Optimal blood glucose control in diabetes mellitus treatment using dynamic programming based on Ackerman’s linear model

NASA Astrophysics Data System (ADS)

Pradanti, Paskalia; Hartono

2018-03-01

Determination of insulin injection dose in diabetes mellitus treatment can be considered as an optimal control problem. This article is aimed to simulate optimal blood glucose control for patient with diabetes mellitus. The blood glucose regulation of diabetic patient is represented by Ackerman’s Linear Model. This problem is then solved using dynamic programming method. The desired blood glucose level is obtained by minimizing the performance index in Lagrange form. The results show that dynamic programming based on Ackerman’s Linear Model is quite good to solve the problem.

Investigation on the use of optimization techniques for helicopter airframe vibrations design studies

NASA Technical Reports Server (NTRS)

Sreekanta Murthy, T.

1992-01-01

Results of the investigation of formal nonlinear programming-based numerical optimization techniques of helicopter airframe vibration reduction are summarized. The objective and constraint function and the sensitivity expressions used in the formulation of airframe vibration optimization problems are presented and discussed. Implementation of a new computational procedure based on MSC/NASTRAN and CONMIN in a computer program system called DYNOPT for optimizing airframes subject to strength, frequency, dynamic response, and dynamic stress constraints is described. An optimization methodology is proposed which is thought to provide a new way of applying formal optimization techniques during the various phases of the airframe design process. Numerical results obtained from the application of the DYNOPT optimization code to a helicopter airframe are discussed.

AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm.

PubMed

Xi, Shibo; Borgna, Lucas Santiago; Zheng, Lirong; Du, Yonghua; Hu, Tiandou

2017-01-01

In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.

Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent.

PubMed

Yao, Rui; Templeton, Alistair K; Liao, Yixiang; Turian, Julius V; Kiel, Krystyna D; Chu, James C H

2014-01-01

To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer. Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program. The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions. Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA. Copyright © 2014 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

NASA Astrophysics Data System (ADS)

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios

2016-06-01

In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.

Program manual for ASTOP, an Arbitrary space trajectory optimization program

NASA Technical Reports Server (NTRS)

Horsewood, J. L.

1974-01-01

The ASTOP program (an Arbitrary Space Trajectory Optimization Program) designed to generate optimum low-thrust trajectories in an N-body field while satisfying selected hardware and operational constraints is presented. The trajectory is divided into a number of segments or arcs over which the control is held constant. This constant control over each arc is optimized using a parameter optimization scheme based on gradient techniques. A modified Encke formulation of the equations of motion is employed. The program provides a wide range of constraint, end conditions, and performance index options. The basic approach is conducive to future expansion of features such as the incorporation of new constraints and the addition of new end conditions.

Nonlinear program based optimization of boost and buck-boost converter designs

NASA Astrophysics Data System (ADS)

Rahman, S.; Lee, F. C.

The facility of an Augmented Lagrangian (ALAG) multiplier based nonlinear programming technique is demonstrated for minimum-weight design optimizations of boost and buck-boost power converters. Certain important features of ALAG are presented in the framework of a comprehensive design example for buck-boost power converter design optimization. The study provides refreshing design insight of power converters and presents such information as weight and loss profiles of various semiconductor components and magnetics as a function of the switching frequency.

Numerical optimization of Ignition and Growth reactive flow modeling for PAX2A

NASA Astrophysics Data System (ADS)

Baker, E. L.; Schimel, B.; Grantham, W. J.

1996-05-01

Variable metric nonlinear optimization has been successfully applied to the parameterization of unreacted and reacted products thermodynamic equations of state and reactive flow modeling of the HMX based high explosive PAX2A. The NLQPEB nonlinear optimization program has been recently coupled to the LLNL developed two-dimensional high rate continuum modeling programs DYNA2D and CALE. The resulting program has the ability to optimize initial modeling parameters. This new optimization capability was used to optimally parameterize the Ignition and Growth reactive flow model to experimental manganin gauge records. The optimization varied the Ignition and Growth reaction rate model parameters in order to minimize the difference between the calculated pressure histories and the experimental pressure histories.

Structural optimization: Status and promise

NASA Astrophysics Data System (ADS)

Kamat, Manohar P.

