha

    2015-07-01

    Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

  9. Optimal placement and decentralized robust vibration control for spacecraft smart solar panel structures

    International Nuclear Information System (INIS)

    Jiang, Jian-ping; Li, Dong-xu

    2010-01-01

    The decentralized robust vibration control with collocated piezoelectric actuator and strain sensor pairs is considered in this paper for spacecraft solar panel structures. Each actuator is driven individually by the output of the corresponding sensor so that only local feedback control is implemented, with each actuator, sensor and controller operating independently. Firstly, an optimal placement method for the location of the collocated piezoelectric actuator and strain gauge sensor pairs is developed based on the degree of observability and controllability indices for solar panel structures. Secondly, a decentralized robust H ∞ controller is designed to suppress the vibration induced by external disturbance. Finally, a numerical comparison between centralized and decentralized control systems is performed in order to investigate their effectiveness to suppress vibration of the smart solar panel. The simulation results show that the vibration can be significantly suppressed with permitted actuator voltages by the controllers. The decentralized control system almost has the same disturbance attenuation level as the centralized control system with a bit higher control voltages. More importantly, the decentralized controller composed of four three-order systems is a better practical implementation than a high-order centralized controller is

  10. Robust and efficient multi-frequency temporal phase unwrapping: optimal fringe frequency and pattern sequence selection.

    Science.gov (United States)

    Zhang, Minliang; Chen, Qian; Tao, Tianyang; Feng, Shijie; Hu, Yan; Li, Hui; Zuo, Chao

    2017-08-21

    Temporal phase unwrapping (TPU) is an essential algorithm in fringe projection profilometry (FPP), especially when measuring complex objects with discontinuities and isolated surfaces. Among others, the multi-frequency TPU has been proven to be the most reliable algorithm in the presence of noise. For a practical FPP system, in order to achieve an accurate, efficient, and reliable measurement, one needs to make wise choices about three key experimental parameters: the highest fringe frequency, the phase-shifting steps, and the fringe pattern sequence. However, there was very little research on how to optimize these parameters quantitatively, especially considering all three aspects from a theoretical and analytical perspective simultaneously. In this work, we propose a new scheme to determine simultaneously the optimal fringe frequency, phase-shifting steps and pattern sequence under multi-frequency TPU, robustly achieving high accuracy measurement by a minimum number of fringe frames. Firstly, noise models regarding phase-shifting algorithms as well as 3-D coordinates are established under a projector defocusing condition, which leads to the optimal highest fringe frequency for a FPP system. Then, a new concept termed frequency-to-frame ratio (FFR) that evaluates the magnitude of the contribution of each frame for TPU is defined, on which an optimal phase-shifting combination scheme is proposed. Finally, a judgment criterion is established, which can be used to judge whether the ratio between adjacent fringe frequencies is conducive to stably and efficiently unwrapping the phase. The proposed method provides a simple and effective theoretical framework to improve the accuracy, efficiency, and robustness of a practical FPP system in actual measurement conditions. The correctness of the derived models as well as the validity of the proposed schemes have been verified through extensive simulations and experiments. Based on a normal monocular 3-D FPP hardware system

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

    Science.gov (United States)

    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.

  12. Robust Trajectory Optimization of a Ski Jumper for Uncertainty Influence and Safety Quantification

    Directory of Open Access Journals (Sweden)

    Patrick Piprek

    2018-02-01

    Full Text Available This paper deals with the development of a robust optimal control framework for a previously developed multi-body ski jumper simulation model by the authors. This framework is used to model uncertainties acting on the jumper during his jump, e.g., wind or mass, to enhance the performance, but also to increase the fairness and safety of the competition. For the uncertainty modeling the method of generalized polynomial chaos together with the discrete expansion by stochastic collocation is applied: This methodology offers a very flexible framework to model multiple uncertainties using a small number of required optimizations to calculate an uncertain trajectory. The results are then compared to the results of the Latin-Hypercube sampling method to show the correctness of the applied methods. Finally, the results are examined with respect to two major metrics: First, the influence of the uncertainties on the jumper, his positioning with respect to the air, and his maximal achievable flight distance are examined. Then, the results are used in a further step to quantify the safety of the jumper.

  13. An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

    Science.gov (United States)

    Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

    2018-03-01

    Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

  14. Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

    Science.gov (United States)

    Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun

    2018-05-01

    The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

  15. Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer

    KAUST Repository

    Lima, Ricardo

    2015-01-01

    This work address the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two-stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. Two variants of a constraint generation algorithm are proposed, namely a primal and dual version, and they are used to solve two case studies based on two different producers. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

  16. A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2015-12-01

    Full Text Available This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss–Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

  17. Robust Optimization of the Self- scheduling and Market Involvement for an Electricity Producer

    KAUST Repository

    Lima, Ricardo

    2015-01-07

    This work address the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two-stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. Two variants of a constraint generation algorithm are proposed, namely a primal and dual version, and they are used to solve two case studies based on two different producers. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority

  18. SU-E-J-137: Incorporating Tumor Regression Into Robust Plan Optimization for Head and Neck Radiotherapy

    International Nuclear Information System (INIS)

    Zhang, P; Hu, J; Tyagi, N; Mageras, G; Lee, N; Hunt, M

    2014-01-01

    Purpose: To develop a robust planning paradigm which incorporates a tumor regression model into the optimization process to ensure tumor coverage in head and neck radiotherapy. Methods: Simulation and weekly MR images were acquired for a group of head and neck patients to characterize tumor regression during radiotherapy. For each patient, the tumor and parotid glands were segmented on the MR images and the weekly changes were formulated with an affine transformation, where morphological shrinkage and positional changes are modeled by a scaling factor, and centroid shifts, respectively. The tumor and parotid contours were also transferred to the planning CT via rigid registration. To perform the robust planning, weekly predicted PTV and parotid structures were created by transforming the corresponding simulation structures according to the weekly affine transformation matrix averaged over patients other than him/herself. Next, robust PTV and parotid structures were generated as the union of the simulation and weekly prediction contours. In the subsequent robust optimization process, attainment of the clinical dose objectives was required for the robust PTV and parotids, as well as other organs at risk (OAR). The resulting robust plans were evaluated by looking at the weekly and total accumulated dose to the actual weekly PTV and parotid structures. The robust plan was compared with the original plan based on the planning CT to determine its potential clinical benefit. Results: For four patients, the average weekly change to tumor volume and position was −4% and 1.2 mm laterally-posteriorly. Due to these temporal changes, the robust plans resulted in an accumulated PTV D95 that was, on average, 2.7 Gy higher than the plan created from the planning CT. OAR doses were similar. Conclusion: Integration of a tumor regression model into target delineation and plan robust optimization is feasible and may yield improved tumor coverage. Part of this research is supported

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

    International Nuclear Information System (INIS)

    Barragán, A. M.; Differding, S.; Lee, J. A.; Sterpin, E.; Janssens, G.

    2015-01-01

    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 PET ) was calculated from 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 of 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 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 PET (worst case) were above 5% of DPBN

  20. Research on the robust optimization of the enterprise's decision on the investment to the collaborative innovation: Under the risk constraints

    International Nuclear Information System (INIS)

    Zhou, Qing; Fang, Gang; Wang, Dong-peng; Yang, Wei

    2016-01-01

    Abstracts: The robust optimization model is applied to analyze the enterprise's decision of the investment portfolio for the collaborative innovation under the risk constraints. Through the mathematical model deduction and the simulation analysis, the research result shows that the enterprise's investment to the collaborative innovation has relatively obvious robust effect. As for the collaborative innovation, the return from the investment coexists with the risk of it. Under the risk constraints, the robust optimization method could solve the minimum risk as well as the proportion of each investment scheme in the portfolio on the condition of different target returns from the investment. On the basis of the result, the enterprise could balance between the investment return and risk and make optimal decision on the investment scheme.

  1. Robust active combustion control for the optimization of environmental performance and energy efficiency

    Science.gov (United States)

    Demayo, Trevor Nat

    optimization. The active control system was demonstrated and evaluated by optimizing the burners under practical conditions. In most cases, the controller was able to locate, within 10--15 min, a global performance peak that simultaneously minimized emissions and maximized system efficiency within specified stability limits. The active controller demonstrated flexibility and robustness by (a) successfully optimizing different burners for different J functions, initial conditions, and sensor combinations, and (b) successfully reoptimizing a burner under the effect of simulated window fouling and following sudden inlet perturbations, including load cycling and a misaligned fuel injector.

  2. Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making.

    Directory of Open Access Journals (Sweden)

    Ritwik K Niyogi

    experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.

  3. Robust optimization for load scheduling of a smart home with photovoltaic system

    International Nuclear Information System (INIS)

    Wang, Chengshan; Zhou, Yue; Jiao, Bingqi; Wang, Yamin; Liu, Wenjian; Wang, Dan

    2015-01-01

    Highlights: • Robust household load scheduling is presented for smart homes with PV system. • A robust counterpart is formulated to deal with PV output uncertainty. • The robust counterpart is finally transformed to a quadratic programming problem. • Load schedules with different robustness can be made by the proposed method. • Feed-in tariff and PV output would affect the significance of the proposed method. - Abstract: In this paper, a robust approach is developed to tackle the uncertainty of PV power output for load scheduling of smart homes integrated with household PV system. Specifically, a robust formulation is proposed and further transformed to an equivalent quadratic programming problem. Day-ahead load schedules with different robustness can be generated by solving the proposed robust formulation with different predefined parameters. The validity and advantage of the proposed approach has been verified by simulation results. Also, the effects of feed-in tariff and PV output have been evaluated

  4. The role of robust optimization in single-leg airline revenue management

    NARCIS (Netherlands)

    Birbil, S.I.; Frenk, J.B.G.; Gromicho Dos Santos, J.A.; Zhang, S.

    2009-01-01

    In this paper, we introduce robust versions of the classical static and dynamic single-leg seat allocation models. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments, it turns out that for these robust

  5. A novel spatial performance metric for robust pattern optimization of distributed hydrological models

    Science.gov (United States)

    Stisen, S.; Demirel, C.; Koch, J.

    2017-12-01

    Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing

  6. Robust economic optimization and environmental policy analysis for microgrid planning: An application to Taichung Industrial Park, Taiwan

    International Nuclear Information System (INIS)

    Yu, Nan; Kang, Jin-Su; Chang, Chung-Chuan; Lee, Tai-Yong; Lee, Dong-Yup

    2016-01-01

    This study aims to provide economical and environmentally friendly solutions for a microgrid system with distributed energy resources in the design stage, considering multiple uncertainties during operation and conflicting interests among diverse microgrid stakeholders. For the purpose, we develop a multi-objective optimization model for robust microgrid planning, on the basis of an economic robustness measure, i.e. the worst-case cost among possible scenarios, to reduce the variability among scenario costs caused by uncertainties. The efficacy of the model is successfully demonstrated by applying it to Taichung Industrial Park in Taiwan, an industrial complex, where significant amount of greenhouse gases are emitted. Our findings show that the most robust solution, but the highest cost, mainly includes 45% (26.8 MW) of gas engine and 47% (28 MW) of photovoltaic panel with the highest system capacity (59 MW). Further analyses reveal the environmental benefits from the significant reduction of the expected annual CO_2 emission and carbon tax by about half of the current utility facilities in the region. In conclusion, the developed model provides an efficient decision-making tool for robust microgrid planning at the preliminary stage. - Highlights: • Developed robust economic and environmental optimization model for microgrid planning. • Provided Pareto optimal planning solutions for Taichung Industrial Park, Taiwan. • Suggested microgrid configuration with significant economic and environmental benefits. • Identified gas engine and photovoltaic panel as two promising energy sources.

  7. Emergence of robust growth laws from optimal regulation of ribosome synthesis.

    Science.gov (United States)

    Scott, Matthew; Klumpp, Stefan; Mateescu, Eduard M; Hwa, Terence

    2014-08-22

    Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.

  8. RSMDP-based Robust Q-learning for Optimal Path Planning in a Dynamic Environment

    Directory of Open Access Journals (Sweden)

    Yunfei Zhang

    2014-07-01

    Full Text Available This paper presents arobust Q-learning method for path planningin a dynamic environment. The method consists of three steps: first, a regime-switching Markov decision process (RSMDP is formed to present the dynamic environment; second a probabilistic roadmap (PRM is constructed, integrated with the RSMDP and stored as a graph whose nodes correspond to a collision-free world state for the robot; and third, an onlineQ-learning method with dynamic stepsize, which facilitates robust convergence of the Q-value iteration, is integrated with the PRM to determine an optimal path for reaching the goal. In this manner, the robot is able to use past experience for improving its performance in avoiding not only static obstacles but also moving obstacles, without knowing the nature of the obstacle motion. The use ofregime switching in the avoidance of obstacles with unknown motion is particularly innovative.  The developed approach is applied to a homecare robot in computer simulation. The results show that the online path planner with Q-learning is able torapidly and successfully converge to the correct path.

  9. Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty.

    Science.gov (United States)

    Zhang, Yong; Jiang, Yunjian

    2017-02-01

    Waste cooking oil (WCO)-for-biodiesel conversion is regarded as the "waste-to-wealthy" industry. This paper addresses the design of a WCO-for-biodiesel supply chain at both strategic and tactical levels. The supply chain of this problem is studied, which is based on a typical mode of the waste collection (from restaurants' kitchen) and conversion in the cities. The supply chain comprises three stakeholders: WCO supplier, integrated bio-refinery and demand zone. Three key problems should be addressed for the optimal design of the supply chain: (1) the number, sizes and locations of bio-refinery; (2) the sites and amount of WCO collected; (3) the transportation plans of WCO and biodiesel. A robust mixed integer linear model with muti-objective (economic, environmental and social objectives) is proposed for these problems. Finally, a large-scale practical case study is adopted based on Suzhou, a city in the east of China, to verify the proposed models. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Optimizing Diamond Structured Automobile Supply Chain Network Towards a Robust Business Continuity Management

    Directory of Open Access Journals (Sweden)

    Abednico Montshiwa

    2016-02-01

    Full Text Available This paper presents an optimized diamond structured automobile supply chain network towards a robust Business Continuity Management model. The model is necessitated by the nature of the automobile supply chain. Companies in tier two are centralized and numerically limited and have to supply multiple tier one companies with goods and services. The challenge with this supply chain structure is the inherent risks in the supply chain. Once supply chain disruption takes place at tier 2 level, the whole supply chain network suffers huge loses. To address this challenge, the paper replaces Risk Analysis with Risk Ranking and it introduces Supply Chain Cooperation (SCC to the traditional Business Continuity Plan (BCP concept. The paper employed three statistical analysis techniques (correlation analysis, regression analysis and Smart PLS 3.0 calculations. In this study, correlation and regression analysis results on risk rankings, SCC and Business Impact Analysis were significant, ascertaining the value of the model. The multivariate data analysis calculations demonstrated that SCC has a positive total significant effect on risk rankings and BCM while BIA has strongest positive effects on all BCP factors. Finally, sensitivity analysis demonstrated that company size plays a role in BCM.

  11. An optimization of robust SMES with specified structure H∞ controller for power system stabilization considering superconducting magnetic coil size

    International Nuclear Information System (INIS)

    Ngamroo, Issarachai

    2011-01-01

    Even the superconducting magnetic energy storage (SMES) is the smart stabilizing device in electric power systems, the installation cost of SMES is very high. Especially, the superconducting magnetic coil size which is the critical part of SMES, must be well designed. On the contrary, various system operating conditions result in system uncertainties. The power controller of SMES designed without taking such uncertainties into account, may fail to stabilize the system. By considering both coil size and system uncertainties, this paper copes with the optimization of robust SMES controller. No need of exact mathematic equations, the normalized coprime factorization is applied to model system uncertainties. Based on the normalized integral square error index of inter-area rotor angle difference and specified structured H ∞ loop shaping optimization, the robust SMES controller with the smallest coil size, can be achieved by the genetic algorithm. The robustness of the proposed SMES with the smallest coil size can be confirmed by simulation study.

  12. DETERMINING A ROBUST D-OPTIMAL DESIGN FOR TESTING FOR DEPARTURE FROM ADDITIVITY IN A MIXTURE OF FOUR PFAAS

    Science.gov (United States)

    Our objective was to determine an optimal experimental design for a mixture of perfluoroalkyl acids (PFAAs) that is robust to the assumption of additivity. Of particular focus to this research project is whether an environmentally relevant mixture of four PFAAs with long half-liv...

  13. Determining a Robust D-Optimal Design for Testing for Departure from Additivity in a Mixture of Four Perfluoroalkyl Acids.

    Science.gov (United States)

    Our objective is to determine an optimal experimental design for a mixture of perfluoroalkyl acids (PFAAs) that is robust to the assumption of additivity. PFAAs are widely used in consumer products and industrial applications. The presence and persistence of PFAAs, especially in ...

  14. On robust multi-period pre-commitment and time-consistent mean-variance portfolio optimization

    NARCIS (Netherlands)

    F. Cong (Fei); C.W. Oosterlee (Kees)

    2017-01-01

    textabstractWe consider robust pre-commitment and time-consistent mean-variance optimal asset allocation strategies, that are required to perform well also in a worst-case scenario regarding the development of the asset price. We show that worst-case scenarios for both strategies can be found by

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

    Science.gov (United States)

    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 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.

  16. Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization

    Czech Academy of Sciences Publication Activity Database

    Branda, Martin; Bucher, M.; Červinka, Michal; Schwartz, A.

    2018-01-01

    Roč. 70, č. 2 (2018), s. 503-530 ISSN 0926-6003 R&D Projects: GA ČR GA15-00735S Institutional support: RVO:67985556 Keywords : Cardinality constraints * Regularization method * Scholtes regularization * Strong stationarity * Sparse portfolio optimization * Robust portfolio optimization Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.520, year: 2016 http://library.utia.cas.cz/separaty/2018/MTR/branda-0489264.pdf

  17. Impact of Spot Size and Spacing on the Quality of Robustly Optimized Intensity Modulated Proton Therapy Plans for Lung Cancer.

    Science.gov (United States)

    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

  18. SU-E-T-527: Is CTV-Based Robust Optimized IMPT in Non-Small-Cell Lung Cancer Robust Against Respiratory Motion?

    International Nuclear Information System (INIS)

    Anetai, Y; Mizuno, H; Sumida, I; Ogawa, K; Takegawa, H; Inoue, T; Koizumi, M; Veld, A van’t; Korevaar, E

    2015-01-01

    Purpose: To determine which proton planning technique on average-CT is more vulnerable to respiratory motion induced density changes and interplay effect among (a) IMPT of CTV-based minimax robust optimization with 5mm set-up error considered, (b, c) IMPT/SFUD of 5mm-expanded PTV optimization. Methods: Three planning techniques were optimized in Raystation with a prescription of 60/25 (Gy/fractions) and almost the same OAR constraints/objectives for each of 10 NSCLC patients. 4D dose without/with interplay effect was recalculated on eight 4D-CT phases and accumulated after deforming the dose of each phase to a reference (exhalation phase). The change of D98% of each CTV caused by density changes and interplay was determined. In addition, evaluation of the DVH information vector (D99%, D98%, D95%, Dave, D50%, D2%, D1%) which compares the whole DVH by η score = (cosine similarity × Pearson correlation coefficient − 0.9) × 1000 quantified the degree of DVH change: score below 100 indicates changed DVH. Results: Three 3D plans of each technique satisfied our clinical goals. D98% shift mean±SD (Gy) due to density changes was largest in (c): −0.78±1.1 while (a): −0.11±0.65 and (b): − 0.59±0.93. Also the shift due to interplay effect most was (c): −.54±0.70 whereas (a): −0.25±0.93 and (b): −0.12±0.13. Moreover lowest η score caused by density change was also (c): 69, while (a) and (b) kept around 90. η score also indicated less effect of interplay than density changes. Note that generally the changed DVH were still acceptable clinically. Paired T-tests showed a significantly smaller density change effect in (a) (p<0.05) than in (b) or (c) and no significant difference in interplay effect. Conclusion: CTV-based robust optimized IMPT was more robust against respiratory motion induced density changes than PTV-based IMPT and SFUD. The interplay effect was smaller than the effect of density changes and similar among the three techniques. The JSPS Core

  19. An Integrated Approach to Single-Leg Airline Revenue Management: The Role of Robust Optimization

    OpenAIRE

    Birbil, S.I.; Frenk, J.B.G.; Gromicho, J.A.S.; Zhang, S.

    2006-01-01

    textabstractIn this paper we introduce robust versions of the classical static and dynamic single leg seat allocation models as analyzed by Wollmer, and Lautenbacher and Stidham, respectively. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments it turns out that for these robust versions the variability compared to their classical counter parts is considerably reduced with a negligible decrease of av...

  20. Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques

    International Nuclear Information System (INIS)

    Chao, Ming; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi; Wei, Jie; Li, Tianfang

    2016-01-01

    We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as  −0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. (paper)

  1. A robust algorithm for optimizing protein structures with NMR chemical shifts

    Energy Technology Data Exchange (ETDEWEB)

    Berjanskii, Mark; Arndt, David; Liang, Yongjie; Wishart, David S., E-mail: david.wishart@ualberta.ca [University of Alberta, Department of Computing Science (Canada)

    2015-11-15

    Over the past decade, a number of methods have been developed to determine the approximate structure of proteins using minimal NMR experimental information such as chemical shifts alone, sparse NOEs alone or a combination of comparative modeling data and chemical shifts. However, there have been relatively few methods that allow these approximate models to be substantively refined or improved using the available NMR chemical shift data. Here, we present a novel method, called Chemical Shift driven Genetic Algorithm for biased Molecular Dynamics (CS-GAMDy), for the robust optimization of protein structures using experimental NMR chemical shifts. The method incorporates knowledge-based scoring functions and structural information derived from NMR chemical shifts via a unique combination of multi-objective MD biasing, a genetic algorithm, and the widely used XPLOR molecular modelling language. Using this approach, we demonstrate that CS-GAMDy is able to refine and/or fold models that are as much as 10 Å (RMSD) away from the correct structure using only NMR chemical shift data. CS-GAMDy is also able to refine of a wide range of approximate or mildly erroneous protein structures to more closely match the known/correct structure and the known/correct chemical shifts. We believe CS-GAMDy will allow protein models generated by sparse restraint or chemical-shift-only methods to achieve sufficiently high quality to be considered fully refined and “PDB worthy”. The CS-GAMDy algorithm is explained in detail and its performance is compared over a range of refinement scenarios with several commonly used protein structure refinement protocols. The program has been designed to be easily installed and easily used and is available at http://www.gamdy.ca http://www.gamdy.ca.

  2. The optimal shape of elastomer mushroom-like fibers for high and robust adhesion

    Directory of Open Access Journals (Sweden)

    Burak Aksak

    2014-05-01

    Full Text Available Over the last decade, significant effort has been put into mimicking the ability of the gecko lizard to strongly and reversibly cling to surfaces, by using synthetic structures. Among these structures, mushroom-like elastomer fiber arrays have demonstrated promising performance on smooth surfaces matching the adhesive strengths obtained with the natural gecko foot-pads. It is possible to improve the already impressive adhesive performance of mushroom-like fibers provided that the underlying adhesion mechanism is understood. Here, the adhesion mechanism of bio-inspired mushroom-like fibers is investigated by implementing the Dugdale–Barenblatt cohesive zone model into finite elements simulations. It is found that the magnitude of pull-off stress depends on the edge angle θ and the ratio of the tip radius to the stalk radius β of the mushroom-like fiber. Pull-off stress is also found to depend on a dimensionless parameter χ, the ratio of the fiber radius to a length-scale related to the dominance of adhesive stress. As an estimate, the optimal parameters are found to be β = 1.1 and θ = 45°. Further, the location of crack initiation is found to depend on χ for given β and θ. An analytical model for pull-off stress, which depends on the location of crack initiation as well as on θ and β, is proposed and found to agree with the simulation results. Results obtained in this work provide a geometrical guideline for designing robust bio-inspired dry fibrillar adhesives.

  3. An integrated approach to single-leg airline revenue management: The role of robust optimization

    NARCIS (Netherlands)

    S.I. Birbil (Ilker); J.B.G. Frenk (Hans); J.A.S. Gromicho (Joaquim); S. Zhang (Shuzhong)

    2006-01-01

    textabstractIn this paper we introduce robust versions of the classical static and dynamic single leg seat allocation models as analyzed by Wollmer, and Lautenbacher and Stidham, respectively. These robust models take into account the inaccurate estimates of the underlying probability distributions.

  4. An Integrated Approach to Single-Leg Airline Revenue Management: The Role of Robust Optimization

    NARCIS (Netherlands)

    S.I. Birbil (Ilker); J.B.G. Frenk (Hans); J.A.S. Gromicho (Joaquim); S. Zhang (Shuzhong)

    2006-01-01

    textabstractIn this paper we introduce robust versions of the classical static and dynamic single leg seat allocation models as analyzed by Wollmer, and Lautenbacher and Stidham, respectively. These robust models take into account the inaccurate estimates of the underlying probability distributions.

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

    Directory of Open Access Journals (Sweden)

    A. Chebbi

    2013-10-01

    Full Text Available 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

  6. Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

    Science.gov (United States)

    Gros, Charley; De Leener, Benjamin; Dupont, Sara M; Martin, Allan R; Fehlings, Michael G; Bakshi, Rohit; Tummala, Subhash; Auclair, Vincent; McLaren, Donald G; Callot, Virginie; Cohen-Adad, Julien; Sdika, Michaël

    2018-02-01

    During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as "OptiC", from a large dataset involving 20 centers, 4 contrasts (T 2 -weighted n = 287, T 1 -weighted n = 120, T 2 ∗ -weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a

  7. Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

    Science.gov (United States)

    Zaghian, Maryam; Cao, Wenhua; Liu, Wei; Kardar, Laleh; Randeniya, Sharmalee; Mohan, Radhe; Lim, Gino

    2017-03-01

    Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet

  8. Efficient and Robust Data Collection Using Compact Micro Hardware, Distributed Bus Architectures and Optimizing Software

    Science.gov (United States)

    Chau, Savio; Vatan, Farrokh; Randolph, Vincent; Baroth, Edmund C.

    2006-01-01

    Future In-Space propulsion systems for exploration programs will invariably require data collection from a large number of sensors. Consider the sensors needed for monitoring several vehicle systems states of health, including the collection of structural health data, over a large area. This would include the fuel tanks, habitat structure, and science containment of systems required for Lunar, Mars, or deep space exploration. Such a system would consist of several hundred or even thousands of sensors. Conventional avionics system design will require these sensors to be connected to a few Remote Health Units (RHU), which are connected to robust, micro flight computers through a serial bus. This results in a large mass of cabling and unacceptable weight. This paper first gives a survey of several techniques that may reduce the cabling mass for sensors. These techniques can be categorized into four classes: power line communication, serial sensor buses, compound serial buses, and wireless network. The power line communication approach uses the power line to carry both power and data, so that the conventional data lines can be eliminated. The serial sensor bus approach reduces most of the cabling by connecting all the sensors with a single (or redundant) serial bus. Many standard buses for industrial control and sensor buses can support several hundreds of nodes, however, have not been space qualified. Conventional avionics serial buses such as the Mil-Std-1553B bus and IEEE 1394a are space qualified but can support only a limited number of nodes. The third approach is to combine avionics buses to increase their addressability. The reliability, EMI/EMC, and flight qualification issues of wireless networks have to be addressed. Several wireless networks such as the IEEE 802.11 and Ultra Wide Band are surveyed in this paper. The placement of sensors can also affect cable mass. Excessive sensors increase the number of cables unnecessarily. Insufficient number of sensors

  9. A PARTIAL ROBUST OPTIMIZATION APPROACH TO INVENTORY MANAGEMENT FOR THE OFFLINE-TO-ONLINE PROBLEM UNDER DIFFERENT SELLING PRICES

    Institute of Scientific and Technical Information of China (English)

    Hui Yu; Jie Deng

    2017-01-01

    This study examines an optimal inventory strategy when a retailer markets a product at different selling prices through a dual-channel supply chain,comprising an online channel and an offline channel.Using the operating pattern of the offiine-to-online (O2O) business model,we develop a partial robust optimization (PRO) model.Then,we provide a closed-form solution when only the mean and standard deviation of the online channel demand distribution is known and the offline channel demand follows a uniform distribution (partial robust).Specifically,owing to the good structural properties of the solution,we obtain a heuristic ordering formula for the general distribution case (i.e.,the offline channel demand follows a general distribution).In addition,a series of numerical experiments prove the rationality of our conjecture.Moreover,after comparing our solution with other possible policies,we conclude that the PRO approach improves the performance of incorporating the internet into an existing supply chain and,thus,is able to adjust the level of conservativeness of the solution.Finally,in a degenerated situation,we compare our PRO approach with a combination of information approach.The results show that the PRO approach has more "robust" performance.As a result,a reasonable trade-off between robustness and performance is achieved.

  10. Novel Robust Optimization and Power Allocation of Time Reversal-MIMO-UWB Systems in an Imperfect CSI

    Directory of Open Access Journals (Sweden)

    Sajjad Alizadeh

    2013-03-01

    Full Text Available Time Reversal (TR technique is an attractive solution for a scenario where the transmission system employs low complexity receivers with multiple antennas at both transmitter and receiver sides. The TR technique can be combined with a high data rate MIMO-UWB system as TR-MIMO-UWB system. In spite of TR's good performance in MIMO-UWB systems, it suffers from performance degradation in an imperfect Channel State Information (CSI case. In this paper, at first a robust TR pre-filter is designed together with a MMSE equalizer in TR-MIMO-UWB system where is robust against channel imperfection conditions. We show that the robust pre-filter optimization technique, considerably improves the BER performance of TR-MIMO-UWB system in imperfect CSI, where temporal focusing of the TR technique is kept, especially for high SNR values. Then, in order to improve the system performance more than ever, a power loading scheme is developed by minimizing the average symbol error rate in an imperfect CSI. Numerical and simulation results are presented to confirm the performance advantage attained by the proposed robust optimization and power loading in an imperfect CSI scenario.

  11. Optimal JPWL Forward Error Correction Rate Allocation for Robust JPEG 2000 Images and Video Streaming over Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Benoit Macq

    2008-07-01

    Full Text Available Based on the analysis of real mobile ad hoc network (MANET traces, we derive in this paper an optimal wireless JPEG 2000 compliant forward error correction (FEC rate allocation scheme for a robust streaming of images and videos over MANET. The packet-based proposed scheme has a low complexity and is compliant to JPWL, the 11th part of the JPEG 2000 standard. The effectiveness of the proposed method is evaluated using a wireless Motion JPEG 2000 client/server application; and the ability of the optimal scheme to guarantee quality of service (QoS to wireless clients is demonstrated.

  12. Integration of CCS, emissions trading and volatilities of fuel prices into sustainable energy planning, and its robust optimization

    International Nuclear Information System (INIS)

    Koo, Jamin; Han, Kyusang; Yoon, En Sup

    2011-01-01

    In this paper, a new approach has been proposed that allows a robust optimization of sustainable energy planning over a period of years. It is based on the modified energy flow optimization model (EFOM) and minimizes total costs in planning capacities of power plants and CCS to be added, stripped or retrofitted. In the process, it reduces risks due to a high volatility in fuel prices; it also provides robustness against infeasibility with respect to meeting the required emission level by adopting a penalty constant that corresponds to the price level of emission allowances. In this manner, the proposed methodology enables decision makers to determine the optimal capacities of power plants and/or CCS, as well as volumes of emissions trading in the future that will meet the required emission level and satisfy energy demand from various user-sections with minimum costs and maximum robustness. They can also gain valuable insights on the effects that the price of emission allowances has on the competitiveness of RES and CCS technologies; it may be used in, for example, setting appropriate subsidies and tax policies for promoting greater use of these technologies. The proposed methodology is applied to a case based on directions and volumes of energy flows in South Korea during the year 2008. (author)

  13. Robust Optimization on Regional WCO-for-Biodiesel Supply Chain under Supply and Demand Uncertainties

    Directory of Open Access Journals (Sweden)

    Yong Zhang

    2016-01-01

    Full Text Available This paper aims to design a robust waste cooking oil- (WCO- for-biodiesel supply chain under WCO supply and price as well as biodiesel demand and price uncertainties, so as to improve biorefineries’ ability to cope with the poor environment. A regional supply chain is firstly introduced based on the biggest WCO-for-biodiesel company in Changzhou, Jiangsu province, and it comprises three components: WCO supplier, biorefinery, and demand zone. And then a robust mixed integer linear model with multiple objectives (economic, environmental, and social objectives is proposed for both biorefinery location and transportation plans. After that, a heuristic algorithm based on genetic algorithm is proposed to solve this model. Finally, the 27 cities in Yangtze River delta are adopted to verify the proposed models and methods, and the sustainability and robustness of biodiesel supply are discussed.

  14. Optimizing edge detectors for robust automatic threshold selection : Coping with edge curvature and noise

    NARCIS (Netherlands)

    Wilkinson, M.H.F.

    The Robust Automatic Threshold Selection algorithm was introduced as a threshold selection based on a simple image statistic. The statistic is an average of the grey levels of the pixels in an image weighted by the response at each pixel of a specific edge detector. Other authors have suggested that

  15. Testing the robustness of deterministic models of optimal dynamic pricing and lot-sizing for deteriorating items under stochastic conditions

    DEFF Research Database (Denmark)

    Ghoreishi, Maryam

    2018-01-01

    Many models within the field of optimal dynamic pricing and lot-sizing models for deteriorating items assume everything is deterministic and develop a differential equation as the core of analysis. Two prominent examples are the papers by Rajan et al. (Manag Sci 38:240–262, 1992) and Abad (Manag......, we will try to expose the model by Abad (1996) and Rajan et al. (1992) to stochastic inputs; however, designing these stochastic inputs such that they as closely as possible are aligned with the assumptions of those papers. We do our investigation through a numerical test where we test the robustness...... of the numerical results reported in Rajan et al. (1992) and Abad (1996) in a simulation model. Our numerical results seem to confirm that the results stated in these papers are indeed robust when being imposed to stochastic inputs....

  16. Robustness analysis of the Zhang neural network for online time-varying quadratic optimization

    International Nuclear Information System (INIS)

    Zhang Yunong; Ruan Gongqin; Li Kene; Yang Yiwen

    2010-01-01

    A general type of recurrent neural network (termed as Zhang neural network, ZNN) has recently been proposed by Zhang et al for the online solution of time-varying quadratic-minimization (QM) and quadratic-programming (QP) problems. Global exponential convergence of the ZNN could be achieved theoretically in an ideal error-free situation. In this paper, with the normal differentiation and dynamics-implementation errors considered, the robustness properties of the ZNN model are investigated for solving these time-varying problems. In addition, linear activation functions and power-sigmoid activation functions could be applied to such a perturbed ZNN model. Both theoretical-analysis and computer-simulation results demonstrate the good ZNN robustness and superior performance for online time-varying QM and QP problem solving, especially when using power-sigmoid activation functions.

  17. An intrinsic robust rank-one-approximation approach for currencyportfolio optimization

    OpenAIRE

    Hongxuan Huang; Zhengjun Zhang

    2018-01-01

    A currency portfolio is a special kind of wealth whose value fluctuates with foreignexchange rates over time, which possesses 3Vs (volume, variety and velocity) properties of big datain the currency market. In this paper, an intrinsic robust rank one approximation (ROA) approachis proposed to maximize the value of currency portfolios over time. The main results of the paperinclude four parts: Firstly, under the assumptions about the currency market, the currency portfoliooptimization problem ...

  18. Tools for Trustworthy Autonomy: Robust Predictions, Intuitive Control, and Optimized Interaction

    OpenAIRE

    Driggs Campbell, Katherine Rose

    2017-01-01

    In the near future, robotics will impact nearly every aspect of life. Yet for technology to smoothly integrate into society, we need interactive systems to be well modeled and predictable; have robust decision making and control; and be trustworthy to improve cooperation and interaction. To achieve these goals, we propose taking a human-centered approach to ease the transition into human-dominated fields. In this work, our modeling methods and control schemes are validated through user stu...

  19. Scope Oriented Thermoeconomic analysis of energy systems. Part II: Formation Structure of Optimality for robust design

    International Nuclear Information System (INIS)

    Piacentino, Antonio; Cardona, Ennio

    2010-01-01

    This paper represents the Part II of a paper in two parts. In Part I the fundamentals of Scope Oriented Thermoeconomics have been introduced, showing a scarce potential for the cost accounting of existing plants; in this Part II the same concepts are applied to the optimization of a small set of design variables for a vapour compression chiller. The method overcomes the limit of most conventional optimization techniques, which are usually based on hermetic algorithms not enabling the energy analyst to recognize all the margins for improvement. The Scope Oriented Thermoeconomic optimization allows us to disassemble the optimization process, thus recognizing the Formation Structure of Optimality, i.e. the specific influence of any thermodynamic and economic parameter in the path toward the optimal design. Finally, the potential applications of such an in-depth understanding of the inner driving forces of the optimization are discussed in the paper, with a particular focus on the sensitivity analysis to the variation of energy and capital costs and on the actual operation-oriented design.

  20. A robust stochastic approach for design optimization of air cooled heat exchangers

    Energy Technology Data Exchange (ETDEWEB)

    Doodman, A.R.; Fesanghary, M.; Hosseini, R. [Department of Mechanical Engineering, Amirkabir University of Technology, 424-Hafez Avenue, 15875-4413 Tehran (Iran)

    2009-07-15

    This study investigates the use of global sensitivity analysis (GSA) and harmony search (HS) algorithm for design optimization of air cooled heat exchangers (ACHEs) from the economic viewpoint. In order to reduce the size of the optimization problem, GSA is performed to examine the effect of the design parameters and to identify the non-influential parameters. Then HS is applied to optimize influential parameters. To demonstrate the ability of the HS algorithm a case study is considered and for validation purpose, genetic algorithm (GA) is also applied to this case study. Results reveal that the HS algorithm converges to optimum solution with higher accuracy in comparison with GA. (author)

  1. A robust stochastic approach for design optimization of air cooled heat exchangers

    International Nuclear Information System (INIS)

    Doodman, A.R.; Fesanghary, M.; Hosseini, R.

    2009-01-01

    This study investigates the use of global sensitivity analysis (GSA) and harmony search (HS) algorithm for design optimization of air cooled heat exchangers (ACHEs) from the economic viewpoint. In order to reduce the size of the optimization problem, GSA is performed to examine the effect of the design parameters and to identify the non-influential parameters. Then HS is applied to optimize influential parameters. To demonstrate the ability of the HS algorithm a case study is considered and for validation purpose, genetic algorithm (GA) is also applied to this case study. Results reveal that the HS algorithm converges to optimum solution with higher accuracy in comparison with GA

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

    International Nuclear Information System (INIS)

    Liu, W; Schild, S; Bues, M; Liao, Z; Sahoo, N; Park, P; Li, H; Li, Y; Li, X; Shen, J; Anand, A; Dong, L; Zhu, X; Mohan, R

    2014-01-01

    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 the 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

  3. Robust design of decentralized power system stabilizers using meta-heuristic optimization techniques for multimachine systems

    Directory of Open Access Journals (Sweden)

    Jeevanandham Arumugam

    2009-01-01

    Full Text Available In this paper a classical lead-lag power system stabilizer is used for demonstration. The stabilizer parameters are selected in such a manner to damp the rotor oscillations. The problem of selecting the stabilizer parameters is converted to a simple optimization problem with an eigen value based objective function and it is proposed to employ simulated annealing and particle swarm optimization for solving the optimization problem. The objective function allows the selection of the stabilizer parameters to optimally place the closed-loop eigen values in the left hand side of the complex s-plane. The single machine connected to infinite bus system and 10-machine 39-bus system are considered for this study. The effectiveness of the stabilizer tuned using the best technique, in enhancing the stability of power system. Stability is confirmed through eigen value analysis and simulation results and suitable heuristic technique will be selected for the best performance of the system.

  4. Robust output observer-based control of neutral uncertain systems with discrete and distributed time delays: LMI optimization approach

    International Nuclear Information System (INIS)

    Chen, J.-D.

    2007-01-01

    In this paper, the robust control problem of output dynamic observer-based control for a class of uncertain neutral systems with discrete and distributed time delays is considered. Linear matrix inequality (LMI) optimization approach is used to design the new output dynamic observer-based controls. Three classes of observer-based controls are proposed and the maximal perturbed bound is given. Based on the results of this paper, the constraint of matrix equality is not necessary for designing the observer-based controls. Finally, a numerical example is given to illustrate the usefulness of the proposed method

  5. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  6. Experimental Modeling of Monolithic Resistors for Silicon ICS with a Robust Optimizer-Driving Scheme

    Directory of Open Access Journals (Sweden)

    Philippe Leduc

    2002-06-01

    Full Text Available Today, an exhaustive library of models describing the electrical behavior of integrated passive components in the radio-frequency range is essential for the simulation and optimization of complex circuits. In this work, a preliminary study has been done on Tantalum Nitride (TaN resistors integrated on silicon, and this leads to a single p-type lumped-element circuit. An efficient extraction technique will be presented to provide a computer-driven optimizer with relevant initial model parameter values (the "guess-timate". The results show the unicity in most cases of the lumped element determination, which leads to a precise simulation of self-resonant frequencies.

  7. Robust optimization of the laser induced damage threshold of dielectric mirrors for high power lasers.

    Science.gov (United States)

    Chorel, Marine; Lanternier, Thomas; Lavastre, Éric; Bonod, Nicolas; Bousquet, Bruno; Néauport, Jérôme

    2018-04-30

    We report on a numerical optimization of the laser induced damage threshold of multi-dielectric high reflection mirrors in the sub-picosecond regime. We highlight the interplay between the electric field distribution, refractive index and intrinsic laser induced damage threshold of the materials on the overall laser induced damage threshold (LIDT) of the multilayer. We describe an optimization method of the multilayer that minimizes the field enhancement in high refractive index materials while preserving a near perfect reflectivity. This method yields a significant improvement of the damage resistance since a maximum increase of 40% can be achieved on the overall LIDT of the multilayer.

  8. Robust Optimization Approach for Design for a Dynamic Cell Formation Considering Labor Utilization: Bi-objective Mathematical Mode

    Directory of Open Access Journals (Sweden)

    Hiwa Farughi

    2016-05-01

    Full Text Available In this paper, robust optimization of a bi-objective mathematical model in a dynamic cell formation problem considering labor utilization with uncertain data is carried out. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. In this research, cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer programming (MIP model is developed to formulate the related robust dynamic cell formation problem. Then the problem is transformed into a bi-objective linear one. The first objective function seeks to minimize relevant costs of the problem including machine procurement and relocation costs, machine variable cost, inter-cell movement and intra-cell movement costs, overtime cost and labor shifting cost between cells, machine maintenance cost, inventory, holding part cost. The second objective function seeks to minimize total man-hour deviations between cells or indeed labor utilization of the modeled.

  9. Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation

    DEFF Research Database (Denmark)

    Witt, Carsten

    2012-01-01

    The analysis of randomized search heuristics on classes of functions is fundamental for the understanding of the underlying stochastic process and the development of suitable proof techniques. Recently, remarkable progress has been made in bounding the expected optimization time of the simple (1...

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

    NARCIS (Netherlands)

    Laumanns, M.; Lefeber, A.A.J.

    2006-01-01

    We develop a model based on stochastic discrete-time controlleddynamical systems in order to derive optimal policies for controllingthe material flow in supply networks. Each node in the network isdescribed as a transducer such that the dynamics of the material andinformation flows within the entire

  11. The Orienteering Problem under Uncertainty Stochastic Programming and Robust Optimization compared

    NARCIS (Netherlands)

    Evers, L.; Glorie, K.; Ster, S. van der; Barros, A.I.; Monsuur, H.

    2012-01-01

    The Orienteering Problem (OP) is a generalization of the well-known traveling salesman problem and has many interesting applications in logistics, tourism and defense. To reflect real-life situations, we focus on an uncertain variant of the OP. Two main approaches that deal with optimization under

  12. Benefits of using an optimization methodology for identifying robust process integration investments under uncertainty-A pulp mill example

    International Nuclear Information System (INIS)

    Svensson, Elin; Berntsson, Thore; Stroemberg, Ann-Brith

    2009-01-01

    This paper presents a case study on the optimization of process integration investments in a pulp mill considering uncertainties in future electricity and biofuel prices and CO 2 emissions charges. The work follows the methodology described in Svensson et al. [Svensson, E., Berntsson, T., Stroemberg, A.-B., Patriksson, M., 2008b. An optimization methodology for identifying robust process integration investments under uncertainty. Energy Policy, in press, (doi:10.1016/j.enpol.2008.10.023)] where a scenario-based approach is proposed for the modelling of uncertainties. The results show that the proposed methodology provides a way to handle the time dependence and the uncertainties of the parameters. For the analyzed case, a robust solution is found which turns out to be a combination of two opposing investment strategies. The difference between short-term and strategic views for the investment decision is analyzed and it is found that uncertainties are increasingly important to account for as a more strategic view is employed. Furthermore, the results imply that the obvious effect of policy instruments aimed at decreasing CO 2 emissions is, in applications like this, an increased profitability for all energy efficiency investments, and not as much a shift between different alternatives

  13. Benefits of using an optimization methodology for identifying robust process integration investments under uncertainty-A pulp mill example

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, Elin [Department of Energy and Environment, Division of Heat and Power Technology, Chalmers University of Technology, SE-412 96 Goeteborg (Sweden)], E-mail: elin.svensson@chalmers.se; Berntsson, Thore [Department of Energy and Environment, Division of Heat and Power Technology, Chalmers University of Technology, SE-412 96 Goeteborg (Sweden); Stroemberg, Ann-Brith [Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg (Sweden)

    2009-03-15

    This paper presents a case study on the optimization of process integration investments in a pulp mill considering uncertainties in future electricity and biofuel prices and CO{sub 2} emissions charges. The work follows the methodology described in Svensson et al. [Svensson, E., Berntsson, T., Stroemberg, A.-B., Patriksson, M., 2008b. An optimization methodology for identifying robust process integration investments under uncertainty. Energy Policy, in press, (doi:10.1016/j.enpol.2008.10.023)] where a scenario-based approach is proposed for the modelling of uncertainties. The results show that the proposed methodology provides a way to handle the time dependence and the uncertainties of the parameters. For the analyzed case, a robust solution is found which turns out to be a combination of two opposing investment strategies. The difference between short-term and strategic views for the investment decision is analyzed and it is found that uncertainties are increasingly important to account for as a more strategic view is employed. Furthermore, the results imply that the obvious effect of policy instruments aimed at decreasing CO{sub 2} emissions is, in applications like this, an increased profitability for all energy efficiency investments, and not as much a shift between different alternatives.

  14. Benefits of using an optimization methodology for identifying robust process integration investments under uncertainty. A pulp mill example

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, Elin; Berntsson, Thore [Department of Energy and Environment, Division of Heat and Power Technology, Chalmers University of Technology, SE-412 96 Goeteborg (Sweden); Stroemberg, Ann-Brith [Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-412 88 Gothenburg (Sweden)

    2009-03-15

    This paper presents a case study on the optimization of process integration investments in a pulp mill considering uncertainties in future electricity and biofuel prices and CO{sub 2} emissions charges. The work follows the methodology described in Svensson et al. [Svensson, E., Berntsson, T., Stroemberg, A.-B., Patriksson, M., 2008b. An optimization methodology for identifying robust process integration investments under uncertainty. Energy Policy, in press, doi:10.1016/j.enpol.2008.10.023] where a scenario-based approach is proposed for the modelling of uncertainties. The results show that the proposed methodology provides a way to handle the time dependence and the uncertainties of the parameters. For the analyzed case, a robust solution is found which turns out to be a combination of two opposing investment strategies. The difference between short-term and strategic views for the investment decision is analyzed and it is found that uncertainties are increasingly important to account for as a more strategic view is employed. Furthermore, the results imply that the obvious effect of policy instruments aimed at decreasing CO{sub 2} emissions is, in applications like this, an increased profitability for all energy efficiency investments, and not as much a shift between different alternatives. (author)

  15. Dual-loop self-optimizing robust control of wind power generation with Doubly-Fed Induction Generator.

    Science.gov (United States)

    Chen, Quan; Li, Yaoyu; Seem, John E

    2015-09-01

    This paper presents a self-optimizing robust control scheme that can maximize the power generation for a variable speed wind turbine with Doubly-Fed Induction Generator (DFIG) operated in Region 2. A dual-loop control structure is proposed to synergize the conversion from aerodynamic power to rotor power and the conversion from rotor power to the electrical power. The outer loop is an Extremum Seeking Control (ESC) based generator torque regulation via the electric power feedback. The ESC can search for the optimal generator torque constant to maximize the rotor power without wind measurement or accurate knowledge of power map. The inner loop is a vector-control based scheme that can both regulate the generator torque requested by the ESC and also maximize the conversion from the rotor power to grid power. An ℋ(∞) controller is synthesized for maximizing, with performance specifications defined based upon the spectrum of the rotor power obtained by the ESC. Also, the controller is designed to be robust against the variations of some generator parameters. The proposed control strategy is validated via simulation study based on the synergy of several software packages including the TurbSim and FAST developed by NREL, Simulink and SimPowerSystems. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Practical Robust Optimization Method for Unit Commitment of a System with Integrated Wind Resource

    Directory of Open Access Journals (Sweden)

    Yuanchao Yang

    2017-01-01

    Full Text Available Unit commitment, one of the significant tasks in power system operations, faces new challenges as the system uncertainty increases dramatically due to the integration of time-varying resources, such as wind. To address these challenges, we propose the formulation and solution of a generalized unit commitment problem for a system with integrated wind resources. Given the prespecified interval information acquired from real central wind forecasting system for uncertainty representation of nodal wind injections with their correlation information, the proposed unit commitment problem solution is computationally tractable and robust against all uncertain wind power injection realizations. We provide a solution approach to tackle this problem with complex mathematical basics and illustrate the capabilities of the proposed mixed integer solution approach on the large-scale power system of the Northwest China Grid. The numerical results demonstrate that the approach is realistic and not overly conservative in terms of the resulting dispatch cost outcomes.