Chapters contained in this book include fundamental concepts of optimum design, mathematical programming methods for constrained optimization, function approximations, approximate reanalysis methods, dual mathematical programming methods for constrained optimization, a generalized optimality criteria method, and a tutorial and survey of multicriteria optimization in engineering. Also included are chapters on the compromise decision support problem and the adaptive linear programming algorithm, sensitivity analyses of discrete and distributed systems, the design sensitivity analysis of nonlinear structures, optimization by decomposition, mixed elements in shape sensitivity analysis of structures based on local criteria, and optimization of stiffened cylindrical shells subjected to destabilizing loads. Other chapters are on applications to fixed-wing aircraft and spacecraft, integrated optimum structural and control design, modeling concurrency in the design of composite structures, and tools for structural optimization. (No individual items are abstracted in this volume)

A thermal vacuum test optimization procedure

NASA Technical Reports Server (NTRS)

Kruger, R.; Norris, H. P.

1979-01-01

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

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

A nonlinear bi-level programming approach for product portfolio management.

PubMed

Ma, Shuang

2016-01-01

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today's competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors' actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader-follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader-follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader-follower bi-level optimization model is robust and can empower product portfolio optimization.

Effectiveness of an Individualized Training Based on Force-Velocity Profiling during Jumping

PubMed Central

Jiménez-Reyes, Pedro; Samozino, Pierre; Brughelli, Matt; Morin, Jean-Benoît

2017-01-01

Ballistic performances are determined by both the maximal lower limb power output (Pmax) and their individual force-velocity (F-v) mechanical profile, especially the F-v imbalance (FVimb): difference between the athlete's actual and optimal profile. An optimized training should aim to increase Pmax and/or reduce FVimb. The aim of this study was to test whether an individualized training program based on the individual F-v profile would decrease subjects' individual FVimb and in turn improve vertical jump performance. FVimb was used as the reference to assign participants to different training intervention groups. Eighty four subjects were assigned to three groups: an “optimized” group divided into velocity-deficit, force-deficit, and well-balanced sub-groups based on subjects' FVimb, a “non-optimized” group for which the training program was not specifically based on FVimb and a control group. All subjects underwent a 9-week specific resistance training program. The programs were designed to reduce FVimb for the optimized groups (with specific programs for sub-groups based on individual FVimb values), while the non-optimized group followed a classical program exactly similar for all subjects. All subjects in the three optimized training sub-groups (velocity-deficit, force-deficit, and well-balanced) increased their jumping performance (12.7 ± 5.7% ES = 0.93 ± 0.09, 14.2 ± 7.3% ES = 1.00 ± 0.17, and 7.2 ± 4.5% ES = 0.70 ± 0.36, respectively) with jump height improvement for all subjects, whereas the results were much more variable and unclear in the non-optimized group. This greater change in jump height was associated with a markedly reduced FVimb for both force-deficit (57.9 ± 34.7% decrease in FVimb) and velocity-deficit (20.1 ± 4.3%) subjects, and unclear or small changes in Pmax (−0.40 ± 8.4% and +10.5 ± 5.2%, respectively). An individualized training program specifically based on FVimb (gap between the actual and optimal F-v profiles of each individual) was more efficient at improving jumping performance (i.e., unloaded squat jump height) than a traditional resistance training common to all subjects regardless of their FVimb. Although improving both FVimb and Pmax has to be considered to improve ballistic performance, the present results showed that reducing FVimb without even increasing Pmax lead to clearly beneficial jump performance changes. Thus, FVimb could be considered as a potentially useful variable for prescribing optimal resistance training to improve ballistic performance. PMID:28119624

Optimization of fuel-cell tram operation based on two dimension dynamic programming

NASA Astrophysics Data System (ADS)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Optimizing violence prevention programs: an examination of program effectiveness among urban high school students.

PubMed

Thompkins, Amanda C; Chauveron, Lisa M; Harel, Ofer; Perkins, Daniel F

2014-07-01

While demand for youth violence prevention programs increases, the ability of the school-day schedule to accommodate their time requirements has diminished. Viable school-based prevention programs must strike a balance between brevity and effectiveness. This article reports results from an effectiveness trial of a 12-session curriculum-based universal violence prevention program that promotes healthy conflict resolution skills among urban adolescents. Using a review of program record data and a multisite quasi-experimental study design, we examined the effectiveness of a New York City-based violence prevention program entitled the Violence Prevention project (VPP) optimized to meet school needs. We analyzed survey data from 1112 9th- and 10th-grade students in 13 New York City public high schools across 4 consecutive school years. Both participants and nonparticipants were surveyed. Review of program record data indicated that the program was implemented with acceptable fidelity to the core component structure, and that participant responsiveness to the model was high. Multilevel modeling indicated that VPP participation was protective for academic self-concept and promoted conflict resolution skills. Findings indicate that semester-long violence prevention programs optimized to meet the needs of a typical high school can be effective at promoting healthy conflict resolution skills in urban adolescents. © 2014, American School Health Association.