  17. Using Multi-Objective Optimization to Explore Robust Policies in the Colorado River Basin

    Science.gov (United States)

    Alexander, E.; Kasprzyk, J. R.; Zagona, E. A.; Prairie, J. R.; Jerla, C.; Butler, A.

    2017-12-01

    The long term reliability of water deliveries in the Colorado River Basin has degraded due to the imbalance of growing demand and dwindling supply. The Colorado River meanders 1,450 miles across a watershed that covers seven US states and Mexico and is an important cultural, economic, and natural resource for nearly 40 million people. Its complex operating policy is based on the "Law of the River," which has evolved since the Colorado River Compact in 1922. Recent (2007) refinements to address shortage reductions and coordinated operations of Lakes Powell and Mead were negotiated with stakeholders in which thousands of scenarios were explored to identify operating guidelines that could ultimately be agreed on. This study explores a different approach to searching for robust operating policies to inform the policy making process. The Colorado River Simulation System (CRSS), a long-term water management simulation model implemented in RiverWare, is combined with the Borg multi-objective evolutionary algorithm (MOEA) to solve an eight objective problem formulation. Basin-wide performance metrics are closely tied to system health through incorporating critical reservoir pool elevations, duration, frequency and quantity of shortage reductions in the objective set. For example, an objective to minimize the frequency that Lake Powell falls below the minimum power pool elevation of 3,490 feet for Glen Canyon Dam protects a vital economic and renewable energy source for the southwestern US. The decision variables correspond to operating tiers in Lakes Powell and Mead that drive the implementation of various shortage and release policies, thus affecting system performance. The result will be a set of non-dominated solutions that can be compared with respect to their trade-offs based on the various objectives. These could inform policy making processes by eliminating dominated solutions and revealing robust solutions that could remain hidden under conventional analysis.

  18. Robustness, efficiency, and optimality in the Fenna-Matthews-Olson photosynthetic pigment-protein complex

    Energy Technology Data Exchange (ETDEWEB)

    Baker, Lewis A.; Habershon, Scott, E-mail: S.Habershon@warwick.ac.uk [Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry CV4 7AL (United Kingdom)

    2015-09-14

    Pigment-protein complexes (PPCs) play a central role in facilitating excitation energy transfer (EET) from light-harvesting antenna complexes to reaction centres in photosynthetic systems; understanding molecular organisation in these biological networks is key to developing better artificial light-harvesting systems. In this article, we combine quantum-mechanical simulations and a network-based picture of transport to investigate how chromophore organization and protein environment in PPCs impacts on EET efficiency and robustness. In a prototypical PPC model, the Fenna-Matthews-Olson (FMO) complex, we consider the impact on EET efficiency of both disrupting the chromophore network and changing the influence of (local and global) environmental dephasing. Surprisingly, we find a large degree of resilience to changes in both chromophore network and protein environmental dephasing, the extent of which is greater than previously observed; for example, FMO maintains EET when 50% of the constituent chromophores are removed, or when environmental dephasing fluctuations vary over two orders-of-magnitude relative to the in vivo system. We also highlight the fact that the influence of local dephasing can be strongly dependent on the characteristics of the EET network and the initial excitation; for example, initial excitations resulting in rapid coherent decay are generally insensitive to the environment, whereas the incoherent population decay observed following excitation at weakly coupled chromophores demonstrates a more pronounced dependence on dephasing rate as a result of the greater possibility of local exciton trapping. Finally, we show that the FMO electronic Hamiltonian is not particularly optimised for EET; instead, it is just one of many possible chromophore organisations which demonstrate a good level of EET transport efficiency following excitation at different chromophores. Overall, these robustness and efficiency characteristics are attributed to the highly

  19. Robustness, efficiency, and optimality in the Fenna-Matthews-Olson photosynthetic pigment-protein complex

    International Nuclear Information System (INIS)

    Baker, Lewis A.; Habershon, Scott

    2015-01-01

    Pigment-protein complexes (PPCs) play a central role in facilitating excitation energy transfer (EET) from light-harvesting antenna complexes to reaction centres in photosynthetic systems; understanding molecular organisation in these biological networks is key to developing better artificial light-harvesting systems. In this article, we combine quantum-mechanical simulations and a network-based picture of transport to investigate how chromophore organization and protein environment in PPCs impacts on EET efficiency and robustness. In a prototypical PPC model, the Fenna-Matthews-Olson (FMO) complex, we consider the impact on EET efficiency of both disrupting the chromophore network and changing the influence of (local and global) environmental dephasing. Surprisingly, we find a large degree of resilience to changes in both chromophore network and protein environmental dephasing, the extent of which is greater than previously observed; for example, FMO maintains EET when 50% of the constituent chromophores are removed, or when environmental dephasing fluctuations vary over two orders-of-magnitude relative to the in vivo system. We also highlight the fact that the influence of local dephasing can be strongly dependent on the characteristics of the EET network and the initial excitation; for example, initial excitations resulting in rapid coherent decay are generally insensitive to the environment, whereas the incoherent population decay observed following excitation at weakly coupled chromophores demonstrates a more pronounced dependence on dephasing rate as a result of the greater possibility of local exciton trapping. Finally, we show that the FMO electronic Hamiltonian is not particularly optimised for EET; instead, it is just one of many possible chromophore organisations which demonstrate a good level of EET transport efficiency following excitation at different chromophores. Overall, these robustness and efficiency characteristics are attributed to the highly

  20. Robust optimization of the output voltage of nanogenerators by statistical design of experiments

    KAUST Repository

    Song, Jinhui; Xie, Huizhi; Wu, Wenzhuo; Roshan Joseph, V.; Jeff Wu, C. F.; Wang, Zhong Lin

    2010-01-01

    Nanogenerators were first demonstrated by deflecting aligned ZnO nanowires using a conductive atomic force microscopy (AFM) tip. The output of a nanogenerator is affected by three parameters: tip normal force, tip scanning speed, and tip abrasion. In this work, systematic experimental studies have been carried out to examine the combined effects of these three parameters on the output, using statistical design of experiments. A statistical model has been built to analyze the data and predict the optimal parameter settings. For an AFM tip of cone angle 70° coated with Pt, and ZnO nanowires with a diameter of 50 nm and lengths of 600 nm to 1 μm, the optimized parameters for the nanogenerator were found to be a normal force of 137 nN and scanning speed of 40 μm/s, rather than the conventional settings of 120 nN for the normal force and 30 μm/s for the scanning speed. A nanogenerator with the optimized settings has three times the average output voltage of one with the conventional settings. © 2010 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

  1. Robust optimization of the output voltage of nanogenerators by statistical design of experiments

    KAUST Repository

    Song, Jinhui

    2010-09-01

    Nanogenerators were first demonstrated by deflecting aligned ZnO nanowires using a conductive atomic force microscopy (AFM) tip. The output of a nanogenerator is affected by three parameters: tip normal force, tip scanning speed, and tip abrasion. In this work, systematic experimental studies have been carried out to examine the combined effects of these three parameters on the output, using statistical design of experiments. A statistical model has been built to analyze the data and predict the optimal parameter settings. For an AFM tip of cone angle 70° coated with Pt, and ZnO nanowires with a diameter of 50 nm and lengths of 600 nm to 1 μm, the optimized parameters for the nanogenerator were found to be a normal force of 137 nN and scanning speed of 40 μm/s, rather than the conventional settings of 120 nN for the normal force and 30 μm/s for the scanning speed. A nanogenerator with the optimized settings has three times the average output voltage of one with the conventional settings. © 2010 Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

  2. Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

    Directory of Open Access Journals (Sweden)

    Yi Long

    2016-01-01

    Full Text Available A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user’s intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC with a cerebellar model articulation controller (CMAC neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA, and SMC with CMAC compensation (SMC_CMAC, all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.

  3. Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

    Science.gov (United States)

    Long, Yi; Du, Zhi-jiang; Wang, Wei-dong; Dong, Wei

    2016-01-01

    A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems. PMID:27069353

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

    Science.gov (United States)

    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.

  5. Using a Robust Design Approach to Optimize Chair Set-up in Wheelchair Sport

    Directory of Open Access Journals (Sweden)

    David S. Haydon

    2018-02-01

    Full Text Available Optimisation of wheelchairs for court sports is currently a difficult and time-consuming process due to the broad range of impairments across athletes, difficulties in monitoring on-court performance, and the trade-off set-up that parameters have on key performance variables. A robust design approach to this problem can potentially reduce the amount of testing required, and therefore allow for individual on-court assessments. This study used orthogonal design with four set-up factors (seat height, depth, and angle, as well as tyre pressure at three levels (current, decreased, and increased for three elite wheelchair rugby players. Each player performed two maximal effort sprints from a stationary position in nine different set-ups, with this allowing for detailed analysis of each factor and level. Whilst statistical significance is difficult to obtain due to the small sample size, meaningful difference results aligning with previous research findings were identified and provide support for the use of this approach.

  6. Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.

    Science.gov (United States)

    Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N

    2007-07-01

    An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

  7. Product code optimization for determinate state LDPC decoding in robust image transmission.

    Science.gov (United States)

    Thomos, Nikolaos; Boulgouris, Nikolaos V; Strintzis, Michael G

    2006-08-01

    We propose a novel scheme for error-resilient image transmission. The proposed scheme employs a product coder consisting of low-density parity check (LDPC) codes and Reed-Solomon codes in order to deal effectively with bit errors. The efficiency of the proposed scheme is based on the exploitation of determinate symbols in Tanner graph decoding of LDPC codes and a novel product code optimization technique based on error estimation. Experimental evaluation demonstrates the superiority of the proposed system in comparison to recent state-of-the-art techniques for image transmission.

  8. Robust Weighted Sum Harvested Energy Maximization for SWIPT Cognitive Radio Networks Based on Particle Swarm Optimization.

    Science.gov (United States)

    Tuan, Pham Viet; Koo, Insoo

    2017-10-06

    In this paper, we consider multiuser simultaneous wireless information and power transfer (SWIPT) for cognitive radio systems where a secondary transmitter (ST) with an antenna array provides information and energy to multiple single-antenna secondary receivers (SRs) equipped with a power splitting (PS) receiving scheme when multiple primary users (PUs) exist. The main objective of the paper is to maximize weighted sum harvested energy for SRs while satisfying their minimum required signal-to-interference-plus-noise ratio (SINR), the limited transmission power at the ST, and the interference threshold of each PU. For the perfect channel state information (CSI), the optimal beamforming vectors and PS ratios are achieved by the proposed PSO-SDR in which semidefinite relaxation (SDR) and particle swarm optimization (PSO) methods are jointly combined. We prove that SDR always has a rank-1 solution, and is indeed tight. For the imperfect CSI with bounded channel vector errors, the upper bound of weighted sum harvested energy (WSHE) is also obtained through the S-Procedure. Finally, simulation results demonstrate that the proposed PSO-SDR has fast convergence and better performance as compared to the other baseline schemes.

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

    Science.gov (United States)

    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.

  10. Classical gas: Hearty prices, robust demand combine to pump breezy optimism through 2005 forecasts

    International Nuclear Information System (INIS)

    Lunan, D.

    2005-01-01

    The outlook for natural gas in 2005 is said to be a watershed year, with a lengthy list of developments that could have significant effect on the industry for many years to come. In light of continuing high demand and static supply prospects, prices will have to continue to be high in order to ensure the necessary infrastructure investments to keep gas flowing from multiple sources to the consumer. It is predicted that against the backdrop of robust prices several supply initiatives will continue to advance rapidly in 2005, such as the $7 billion Mackenzie Gas Project on which public hearings are expected to start this summer, along with regulatory clarity about the $20 billion Alaska Highway Natural Gas Pipeline Project to move North Slope gas to southern markets. Drilling of new gas wells will continue to approach or even surpass 18,000 new wells, with an increasing number of these being coal-bed methane wells. Despite high level drilling activity, supply is expected to grow only about 400 MMcf per day. Greater supply increments are expected through continued LNG terminal development, although plans for new LNG terminal development have been met with stiff resistance from local residents both in Canada and the United States. Imports of liquefied natural gas into the United States slowed dramatically in 2004 under the severe short-term downward pressure on natural gas prices, nevertheless, these imports are expected to rebound to new record highs in 2005. Capacity is expected to climb from about 2.55 Bcf per day in 2004 to as much as 6.4 Bcf per day by late 2007. At least one Canadian import facility, Anadarko's one Bcf per day Bear Head terminal on Nova Scotia's Strait of Canso, is expected to become operational by late 2007 or early 2008. 6 photos

  11. A Framework for Robust Multivariable Optimization of Integrated Circuits in Space Applications

    Science.gov (United States)

    DuMonthier, Jeffrey; Suarez, George

    2013-01-01

    Application Specific Integrated Circuit (ASIC) design for space applications involves multiple challenges of maximizing performance, minimizing power and ensuring reliable operation in extreme environments. This is a complex multidimensional optimization problem which must be solved early in the development cycle of a system due to the time required for testing and qualification severely limiting opportunities to modify and iterate. Manual design techniques which generally involve simulation at one or a small number of corners with a very limited set of simultaneously variable parameters in order to make the problem tractable are inefficient and not guaranteed to achieve the best possible results within the performance envelope defined by the process and environmental requirements. What is required is a means to automate design parameter variation, allow the designer to specify operational constraints and performance goals, and to analyze the results in a way which facilitates identifying the tradeoffs defining the performance envelope over the full set of process and environmental corner cases. The system developed by the Mixed Signal ASIC Group (MSAG) at the Goddard Space Flight Center is implemented as framework of software modules, templates and function libraries. It integrates CAD tools and a mathematical computing environment, and can be customized for new circuit designs with only a modest amount of effort as most common tasks are already encapsulated. Customization is required for simulation test benches to determine performance metrics and for cost function computation. Templates provide a starting point for both while toolbox functions minimize the code required. Once a test bench has been coded to optimize a particular circuit, it is also used to verify the final design. The combination of test bench and cost function can then serve as a template for similar circuits or be re-used to migrate the design to different processes by re-running it with the

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

    Science.gov (United States)

    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

  13. Robustness of Operational Matrices of Differentiation for Solving State-Space Analysis and Optimal Control Problems

    Directory of Open Access Journals (Sweden)

    Emran Tohidi

    2013-01-01

    Full Text Available The idea of approximation by monomials together with the collocation technique over a uniform mesh for solving state-space analysis and optimal control problems (OCPs has been proposed in this paper. After imposing the Pontryagins maximum principle to the main OCPs, the problems reduce to a linear or nonlinear boundary value problem. In the linear case we propose a monomial collocation matrix approach, while in the nonlinear case, the general collocation method has been applied. We also show the efficiency of the operational matrices of differentiation with respect to the operational matrices of integration in our numerical examples. These matrices of integration are related to the Bessel, Walsh, Triangular, Laguerre, and Hermite functions.

  14. Emergence of robust solutions to 0-1 optimization problems in multi-agent systems

    DEFF Research Database (Denmark)

    constructive application in engineering. The approach is demonstrated by giving two examples: First, time-dependent robot-target assignment problems with several autonomous robots and several targets are considered as model of flexible manufacturing systems. Each manufacturing target has to be served...... of autonomous space robots building a space station by a distributed transportation of several parts from a space shuttle to defined positions at the space station. Second, the suggested approach is used for the design and selection of traffic networks. The topology of the network is optimized with respect...... to an additive quantity like the length of route segments and an upper bound for the number of route segments. For this, the dynamics of the selection processes of the previous example is extended such that for each vertex several choices for the edges can be made simultaneously up to an individually given upper...

  15. Microwave potentials and optimal control for robust quantum gates on an atom chip

    International Nuclear Information System (INIS)

    Treutlein, Philipp; Haensch, Theodor W.; Reichel, Jakob; Negretti, Antonio; Cirone, Markus A.; Calarco, Tommaso

    2006-01-01

    We propose a two-qubit collisional phase gate that can be implemented with available atom chip technology and present a detailed theoretical analysis of its performance. The gate is based on earlier phase gate schemes, but uses a qubit state pair with an experimentally demonstrated, very long coherence lifetime. Microwave near fields play a key role in our implementation as a means to realize the state-dependent potentials required for conditional dynamics. Quantum control algorithms are used to optimize gate performance. We employ circuit configurations that can be built with current fabrication processes and extensively discuss the impact of technical noise and imperfections that characterize an actual atom chip. We find an overall infidelity compatible with requirements for fault-tolerant quantum computation

  16. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    CERN Document Server

    Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...

  17. MO-FG-CAMPUS-TeP3-04: Deliverable Robust Optimization in IMPT Using Quadratic Objective Function

    Energy Technology Data Exchange (ETDEWEB)

    Shan, J; Liu, W; Bues, M; Schild, S [Mayo Clinic Arizona, Phoenix, AZ (United States)

    2016-06-15

    Purpose: To find and evaluate the way of applying deliverable MU constraints into robust spot intensity optimization in Intensity-Modulated- Proton-Therapy (IMPT) to prevent plan quality and robustness from degrading due to machine deliverable MU-constraints. Methods: Currently, the influence of the deliverable MU-constraints is retrospectively evaluated by post-processing immediately following optimization. In this study, we propose a new method based on the quasi-Newton-like L-BFGS-B algorithm with which we turn deliverable MU-constraints on and off alternatively during optimization. Seven patients with two different machine settings (small and large spot size) were planned with both conventional and new methods. For each patient, three kinds of plans were generated — conventional non-deliverable plan (plan A), conventional deliverable plan with post-processing (plan B), and new deliverable plan (plan C). We performed this study with both realistic (small) and artificial (large) deliverable MU-constraints. Results: With small minimum MU-constraints considered, new method achieved a slightly better plan quality than conventional method (D95% CTV normalized to the prescription dose: 0.994[0.992∼0.996] (Plan C) vs 0.992[0.986∼0.996] (Plan B)). With large minimum MU constraints considered, results show that the new method maintains plan quality while plan quality from the conventional method is degraded greatly (D95% CTV normalized to the prescription dose: 0.987[0.978∼0.994] (Plan C) vs 0.797[0.641∼1.000] (Plan B)). Meanwhile, plan robustness of these two method’s results is comparable. (For all 7 patients, CTV DVH band gap at D95% normalized to the prescription dose: 0.015[0.005∼0.043] (Plan C) vs 0.012[0.006∼0.038] (Plan B) with small MU-constraints and 0.019[0.009∼0.039] (Plan C) vs 0.030[0.015∼0.041] (Plan B) with large MU-constraints) Conclusion: Positive correlation has been found between plan quality degeneration and magnitude of

  18. MO-FG-CAMPUS-TeP3-04: Deliverable Robust Optimization in IMPT Using Quadratic Objective Function

    International Nuclear Information System (INIS)

    Shan, J; Liu, W; Bues, M; Schild, S

    2016-01-01

    Purpose: To find and evaluate the way of applying deliverable MU constraints into robust spot intensity optimization in Intensity-Modulated- Proton-Therapy (IMPT) to prevent plan quality and robustness from degrading due to machine deliverable MU-constraints. Methods: Currently, the influence of the deliverable MU-constraints is retrospectively evaluated by post-processing immediately following optimization. In this study, we propose a new method based on the quasi-Newton-like L-BFGS-B algorithm with which we turn deliverable MU-constraints on and off alternatively during optimization. Seven patients with two different machine settings (small and large spot size) were planned with both conventional and new methods. For each patient, three kinds of plans were generated — conventional non-deliverable plan (plan A), conventional deliverable plan with post-processing (plan B), and new deliverable plan (plan C). We performed this study with both realistic (small) and artificial (large) deliverable MU-constraints. Results: With small minimum MU-constraints considered, new method achieved a slightly better plan quality than conventional method (D95% CTV normalized to the prescription dose: 0.994[0.992∼0.996] (Plan C) vs 0.992[0.986∼0.996] (Plan B)). With large minimum MU constraints considered, results show that the new method maintains plan quality while plan quality from the conventional method is degraded greatly (D95% CTV normalized to the prescription dose: 0.987[0.978∼0.994] (Plan C) vs 0.797[0.641∼1.000] (Plan B)). Meanwhile, plan robustness of these two method’s results is comparable. (For all 7 patients, CTV DVH band gap at D95% normalized to the prescription dose: 0.015[0.005∼0.043] (Plan C) vs 0.012[0.006∼0.038] (Plan B) with small MU-constraints and 0.019[0.009∼0.039] (Plan C) vs 0.030[0.015∼0.041] (Plan B) with large MU-constraints) Conclusion: Positive correlation has been found between plan quality degeneration and magnitude of

  19. Robust and Optimal Control of Magnetic Microparticles inside Fluidic Channels with Time-Varying Flow Rates

    Directory of Open Access Journals (Sweden)

    Islam S.M. Khalil

    2016-06-01

    Full Text Available Targeted therapy using magnetic microparticles and nanoparticles has the potential to mitigate the negative side-effects associated with conventional medical treatment. Major technological challenges still need to be addressed in order to translate these particles into in vivo applications. For example, magnetic particles need to be navigated controllably in vessels against flowing streams of body fluid. This paper describes the motion control of paramagnetic microparticles in the flowing streams of fluidic channels with time-varying flow rates (maximum flow is 35 ml.hr−1. This control is designed using a magnetic-based proportional-derivative (PD control system to compensate for the time-varying flow inside the channels (with width and depth of 2 mm and 1.5 mm, respectively. First, we achieve point-to-point motion control against and along flow rates of 4 ml.hr−1, 6 ml.hr−1, 17 ml.hr−1, and 35 ml.hr−1. The average speeds of single microparticle (with average diameter of 100 μm against flow rates of 6 ml.hr−1 and 30 ml.hr−1 are calculated to be 45 μm.s−1 and 15 μm.s−1, respectively. Second, we implement PD control with disturbance estimation and compensation. This control decreases the steady-state error by 50%, 70%, 73%, and 78% at flow rates of 4 ml.hr−1, 6 ml.hr−1, 17 ml.hr−1, and 35 ml.hr−1, respectively. Finally, we consider the problem of finding the optimal path (minimal kinetic energy between two points using calculus of variation, against the mentioned flow rates. Not only do we find that an optimal path between two collinear points with the direction of maximum flow (middle of the fluidic channel decreases the rise time of the microparticles, but we also decrease the input current that is supplied to the electromagnetic coils by minimizing the kinetic energy of the microparticles, compared to a PD control with disturbance compensation.

  20. A Robust Bayesian Approach to an Optimal Replacement Policy for Gas Pipelines

    Directory of Open Access Journals (Sweden)

    José Pablo Arias-Nicolás

    2015-06-01

    Full Text Available In the paper, we address Bayesian sensitivity issues when integrating experts’ judgments with available historical data in a case study about strategies for the preventive maintenance of low-pressure cast iron pipelines in an urban gas distribution network. We are interested in replacement priorities, as determined by the failure rates of pipelines deployed under different conditions. We relax the assumptions, made in previous papers, about the prior distributions on the failure rates and study changes in replacement priorities under different choices of generalized moment-constrained classes of priors. We focus on the set of non-dominated actions, and among them, we propose the least sensitive action as the optimal choice to rank different classes of pipelines, providing a sound approach to the sensitivity problem. Moreover, we are also interested in determining which classes have a failure rate exceeding a given acceptable value, considered as the threshold determining no need for replacement. Graphical tools are introduced to help decisionmakers to determine if pipelines are to be replaced and the corresponding priorities.

  1. TCSC robust damping controller design based on particle swarm optimization for a multi-machine power system

    Energy Technology Data Exchange (ETDEWEB)

    Shayeghi, H., E-mail: hshayeghi@gmail.co [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power System Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Jalilzadeh, S.; Safari, A. [Technical Engineering Department, Zanjan University, Zanjan (Iran, Islamic Republic of)

    2010-10-15

    In this paper, a new approach based on the particle swarm optimization (PSO) technique is proposed to tune the parameters of the thyristor controlled series capacitor (TCSC) power oscillation damping controller. The design problem of the damping controller is converted to an optimization problem with the time-domain-based objective function which is solved by a PSO technique which has a strong ability to find the most optimistic results. To ensure the robustness of the proposed stabilizers, the design process takes a wide range of operating conditions into account. The performance of the newly designed controller is evaluated in a four-machine power system subjected to the different types of disturbances in comparison with the genetic algorithm based damping controller. The effectiveness of the proposed controller is demonstrated through the nonlinear time-domain simulation and some performance indices studies. The results analysis reveals that the tuned PSO based TCSC damping controller using the proposed fitness function has an excellent capability in damping power system inter-area oscillations and enhances greatly the dynamic stability of the power systems. Moreover, it is superior to the genetic algorithm based damping controller.

  2. Robustness in laying hens

    NARCIS (Netherlands)

    Star, L.

    2008-01-01

    The aim of the project ‘The genetics of robustness in laying hens’ was to investigate nature and regulation of robustness in laying hens under sub-optimal conditions and the possibility to increase robustness by using animal breeding without loss of production. At the start of the project, a robust

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

    Science.gov (United States)

    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

  4. Impact of mechanism vibration characteristics by joint clearance and optimization design of its multi-objective robustness

    Science.gov (United States)

    Zeng, Baoping; Wang, Chao; Zhang, Yu; Gong, Yajun; Hu, Sanbao

    2017-12-01

    Joint clearances and friction characteristics significantly influence the mechanism vibration characteristics; for example: as for joint clearances, the shaft and bearing of its clearance joint collide to bring about the dynamic normal contact force and tangential coulomb friction force while the mechanism works; thus, the whole system may vibrate; moreover, the mechanism is under contact-impact with impact force constraint from free movement under action of the above dynamic forces; in addition, the mechanism topology structure also changes. The constraint relationship between joints may be established by a repeated complex nonlinear dynamic process (idle stroke - contact-impact - elastic compression - rebound - impact relief - idle stroke movement - contact-impact). Analysis of vibration characteristics of joint parts is still a challenging open task by far. The dynamic equations for any mechanism with clearance is often a set of strong coupling, high-dimensional and complex time-varying nonlinear differential equations which are solved very difficultly. Moreover, complicated chaotic motions very sensitive to initial values in impact and vibration due to clearance let high-precision simulation and prediction of their dynamic behaviors be more difficult; on the other hand, their subsequent wearing necessarily leads to some certain fluctuation of structure clearance parameters, which acts as one primary factor for vibration of the mechanical system. A dynamic model was established to the device for opening the deepwater robot cabin door with joint clearance by utilizing the finite element method and analysis was carried out to its vibration characteristics in this study. Moreover, its response model was carried out by utilizing the DOE method and then the robust optimization design was performed to sizes of the joint clearance and the friction coefficient change range so that the optimization design results may be regarded as reference data for selecting bearings

  5. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    Science.gov (United States)

    Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions. PMID:25615734

  6. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    Directory of Open Access Journals (Sweden)

    Ramviyas Parasuraman

    2014-12-01

    Full Text Available The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS. When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities, there is a possibility that some electronic components may fail randomly (due to radiation effects, which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.

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

    Science.gov (United States)

    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

  8. Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

    NARCIS (Netherlands)

    Inoue, Tatsuya; Widder, Joachim; van Dijk, Lisanne V; Takegawa, Hideki; Koizumi, Masahiko; Takashina, Masaaki; Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru; Saito, Anneyuko I; Sasai, Keisuke; Van't Veld, Aart A; Langendijk, Johannes A; Korevaar, Erik W

    2016-01-01

    Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans

  9. Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

    International Nuclear Information System (INIS)

    Inoue, Tatsuya; Widder, Joachim; Dijk, Lisanne V. van; Takegawa, Hideki; Koizumi, Masahiko; Takashina, Masaaki; Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru; Saito, Anneyuko I.; Sasai, Keisuke; Veld, Aart A. van't; Langendijk, Johannes A.; Korevaar, Erik W.

    2016-01-01

    Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans were created using a minimax robust optimization technique for 10 NSCLC patients. The plans accounted for 5- or 7-mm setup errors with ±3% range uncertainties. The robustness of the IMPT nominal plans was evaluated considering (1) isotropic 5-mm setup errors with ±3% range uncertainties; (2) breathing motion; (3) interplay effects; and (4) a combination of items 1 and 2. The plans were calculated using 4-dimensional and average intensity projection computed tomography images. The target coverage (TC, volume receiving 95% of prescribed dose) and homogeneity index (D_2 − D_9_8, where D_2 and D_9_8 are the least doses received by 2% and 98% of the volume) for the internal clinical target volume, and dose indexes for lung, esophagus, heart and spinal cord were compared with that of clinical volumetric modulated arc therapy plans. Results: The TC and homogeneity index for all plans were within clinical limits when considering the breathing motion and interplay effects independently. The setup and range uncertainties had a larger effect when considering their combined effect. The TC decreased to 98% for robust 7-mm evaluations for all patients. The organ at risk dose parameters did not significantly vary between the respective robust 5-mm and robust 7-mm evaluations for the 4 error types. Compared with the volumetric modulated arc therapy plans, the IMPT plans showed better target homogeneity and mean lung and heart dose parameters reduced by about 40% and 60%, respectively. Conclusions: In robustly optimized IMPT for stage III NSCLC, the setup and range uncertainties, breathing motion, and interplay effects have limited impact on target coverage, dose homogeneity, and

  10. Asymmetric underlap optimization of sub-10nm finfets for realizing energy-efficient logic and robust memories

    Science.gov (United States)

    Akkala, Arun Goud

    Leakage currents in CMOS transistors have risen dramatically with technology scaling leading to significant increase in standby power consumption. Among the various transistor candidates, the excellent short channel immunity of Silicon double gate FinFETs have made them the best contender for successful scaling to sub-10nm nodes. For sub-10nm FinFETs, new quantum mechanical leakage mechanisms such as direct source to drain tunneling (DSDT) of charge carriers through channel potential energy barrier arising due to proximity of source/drain regions coupled with the high transport direction electric field is expected to dominate overall leakage. To counter the effects of DSDT and worsening short channel effects and to maintain Ion/ Ioff, performance and power consumption at reasonable values, device optimization techniques are necessary for deeply scaled transistors. In this work, source/drain underlapping of FinFETs has been explored using quantum mechanical device simulations as a potentially promising method to lower DSDT while maintaining the Ion/ Ioff ratio at acceptable levels. By adopting a device/circuit/system level co-design approach, it is shown that asymmetric underlapping, where the drain side underlap is longer than the source side underlap, results in optimal energy efficiency for logic circuits in near-threshold as well as standard, super-threshold operating regimes. In addition, read/write conflict in 6T SRAMs and the degradation in cell noise margins due to the low supply voltage can be mitigated by using optimized asymmetric underlapped n-FinFETs for the access transistor, thereby leading to robust cache memories. When gate-workfunction tuning is possible, using asymmetric underlapped n-FinFETs for both access and pull-down devices in an SRAM bit cell can lead to high-speed and low-leakage caches. Further, it is shown that threshold voltage degradation in the presence of Hot Carrier Injection (HCI) is less severe in asymmetric underlap n-FinFETs. A

  11. TH-CD-209-05: Impact of Spot Size and Spacing On the Quality of Robustly-Optimized Intensity-Modulated Proton Therapy Plans for Lung Cancer

    International Nuclear Information System (INIS)

    Liu, W; Ding, X; Hu, Y; Shen, J; Korte, S; Bues, M; Schild, S; Wong, W; Chang, J; Liao, Z; Sahoo, N; Herman, M

    2016-01-01

    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). The 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

  12. TH-CD-209-05: Impact of Spot Size and Spacing On the Quality of Robustly-Optimized Intensity-Modulated Proton Therapy Plans for Lung Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Liu, W; Ding, X; Hu, Y; Shen, J; Korte, S; Bues, M [Mayo Clinic Arizona, Phoenix, AZ (United States); Schild, S; Wong, W [Mayo Clinic AZ, Phoenix, AZ (United States); Chang, J [MD Anderson Cancer Center, Houston, TX (United States); Liao, Z; Sahoo, N [UT MD Anderson Cancer Center, Houston, TX (United States); Herman, M [Mayo Clinic, Rochester, MN (United States)

    2016-06-15

    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). The 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

  13. The optimization model for multi-type customers assisting wind power consumptive considering uncertainty and demand response based on robust stochastic theory

    International Nuclear Information System (INIS)

    Tan, Zhongfu; Ju, Liwei; Reed, Brent; Rao, Rao; Peng, Daoxin; Li, Huanhuan; Pan, Ge

    2015-01-01

    Highlights: • Our research focuses on demand response behaviors of multi-type customers. • A wind power simulation method is proposed based on the Brownian motion theory. • Demand response revenue functions are proposed for multi-type customers. • A robust stochastic optimization model is proposed for wind power consumptive. • Models are built to measure the impacts of demand response on wind power consumptive. - Abstract: In order to relieve the influence of wind power uncertainty on power system operation, demand response and robust stochastic theory are introduced to build a stochastic scheduling optimization model. Firstly, this paper presents a simulation method for wind power considering external environment based on Brownian motion theory. Secondly, price-based demand response and incentive-based demand response are introduced to build demand response model. Thirdly, the paper constructs the demand response revenue functions for electric vehicle customers, business customers, industry customers and residential customers. Furthermore, robust stochastic optimization theory is introduced to build a wind power consumption stochastic optimization model. Finally, simulation analysis is taken in the IEEE 36 nodes 10 units system connected with 650 MW wind farms. The results show the robust stochastic optimization theory is better to overcome wind power uncertainty. Demand response can improve system wind power consumption capability. Besides, price-based demand response could transform customers’ load demand distribution, but its load curtailment capacity is not as obvious as incentive-based demand response. Since price-based demand response cannot transfer customer’s load demand as the same as incentive-based demand response, the comprehensive optimization effect will reach best when incentive-based demand response and price-based demand response are both introduced.

  14. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  15. SU-F-BRD-01: A Novel 4D Robust Optimization Mitigates Interplay Effect in Intensity-Modulated Proton Therapy for Lung Cancer

    International Nuclear Information System (INIS)

    Liu, W; Shen, J; Stoker, J; Bues, M; Schild, S; Wong, W; Chang, J; Liao, Z; Wen, Z; Sahoo, N; Herman, M; Mohan, R

    2015-01-01

    Purpose: To compare the impact of interplay effect on 3D and 4D robustly optimized intensity-modulated proton therapy (IMPT) plans to treat lung cancer. Methods: Two IMPT plans were created for 11 non-small-cell-lung-cancer cases with 6–14 mm spots. 3D robust optimization generated plans on average CTs with the internal gross tumor volume density overridden to deliver 66 CGyE in 33 fractions to the internal target volume (ITV). 4D robust optimization generated plans on 4D CTs with the delivery of prescribed dose to the clinical target volume (CTV). In 4D optimization, the CTV of individual 4D CT phases received non-uniform doses to achieve a uniform cumulative dose. 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. Indices from dose-volume histograms were used to compare target coverage, dose homogeneity, and normal-tissue sparing. DVH indices were compared using Wilcoxon test. Results: Given the presence of interplay effect, 4D robust optimization produced IMPT plans with better target coverage and homogeneity, but slightly worse normal tissue sparing compared to 3D robust optimization (unit: Gy) [D95% ITV: 63.5 vs 62.0 (p=0.014), D5% - D95% ITV: 6.2 vs 7.3 (p=0.37), D1% spinal cord: 29.0 vs 29.5 (p=0.52), Dmean total lung: 14.8 vs 14.5 (p=0.12), D33% esophagus: 33.6 vs 33.1 (p=0.28)]. The improvement of target coverage (D95%,4D – D95%,3D) was related to the ratio RMA3/(TVx10−4), with RMA and TV being respiratory motion amplitude (RMA) and tumor volume (TV), respectively. Peak benefit was observed at ratios between 2 and 10. This corresponds to 125 – 625 cm3 TV with 0.5-cm RMA. Conclusion: 4D optimization produced more interplay-effect-resistant plans compared to 3D optimization. It is most effective when respiratory motion is modest

  16. SU-F-BRD-01: A Novel 4D Robust Optimization Mitigates Interplay Effect in Intensity-Modulated Proton Therapy for Lung Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Liu, W; Shen, J; Stoker, J; Bues, M [Mayo Clinic Arizona, Phoenix, AZ (United States); Schild, S; Wong, W [Mayo Clinic, Phoenix, Arizona (United States); Chang, J; Liao, Z; Wen, Z; Sahoo, N [MD Anderson Cancer Center, Houston, TX (United States); Herman, M [Mayo Clinic, Rochester, MN (United States); Mohan, R [UT MD Anderson Cancer Center, Houston, TX (United States)

    2015-06-15

    Purpose: To compare the impact of interplay effect on 3D and 4D robustly optimized intensity-modulated proton therapy (IMPT) plans to treat lung cancer. Methods: Two IMPT plans were created for 11 non-small-cell-lung-cancer cases with 6–14 mm spots. 3D robust optimization generated plans on average CTs with the internal gross tumor volume density overridden to deliver 66 CGyE in 33 fractions to the internal target volume (ITV). 4D robust optimization generated plans on 4D CTs with the delivery of prescribed dose to the clinical target volume (CTV). In 4D optimization, the CTV of individual 4D CT phases received non-uniform doses to achieve a uniform cumulative dose. 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. Indices from dose-volume histograms were used to compare target coverage, dose homogeneity, and normal-tissue sparing. DVH indices were compared using Wilcoxon test. Results: Given the presence of interplay effect, 4D robust optimization produced IMPT plans with better target coverage and homogeneity, but slightly worse normal tissue sparing compared to 3D robust optimization (unit: Gy) [D95% ITV: 63.5 vs 62.0 (p=0.014), D5% - D95% ITV: 6.2 vs 7.3 (p=0.37), D1% spinal cord: 29.0 vs 29.5 (p=0.52), Dmean total lung: 14.8 vs 14.5 (p=0.12), D33% esophagus: 33.6 vs 33.1 (p=0.28)]. The improvement of target coverage (D95%,4D – D95%,3D) was related to the ratio RMA3/(TVx10−4), with RMA and TV being respiratory motion amplitude (RMA) and tumor volume (TV), respectively. Peak benefit was observed at ratios between 2 and 10. This corresponds to 125 – 625 cm3 TV with 0.5-cm RMA. Conclusion: 4D optimization produced more interplay-effect-resistant plans compared to 3D optimization. It is most effective when respiratory motion is modest

  17. Innovation: Submissions

    African Journals Online (AJOL)

    a critical understanding of the socio-political, educational and economic realities of ... Book reviews are also welcome. Submissions ... The author-date (Harvard) citation system based on the 15th edition of The Chicago manual of style is used.

  18. Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Inoue, Tatsuya [Department of Radiology, Juntendo University Urayasu Hospital, Chiba (Japan); Widder, Joachim; Dijk, Lisanne V. van [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Takegawa, Hideki [Department of Radiation Oncology, Kansai Medical University Hirakata Hospital, Osaka (Japan); Koizumi, Masahiko; Takashina, Masaaki [Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka (Japan); Usui, Keisuke; Kurokawa, Chie; Sugimoto, Satoru [Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Saito, Anneyuko I. [Department of Radiology, Juntendo University Urayasu Hospital, Chiba (Japan); Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Sasai, Keisuke [Department of Radiation Oncology, Juntendo University Graduate School of Medicine, Tokyo (Japan); Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Korevaar, Erik W., E-mail: e.w.korevaar@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2016-11-01

    Purpose: To investigate the impact of setup and range uncertainties, breathing motion, and interplay effects using scanning pencil beams in robustly optimized intensity modulated proton therapy (IMPT) for stage III non-small cell lung cancer (NSCLC). Methods and Materials: Three-field IMPT plans were created using a minimax robust optimization technique for 10 NSCLC patients. The plans accounted for 5- or 7-mm setup errors with ±3% range uncertainties. The robustness of the IMPT nominal plans was evaluated considering (1) isotropic 5-mm setup errors with ±3% range uncertainties; (2) breathing motion; (3) interplay effects; and (4) a combination of items 1 and 2. The plans were calculated using 4-dimensional and average intensity projection computed tomography images. The target coverage (TC, volume receiving 95% of prescribed dose) and homogeneity index (D{sub 2} − D{sub 98}, where D{sub 2} and D{sub 98} are the least doses received by 2% and 98% of the volume) for the internal clinical target volume, and dose indexes for lung, esophagus, heart and spinal cord were compared with that of clinical volumetric modulated arc therapy plans. Results: The TC and homogeneity index for all plans were within clinical limits when considering the breathing motion and interplay effects independently. The setup and range uncertainties had a larger effect when considering their combined effect. The TC decreased to <98% (clinical threshold) in 3 of 10 patients for robust 5-mm evaluations. However, the TC remained >98% for robust 7-mm evaluations for all patients. The organ at risk dose parameters did not significantly vary between the respective robust 5-mm and robust 7-mm evaluations for the 4 error types. Compared with the volumetric modulated arc therapy plans, the IMPT plans showed better target homogeneity and mean lung and heart dose parameters reduced by about 40% and 60%, respectively. Conclusions: In robustly optimized IMPT for stage III NSCLC, the setup and range

  19. TH-CD-209-04: Fuzzy Robust Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

    International Nuclear Information System (INIS)

    An, Y; Bues, M; Schild, S; Liu, W

    2016-01-01

    Purpose: We propose to apply a robust optimization model based on fuzzy-logic constraints in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade-off between tumor dose coverage and organ-at-risk(OAR) sparing. Methods: Two IMPT plans were generated for 3 head-and-neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed - the nominal one and one each for ±3mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose-volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%-D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem. Results: Our model could yield clinically acceptable plans. The fuzzy-logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% - D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing. Conclusion: The fuzzy-logic robust optimization generates superior IMPT with minimum compromise of OAR sparing. This research

  20. TH-CD-209-04: Fuzzy Robust Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    An, Y; Bues, M; Schild, S; Liu, W [Mayo Clinic Arizona, Phoenix, AZ (United States)

    2016-06-15

    Purpose: We propose to apply a robust optimization model based on fuzzy-logic constraints in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade-off between tumor dose coverage and organ-at-risk(OAR) sparing. Methods: Two IMPT plans were generated for 3 head-and-neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed - the nominal one and one each for ±3mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose-volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%-D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem. Results: Our model could yield clinically acceptable plans. The fuzzy-logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% - D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing. Conclusion: The fuzzy-logic robust optimization generates superior IMPT with minimum compromise of OAR sparing. This research

  1. Optimization for set-points and robust model predictive control for steam generator in nuclear power plants

    International Nuclear Information System (INIS)

    Osgouee, Ahmad

    2010-01-01

    many advanced control methods proposed for the control of nuclear SG water level, operators are still experiencing difficulties especially at low powers. Therefore, it seems that a suitable controller to replace the manual operations is still needed. In this paper optimization of SGL set-points and designing a robust control for SGL control system using will be discussed

  2. Reducing regional vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty

    Science.gov (United States)

    Reed, Patrick; Trindade, Bernardo; Jonathan, Herman; Harrison, Zeff; Gregory, Characklis

    2016-04-01

    Emerging water scarcity concerns in southeastern US are associated with several deeply uncertain factors, including rapid population growth, limited coordination across adjacent municipalities and the increasing risks for sustained regional droughts. Managing these uncertainties will require that regional water utilities identify regionally coordinated, scarcity-mitigating strategies that trigger the appropriate actions needed to avoid water shortages and financial instabilities. This research focuses on the Research Triangle area of North Carolina, seeking to engage the water utilities within Raleigh, Durham, Cary and Chapel Hill in cooperative and robust regional water portfolio planning. Prior analysis of this region through the year 2025 has identified significant regional vulnerabilities to volumetric shortfalls and financial losses. Moreover, efforts to maximize the individual robustness of any of the mentioned utilities also have the potential to strongly degrade the robustness of the others. This research advances a multi-stakeholder Many-Objective Robust Decision Making (MORDM) framework to better account for deeply uncertain factors when identifying cooperative management strategies. Results show that the sampling of deeply uncertain factors in the computational search phase of MORDM can aid in the discovery of management actions that substantially improve the robustness of individual utilities as well as the overall region to water scarcity. Cooperative water transfers, financial risk mitigation tools, and coordinated regional demand management must be explored jointly to decrease robustness conflicts between the utilities. The insights from this work have general merit for regions where adjacent municipalities can benefit from cooperative regional water portfolio planning.

  3. Reducing regional drought vulnerabilities and multi-city robustness conflicts using many-objective optimization under deep uncertainty

    Science.gov (United States)

    Trindade, B. C.; Reed, P. M.; Herman, J. D.; Zeff, H. B.; Characklis, G. W.

    2017-06-01

    Emerging water scarcity concerns in many urban regions are associated with several deeply uncertain factors, including rapid population growth, limited coordination across adjacent municipalities and the increasing risks for sustained regional droughts. Managing these uncertainties will require that regional water utilities identify coordinated, scarcity-mitigating strategies that trigger the appropriate actions needed to avoid water shortages and financial instabilities. This research focuses on the Research Triangle area of North Carolina, seeking to engage the water utilities within Raleigh, Durham, Cary and Chapel Hill in cooperative and robust regional water portfolio planning. Prior analysis of this region through the year 2025 has identified significant regional vulnerabilities to volumetric shortfalls and financial losses. Moreover, efforts to maximize the individual robustness of any of the mentioned utilities also have the potential to strongly degrade the robustness of the others. This research advances a multi-stakeholder Many-Objective Robust Decision Making (MORDM) framework to better account for deeply uncertain factors when identifying cooperative drought management strategies. Our results show that appropriately designing adaptive risk-of-failure action triggers required stressing them with a comprehensive sample of deeply uncertain factors in the computational search phase of MORDM. Search under the new ensemble of states-of-the-world is shown to fundamentally change perceived performance tradeoffs and substantially improve the robustness of individual utilities as well as the overall region to water scarcity. Search under deep uncertainty enhanced the discovery of how cooperative water transfers, financial risk mitigation tools, and coordinated regional demand management must be employed jointly to improve regional robustness and decrease robustness conflicts between the utilities. Insights from this work have general merit for regions where

  4. Synthesis of multi-wavelength temporal phase-shifting algorithms optimized for high signal-to-noise ratio and high detuning robustness using the frequency transfer function.

    Science.gov (United States)

    Servin, Manuel; Padilla, Moises; Garnica, Guillermo

    2016-05-02

    Synthesis of single-wavelength temporal phase-shifting algorithms (PSA) for interferometry is well-known and firmly based on the frequency transfer function (FTF) paradigm. Here we extend the single-wavelength FTF-theory to dual and multi-wavelength PSA-synthesis when several simultaneous laser-colors are present. The FTF-based synthesis for dual-wavelength (DW) PSA is optimized for high signal-to-noise ratio and minimum number of temporal phase-shifted interferograms. The DW-PSA synthesis herein presented may be used for interferometric contouring of discontinuous industrial objects. Also DW-PSA may be useful for DW shop-testing of deep free-form aspheres. As shown here, using the FTF-based synthesis one may easily find explicit DW-PSA formulae optimized for high signal-to-noise and high detuning robustness. To this date, no general synthesis and analysis for temporal DW-PSAs has been given; only ad hoc DW-PSAs formulas have been reported. Consequently, no explicit formulae for their spectra, their signal-to-noise, their detuning and harmonic robustness has been given. Here for the first time a fully general procedure for designing DW-PSAs (or triple-wavelengths PSAs) with desire spectrum, signal-to-noise ratio and detuning robustness is given. We finally generalize DW-PSA to higher number of wavelength temporal PSAs.

  5. A Multiobjective Robust Scheduling Optimization Mode for Multienergy Hybrid System Integrated by Wind Power, Solar Photovoltaic Power, and Pumped Storage Power

    Directory of Open Access Journals (Sweden)

    Lihui Zhang

    2017-01-01

    Full Text Available Wind power plant (WPP, photovoltaic generators (PV, cell-gas turbine (CGT, and pumped storage power station (PHSP are integrated into multienergy hybrid system (MEHS. Firstly, this paper presents MEHS structure and constructs a scheduling model with the objective functions of maximum economic benefit and minimum power output fluctuation. Secondly, in order to relieve the uncertainty influence of WPP and PV on system, robust stochastic theory is introduced to describe uncertainty and propose a multiobjective stochastic scheduling optimization mode by transforming constraint conditions with uncertain variables. Finally, a 9.6 MW WPP, a 6.5 MW PV, three CGT units, and an upper reservoir with 10 MW·h equivalent capacity are chosen as simulation system. The results show MEHS system can achieve the best operation result by using the multienergy hybrid generation characteristic. PHSP could shave peak and fill valley of load curve by optimizing pumping storage and inflowing generating behaviors based on the load supply and demand status and the available power of WPP and PV. Robust coefficients can relieve the uncertainty of WPP and PV and provide flexible scheduling decision tools for decision-makers with different risk attitudes by setting different robust coefficients, which could maximize economic benefits and minimize operation risks at the same time.

  6. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    DEFF Research Database (Denmark)

    Jiang, Yuewen; Chen, Meisen; You, Shi

    2017-01-01

    In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase...... in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view...... of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO) is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle...

  7. Robustness Beamforming Algorithms

    Directory of Open Access Journals (Sweden)

    Sajad Dehghani

    2014-04-01

    Full Text Available Adaptive beamforming methods are known to degrade in the presence of steering vector and covariance matrix uncertinity. In this paper, a new approach is presented to robust adaptive minimum variance distortionless response beamforming make robust against both uncertainties in steering vector and covariance matrix. This method minimize a optimization problem that contains a quadratic objective function and a quadratic constraint. The optimization problem is nonconvex but is converted to a convex optimization problem in this paper. It is solved by the interior-point method and optimum weight vector to robust beamforming is achieved.