DE and NLP Based QPLS Algorithm

NASA Astrophysics Data System (ADS)

Yu, Xiaodong; Huang, Dexian; Wang, Xiong; Liu, Bo

As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

A Language for Specifying Compiler Optimizations for Generic Software

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

Willcock, Jeremiah J.

2007-01-01

Compiler optimization is important to software performance, and modern processor architectures make optimization even more critical. However, many modern software applications use libraries providing high levels of abstraction. Such libraries often hinder effective optimization — the libraries are difficult to analyze using current compiler technology. For example, high-level libraries often use dynamic memory allocation and indirectly expressed control structures, such as iteratorbased loops. Programs using these libraries often cannot achieve an optimal level of performance. On the other hand, software libraries have also been recognized as potentially aiding in program optimization. One proposed implementation of library-based optimization is to allowmore » the library author, or a library user, to define custom analyses and optimizations. Only limited systems have been created to take advantage of this potential, however. One problem in creating a framework for defining new optimizations and analyses is how users are to specify them: implementing them by hand inside a compiler is difficult and prone to errors. Thus, a domain-specific language for librarybased compiler optimizations would be beneficial. Many optimization specification languages have appeared in the literature, but they tend to be either limited in power or unnecessarily difficult to use. Therefore, I have designed, implemented, and evaluated the Pavilion language for specifying program analyses and optimizations, designed for library authors and users. These analyses and optimizations can be based on the implementation of a particular library, its use in a specific program, or on the properties of a broad range of types, expressed through concepts. The new system is intended to provide a high level of expressiveness, even though the intended users are unlikely to be compiler experts.« less

How to formulate and solve "optimal stand density over time" problems for even-aged stands using dynamic programming.

Treesearch

Chung M. Chen; Dietmar W. Rose; Rolfe A. Leary

1980-01-01

Describes how dynamic programming can be used to solve optimal stand density problems when yields are given by prior simulation or by a new stand growth equation that is a function of the decision variable. Formulations of the latter type allow use of a calculus-based search procedure; they determine exact optimal residual density at each stage.

PharmDock: a pharmacophore-based docking program

PubMed Central

2014-01-01

Background Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. Results Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking. Conclusion A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available from http://people.pharmacy.purdue.edu/~mlill/software/pharmdock. PMID:24739488

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

PubMed

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 tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull-based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV-based optimization, and the NLP-PTV-based optimization outperformed the LP-PTV-based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP-based methods had notably fewer scanning spots than did those generated using NLP-based methods. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

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

The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

A hybrid nonlinear programming method for design optimization

NASA Technical Reports Server (NTRS)

Rajan, S. D.

1986-01-01

Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.

Power Grid Construction Project Portfolio Optimization Based on Bi-level programming model

NASA Astrophysics Data System (ADS)

Zhao, Erdong; Li, Shangqi

2017-08-01

As the main body of power grid operation, county-level power supply enterprises undertake an important emission to guarantee the security of power grid operation and safeguard social power using order. The optimization of grid construction projects has been a key issue of power supply capacity and service level of grid enterprises. According to the actual situation of power grid construction project optimization of county-level power enterprises, on the basis of qualitative analysis of the projects, this paper builds a Bi-level programming model based on quantitative analysis. The upper layer of the model is the target restriction of the optimal portfolio; the lower layer of the model is enterprises’ financial restrictions on the size of the enterprise project portfolio. Finally, using a real example to illustrate operation proceeding and the optimization result of the model. Through qualitative analysis and quantitative analysis, the bi-level programming model improves the accuracy and normative standardization of power grid enterprises projects.

Optimal Control Strategy Design Based on Dynamic Programming for a Dual-Motor Coupling-Propulsion System

PubMed Central

Zhang, Shuo; Zhang, Chengning; Han, Guangwei; Wang, Qinghui

2014-01-01

A dual-motor coupling-propulsion electric bus (DMCPEB) is modeled, and its optimal control strategy is studied in this paper. The necessary dynamic features of energy loss for subsystems is modeled. Dynamic programming (DP) technique is applied to find the optimal control strategy including upshift threshold, downshift threshold, and power split ratio between the main motor and auxiliary motor. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement in reducing energy loss due to the dual-motor coupling-propulsion system (DMCPS) running is realized without increasing the frequency of the mode switch. PMID:25540814

Optimal control strategy design based on dynamic programming for a dual-motor coupling-propulsion system.