  8. Solusi Optimal Model Optimisasi Robust Untuk Masalah Traveling Salesman Dengan Ketidaktentuan Kotak Dan Pendekatan Metode Branch And Bound

    Directory of Open Access Journals (Sweden)

    Poppy Amriyati

    2015-12-01

    Full Text Available Traveling Salesman Problem (TSP merupakan teknik pencarian rute yang dimulai dari satu titik awal, setiap kota harus dikunjungi sekali dan kemudian kembali ke tempat asal sehingga total jarak atau waktu perjalanan adalah minimum. Untuk mengatasi kedakpastian jarak atau waktu perjalanan, maka perlu dilakukan pengembangan model TSP. Salah satu bidang Optimisasi yang mampu menyelesaikan permasalahan terkait ketidakpastian adalah Optimisasi Robust. Dalam makalah ini dibahas mengenai penerapan Optimisasi Robust pada TSP (RTSP menggunakan pendekatan Box Uncertainty dan diselesaikan dengan menggunakan Metode Branch and Bound. Disajikan simulasi numerik pada software aplikasi Maple untuk beberapa kasus nyata terkait penerapan Optimisasi RTSP , seperti masalah manajemen konstruksi, penentuan jarak tempuh kota di Pulau Jawa, dan Penentuan Rute Mandiri Fun Run.

  9. A fast and robust kinematic model for a 12 DoF hyper-redundant robot positioning: An optimization proposal

    Science.gov (United States)

    Lima, José; Pereira, Ana I.; Costa, Paulo; Pinto, Andry; Costa, Pedro

    2017-07-01

    This paper describes an optimization procedure for a robot with 12 degrees of freedom avoiding the inverse kinematics problem, which is a hard task for this type of robot manipulator. This robot can be used to pick and place tasks in complex designs. Combining an accurate and fast direct kinematics model with optimization strategies, it is possible to achieve the joints angles for a desired end-effector position and orientation. The optimization methods stretched simulated annealing algorithm and genetic algorithm were used. The solutions found were validated using data originated by a real and by a simulated robot formed by 12 servomotors with a gripper.

  10. A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

    Directory of Open Access Journals (Sweden)

    Bego Blanco

    2017-01-01

    Full Text Available In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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.

  13. Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights.

    Science.gov (United States)

    Mehrotra, Sanjay; Kim, Kibaek

    2011-12-01

    We consider the problem of outcomes based budget allocations to chronic disease prevention programs across the United States (US) to achieve greater geographical healthcare equity. We use Diabetes Prevention and Control Programs (DPCP) by the Center for Disease Control and Prevention (CDC) as an example. We present a multi-criteria robust weighted sum model for such multi-criteria decision making in a group decision setting. The principal component analysis and an inverse linear programming techniques are presented and used to study the actual 2009 budget allocation by CDC. Our results show that the CDC budget allocation process for the DPCPs is not likely model based. In our empirical study, the relative weights for different prevalence and comorbidity factors and the corresponding budgets obtained under different weight regions are discussed. Parametric analysis suggests that money should be allocated to states to promote diabetes education and to increase patient-healthcare provider interactions to reduce disparity across the US.

  14. Uncertainty of Blood Alcohol Concentration (BAC Results as Related to Instrumental Conditions: Optimization and Robustness of BAC Analysis Headspace Parameters

    Directory of Open Access Journals (Sweden)

    Haleigh A. Boswell

    2015-12-01

    Full Text Available Analysis of blood alcohol concentration is a routine analysis performed in many forensic laboratories. This analysis commonly utilizes static headspace sampling, followed by gas chromatography combined with flame ionization detection (GC-FID. Studies have shown several “optimal” methods for instrumental operating conditions, which are intended to yield accurate and precise data. Given that different instruments, sampling methods, application specific columns and parameters are often utilized, it is much less common to find information on the robustness of these reported conditions. A major problem can arise when these “optimal” conditions may not also be robust, thus producing data with higher than desired uncertainty or potentially inaccurate results. The goal of this research was to incorporate the principles of quality by design (QBD in the adjustment and determination of BAC (blood alcohol concentration instrumental headspace parameters, thereby ensuring that minor instrumental variations, which occur as a matter of normal work, do not appreciably affect the final results of this analysis. This study discusses both the QBD principles as well as the results of the experiments, which allow for determination of more favorable instrumental headspace conditions. Additionally, method detection limits will also be reported in order to determine a reporting threshold and the degree of uncertainty at the common threshold value of 0.08 g/dL. Furthermore, the comparison of two internal standards, n-propanol and t-butanol, will be investigated. The study showed that an altered parameter of 85 °C headspace oven temperature and 15 psi headspace vial pressurization produces the lowest percent relative standard deviation of 1.3% when t-butanol is implemented as an internal standard, at least for one very common platform. The study also showed that an altered parameter of 100 °C headspace oven temperature and 15-psi headspace vial pressurization

  15. The Key Role of the Vector Optimization Algorithm and Robust Design Approach for the Design of Polygeneration Systems

    Directory of Open Access Journals (Sweden)

    Alfredo Gimelli

    2018-04-01

    Full Text Available In recent decades, growing concerns about global warming and climate change effects have led to specific directives, especially in Europe, promoting the use of primary energy-saving techniques and renewable energy systems. The increasingly stringent requirements for carbon dioxide reduction have led to a more widespread adoption of distributed energy systems. In particular, besides renewable energy systems for power generation, one of the most effective techniques used to face the energy-saving challenges has been the adoption of polygeneration plants for combined heating, cooling, and electricity generation. This technique offers the possibility to achieve a considerable enhancement in energy and cost savings as well as a simultaneous reduction of greenhouse gas emissions. However, the use of small-scale polygeneration systems does not ensure the achievement of mandatory, but sometimes conflicting, aims without the proper sizing and operation of the plant. This paper is focused on a methodology based on vector optimization algorithms and developed by the authors for the identification of optimal polygeneration plant solutions. To this aim, a specific calculation algorithm for the study of cogeneration systems has also been developed. This paper provides, after a detailed description of the proposed methodology, some specific applications to the study of combined heat and power (CHP and organic Rankine cycle (ORC plants, thus highlighting the potential of the proposed techniques and the main results achieved.

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

    Science.gov (United States)

    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.

  17. Robust Co-Optimization to Energy and Reserve Joint Dispatch Considering Wind Power Generation and Zonal Reserve Constraints in Real-Time Electricity Markets

    Directory of Open Access Journals (Sweden)

    Chunlai Li

    2017-07-01

    Full Text Available This paper proposes an energy and reserve joint dispatch model based on a robust optimization approach in real-time electricity markets, considering wind power generation uncertainties as well as zonal reserve constraints under both normal and N-1 contingency conditions. In the proposed model, the operating reserves are classified as regulating reserve and spinning reserve according to the response performance. More specifically, the regulating reserve is usually utilized to reduce the gap due to forecasting errors, while the spinning reserve is commonly adopted to enhance the ability for N-1 contingencies. Since the transmission bottlenecks may inhibit the deliverability of reserve, the zonal placement of spinning reserve is considered in this paper to improve the reserve deliverability under the contingencies. Numerical results on the IEEE 118-bus test system show the effectiveness of the proposed model.

  18. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    Directory of Open Access Journals (Sweden)

    Yuewen Jiang

    2017-04-01

    Full Text Available In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle swarm algorithm (QPSO. Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm.

  19. Optimization and validation of an existing, surgical and robust dry eye rat model for the evaluation of therapeutic compounds.

    Science.gov (United States)

    Joossen, Cedric; Lanckacker, Ellen; Zakaria, Nadia; Koppen, Carina; Joossens, Jurgen; Cools, Nathalie; De Meester, Ingrid; Lambeir, Anne-Marie; Delputte, Peter; Maes, Louis; Cos, Paul

    2016-05-01

    The aim of this research was to optimize and validate an animal model for dry eye, adopting clinically relevant evaluation parameters. Dry eye was induced in female Wistar rats by surgical removal of the exorbital lacrimal gland. The clinical manifestations of dry eye were evaluated by tear volume measurements, corneal fluorescein staining, cytokine measurements in tear fluid, MMP-9 mRNA expression and CD3(+) cell infiltration in the conjunctiva. The animal model was validated by treatment with Restasis(®) (4 weeks) and commercial dexamethasone eye drops (2 weeks). Removal of the exorbital lacrimal gland resulted in 50% decrease in tear volume and a gradual increase in corneal fluorescein staining. Elevated levels of TNF-α and IL-1α have been registered in tear fluid together with an increase in CD3(+) cells in the palpebral conjunctiva when compared to control animals. Additionally, an increase in MMP-9 mRNA expression was recorded in conjunctival tissue. Reference treatment with Restasis(®) and dexamethasone eye drops had a positive effect on all evaluation parameters, except on tear volume. This rat dry eye model was validated extensively and judged appropriate for the evaluation of novel compounds and therapeutic preparations for dry eye disease. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Combined mask and illumination scheme optimization for robust contact patterning on 45nm technology node flash memory devices

    Science.gov (United States)

    Vaglio Pret, Alessandro; Capetti, Gianfranco; Bollin, Maddalena; Cotti, Gina; De Simone, Danilo; Cantù, Pietro; Vaccaro, Alessandro; Soma, Laura

    2008-03-01

    Immersion Lithography is the most important technique for extending optical lithography's capabilities and meeting the requirements of Semiconductor Roadmap. The introduction of immersion tools has recently allowed the development of 45nm technology node in single exposure. Nevertheless the usage of hyper-high NA scanners (NA > 1), some levels still remain very critical to be imaged with sufficient process performances. For memory devices, contact mask is for sure the most challenging layer. Aim of this paper is to present the lithographic assessment of 193nm contact holes process, with k I value of ~0.30 using NA 1.20 immersion lithography (minimum pitch is 100nm). Different issues will be reported, related to mask choices (Binary or Attenuated Phase Shift) and illuminator configurations. First phase of the work will be dedicated to a preliminary experimental screening on a simple test case in order to reduce the variables in the following optimization sections. Based on this analysis we will discard X-Y symmetrical illuminators (Annular, C-Quad) due to poor contrast. Second phase will be dedicated to a full simulation assessment. Different illuminators will be compared, with both mask type and several mask biases. From this study, we will identify some general trends of lithography performances that can be used for the fine tuning of the RET settings. The last phase of the work will be dedicated to find the sensitivity trends for one of the analyzed illuminators. In particular we study the effect of Numerical Aperture, mask bias in both X and Y direction and poles sigma ring-width and centre.

  1. Optimization of the HyPer sensor for robust real-time detection of hydrogen peroxide in the rice blast fungus.

    Science.gov (United States)

    Huang, Kun; Caplan, Jeff; Sweigard, James A; Czymmek, Kirk J; Donofrio, Nicole M

    2017-02-01

    Reactive oxygen species (ROS) production and breakdown have been studied in detail in plant-pathogenic fungi, including the rice blast fungus, Magnaporthe oryzae; however, the examination of the dynamic process of ROS production in real time has proven to be challenging. We resynthesized an existing ROS sensor, called HyPer, to exhibit optimized codon bias for fungi, specifically Neurospora crassa, and used a combination of microscopy and plate reader assays to determine whether this construct could detect changes in fungal ROS during the plant infection process. Using confocal microscopy, we were able to visualize fluctuating ROS levels during the formation of an appressorium on an artificial hydrophobic surface, as well as during infection on host leaves. Using the plate reader, we were able to ascertain measurements of hydrogen peroxide (H 2 O 2 ) levels in conidia as detected by the MoHyPer sensor. Overall, by the optimization of codon usage for N. crassa and related fungal genomes, the MoHyPer sensor can be used as a robust, dynamic and powerful tool to both monitor and quantify H 2 O 2 dynamics in real time during important stages of the plant infection process. © 2016 BSPP AND JOHN WILEY & SONS LTD.

  2. Optimization of Tape Winding Process Parameters to Enhance the Performance of Solid Rocket Nozzle Throat Back Up Liners using Taguchi's Robust Design Methodology

    Science.gov (United States)

    Nath, Nayani Kishore

    2017-08-01

    The throat back up liners is used to protect the nozzle structural members from the severe thermal environment in solid rocket nozzles. The throat back up liners is made with E-glass phenolic prepregs by tape winding process. The objective of this work is to demonstrate the optimization of process parameters of tape winding process to achieve better insulative resistance using Taguchi's robust design methodology. In this method four control factors machine speed, roller pressure, tape tension, tape temperature that were investigated for the tape winding process. The presented work was to study the cogency and acceptability of Taguchi's methodology in manufacturing of throat back up liners. The quality characteristic identified was Back wall temperature. Experiments carried out using L 9 ' (34) orthogonal array with three levels of four different control factors. The test results were analyzed using smaller the better criteria for Signal to Noise ratio in order to optimize the process. The experimental results were analyzed conformed and successfully used to achieve the minimum back wall temperature of the throat back up liners. The enhancement in performance of the throat back up liners was observed by carrying out the oxy-acetylene tests. The influence of back wall temperature on the performance of throat back up liners was verified by ground firing test.

  3. Robust sampling-sourced numerical retrieval algorithm for optical energy loss function based on log–log mesh optimization and local monotonicity preserving Steffen spline

    Energy Technology Data Exchange (ETDEWEB)

    Maglevanny, I.I., E-mail: sianko@list.ru [Volgograd State Social Pedagogical University, 27 Lenin Avenue, Volgograd 400131 (Russian Federation); Smolar, V.A. [Volgograd State Technical University, 28 Lenin Avenue, Volgograd 400131 (Russian Federation)

    2016-01-15

    We introduce a new technique of interpolation of the energy-loss function (ELF) in solids sampled by empirical optical spectra. Finding appropriate interpolation methods for ELFs poses several challenges. The sampled ELFs are usually very heterogeneous, can originate from various sources thus so called “data gaps” can appear, and significant discontinuities and multiple high outliers can be present. As a result an interpolation based on those data may not perform well at predicting reasonable physical results. Reliable interpolation tools, suitable for ELF applications, should therefore satisfy several important demands: accuracy and predictive power, robustness and computational efficiency, and ease of use. We examined the effect on the fitting quality due to different interpolation schemes with emphasis on ELF mesh optimization procedures and we argue that the optimal fitting should be based on preliminary log–log scaling data transforms by which the non-uniformity of sampled data distribution may be considerably reduced. The transformed data are then interpolated by local monotonicity preserving Steffen spline. The result is a piece-wise smooth fitting curve with continuous first-order derivatives that passes through all data points without spurious oscillations. Local extrema can occur only at grid points where they are given by the data, but not in between two adjacent grid points. It is found that proposed technique gives the most accurate results and also that its computational time is short. Thus, it is feasible using this simple method to address practical problems associated with interaction between a bulk material and a moving electron. A compact C++ implementation of our algorithm is also presented.

  4. Robust sampling-sourced numerical retrieval algorithm for optical energy loss function based on log–log mesh optimization and local monotonicity preserving Steffen spline

    International Nuclear Information System (INIS)

    Maglevanny, I.I.; Smolar, V.A.

    2016-01-01

    We introduce a new technique of interpolation of the energy-loss function (ELF) in solids sampled by empirical optical spectra. Finding appropriate interpolation methods for ELFs poses several challenges. The sampled ELFs are usually very heterogeneous, can originate from various sources thus so called “data gaps” can appear, and significant discontinuities and multiple high outliers can be present. As a result an interpolation based on those data may not perform well at predicting reasonable physical results. Reliable interpolation tools, suitable for ELF applications, should therefore satisfy several important demands: accuracy and predictive power, robustness and computational efficiency, and ease of use. We examined the effect on the fitting quality due to different interpolation schemes with emphasis on ELF mesh optimization procedures and we argue that the optimal fitting should be based on preliminary log–log scaling data transforms by which the non-uniformity of sampled data distribution may be considerably reduced. The transformed data are then interpolated by local monotonicity preserving Steffen spline. The result is a piece-wise smooth fitting curve with continuous first-order derivatives that passes through all data points without spurious oscillations. Local extrema can occur only at grid points where they are given by the data, but not in between two adjacent grid points. It is found that proposed technique gives the most accurate results and also that its computational time is short. Thus, it is feasible using this simple method to address practical problems associated with interaction between a bulk material and a moving electron. A compact C++ implementation of our algorithm is also presented.

  5. Robust Airline Schedules

    OpenAIRE

    Eggenberg, Niklaus; Salani, Matteo; Bierlaire, Michel

    2010-01-01

    Due to economic pressure industries, when planning, tend to focus on optimizing the expected profit or the yield. The consequence of highly optimized solutions is an increased sensitivity to uncertainty. This generates additional "operational" costs, incurred by possible modifications of the original plan to be performed when reality does not reflect what was expected in the planning phase. The modern research trend focuses on "robustness" of solutions instead of yield or profit. Although ro...

  6. Robustness of structures

    DEFF Research Database (Denmark)

    Vrouwenvelder, T.; Sørensen, John Dalsgaard

    2009-01-01

    After the collapse of the World Trade Centre towers in 2001 and a number of collapses of structural systems in the beginning of the century, robustness of structural systems has gained renewed interest. Despite many significant theoretical, methodical and technological advances, structural...... of robustness for structural design such requirements are not substantiated in more detail, nor have the engineering profession been able to agree on an interpretation of robustness which facilitates for its uantification. A European COST action TU 601 on ‘Robustness of structures' has started in 2007...... by a group of members of the CSS. This paper describes the ongoing work in this action, with emphasis on the development of a theoretical and risk based quantification and optimization procedure on the one side and a practical pre-normative guideline on the other....

  7. Robust Scientists

    DEFF Research Database (Denmark)

    Gorm Hansen, Birgitte

    their core i nterests, 2) developing a selfsupply of industry interests by becoming entrepreneurs and thus creating their own compliant industry partner and 3) balancing resources within a larger collective of researchers, thus countering changes in the influx of funding caused by shifts in political...... knowledge", Danish research policy seems to have helped develop politically and economically "robust scientists". Scientific robustness is acquired by way of three strategies: 1) tasting and discriminating between resources so as to avoid funding that erodes academic profiles and push scientists away from...

  8. Nigerian Music Review: Submissions

    African Journals Online (AJOL)

    Authors of accepted articles may have to pay token amount as part of the publication. expenses. 12. Contributors shall be given two (2) copies of the journal at no extra cost. 13. Submission of papers is welcome for assessment throughout the year. 14. Submission of Articles should be addressed to: Editor-in- Chief, Nigerian ...

  9. Mizan Law Review: Submissions

    African Journals Online (AJOL)

    Author Guidelines. SUBMISSION GUIDELINES The following submissions are acceptable for publication upon approval by the Editorial Board. Publication of an ... and development of laws; Comments: Case comments that highlight and analyze issues, laws and their interpretation and application in case decisions or fact ...

  10. Integration of uniform design and quantum-behaved particle swarm optimization to the robust design for a railway vehicle suspension system under different wheel conicities and wheel rolling radii

    Science.gov (United States)

    Cheng, Yung-Chang; Lee, Cheng-Kang

    2017-10-01

    This paper proposes a systematic method, integrating the uniform design (UD) of experiments and quantum-behaved particle swarm optimization (QPSO), to solve the problem of a robust design for a railway vehicle suspension system. Based on the new nonlinear creep model derived from combining Hertz contact theory, Kalker's linear theory and a heuristic nonlinear creep model, the modeling and dynamic analysis of a 24 degree-of-freedom railway vehicle system were investigated. The Lyapunov indirect method was used to examine the effects of suspension parameters, wheel conicities and wheel rolling radii on critical hunting speeds. Generally, the critical hunting speeds of a vehicle system resulting from worn wheels with different wheel rolling radii are lower than those of a vehicle system having original wheels without different wheel rolling radii. Because of worn wheels, the critical hunting speed of a running railway vehicle substantially declines over the long term. For safety reasons, it is necessary to design the suspension system parameters to increase the robustness of the system and decrease the sensitive of wheel noises. By applying UD and QPSO, the nominal-the-best signal-to-noise ratio of the system was increased from -48.17 to -34.05 dB. The rate of improvement was 29.31%. This study has demonstrated that the integration of UD and QPSO can successfully reveal the optimal solution of suspension parameters for solving the robust design problem of a railway vehicle suspension system.

  11. Robust portfolio selection under norm uncertainty

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2016-06-01

    Full Text Available Abstract In this paper, we consider the robust portfolio selection problem which has a data uncertainty described by the ( p , w $(p,w$ -norm in the objective function. We show that the robust formulation of this problem is equivalent to a linear optimization problem. Moreover, we present some numerical results concerning our robust portfolio selection problem.

  12. Electronic Submission of Labels

    Science.gov (United States)

    Pesticide registrants can provide draft and final labels to EPA electronically for our review as part of the pesticide registration process. The electronic submission of labels by registrants is voluntary but strongly encouraged.

  13. Ghana Medical Journal: Submissions

    African Journals Online (AJOL)

    Journal Home > About the Journal > Ghana Medical Journal: Submissions ... Works publishable under this section include original work of suitable standard. ... interest statement of all types of manuscript should be submitted as a separate file.

  14. A thematic analysis of the strengths and weaknesses of manufacturers' submissions to the NICE Single Technology Assessment (STA) process.

    Science.gov (United States)

    Carroll, Christopher; Kaltenthaler, Eva; FitzGerald, Patrick; Boland, Angela; Dickson, Rumona

    2011-10-01

    The NICE Single Technology Appraisal (STA) process in the UK has been underway for five years. Evidence Review Groups (ERGs) critically appraise submissions from manufacturers on the clinical and cost effectiveness of new technologies. This study analysed the ERGs' assessment of the strengths and weaknesses of 30 manufacturers' submissions to the STA process. Thematic analysis was performed on the textual descriptions of the strengths and weakness of manufacturer submissions, as outlined by the ERGs in their reports. Various themes emerged from the data. These themes related to the processes applied in the submissions; the content of the submission (e.g. the amount and quality of evidence); the reporting of the submissions' review and analysis processes; the reliability and validity of the submissions' findings; and how far the submission had satisfied the STA process objectives. STA submissions could be improved if attention were paid to transparency in the reporting, conduct and justification of review and modelling processes and analyses, as well as greater robustness in the choice of data and closer adherence to the scope or decision problem. Where this adherence is not possible, more detailed justification of the choice of evidence or data is required. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  15. Synthesis of multi-wavelength temporal phase-shifting algorithms optimized for high signal-to-noise ratio and high detuning robustness using the frequency transfer function

    OpenAIRE

    Servin, Manuel; Padilla, Moises; Garnica, Guillermo

    2016-01-01

    Synthesis of single-wavelength temporal phase-shifting algorithms (PSA) for interferometry is well-known and firmly based on the frequency transfer function (FTF) paradigm. Here we extend the single-wavelength FTF-theory to dual and multi-wavelength PSA-synthesis when several simultaneous laser-colors are present. The FTF-based synthesis for dual-wavelength PSA (DW-PSA) is optimized for high signal-to-noise ratio and minimum number of temporal phase-shifted interferograms. The DW-PSA synthesi...

  16. Optimal Throughput and Self-adaptability of Robust Real-Time IEEE 802.15.4 MAC for AMI Mesh Network

    International Nuclear Information System (INIS)

    Shabani, Hikma; Ahmed, Musse Mohamud; Khan, Sheroz; Hameed, Shahab Ahmed; Habaebi, Mohamed Hadi

    2013-01-01

    A smart grid refers to a modernization of the electricity system that brings intelligence, reliability, efficiency and optimality to the power grid. To provide an automated and widely distributed energy delivery, the smart grid will be branded by a two-way flow of electricity and information system between energy suppliers and their customers. Thus, the smart grid is a power grid that integrates data communication networks which provide the collected and analysed data at all levels in real time. Therefore, the performance of communication systems is so vital for the success of smart grid. Merit to the ZigBee/IEEE802.15.4std low cost, low power, low data rate, short range, simplicity and free licensed spectrum that makes wireless sensor networks (WSNs) the most suitable wireless technology for smart grid applications. Unfortunately, almost all ZigBee channels overlap with wireless local area network (WLAN) channels, resulting in severe performance degradation due to interference. In order to improve the performance of communication systems, this paper proposes an optimal throughput and self-adaptability of ZigBee/IEEE802.15.4std for smart grid

  17. Philosophical Papers: Submissions

    African Journals Online (AJOL)

    Articles should be relevant to the analytic tradition in philosophy, understood broadly and including critiques of that tradition. All submissions are independently refereed. In most cases, decisions are based on reports from at least two referees. Final editorial decisions are made by the Editorial Board of Philosophical Papers.

  18. Kioo cha Lugha: Submissions

    African Journals Online (AJOL)

    AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search · USING AJOL ... Submissions should be made in soft or hard copy, by either sending two hard copies to IKS mailing address or sending soft copy to iks@udsm.ac.tz or ...

  19. African Studies Monographs: Submissions

    African Journals Online (AJOL)

    Author Guidelines. Manuscripts should be sent to The Series Editor, African Studies Monographs, OOP Ltd, P.O. Box 4893, Somolu, Lagos State, Nigeria or Dr Karo Ogbinaka, Department of Philosophy, Faculty of Arts, University of Lagos, Akoka, Yaba, Lagos, Nigeria. Electronic submission should be on Microsoft Word and ...

  20. Methods for robustness programming

    NARCIS (Netherlands)

    Olieman, N.J.

    2008-01-01

    Robustness of an object is defined as the probability that an object will have properties as required. Robustness Programming (RP) is a mathematical approach for Robustness estimation and Robustness optimisation. An example in the context of designing a food product, is finding the best composition

  1. Uma abordagem de otimização robusta no planejamento agregado de produção na indústria cítrica A robust optimization approach for the aggregate production planning in the citrus industry

    Directory of Open Access Journals (Sweden)

    José Renato Munhoz

    2012-01-01

    Full Text Available Neste trabalho o planejamento agregado de produção de sucos concentrados congelados de laranja é modelado com considerações de incertezas em alguns parâmetros, de modo a constituir uma ferramenta efetiva de suporte à tomada de decisões. A abordagem de otimização robusta é baseada em um modelo determinístico de programação linear com múltiplos produtos, estágios e períodos proposto em Munhoz e Morabito (2010. Além das decisões de produção, mistura e estocagem de sucos, esse modelo também incorpora o planejamento de colheita da laranja, levando-se em consideração as curvas de maturação das frutas. Um estudo de caso foi realizado em uma empresa do setor localizada no estado de São Paulo, envolvendo várias plantas e uma rede de distribuição internacional. Os resultados computacionais obtidos com a abordagem de otimização robusta, utilizando uma linguagem de modelagem algébrica e um aplicativo de última geração de solução de problemas de programação matemática, indicam que a abordagem tem potencial para ser aplicada com sucesso em situações reais.In this work, a frozen concentrated orange juice aggregate production planning is modeled taking into account uncertainty in some parameters. This generates an effective tool to support decision making. The robust optimization approach is based on a linear programming model with multiple products, stages and periods proposed by Munhoz and Morabito (2010. Besides decisions in the production, blending and storage of juices, the model also includes an orange harvesting plan, which considers orange maturation curves. A case study was developed in an orange juice company located in the State of Sao Paulo. This company has different facilities and a worldwide distribution system. The computational results obtained from this robust optimization approach, using an algebraic modeling language and a state-of-the-art optimization solver; indicate that the approach can be

  2. Uma abordagem de otimização robusta no planejamento agregado de produção na indústria cítrica A robust optimization approach for the aggregate production planning in the citrus industry

    Directory of Open Access Journals (Sweden)

    José Renato Munhoz

    2013-06-01

    Full Text Available Neste trabalho o planejamento agregado de produção de sucos concentrados congelados de laranja é modelado com considerações de incertezas em alguns parâmetros, de modo a constituir uma ferramenta efetiva de suporte à tomada de decisões. A abordagem de otimização robusta é baseada em um modelo determinístico de programação linear com múltiplos produtos, estágios e períodos proposto em Munhoz e Morabito (2010. Além das decisões de produção, mistura e estocagem de sucos, esse modelo também incorpora o planejamento de colheita da laranja, levando-se em consideração as curvas de maturação das frutas. Um estudo de caso foi realizado em uma empresa do setor localizada no estado de São Paulo, envolvendo várias plantas e uma rede de distribuição internacional. Os resultados computacionais obtidos com a abordagem de otimização robusta, utilizando uma linguagem de modelagem algébrica e um aplicativo de última geração de solução de problemas de programação matemática, indicam que a abordagem tem potencial para ser aplicada com sucesso em situações reais.In this work, a frozen concentrated orange juice aggregate production planning is modeled taking into account uncertainty in some parameters. This generates an effective tool to support decision making. The robust optimization approach is based on a linear programming model with multiple products, stages and periods proposed by Munhoz and Morabito (2010. Besides decisions in the production, blending and storage of juices, the model also includes an orange harvesting plan, which considers orange maturation curves. A case study was developed in an orange juice company located in the State of Sao Paulo. This company has different facilities and a worldwide distribution system. The computational results obtained from this robust optimization approach, using an algebraic modeling language and a state-of-the-art optimization solver; indicate that the approach can be

  3. Robust continuous clustering.

    Science.gov (United States)

    Shah, Sohil Atul; Koltun, Vladlen

    2017-09-12

    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.

  4. Robust estimation and hypothesis testing

    CERN Document Server

    Tiku, Moti L

    2004-01-01

    In statistical theory and practice, a certain distribution is usually assumed and then optimal solutions sought. Since deviations from an assumed distribution are very common, one cannot feel comfortable with assuming a particular distribution and believing it to be exactly correct. That brings the robustness issue in focus. In this book, we have given statistical procedures which are robust to plausible deviations from an assumed mode. The method of modified maximum likelihood estimation is used in formulating these procedures. The modified maximum likelihood estimators are explicit functions of sample observations and are easy to compute. They are asymptotically fully efficient and are as efficient as the maximum likelihood estimators for small sample sizes. The maximum likelihood estimators have computational problems and are, therefore, elusive. A broad range of topics are covered in this book. Solutions are given which are easy to implement and are efficient. The solutions are also robust to data anomali...

  5. Perceptual Robust Design

    DEFF Research Database (Denmark)

    Pedersen, Søren Nygaard

    The research presented in this PhD thesis has focused on a perceptual approach to robust design. The results of the research and the original contribution to knowledge is a preliminary framework for understanding, positioning, and applying perceptual robust design. Product quality is a topic...... been presented. Therefore, this study set out to contribute to the understanding and application of perceptual robust design. To achieve this, a state-of-the-art and current practice review was performed. From the review two main research problems were identified. Firstly, a lack of tools...... for perceptual robustness was found to overlap with the optimum for functional robustness and at most approximately 2.2% out of the 14.74% could be ascribed solely to the perceptual robustness optimisation. In conclusion, the thesis have offered a new perspective on robust design by merging robust design...

  6. Defining robustness protocols: a method to include and evaluate robustness in clinical plans

    International Nuclear Information System (INIS)

    McGowan, S E; Albertini, F; Lomax, A J; Thomas, S J

    2015-01-01

    We aim to define a site-specific robustness protocol to be used during the clinical plan evaluation process. Plan robustness of 16 skull base IMPT plans to systematic range and random set-up errors have been retrospectively and systematically analysed. This was determined by calculating the error-bar dose distribution (ebDD) for all the plans and by defining some metrics used to define protocols aiding the plan assessment. Additionally, an example of how to clinically use the defined robustness database is given whereby a plan with sub-optimal brainstem robustness was identified. The advantage of using different beam arrangements to improve the plan robustness was analysed. Using the ebDD it was found range errors had a smaller effect on dose distribution than the corresponding set-up error in a single fraction, and that organs at risk were most robust to the range errors, whereas the target was more robust to set-up errors. A database was created to aid planners in terms of plan robustness aims in these volumes. This resulted in the definition of site-specific robustness protocols. The use of robustness constraints allowed for the identification of a specific patient that may have benefited from a treatment of greater individuality. A new beam arrangement showed to be preferential when balancing conformality and robustness for this case. The ebDD and error-bar volume histogram proved effective in analysing plan robustness. The process of retrospective analysis could be used to establish site-specific robustness planning protocols in proton therapy. These protocols allow the planner to determine plans that, although delivering a dosimetrically adequate dose distribution, have resulted in sub-optimal robustness to these uncertainties. For these cases the use of different beam start conditions may improve the plan robustness to set-up and range uncertainties. (paper)

  7. Defining robustness protocols: a method to include and evaluate robustness in clinical plans

    Science.gov (United States)

    McGowan, S. E.; Albertini, F.; Thomas, S. J.; Lomax, A. J.

    2015-04-01

    We aim to define a site-specific robustness protocol to be used during the clinical plan evaluation process. Plan robustness of 16 skull base IMPT plans to systematic range and random set-up errors have been retrospectively and systematically analysed. This was determined by calculating the error-bar dose distribution (ebDD) for all the plans and by defining some metrics used to define protocols aiding the plan assessment. Additionally, an example of how to clinically use the defined robustness database is given whereby a plan with sub-optimal brainstem robustness was identified. The advantage of using different beam arrangements to improve the plan robustness was analysed. Using the ebDD it was found range errors had a smaller effect on dose distribution than the corresponding set-up error in a single fraction, and that organs at risk were most robust to the range errors, whereas the target was more robust to set-up errors. A database was created to aid planners in terms of plan robustness aims in these volumes. This resulted in the definition of site-specific robustness protocols. The use of robustness constraints allowed for the identification of a specific patient that may have benefited from a treatment of greater individuality. A new beam arrangement showed to be preferential when balancing conformality and robustness for this case. The ebDD and error-bar volume histogram proved effective in analysing plan robustness. The process of retrospective analysis could be used to establish site-specific robustness planning protocols in proton therapy. These protocols allow the planner to determine plans that, although delivering a dosimetrically adequate dose distribution, have resulted in sub-optimal robustness to these uncertainties. For these cases the use of different beam start conditions may improve the plan robustness to set-up and range uncertainties.

  8. Automated Robust Maneuver Design and Optimization

    Data.gov (United States)

    National Aeronautics and Space Administration — NASA is seeking improvements to the current technologies related to Position, Navigation and Timing. In particular, it is desired to automate precise maneuver...

  9. Adaptive Critic Nonlinear Robust Control: A Survey.

    Science.gov (United States)

    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.

  10. Robustness of Structural Systems

    DEFF Research Database (Denmark)

    Canisius, T.D.G.; Sørensen, John Dalsgaard; Baker, J.W.

    2007-01-01

    The importance of robustness as a property of structural systems has been recognised following several structural failures, such as that at Ronan Point in 1968,where the consequenceswere deemed unacceptable relative to the initiating damage. A variety of research efforts in the past decades have...... attempted to quantify aspects of robustness such as redundancy and identify design principles that can improve robustness. This paper outlines the progress of recent work by the Joint Committee on Structural Safety (JCSS) to develop comprehensive guidance on assessing and providing robustness in structural...... systems. Guidance is provided regarding the assessment of robustness in a framework that considers potential hazards to the system, vulnerability of system components, and failure consequences. Several proposed methods for quantifying robustness are reviewed, and guidelines for robust design...

  11. Robust multivariate analysis

    CERN Document Server

    J Olive, David

    2017-01-01

    This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

  12. A deficit in optimizing task solution but robust and well-retained speed and accuracy gains in complex skill acquisition in Parkinson׳s disease: multi-session training on the Tower of Hanoi Puzzle.

    Science.gov (United States)

    Vakil, Eli; Hassin-Baer, Sharon; Karni, Avi

    2014-05-01

    There are inconsistent results in the research literature relating to whether a procedural memory dysfunction exists as a core deficit in Parkinson׳s disease (PD). To address this issue, we examined the acquisition and long-term retention of a cognitive skill in patients with moderately severe PD. To this end, we used a computerized version of the Tower of Hanoi Puzzle. Sixteen patients with PD (11 males, age 60.9±10.26 years, education 13.8±3.5 years, disease duration 8.6±4.7 years, UPDRS III "On" score 16±5.3) were compared with 20 healthy individuals matched for age, gender, education and MMSE scores. The patients were assessed while taking their anti-Parkinsonian medication. All participants underwent three consecutive practice sessions, 24-48h apart, and a retention-test session six months later. A computerized version of the Tower of Hanoi Puzzle, with four disks, was used for training. Participants completed the task 18 times in each session. Number of moves (Nom) to solution, and time per move (Tpm), were used as measures of acquisition and retention of the learned skill. Robust learning, a significant reduction in Nom and a concurrent decrease in Tpm, were found across all three training sessions, in both groups. Moreover, both patients and controls showed significant savings for both measures at six months post-training. However, while their Tpm was no slower than that of controls, patients with PD required more Nom (in 3rd and 4th sessions) and tended to stabilize on less-than-optimal solutions. The results do not support the notion of a core deficit in gaining speed (fluency) or generating procedural memory in PD. However, PD patients settled on less-than-optimal solutions of the task, i.e., less efficient task solving process. The results are consistent with animal studies of the effects of dopamine depletion on task exploration. Thus, patients with PD may have a problem in exploring for optimal task solution rather than in skill acquisition and

  13. Robust control design with MATLAB

    CERN Document Server

    Gu, Da-Wei; Konstantinov, Mihail M

    2013-01-01

    Robust Control Design with MATLAB® (second edition) helps the student to learn how to use well-developed advanced robust control design methods in practical cases. To this end, several realistic control design examples from teaching-laboratory experiments, such as a two-wheeled, self-balancing robot, to complex systems like a flexible-link manipulator are given detailed presentation. All of these exercises are conducted using MATLAB® Robust Control Toolbox 3, Control System Toolbox and Simulink®. By sharing their experiences in industrial cases with minimum recourse to complicated theories and formulae, the authors convey essential ideas and useful insights into robust industrial control systems design using major H-infinity optimization and related methods allowing readers quickly to move on with their own challenges. The hands-on tutorial style of this text rests on an abundance of examples and features for the second edition: ·        rewritten and simplified presentation of theoretical and meth...

  14. Journal of Pharmacy & Bioresources: Submissions

    African Journals Online (AJOL)

    Journal Home > About the Journal > Journal of Pharmacy & Bioresources: Submissions ... The reference section should be typed on separate pages. ... human subjects in their work to seek approval from the appropriate Ethical Committees.

  15. Robustness of Structures

    DEFF Research Database (Denmark)

    Faber, Michael Havbro; Vrouwenvelder, A.C.W.M.; Sørensen, John Dalsgaard

    2011-01-01

    In 2005, the Joint Committee on Structural Safety (JCSS) together with Working Commission (WC) 1 of the International Association of Bridge and Structural Engineering (IABSE) organized a workshop on robustness of structures. Two important decisions resulted from this workshop, namely...... ‘COST TU0601: Robustness of Structures’ was initiated in February 2007, aiming to provide a platform for exchanging and promoting research in the area of structural robustness and to provide a basic framework, together with methods, strategies and guidelines enhancing robustness of structures...... the development of a joint European project on structural robustness under the COST (European Cooperation in Science and Technology) programme and the decision to develop a more elaborate document on structural robustness in collaboration between experts from the JCSS and the IABSE. Accordingly, a project titled...

  16. Robust Growth Determinants

    OpenAIRE

    Doppelhofer, Gernot; Weeks, Melvyn

    2011-01-01

    This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsi- monious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth ...

  17. Robust Programming by Example

    OpenAIRE

    Bishop , Matt; Elliott , Chip

    2011-01-01

    Part 2: WISE 7; International audience; Robust programming lies at the heart of the type of coding called “secure programming”. Yet it is rarely taught in academia. More commonly, the focus is on how to avoid creating well-known vulnerabilities. While important, that misses the point: a well-structured, robust program should anticipate where problems might arise and compensate for them. This paper discusses one view of robust programming and gives an example of how it may be taught.

  18. Nigerian Quarterly Journal of Hospital Medicine: Submissions

    African Journals Online (AJOL)

    Nigerian Quarterly Journal of Hospital Medicine: Submissions. Journal Home > About the Journal > Nigerian Quarterly Journal of Hospital Medicine: Submissions. Log in or Register to get access to full text downloads.

  19. Robust recognition via information theoretic learning

    CERN Document Server

    He, Ran; Yuan, Xiaotong; Wang, Liang

    2014-01-01

    This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.The?authors?resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency,?the brief?introduces the additive and multip

  20. Robust procedures in chemometrics

    DEFF Research Database (Denmark)

    Kotwa, Ewelina

    properties of the analysed data. The broad theoretical background of robust procedures was given as a very useful supplement to the classical methods, and a new tool, based on robust PCA, aiming at identifying Rayleigh and Raman scatters in excitation-mission (EEM) data was developed. The results show...

  1. Journal of Surgical Technique and Case Report: Submissions

    African Journals Online (AJOL)

    Submission Preparation Checklist. As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines. The submission has not been previously published, nor is it before another ...

  2. LDRD final report : robust analysis of large-scale combinatorial applications.

    Energy Technology Data Exchange (ETDEWEB)

    Carr, Robert D.; Morrison, Todd (University of Colorado, Denver, CO); Hart, William Eugene; Benavides, Nicolas L. (Santa Clara University, Santa Clara, CA); Greenberg, Harvey J. (University of Colorado, Denver, CO); Watson, Jean-Paul; Phillips, Cynthia Ann

    2007-09-01

    Discrete models of large, complex systems like national infrastructures and complex logistics frameworks naturally incorporate many modeling uncertainties. Consequently, there is a clear need for optimization techniques that can robustly account for risks associated with modeling uncertainties. This report summarizes the progress of the Late-Start LDRD 'Robust Analysis of Largescale Combinatorial Applications'. This project developed new heuristics for solving robust optimization models, and developed new robust optimization models for describing uncertainty scenarios.

  3. Efficient robust conditional random fields.

    Science.gov (United States)

    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.

  4. Robustness Metrics: Consolidating the multiple approaches to quantify Robustness

    DEFF Research Database (Denmark)

    Göhler, Simon Moritz; Eifler, Tobias; Howard, Thomas J.

    2016-01-01

    robustness metrics; 3) Functional expectancy and dispersion robustness metrics; and 4) Probability of conformance robustness metrics. The goal was to give a comprehensive overview of robustness metrics and guidance to scholars and practitioners to understand the different types of robustness metrics...

  5. Robustness of Structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2008-01-01

    This paper describes the background of the robustness requirements implemented in the Danish Code of Practice for Safety of Structures and in the Danish National Annex to the Eurocode 0, see (DS-INF 146, 2003), (DS 409, 2006), (EN 1990 DK NA, 2007) and (Sørensen and Christensen, 2006). More...... frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure combined with increased requirements to efficiency in design and execution followed by increased risk of human errors has made the need of requirements to robustness of new structures essential....... According to Danish design rules robustness shall be documented for all structures in high consequence class. The design procedure to document sufficient robustness consists of: 1) Review of loads and possible failure modes / scenarios and determination of acceptable collapse extent; 2) Review...

  6. Robust Approaches to Forecasting

    OpenAIRE

    Jennifer Castle; David Hendry; Michael P. Clements

    2014-01-01

    We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods ar...

  7. Robustness - theoretical framework

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Rizzuto, Enrico; Faber, Michael H.

    2010-01-01

    More frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure combined with increased requirements to efficiency in design and execution followed by increased risk of human errors has made the need of requirements to robustness of new struct...... of this fact sheet is to describe a theoretical and risk based framework to form the basis for quantification of robustness and for pre-normative guidelines....

  8. Measure of robustness for complex networks

    Science.gov (United States)

    Youssef, Mina Nabil

    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

  9. Second order statistics of bilinear forms of robust scatter estimators

    KAUST Repository

    Kammoun, Abla

    2015-08-12

    This paper lies in the lineage of recent works studying the asymptotic behaviour of robust-scatter estimators in the case where the number of observations and the dimension of the population covariance matrix grow at infinity with the same pace. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged in order to improve the design of robust detection methods. As an example, we provide an improved generalized likelihood ratio based detector which combines robustness to impulsive observations and optimality across the shrinkage parameter, the optimality being considered for the false alarm regulation.

  10. Qualitative Robustness in Estimation

    Directory of Open Access Journals (Sweden)

    Mohammed Nasser

    2012-07-01

    Full Text Available Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif";} Qualitative robustness, influence function, and breakdown point are three main concepts to judge an estimator from the viewpoint of robust estimation. It is important as well as interesting to study relation among them. This article attempts to present the concept of qualitative robustness as forwarded by first proponents and its later development. It illustrates intricacies of qualitative robustness and its relation with consistency, and also tries to remove commonly believed misunderstandings about relation between influence function and qualitative robustness citing some examples from literature and providing a new counter-example. At the end it places a useful finite and a simulated version of   qualitative robustness index (QRI. In order to assess the performance of the proposed measures, we have compared fifteen estimators of correlation coefficient using simulated as well as real data sets.

  11. Robust Utility Maximization Under Convex Portfolio Constraints

    International Nuclear Information System (INIS)

    Matoussi, Anis; Mezghani, Hanen; Mnif, Mohamed

    2015-01-01

    We study a robust maximization problem from terminal wealth and consumption under a convex constraints on the portfolio. We state the existence and the uniqueness of the consumption–investment strategy by studying the associated quadratic backward stochastic differential equation. We characterize the optimal control by using the duality method and deriving a dynamic maximum principle

  12. Robust Active Portfolio Management

    National Research Council Canada - National Science Library

    Erdogan, E; Goldfarb, D; Iyengar, G

    2006-01-01

    ... on the portfolio beta, and limits on cash and industry exposure. We show that the optimal portfolios can be computed by solving second-order cone programs -- a class of optimization problems with a worst case complexity (i.e...

  13. Robustness in econometrics

    CERN Document Server

    Sriboonchitta, Songsak; Huynh, Van-Nam

    2017-01-01

    This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.

  14. Robust Manufacturing Control

    CERN Document Server

    2013-01-01

    This contributed volume collects research papers, presented at the CIRP Sponsored Conference Robust Manufacturing Control: Innovative and Interdisciplinary Approaches for Global Networks (RoMaC 2012, Jacobs University, Bremen, Germany, June 18th-20th 2012). These research papers present the latest developments and new ideas focusing on robust manufacturing control for global networks. Today, Global Production Networks (i.e. the nexus of interconnected material and information flows through which products and services are manufactured, assembled and distributed) are confronted with and expected to adapt to: sudden and unpredictable large-scale changes of important parameters which are occurring more and more frequently, event propagation in networks with high degree of interconnectivity which leads to unforeseen fluctuations, and non-equilibrium states which increasingly characterize daily business. These multi-scale changes deeply influence logistic target achievement and call for robust planning and control ...

  15. Robust plasmonic substrates

    DEFF Research Database (Denmark)

    Kostiučenko, Oksana; Fiutowski, Jacek; Tamulevicius, Tomas

    2014-01-01

    Robustness is a key issue for the applications of plasmonic substrates such as tip-enhanced Raman spectroscopy, surface-enhanced spectroscopies, enhanced optical biosensing, optical and optoelectronic plasmonic nanosensors and others. A novel approach for the fabrication of robust plasmonic...... substrates is presented, which relies on the coverage of gold nanostructures with diamond-like carbon (DLC) thin films of thicknesses 25, 55 and 105 nm. DLC thin films were grown by direct hydrocarbon ion beam deposition. In order to find the optimum balance between optical and mechanical properties...

  16. Robust Self Tuning Controllers

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad

    1985-01-01

    The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...... has several operation modes and a detector for controlling the mode. A special self tuning controller has been developed to regulate plant with changing time delay.......The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...

  17. Economic and Policy Review: Submissions

    African Journals Online (AJOL)

    Manuscripts should be appropriately referenced using the American Psychological Association (APA) style • Electronic copy of the manuscripts should be submitted to the NESG designated email address on or before the submission deadline • Manuscripts published elsewhere or those under • All manuscripts should be ...

  18. Medical Journal of Zambia: Submissions

    African Journals Online (AJOL)

    Authors from the University Teaching Hospital in Lusaka may find it quicker to submit their articles through the e-mail: MedJZambia@yahoo.com. Manuscripts Papers should be clear, precise and logical. The Journal welcomes submissions in any of the following categories: original articles, review articles, short reports, case ...

  19. Research in Hospitality Management: Submissions

    African Journals Online (AJOL)

    Before submitting a manuscript, authors should peruse and consult a recent issue of the Journal for format and style. ... The submission of a manuscript by the authors implies that they automatically agree to assign exclusive copyright to the publishers of the Research in Hospitality Management, NISC (Pty) Ltd. There are no ...

  20. Review of submissions: competition impacts

    NARCIS (Netherlands)

    Burghouwt, G.; de Wit, W.

    2015-01-01

    The Airports Commission requested ITF/SEO to provide technical assistance with analysing responses that pertain to the previous work undertaken by the ITF/SEO. This report assesses the submissions by the stakeholders in relation to competition impacts and compares them with the ITF/SEO results and

  1. South African Music Studies: Submissions

    African Journals Online (AJOL)

    SAMUS: South African Music Studies is accredited with the South African ... Only one submission at a time per author will be considered. 2. Articles ... The Editor reserves the right to make language and punctuation changes and other ... We may require a subvention (page fees) from authors of articles to cover printing costs.

  2. Shakespeare in Southern Africa: Submissions

    African Journals Online (AJOL)

    Shakespeare in Southern Africa publishes articles, commentary and reviews on all aspects of Shakespearean studies and performance, with a particular emphasis on responses to Shakespeare in southern Africa. Submissions are reviewed by at least two referees. The practice of 'blind' reviewing is adhered to. The Journal ...

  3. Annals of Biomedical Sciences: Submissions

    African Journals Online (AJOL)

    Authors must comply with “Instructions to authors” which is published in each issue of the journal. TYPES OF CONTRIBUTION The following categories of articles are published. Authors should indicate into which of listed categories their submission is intended for. Original articles. These are reports of original research of ...

  4. Robust distributed cognitive relay beamforming

    KAUST Repository

    Pandarakkottilil, Ubaidulla

    2012-05-01

    In this paper, we present a distributed relay beamformer design for a cognitive radio network in which a cognitive (or secondary) transmit node communicates with a secondary receive node assisted by a set of cognitive non-regenerative relays. The secondary nodes share the spectrum with a licensed primary user (PU) node, and each node is assumed to be equipped with a single transmit/receive antenna. The interference to the PU resulting from the transmission from the cognitive nodes is kept below a specified limit. The proposed robust cognitive relay beamformer design seeks to minimize the total relay transmit power while ensuring that the transceiver signal-to-interference- plus-noise ratio and PU interference constraints are satisfied. The proposed design takes into account a parameter of the error in the channel state information (CSI) to render the performance of the beamformer robust in the presence of imperfect CSI. Though the original problem is non-convex, we show that the proposed design can be reformulated as a tractable convex optimization problem that can be solved efficiently. Numerical results are provided and illustrate the performance of the proposed designs for different network operating conditions and parameters. © 2012 IEEE.

  5. Robust surgery loading

    NARCIS (Netherlands)

    Hans, Elias W.; Wullink, Gerhard; van Houdenhoven, Mark; Kazemier, Geert

    2008-01-01

    We consider the robust surgery loading problem for a hospital’s operating theatre department, which concerns assigning surgeries and sufficient planned slack to operating room days. The objective is to maximize capacity utilization and minimize the risk of overtime, and thus cancelled patients. This

  6. Robustness Envelopes of Networks

    NARCIS (Netherlands)

    Trajanovski, S.; Martín-Hernández, J.; Winterbach, W.; Van Mieghem, P.

    2013-01-01

    We study the robustness of networks under node removal, considering random node failure, as well as targeted node attacks based on network centrality measures. Whilst both of these have been studied in the literature, existing approaches tend to study random failure in terms of average-case

  7. Robust Parametric Control of Spacecraft Rendezvous

    Directory of Open Access Journals (Sweden)

    Dake Gu

    2014-01-01

    Full Text Available This paper proposes a method to design the robust parametric control for autonomous rendezvous of spacecrafts with the inertial information with uncertainty. We consider model uncertainty of traditional C-W equation to formulate the dynamic model of the relative motion. Based on eigenstructure assignment and model reference theory, a concise control law for spacecraft rendezvous is proposed which could be fixed through solving an optimization problem. The cost function considers the stabilization of the system and other performances. Simulation results illustrate the robustness and effectiveness of the proposed control.

  8. Robust bayesian analysis of an autoregressive model with ...

    African Journals Online (AJOL)

    In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...

  9. Assertiveness, submissive behaviour and social comparison.

    Science.gov (United States)

    Gilbert, P; Allan, S

    1994-09-01

    This paper explores the relationship between a new assertiveness measure (the Scale for Interpersonal Behaviour--SIB), social comparison and submissive behaviour. The paper investigates these measures in relation to the personality traits of neuroticism and introversion. Findings suggest: (a) that social comparison may be an important variable in assertiveness and submissive behaviour and shows a strong relationship to neuroticism and introversion; (b) that submissive behaviour is not the mirror opposite of assertive behaviour; and (c) submissive behaviour seems more strongly associated with introversion and neuroticism than assertive performance.

  10. A robust classic.

    Science.gov (United States)

    Kutzner, Florian; Vogel, Tobias; Freytag, Peter; Fiedler, Klaus

    2011-01-01

    In the present research, we argue for the robustness of illusory correlations (ICs, Hamilton & Gifford, 1976) regarding two boundary conditions suggested in previous research. First, we argue that ICs are maintained under extended experience. Using simulations, we derive conflicting predictions. Whereas noise-based accounts predict ICs to be maintained (Fielder, 2000; Smith, 1991), a prominent account based on discrepancy-reducing feedback learning predicts ICs to disappear (Van Rooy et al., 2003). An experiment involving 320 observations with majority and minority members supports the claim that ICs are maintained. Second, we show that actively using the stereotype to make predictions that are met with reward and punishment does not eliminate the bias. In addition, participants' operant reactions afford a novel online measure of ICs. In sum, our findings highlight the robustness of ICs that can be explained as a result of unbiased but noisy learning.

  11. Defending submission-year analyses of new drug approvals.

    Science.gov (United States)

    Carpenter, Daniel P

    2003-01-01

    In response to the critique of Mary Olsen, Daniel Carpenter, on behalf of his co-authors, addresses the issue of analysis based on the year a new drug is submitted for Food and Drug Administration (FDA) approval, not the year it is approved. Both substantive knowledge of the FDA drug review process and sound social science theory favor submission-year averaging. The history and bureaucratic mechanics of the Center for Drug Evaluation and Review (CDER) conform to the author's assumption. The statistical theory of optimal experimentation also points to the beginning of review as a locus for effects upon decisions.

  12. Incentive-Compatible Robust Line Planning

    Science.gov (United States)

    Bessas, Apostolos; Kontogiannis, Spyros; Zaroliagis, Christos

    The problem of robust line planning requests for a set of origin-destination paths (lines) along with their frequencies in an underlying railway network infrastructure, which are robust to fluctuations of real-time parameters of the solution. In this work, we investigate a variant of robust line planning stemming from recent regulations in the railway sector that introduce competition and free railway markets, and set up a new application scenario: there is a (potentially large) number of line operators that have their lines fixed and operate as competing entities issuing frequency requests, while the management of the infrastructure itself remains the responsibility of a single entity, the network operator. The line operators are typically unwilling to reveal their true incentives, while the network operator strives to ensure a fair (or socially optimal) usage of the infrastructure, e.g., by maximizing the (unknown to him) aggregate incentives of the line operators.

  13. The Crane Robust Control

    Directory of Open Access Journals (Sweden)

    Marek Hicar

    2004-01-01

    Full Text Available The article is about a control design for complete structure of the crane: crab, bridge and crane uplift.The most important unknown parameters for simulations are burden weight and length of hanging rope. We will use robustcontrol for crab and bridge control to ensure adaptivity for burden weight and rope length. Robust control will be designed for current control of the crab and bridge, necessary is to know the range of unknown parameters. Whole robust will be splitto subintervals and after correct identification of unknown parameters the most suitable robust controllers will be chosen.The most important condition at the crab and bridge motion is avoiding from burden swinging in the final position. Crab and bridge drive is designed by asynchronous motor fed from frequency converter. We will use crane uplift with burden weightobserver in combination for uplift, crab and bridge drive with cooperation of their parameters: burden weight, rope length and crab and bridge position. Controllers are designed by state control method. We will use preferably a disturbance observerwhich will identify burden weight as a disturbance. The system will be working in both modes at empty hook as well asat maximum load: burden uplifting and dropping down.

  14. Design of Robust Neural Network Classifiers

    DEFF Research Database (Denmark)

    Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads

    1998-01-01

    This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...

  15. Robust estimation for ordinary differential equation models.

    Science.gov (United States)

    Cao, J; Wang, L; Xu, J

    2011-12-01

    Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data. © 2011, The International Biometric Society.

  16. Robust and Adaptive Control With Aerospace Applications

    CERN Document Server

    Lavretsky, Eugene

    2013-01-01

    Robust and Adaptive Control shows the reader how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. Driven by aerospace applications the focus of the book is primarily on continuous-dynamical systems.  The text is a three-part treatment, beginning with robust and optimal linear control methods and moving on to a self-contained presentation of the design and analysis of model reference adaptive control (MRAC) for nonlinear uncertain dynamical systems. Recent extensions and modifications to MRAC design are included, as are guidelines for combining robust optimal and MRAC controllers. Features of the text include: ·         case studies that demonstrate the benefits of robust and adaptive control for piloted, autonomous and experimental aerial platforms; ·         detailed background material for each chapter to motivate theoretical developments; ·         realistic examples and simulation data illustrating key features ...

  17. Specimen Collection and Submission Manual

    Science.gov (United States)

    2016-06-01

    collection procedures. Urine Sterile screw-capped, leak proof, container or sterile urine container A clean catch mid-stream sample is preferred. Follow...several days cannot be avoided, freeze samples for storage and ship frozen on dry ice. TR-16-161 DISTRIBUTION STATEMENT A: Approved for public...designed to provide detailed instructions for submission of samples that will be analyzed in the SPL. Tests that are not listed may require special

  18. Nigerian Journal of Gastroenterology and Hepatology: Submissions

    African Journals Online (AJOL)

    Submission of manuscript could be by either electronic and/or surface mails. Electronic submission shall be by e-mail, flash drive or CD. ... When books are cited, the name of authors, title of publication, In (title of book), chapter, edition, editors, publishers, city and year of publication and pages must be cited in order.

  19. Robust gates for holonomic quantum computation

    International Nuclear Information System (INIS)

    Florio, Giuseppe; Pascazio, Saverio; Facchi, Paolo; Fazio, Rosario; Giovannetti, Vittorio

    2006-01-01

    Non-Abelian geometric phases are attracting increasing interest because of possible experimental application in quantum computation. We study the effects of the environment (modeled as an ensemble of harmonic oscillators) on a holonomic transformation and write the corresponding master equation. The solution is analytically and numerically investigated and the behavior of the fidelity analyzed: fidelity revivals are observed and an optimal finite operation time is determined at which the gate is most robust against noise

  20. Robust efficient video fingerprinting

    Science.gov (United States)

    Puri, Manika; Lubin, Jeffrey

    2009-02-01

    We have developed a video fingerprinting system with robustness and efficiency as the primary and secondary design criteria. In extensive testing, the system has shown robustness to cropping, letter-boxing, sub-titling, blur, drastic compression, frame rate changes, size changes and color changes, as well as to the geometric distortions often associated with camcorder capture in cinema settings. Efficiency is afforded by a novel two-stage detection process in which a fast matching process first computes a number of likely candidates, which are then passed to a second slower process that computes the overall best match with minimal false alarm probability. One key component of the algorithm is a maximally stable volume computation - a three-dimensional generalization of maximally stable extremal regions - that provides a content-centric coordinate system for subsequent hash function computation, independent of any affine transformation or extensive cropping. Other key features include an efficient bin-based polling strategy for initial candidate selection, and a final SIFT feature-based computation for final verification. We describe the algorithm and its performance, and then discuss additional modifications that can provide further improvement to efficiency and accuracy.

  1. Robustness of mission plans for unmanned aircraft

    Science.gov (United States)

    Niendorf, Moritz

    This thesis studies the robustness of optimal mission plans for unmanned aircraft. Mission planning typically involves tactical planning and path planning. Tactical planning refers to task scheduling and in multi aircraft scenarios also includes establishing a communication topology. Path planning refers to computing a feasible and collision-free trajectory. For a prototypical mission planning problem, the traveling salesman problem on a weighted graph, the robustness of an optimal tour is analyzed with respect to changes to the edge costs. Specifically, the stability region of an optimal tour is obtained, i.e., the set of all edge cost perturbations for which that tour is optimal. The exact stability region of solutions to variants of the traveling salesman problems is obtained from a linear programming relaxation of an auxiliary problem. Edge cost tolerances and edge criticalities are derived from the stability region. For Euclidean traveling salesman problems, robustness with respect to perturbations to vertex locations is considered and safe radii and vertex criticalities are introduced. For weighted-sum multi-objective problems, stability regions with respect to changes in the objectives, weights, and simultaneous changes are given. Most critical weight perturbations are derived. Computing exact stability regions is intractable for large instances. Therefore, tractable approximations are desirable. The stability region of solutions to relaxations of the traveling salesman problem give under approximations and sets of tours give over approximations. The application of these results to the two-neighborhood and the minimum 1-tree relaxation are discussed. Bounds on edge cost tolerances and approximate criticalities are obtainable likewise. A minimum spanning tree is an optimal communication topology for minimizing the cumulative transmission power in multi aircraft missions. The stability region of a minimum spanning tree is given and tolerances, stability balls

  2. Robust input design for nonlinear dynamic modeling of AUV.

    Science.gov (United States)

    Nouri, Nowrouz Mohammad; Valadi, Mehrdad

    2017-09-01

    Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system's inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Robust portfolio choice with ambiguity and learning about return predictability

    DEFF Research Database (Denmark)

    Larsen, Linda Sandris; Branger, Nicole; Munk, Claus

    2013-01-01

    We analyze the optimal stock-bond portfolio under both learning and ambiguity aversion. Stock returns are predictable by an observable and an unobservable predictor, and the investor has to learn about the latter. Furthermore, the investor is ambiguity-averse and has a preference for investment...... strategies that are robust to model misspecifications. We derive a closed-form solution for the optimal robust investment strategy. We find that both learning and ambiguity aversion impact the level and structure of the optimal stock investment. Suboptimal strategies resulting either from not learning...... or from not considering ambiguity can lead to economically significant losses....

  4. Robust automated knowledge capture.

    Energy Technology Data Exchange (ETDEWEB)

    Stevens-Adams, Susan Marie; Abbott, Robert G.; Forsythe, James Chris; Trumbo, Michael Christopher Stefan; Haass, Michael Joseph; Hendrickson, Stacey M. Langfitt

    2011-10-01

    This report summarizes research conducted through the Sandia National Laboratories Robust Automated Knowledge Capture Laboratory Directed Research and Development project. The objective of this project was to advance scientific understanding of the influence of individual cognitive attributes on decision making. The project has developed a quantitative model known as RumRunner that has proven effective in predicting the propensity of an individual to shift strategies on the basis of task and experience related parameters. Three separate studies are described which have validated the basic RumRunner model. This work provides a basis for better understanding human decision making in high consequent national security applications, and in particular, the individual characteristics that underlie adaptive thinking.

  5. Passion, Robustness and Perseverance

    DEFF Research Database (Denmark)

    Lim, Miguel Antonio; Lund, Rebecca

    2016-01-01

    Evaluation and merit in the measured university are increasingly based on taken-for-granted assumptions about the “ideal academic”. We suggest that the scholar now needs to show that she is passionate about her work and that she gains pleasure from pursuing her craft. We suggest that passion...... and pleasure achieve an exalted status as something compulsory. The scholar ought to feel passionate about her work and signal that she takes pleasure also in the difficult moments. Passion has become a signal of robustness and perseverance in a job market characterised by funding shortages, increased pressure...... way to demonstrate their potential and, crucially, their passion for their work. Drawing on the literature on technologies of governance, we reflect on what is captured and what is left out by these two evaluation instruments. We suggest that bibliometric analysis at the individual level is deeply...

  6. Robust Optical Flow Estimation

    Directory of Open Access Journals (Sweden)

    Javier Sánchez Pérez

    2013-10-01

    Full Text Available n this work, we describe an implementation of the variational method proposed by Brox etal. in 2004, which yields accurate optical flows with low running times. It has several benefitswith respect to the method of Horn and Schunck: it is more robust to the presence of outliers,produces piecewise-smooth flow fields and can cope with constant brightness changes. Thismethod relies on the brightness and gradient constancy assumptions, using the information ofthe image intensities and the image gradients to find correspondences. It also generalizes theuse of continuous L1 functionals, which help mitigate the effect of outliers and create a TotalVariation (TV regularization. Additionally, it introduces a simple temporal regularizationscheme that enforces a continuous temporal coherence of the flow fields.

  7. Robust Multimodal Dictionary Learning

    Science.gov (United States)

    Cao, Tian; Jojic, Vladimir; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

    2014-01-01

    We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities. Dictionaries learned with such non-corresponding data will induce uncertainty about image representation. In this paper, we propose a probabilistic model that accounts for image areas that are poorly corresponding between the image modalities. We cast the problem of learning a dictionary in presence of problematic image patches as a likelihood maximization problem and solve it with a variant of the EM algorithm. Our algorithm iterates identification of poorly corresponding patches and re-finements of the dictionary. We tested our method on synthetic and real data. We show improvements in image prediction quality and alignment accuracy when using the method for multimodal image registration. PMID:24505674

  8. Robust snapshot interferometric spectropolarimetry.

    Science.gov (United States)

    Kim, Daesuk; Seo, Yoonho; Yoon, Yonghee; Dembele, Vamara; Yoon, Jae Woong; Lee, Kyu Jin; Magnusson, Robert

    2016-05-15

    This Letter describes a Stokes vector measurement method based on a snapshot interferometric common-path spectropolarimeter. The proposed scheme, which employs an interferometric polarization-modulation module, can extract the spectral polarimetric parameters Ψ(k) and Δ(k) of a transmissive anisotropic object by which an accurate Stokes vector can be calculated in the spectral domain. It is inherently strongly robust to the object 3D pose variation, since it is designed distinctly so that the measured object can be placed outside of the interferometric module. Experiments are conducted to verify the feasibility of the proposed system. The proposed snapshot scheme enables us to extract the spectral Stokes vector of a transmissive anisotropic object within tens of msec with high accuracy.

  9. International Conference on Robust Statistics

    CERN Document Server

    Filzmoser, Peter; Gather, Ursula; Rousseeuw, Peter

    2003-01-01

    Aspects of Robust Statistics are important in many areas. Based on the International Conference on Robust Statistics 2001 (ICORS 2001) in Vorau, Austria, this volume discusses future directions of the discipline, bringing together leading scientists, experienced researchers and practitioners, as well as younger researchers. The papers cover a multitude of different aspects of Robust Statistics. For instance, the fundamental problem of data summary (weights of evidence) is considered and its robustness properties are studied. Further theoretical subjects include e.g.: robust methods for skewness, time series, longitudinal data, multivariate methods, and tests. Some papers deal with computational aspects and algorithms. Finally, the aspects of application and programming tools complete the volume.

  10. 76 FR 28994 - Submission for OMB Review; Comment Request; A Generic Submission for Formative Research, Pre...

    Science.gov (United States)

    2011-05-19

    ... displays a currently valid OMB control number. Proposed Collection: Title: A Generic Submission for... advocates; members of the public; health care professionals; organizational representatives. Table 1...

  11. Robust on-off pulse control of flexible space vehicles

    Science.gov (United States)

    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.

  12. Robustness Analyses of Timber Structures

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Hald, Frederik

    2013-01-01

    The robustness of structural systems has obtained a renewed interest arising from a much more frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure. In order to minimise the likelihood of such disproportionate structural failures, many mo...... with respect to robustness of timber structures and will discuss the consequences of such robustness issues related to the future development of timber structures.......The robustness of structural systems has obtained a renewed interest arising from a much more frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure. In order to minimise the likelihood of such disproportionate structural failures, many...... modern building codes consider the need for the robustness of structures and provide strategies and methods to obtain robustness. Therefore, a structural engineer may take necessary steps to design robust structures that are insensitive to accidental circumstances. The present paper summaries issues...

  13. Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: Case study for the analysis of an etching solution containing hydrogen peroxide

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Youngbok [Department of Chemistry, College of Natural Sciences, Hanyang University Haengdang-Dong, Seoul 133-791 (Korea, Republic of); Chung, Hoeil [Department of Chemistry, College of Natural Sciences, Hanyang University Haengdang-Dong, Seoul 133-791 (Korea, Republic of)]. E-mail: hoeil@hanyang.ac.kr; Arnold, Mark A. [Optical Science and Technology Center and Department of Chemistry, University of Iowa, Iowa City, IA 52242 (United States)

    2006-07-14

    Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH{sub 4}OH, H{sub 2}O{sub 2} and H{sub 2}O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000-7500 cm{sup -1}) were used to build PLS models. The selective determination of H{sub 2}O{sub 2} without influences from NH{sub 4}OH and H{sub 2}O was a key issue since its molecular structure is similar to that of H{sub 2}O and NH{sub 4}OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH{sub 4}OH and H{sub 2}O{sub 2} were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH{sub 4}OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H{sub 2}O{sub 2} was broadly overlapping and much less distinct than that from NH{sub 4}OH, the selectivity and prediction performance for the H{sub 2}O{sub 2} calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H{sub 2}O{sub 2} calibration. A robust H{sub 2}O{sub 2} calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.

  14. Robustness of dynamic systems with parameter uncertainties

    CERN Document Server

    Balemi, S; Truöl, W

    1992-01-01

    Robust Control is one of the fastest growing and promising areas of research today. In many practical systems there exist uncertainties which have to be considered in the analysis and design of control systems. In the last decade methods were developed for dealing with dynamic systems with unstructured uncertainties such as HOO_ and £I-optimal control. For systems with parameter uncertainties, the seminal paper of V. L. Kharitonov has triggered a large amount of very promising research. An international workshop dealing with all aspects of robust control was successfully organized by S. P. Bhattacharyya and L. H. Keel in San Antonio, Texas, USA in March 1991. We organized the second international workshop in this area in Ascona, Switzer­ land in April 1992. However, this second workshop was restricted to robust control of dynamic systems with parameter uncertainties with the objective to concentrate on some aspects of robust control. This book contains a collection of papers presented at the International W...

  15. Dynamics robustness of cascading systems.

    Directory of Open Access Journals (Sweden)

    Jonathan T Young

    2017-03-01

    Full Text Available A most important property of biochemical systems is robustness. Static robustness, e.g., homeostasis, is the insensitivity of a state against perturbations, whereas dynamics robustness, e.g., homeorhesis, is the insensitivity of a dynamic process. In contrast to the extensively studied static robustness, dynamics robustness, i.e., how a system creates an invariant temporal profile against perturbations, is little explored despite transient dynamics being crucial for cellular fates and are reported to be robust experimentally. For example, the duration of a stimulus elicits different phenotypic responses, and signaling networks process and encode temporal information. Hence, robustness in time courses will be necessary for functional biochemical networks. Based on dynamical systems theory, we uncovered a general mechanism to achieve dynamics robustness. Using a three-stage linear signaling cascade as an example, we found that the temporal profiles and response duration post-stimulus is robust to perturbations against certain parameters. Then analyzing the linearized model, we elucidated the criteria of when signaling cascades will display dynamics robustness. We found that changes in the upstream modules are masked in the cascade, and that the response duration is mainly controlled by the rate-limiting module and organization of the cascade's kinetics. Specifically, we found two necessary conditions for dynamics robustness in signaling cascades: 1 Constraint on the rate-limiting process: The phosphatase activity in the perturbed module is not the slowest. 2 Constraints on the initial conditions: The kinase activity needs to be fast enough such that each module is saturated even with fast phosphatase activity and upstream changes are attenuated. We discussed the relevance of such robustness to several biological examples and the validity of the above conditions therein. Given the applicability of dynamics robustness to a variety of systems, it

  16. Track filtering by robust neural network

    International Nuclear Information System (INIS)

    Baginyan, S.A.; Kisel', I.V.; Konotopskaya, E.V.; Ososkov, G.A.

    1993-01-01

    In the present paper we study the following problems of track information extraction by the artificial neural network (ANN) rotor model: providing initial ANN configuration by an algorithm general enough to be applicable for any discrete detector in- or out of a magnetic field; robustness to heavy contaminated raw data (up to 100% signal-to-noise ratio); stability to the growing event multiplicity. These problems were carried out by corresponding innovations of our model, namely: by a special one-dimensional histogramming, by multiplying weights by a specially designed robust multiplier, and by replacing the simulated annealing schedule by ANN dynamics with an optimally fixed temperature. Our approach is valid for both circular and straight (non-magnetic) tracks and tested on 2D simulated data contaminated by 100% noise points distributed uniformly. To be closer to some reality in our simulation, we keep parameters of the cylindrical spectrometer ARES. 12 refs.; 9 figs

  17. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  18. Robust facility location: Hedging against failures

    International Nuclear Information System (INIS)

    Hernandez, Ivan; Emmanuel Ramirez-Marquez, Jose; Rainwater, Chase; Pohl, Edward; Medal, Hugh

    2014-01-01

    While few companies would be willing to sacrifice day-to-day operations to hedge against disruptions, designing for robustness can yield solutions that perform well before and after failures have occurred. Through a multi-objective optimization approach this paper provides decision makers the option to trade-off total weighted distance before and after disruptions in the Facility Location Problem. Additionally, this approach allows decision makers to understand the impact on the opening of facilities on total distance and on system robustness (considering the system as the set of located facilities). This approach differs from previous studies in that hedging against failures is done without having to elicit facility failure probabilities concurrently without requiring the allocation of additional hardening/protections resources. The approach is applied to two datasets from the literature

  19. Robust Trust in Expert Testimony

    Directory of Open Access Journals (Sweden)

    Christian Dahlman

    2015-05-01

    Full Text Available The standard of proof in criminal trials should require that the evidence presented by the prosecution is robust. This requirement of robustness says that it must be unlikely that additional information would change the probability that the defendant is guilty. Robustness is difficult for a judge to estimate, as it requires the judge to assess the possible effect of information that the he or she does not have. This article is concerned with expert witnesses and proposes a method for reviewing the robustness of expert testimony. According to the proposed method, the robustness of expert testimony is estimated with regard to competence, motivation, external strength, internal strength and relevance. The danger of trusting non-robust expert testimony is illustrated with an analysis of the Thomas Quick Case, a Swedish legal scandal where a patient at a mental institution was wrongfully convicted for eight murders.

  20. Rwanda Journal of Health Sciences: Submissions

    African Journals Online (AJOL)

    Rwanda Journal of Health Sciences: Submissions ... in various health related fields including public health, allied health sciences, nursing ... Following the abstract, about 3 to 10 key words that will provide indexing references should be listed.

  1. Filing a Biotechnology Submission under TSCA

    Science.gov (United States)

    Section 5 of the Toxic Substances Control Act (TSCA) requires the submission of certain information to EPA if a person wishes to commercialize an intergeneric microorganism or Introduce such microorganisms into the environment for research purposes.

  2. African Journal of Infectious Diseases: Submissions

    African Journals Online (AJOL)

    Authors should submit data that have arisen from animal and human studies ... present the topic, state the scope of the experiments, indicate significant data .... The submission file is in Microsoft Word, RTF, or WordPerfect document file format.

  3. South African Journal of Animal Science: Submissions

    African Journals Online (AJOL)

    The South African Society for Animal Science (SASAS) welcomes the submission ... aspects of their products and their relationship to the social or physical environment. ... experimental data that have been analysed using statistical methods.

  4. International Journal of Health Research: Submissions

    African Journals Online (AJOL)

    International Journal of Health Research: Submissions ... The journal is devoted to the promotion of pharmaceutical sciences and related disciplines ... adverse drug events, medical and other life sciences, and related engineering fields).

  5. 2008 wind farm submission requirements

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2008-05-01

    Wind turbines used to generate electricity can have important visual, environmental and amenity impacts depending on their location. Planning can contribute to the provision of renewable energy by enabling wind energy development in appropriate locations in a manner that appropriately balances their environmental, social and economic benefits with any demonstrated visual, environmental and amenity impacts. The County of Bruce, Ontario and member municipalities are attempting to balance these matters in favour of an overall net community benefit. Planning applications need to include sufficient information and explanation to allow the approval authorities to come to a sound and timely decision, in order to facilitate a viable wind energy industry. This document outlined the County of Bruce application requirements in order to assist in the design and siting of proposed wind energy facilities and in preparing planning applications. The County of Bruce official plan and policies for large wind energy conversion systems (LWECS) were presented. Submissions requirements include an environmental screening report; a federal environmental assessment clearance; a prime agricultural land justification report; a general project description; turbine specifications; noise evaluation; shadow flicker calculations; visual effect modelling; NAV Canada/Transport Canada clearance; grid connections and routing; project staging; overshadowing study; electromagnetic interference study; turbine foundation drawings; and an environmental management plan. Other requirements include lot level maps; project area maps; sensitive receptor table; shadow flicker; stage 2 archaeological study; type certification; statement of planning policy conformity; and a submission checklist. 6 refs., 1 tab., 3 appendices.

  6. Robust Performance And Dissipation of Stochastic Control Systems

    DEFF Research Database (Denmark)

    Thygesen, Uffe Høgsbro

    and topology on the space of supply rates. For instance, we give conditions under which the available storage is a continuous convex function of the supply rate. Dissipation theory in the existing literature applies only to deterministic systems. This is unfortunate since robust control applications typically...... is a prototype of robust adaptive control problems. We show that the optimal (minimax) controller for this problem is finite dimensional but not based on certainty equivalence, and we discuss the heuristic certainty equivalence controller....

  7. Robust design of large-displacement compliant mechanisms

    DEFF Research Database (Denmark)

    Lazarov, Boyan Stefanov; Schevenels, M.; Sigmund, Ole

    2011-01-01

    The aim of this article is to introduce a new topology optimisation formulation for optimal robust design of Micro Electro Mechanical Systems. Mesh independence in topology optimisation is most often ensured by using filtering techniques, which result in transition grey regions difficult to inter...... in nearly black and white mechanism designs, robust with respect to uncertainties in the production process, i.e. without any hinges or small details which can create manufacturing difficulties....

  8. Muscle Synergy-Driven Robust Motion Control.

    Science.gov (United States)

    Min, Kyuengbo; Iwamoto, Masami; Kakei, Shinji; Kimpara, Hideyuki

    2018-04-01

    Humans are able to robustly maintain desired motion and posture under dynamically changing circumstances, including novel conditions. To accomplish this, the brain needs to optimize the synergistic control between muscles against external dynamic factors. However, previous related studies have usually simplified the control of multiple muscles using two opposing muscles, which are minimum actuators to simulate linear feedback control. As a result, they have been unable to analyze how muscle synergy contributes to motion control robustness in a biological system. To address this issue, we considered a new muscle synergy concept used to optimize the synergy between muscle units against external dynamic conditions, including novel conditions. We propose that two main muscle control policies synergistically control muscle units to maintain the desired motion against external dynamic conditions. Our assumption is based on biological evidence regarding the control of multiple muscles via the corticospinal tract. One of the policies is the group control policy (GCP), which is used to control muscle group units classified based on functional similarities in joint control. This policy is used to effectively resist external dynamic circumstances, such as disturbances. The individual control policy (ICP) assists the GCP in precisely controlling motion by controlling individual muscle units. To validate this hypothesis, we simulated the reinforcement of the synergistic actions of the two control policies during the reinforcement learning of feedback motion control. Using this learning paradigm, the two control policies were synergistically combined to result in robust feedback control under novel transient and sustained disturbances that did not involve learning. Further, by comparing our data to experimental data generated by human subjects under the same conditions as those of the simulation, we showed that the proposed synergy concept may be used to analyze muscle synergy

  9. Robust Learning Control Design for Quantum Unitary Transformations.

    Science.gov (United States)

    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.

  10. 76 FR 62421 - Submission for OMB Review; Comment Request; A Generic Submission for Theory Development and...

    Science.gov (United States)

    2011-10-07

    ...; Comment Request; A Generic Submission for Theory Development and Validation (NCI) SUMMARY: Under the... control number. Proposed Collection: Title: A Generic Submission for Theory Development and Validation...), to conduct and support behavioral research informed by and informing theory. Formative research in...

  11. 75 FR 33798 - Submission for OMB Review; Comment Request

    Science.gov (United States)

    2010-06-15

    ... Management, publishes that notice containing proposed information collection requests prior to submission of.... SUMMARY: The Director, Information Collection Clearance Division, Regulatory Information Management Services, Office of Management invites comments on the submission for OMB review as required by the...

  12. 75 FR 16762 - Submission for OMB Review; Comment Request

    Science.gov (United States)

    2010-04-02

    ... Management, publishes that notice containing proposed information collection requests prior to submission of.... SUMMARY: The Director, Information Collection Clearance Division, Regulatory Information Management Services, Office of Management invites comments on the submission for OMB review as required by the...

  13. 21 CFR 13.20 - Submissions to a Board.

    Science.gov (United States)

    2010-04-01

    ... to be filed with the Division of Dockets Management under § 10.20. (b) The person making a submission... 13.45. Submissions of documentary data and information need not be sent to each participant, but any...

  14. 77 FR 37870 - Submission for OMB Review; Comment Request

    Science.gov (United States)

    2012-06-25

    ... DEPARTMENT OF COMMERCE Submission for OMB Review; Comment Request The Department of Commerce will...: National Telecommunications and Information Administration (NTIA). Title: Computer and Internet Use...): None. Type of Request: Regular submission (Reinstatement with change of a previously approved...

  15. 76 FR 71621 - Submission for OMB Review; Comment Request

    Science.gov (United States)

    2011-11-18

    ... publication of this notice. Copies of the submission(s) may be obtained by calling the Treasury Bureau... Clearance Officer: Yvonne Pollard, United States Mint, 799 9th Street NW., 4th Floor, Washington, DC 20220...

  16. Robust Transceiver Design for Multiuser MIMO Downlink with Channel Uncertainties

    Science.gov (United States)

    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.

  17. Assessment of a robust model protocol with accelerated throughput for a human recombinant full length estrogen receptor-alpha binding assay: protocol optimization and intralaboratory assay performance as initial steps towards validation.

    Science.gov (United States)

    Freyberger, Alexius; Wilson, Vickie; Weimer, Marc; Tan, Shirlee; Tran, Hoai-Son; Ahr, Hans-Jürgen

    2010-08-01

    Despite about two decades of research in the field of endocrine active compounds, still no validated human recombinant (hr) estrogen receptor-alpha (ERalpha) binding assay is available, although hr-ERalpha is available from several sources. In a joint effort, US EPA and Bayer Schering Pharma with funding from the EU-sponsored 6th framework project, ReProTect, developed a model protocol for such a binding assay. Important features of this assay are the use of a full length hr-ERalpha and performance in a 96-well plate format. A full length hr-ERalpha was chosen, as it was considered to provide the most accurate and human-relevant results, whereas truncated receptors could perform differently. Besides three reference compounds [17beta-estradiol, norethynodrel, dibutylphthalate] nine test compounds with different affinities for the ERalpha [diethylstilbestrol (DES), ethynylestradiol, meso-hexestrol, equol, genistein, o,p'-DDT, nonylphenol, n-butylparaben, and corticosterone] were used to explore the performance of the assay. Three independent experiments per compound were performed on different days, and dilutions of test compounds from deep-frozen stocks, solutions of radiolabeled ligand and receptor preparation were freshly prepared for each experiment. The ERalpha binding properties of reference and test compounds were well detected. As expected dibutylphthalate and corticosterone were non-binders in this assay. In terms of the relative ranking of binding affinities, there was good agreement with published data obtained from experiments using a human recombinant ERalpha ligand binding domain. Irrespective of the chemical nature of the compound, individual IC(50)-values for a given compound varied by not more than a factor of 2.5. Our data demonstrate that the assay was robust and reliably ranked compounds with strong, weak, and no affinity for the ERalpha with high accuracy. It avoids the manipulation and use of animals, i.e., the preparation of uterine cytosol as

  18. Robust and distributed hypothesis testing

    CERN Document Server

    Gül, Gökhan

    2017-01-01

    This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the boo...

  19. Robustness of IPTV business models

    NARCIS (Netherlands)

    Bouwman, H.; Zhengjia, M.; Duin, P. van der; Limonard, S.

    2008-01-01

    The final stage in the STOF method is an evaluation of the robustness of the design, for which the method provides some guidelines. For many innovative services, the future holds numerous uncertainties, which makes evaluating the robustness of a business model a difficult task. In this chapter, we

  20. Robustness Evaluation of Timber Structures

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard

    2009-01-01

    Robustness of structural systems has obtained a renewed interest due to a much more frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure.......Robustness of structural systems has obtained a renewed interest due to a much more frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure....

  1. 21 CFR 12.80 - Filing and service of submissions.

    Science.gov (United States)

    2010-04-01

    ...) Submissions, including pleadings in a hearing, are to be filed with the Division of Dockets Management under... Dockets Management. When this part allows a response to a submission and prescribes a period of time for... participants. Submissions of documentary data and information are not required to be served on each participant...

  2. 7 CFR 3403.9 - Submission of proposals.

    Science.gov (United States)

    2010-01-01

    ... Submission and Evaluation of Proposals § 3403.9 Submission of proposals. The SBIR program solicitation for Phase I proposals and the correspondence requesting Phase II proposals will provide the deadline date... 7 Agriculture 15 2010-01-01 2010-01-01 false Submission of proposals. 3403.9 Section 3403.9...

  3. 48 CFR 1842.7101 - Submission of vouchers.

    Science.gov (United States)

    2010-10-01

    ..., Submission of Vouchers for Payment. (b) The auditor shall retain an unpaid copy of the voucher. (c) When a... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Submission of vouchers... ADMINISTRATION CONTRACT MANAGEMENT CONTRACT ADMINISTRATION AND AUDIT SERVICES Submission of Vouchers 1842.7101...

  4. Robust statistical methods with R

    CERN Document Server

    Jureckova, Jana

    2005-01-01

    Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on practical application.The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands-on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It...

  5. Robust video object cosegmentation.

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing; Li, Xuelong; Porikli, Fatih

    2015-10-01

    With ever-increasing volumes of video data, automatic extraction of salient object regions became even more significant for visual analytic solutions. This surge has also opened up opportunities for taking advantage of collective cues encapsulated in multiple videos in a cooperative manner. However, it also brings up major challenges, such as handling of drastic appearance, motion pattern, and pose variations, of foreground objects as well as indiscriminate backgrounds. Here, we present a cosegmentation framework to discover and segment out common object regions across multiple frames and multiple videos in a joint fashion. We incorporate three types of cues, i.e., intraframe saliency, interframe consistency, and across-video similarity into an energy optimization framework that does not make restrictive assumptions on foreground appearance and motion model, and does not require objects to be visible in all frames. We also introduce a spatio-temporal scale-invariant feature transform (SIFT) flow descriptor to integrate across-video correspondence from the conventional SIFT-flow into interframe motion flow from optical flow. This novel spatio-temporal SIFT flow generates reliable estimations of common foregrounds over the entire video data set. Experimental results show that our method outperforms the state-of-the-art on a new extensive data set (ViCoSeg).

  6. Designing Phononic Crystals with Wide and Robust Band Gaps

    Science.gov (United States)

    Jia, Zian; Chen, Yanyu; Yang, Haoxiang; Wang, Lifeng

    2018-04-01

    Phononic crystals (PnCs) engineered to manipulate and control the propagation of mechanical waves have enabled the design of a range of novel devices, such as waveguides, frequency modulators, and acoustic cloaks, for which wide and robust phononic band gaps are highly preferable. While numerous PnCs have been designed in recent decades, to the best of our knowledge, PnCs that possess simultaneous wide and robust band gaps (to randomness and deformations) have not yet been reported. Here, we demonstrate that by combining the band-gap formation mechanisms of Bragg scattering and local resonances (the latter one is dominating), PnCs with wide and robust phononic band gaps can be established. The robustness of the phononic band gaps are then discussed from two aspects: robustness to geometric randomness (manufacture defects) and robustness to deformations (mechanical stimuli). Analytical formulations further predict the optimal design parameters, and an uncertainty analysis quantifies the randomness effect of each designing parameter. Moreover, we show that the deformation robustness originates from a local resonance-dominant mechanism together with the suppression of structural instability. Importantly, the proposed PnCs require only a small number of layers of elements (three unit cells) to obtain broad, robust, and strong attenuation bands, which offer great potential in designing flexible and deformable phononic devices.

  7. Designing Phononic Crystals with Wide and Robust Band Gaps

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yanyu [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jia, Zian [State University of New York at Stony Brook; Yang, Haoxiang [State University of New York at Stony Brook; Wang, Lifeng [State University of New York at Stony Brook

    2018-04-16

    Phononic crystals (PnCs) engineered to manipulate and control the propagation of mechanical waves have enabled the design of a range of novel devices, such as waveguides, frequency modulators, and acoustic cloaks, for which wide and robust phononic band gaps are highly preferable. While numerous PnCs have been designed in recent decades, to the best of our knowledge, PnCs that possess simultaneous wide and robust band gaps (to randomness and deformations) have not yet been reported. Here, we demonstrate that by combining the band-gap formation mechanisms of Bragg scattering and local resonances (the latter one is dominating), PnCs with wide and robust phononic band gaps can be established. The robustness of the phononic band gaps are then discussed from two aspects: robustness to geometric randomness (manufacture defects) and robustness to deformations (mechanical stimuli). Analytical formulations further predict the optimal design parameters, and an uncertainty analysis quantifies the randomness effect of each designing parameter. Moreover, we show that the deformation robustness originates from a local resonance-dominant mechanism together with the suppression of structural instability. Importantly, the proposed PnCs require only a small number of layers of elements (three unit cells) to obtain broad, robust, and strong attenuation bands, which offer great potential in designing flexible and deformable phononic devices.

  8. DIRAC optimized workload management

    CERN Document Server

    Paterson, S K

    2008-01-01

    The LHCb DIRAC Workload and Data Management System employs advanced optimization techniques in order to dynamically allocate resources. The paradigms realized by DIRAC, such as late binding through the Pilot Agent approach, have proven to be highly successful. For example, this has allowed the principles of workload management to be applied not only at the time of user job submission to the Grid but also to optimize the use of computing resources once jobs have been acquired. Along with the central application of job priorities, DIRAC minimizes the system response time for high priority tasks. This paper will describe the recent developments to support Monte Carlo simulation, data processing and distributed user analysis in a consistent way across disparate compute resources including individual PCs, local batch systems, and the Worldwide LHC Computing Grid. The Grid environment is inherently unpredictable and whilst short-term studies have proven to deliver high job efficiencies, the system performance over ...

  9. Robust Path Planning and Feedback Design Under Stochastic Uncertainty

    Science.gov (United States)

    Blackmore, Lars

    2008-01-01

    Autonomous vehicles require optimal path planning algorithms to achieve mission goals while avoiding obstacles and being robust to uncertainties. The uncertainties arise from exogenous disturbances, modeling errors, and sensor noise, which can be characterized via stochastic models. Previous work defined a notion of robustness in a stochastic setting by using the concept of chance constraints. This requires that mission constraint violation can occur with a probability less than a prescribed value.In this paper we describe a novel method for optimal chance constrained path planning with feedback design. The approach optimizes both the reference trajectory to be followed and the feedback controller used to reject uncertainty. Our method extends recent results in constrained control synthesis based on convex optimization to solve control problems with nonconvex constraints. This extension is essential for path planning problems, which inherently have nonconvex obstacle avoidance constraints. Unlike previous approaches to chance constrained path planning, the new approach optimizes the feedback gain as wellas the reference trajectory.The key idea is to couple a fast, nonconvex solver that does not take into account uncertainty, with existing robust approaches that apply only to convex feasible regions. By alternating between robust and nonrobust solutions, the new algorithm guarantees convergence to a global optimum. We apply the new method to an unmanned aircraft and show simulation results that demonstrate the efficacy of the approach.

  10. Robust bearing estimation for 3-component stations

    International Nuclear Information System (INIS)

    CLAASSEN, JOHN P.

    2000-01-01

    A robust bearing estimation process for 3-component stations has been developed and explored. The method, called SEEC for Search, Estimate, Evaluate and Correct, intelligently exploits the inherent information in the arrival at every step of the process to achieve near-optimal results. In particular the approach uses a consistent framework to define the optimal time-frequency windows on which to make estimates, to make the bearing estimates themselves, to construct metrics helpful in choosing the better estimates or admitting that the bearing is immeasurable, and finally to apply bias corrections when calibration information is available to yield a single final estimate. The algorithm was applied to a small but challenging set of events in a seismically active region. It demonstrated remarkable utility by providing better estimates and insights than previously available. Various monitoring implications are noted from these findings

  11. Engineering Robustness of Microbial Cell Factories.

    Science.gov (United States)

    Gong, Zhiwei; Nielsen, Jens; Zhou, Yongjin J

    2017-10-01

    Metabolic engineering and synthetic biology offer great prospects in developing microbial cell factories capable of converting renewable feedstocks into fuels, chemicals, food ingredients, and pharmaceuticals. However, prohibitively low production rate and mass concentration remain the major hurdles in industrial processes even though the biosynthetic pathways are comprehensively optimized. These limitations are caused by a variety of factors unamenable for host cell survival, such as harsh industrial conditions, fermentation inhibitors from biomass hydrolysates, and toxic compounds including metabolic intermediates and valuable target products. Therefore, engineered microbes with robust phenotypes is essential for achieving higher yield and productivity. In this review, the recent advances in engineering robustness and tolerance of cell factories is described to cope with these issues and briefly introduce novel strategies with great potential to enhance the robustness of cell factories, including metabolic pathway balancing, transporter engineering, and adaptive laboratory evolution. This review also highlights the integration of advanced systems and synthetic biology principles toward engineering the harmony of overall cell function, more than the specific pathways or enzymes. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Tension and robustness in multitasking cellular networks.

    Directory of Open Access Journals (Sweden)

    Jeffrey V Wong

    Full Text Available Cellular networks multitask by exhibiting distinct, context-dependent dynamics. However, network states (parameters that generate a particular dynamic are often sub-optimal for others, defining a source of "tension" between them. Though multitasking is pervasive, it is not clear where tension arises, what consequences it has, and how it is resolved. We developed a generic computational framework to examine the source and consequences of tension between pairs of dynamics exhibited by the well-studied RB-E2F switch regulating cell cycle entry. We found that tension arose from task-dependent shifts in parameters associated with network modules. Although parameter sets common to distinct dynamics did exist, tension reduced both their accessibility and resilience to perturbation, indicating a trade-off between "one-size-fits-all" solutions and robustness. With high tension, robustness can be preserved by dynamic shifting of modules, enabling the network to toggle between tasks, and by increasing network complexity, in this case by gene duplication. We propose that tension is a general constraint on the architecture and operation of multitasking biological networks. To this end, our work provides a framework to quantify the extent of tension between any network dynamics and how it affects network robustness. Such analysis would suggest new ways to interfere with network elements to elucidate the design principles of cellular networks.

  13. Hyperspectral Unmixing with Robust Collaborative Sparse Regression

    Directory of Open Access Journals (Sweden)

    Chang Li

    2016-07-01

    Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

  14. Robust loss functions for boosting.

    Science.gov (United States)

    Kanamori, Takafumi; Takenouchi, Takashi; Eguchi, Shinto; Murata, Noboru

    2007-08-01

    Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.

  15. Theoretical Framework for Robustness Evaluation

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2011-01-01

    This paper presents a theoretical framework for evaluation of robustness of structural systems, incl. bridges and buildings. Typically modern structural design codes require that ‘the consequence of damages to structures should not be disproportional to the causes of the damages’. However, although...... the importance of robustness for structural design is widely recognized the code requirements are not specified in detail, which makes the practical use difficult. This paper describes a theoretical and risk based framework to form the basis for quantification of robustness and for pre-normative guidelines...

  16. Robustness of airline route networks

    Science.gov (United States)

    Lordan, Oriol; Sallan, Jose M.; Escorihuela, Nuria; Gonzalez-Prieto, David

    2016-03-01

    Airlines shape their route network by defining their routes through supply and demand considerations, paying little attention to network performance indicators, such as network robustness. However, the collapse of an airline network can produce high financial costs for the airline and all its geographical area of influence. The aim of this study is to analyze the topology and robustness of the network route of airlines following Low Cost Carriers (LCCs) and Full Service Carriers (FSCs) business models. Results show that FSC hubs are more central than LCC bases in their route network. As a result, LCC route networks are more robust than FSC networks.

  17. Robust estimation of hydrological model parameters

    Directory of Open Access Journals (Sweden)

    A. Bárdossy

    2008-11-01

    Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

  18. Identifying multiple submissions in Internet research: preserving data integrity.

    Science.gov (United States)

    Bowen, Anne M; Daniel, Candice M; Williams, Mark L; Baird, Grayson L

    2008-11-01

    Internet-based sexuality research with hidden populations has become increasingly popular. Respondent anonymity may encourage participation and lower social desirability, but associated disinhibition may promote multiple submissions, especially when incentives are offered. The goal of this study was to identify the usefulness of different variables for detecting multiple submissions from repeat responders and to explore incentive effects. The data included 1,900 submissions from a three-session Internet intervention with a pretest and three post-test questionnaires. Participants were men who have sex with men and incentives were offered to rural participants for completing each questionnaire. The final number of submissions included 1,273 "unique", 132 first submissions by "repeat responders" and 495 additional submissions by the "repeat responders" (N = 1,900). Four categories of repeat responders were identified: "infrequent" (2-5 submissions), "persistent" (6-10 submissions), "very persistent" (11-30 submissions), and "hackers" (more than 30 submissions). Internet Provider (IP) addresses, user names, and passwords were the most useful for identifying "infrequent" repeat responders. "Hackers" often varied their IP address and identifying information to prevent easy identification, but investigating the data for small variations in IP, using reverse telephone look up, and patterns across usernames and passwords were helpful. Incentives appeared to play a role in stimulating multiple submissions, especially from the more sophisticated "hackers". Finally, the web is ever evolving and it will be necessary to have good programmers and staff who evolve as fast as "hackers".

  19. Robust methods for data reduction

    CERN Document Server

    Farcomeni, Alessio

    2015-01-01

    Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis.The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analy

  20. A robust data-driven approach for gene ontology annotation.

    Science.gov (United States)

    Li, Yanpeng; Yu, Hong

    2014-01-01

    Gene ontology (GO) and GO annotation are important resources for biological information management and knowledge discovery, but the speed of manual annotation became a major bottleneck of database curation. BioCreative IV GO annotation task aims to evaluate the performance of system that automatically assigns GO terms to genes based on the narrative sentences in biomedical literature. This article presents our work in this task as well as the experimental results after the competition. For the evidence sentence extraction subtask, we built a binary classifier to identify evidence sentences using reference distance estimator (RDE), a recently proposed semi-supervised learning method that learns new features from around 10 million unlabeled sentences, achieving an F1 of 19.3% in exact match and 32.5% in relaxed match. In the post-submission experiment, we obtained 22.1% and 35.7% F1 performance by incorporating bigram features in RDE learning. In both development and test sets, RDE-based method achieved over 20% relative improvement on F1 and AUC performance against classical supervised learning methods, e.g. support vector machine and logistic regression. For the GO term prediction subtask, we developed an information retrieval-based method to retrieve the GO term most relevant to each evidence sentence using a ranking function that combined cosine similarity and the frequency of GO terms in documents, and a filtering method based on high-level GO classes. The best performance of our submitted runs was 7.8% F1 and 22.2% hierarchy F1. We found that the incorporation of frequency information and hierarchy filtering substantially improved the performance. In the post-submission evaluation, we obtained a 10.6% F1 using a simpler setting. Overall, the experimental analysis showed our approaches were robust in both the two tasks. © The Author(s) 2014. Published by Oxford University Press.