PubMed

Zhang, Shuo; Zhang, Chengning; Han, Guangwei; Wang, Qinghui

2014-01-01

A dual-motor coupling-propulsion electric bus (DMCPEB) is modeled, and its optimal control strategy is studied in this paper. The necessary dynamic features of energy loss for subsystems is modeled. Dynamic programming (DP) technique is applied to find the optimal control strategy including upshift threshold, downshift threshold, and power split ratio between the main motor and auxiliary motor. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement in reducing energy loss due to the dual-motor coupling-propulsion system (DMCPS) running is realized without increasing the frequency of the mode switch.

Optimal blood glucose level control using dynamic programming based on minimal Bergman model

NASA Astrophysics Data System (ADS)

Rettian Anggita Sari, Maria; Hartono

2018-03-01

The purpose of this article is to simulate the glucose dynamic and the insulin kinetic of diabetic patient. The model used in this research is a non-linear Minimal Bergman model. Optimal control theory is then applied to formulate the problem in order to determine the optimal dose of insulin in the treatment of diabetes mellitus such that the glucose level is in the normal range for some specific time range. The optimization problem is solved using dynamic programming. The result shows that dynamic programming is quite reliable to represent the interaction between glucose and insulin levels in diabetes mellitus patient.

Impact of electronic health record-based, pharmacist-driven valganciclovir dose optimization in solid organ transplant recipients.

PubMed

Hensler, David; Richardson, Chad L; Brown, Joslyn; Tseng, Christine; DeCamp, Phyllis J; Yang, Amy; Pawlowski, Anna; Ho, Bing; Ison, Michael G

2018-04-01

Prophylaxis with valganciclovir reduces the incidence of cytomegalovirus (CMV) infection following solid organ transplant (SOT). Under-dosing of valganciclovir is associated with an increased risk of CMV infection and development of ganciclovir-resistant CMV. An automated electronic health record (EHR)-based, pharmacist-driven program was developed to optimize dosing of valganciclovir in solid organ transplant recipients at a large transplant center. Two cohorts of kidney, pancreas-kidney, and liver transplant recipients from our center pre-implementation (April 2011-March 2012, n = 303) and post-implementation of the optimization program (September 2012-August 2013, n=263) had demographic and key outcomes data collected for 1 year post-transplant. The 1-year incidence of CMV infection dropped from 56 (18.5%) to 32 (12.2%, P = .05) and the incidence of breakthrough infections on prophylaxis was cut in half (61% vs 34%, P = .03) after implementation of the dose optimization program. The hazard ratio of developing CMV was 1.64 (95% CI 1.06-2.60, P = .027) for the pre-implementation group after adjusting for potential confounders. The program also resulted in a numerical reduction in the number of ganciclovir-resistant CMV cases (2 [0.7%] pre-implementation vs 0 post-implementation). An EHR-based, pharmacist-driven valganciclovir dose optimization program was associated with reduction in CMV infections. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Site Optimization Tracker: Crossley Farm Superfund Site, Hereford and Washington Townships, Berks County, Pennsylvania

EPA Pesticide Factsheets

Pilot Region-Based Optimization Program for Fund-Lead Sites in EPA Region 3. Site Optimization Tracking information for Crossley Farm Superfund Site, Hereford and Washington Townships, Berks County, Pennsylvania.

Optimization-Based Selection of Influential Agents in a Rural Afghan Social Network

DTIC Science & Technology

2010-06-01

nonlethal targeting model, a nonlinear programming ( NLP ) optimization formulation that identifies the k US agent assignment strategy producing the greatest...leader social network, and 3) the nonlethal targeting model, a nonlinear programming ( NLP ) optimization formulation that identifies the k US agent...NATO Coalition in Afghanistan. 55 for Afghanistan ( [54], [31], [48], [55], [30]). While Arab tribes tend to be more hierarchical, Pashtun tribes are

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

PubMed

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

2013-01-01

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

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

PubMed Central

Zou, Rui; Liu, Yong; Yu, Yajuan

2013-01-01

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

Near-Optimal Tracking Control of Mobile Robots Via Receding-Horizon Dual Heuristic Programming.

PubMed

Lian, Chuanqiang; Xu, Xin; Chen, Hong; He, Haibo

2016-11-01

Trajectory tracking control of wheeled mobile robots (WMRs) has been an important research topic in control theory and robotics. Although various tracking control methods with stability have been developed for WMRs, it is still difficult to design optimal or near-optimal tracking controller under uncertainties and disturbances. In this paper, a near-optimal tracking control method is presented for WMRs based on receding-horizon dual heuristic programming (RHDHP). In the proposed method, a backstepping kinematic controller is designed to generate desired velocity profiles and the receding horizon strategy is used to decompose the infinite-horizon optimal control problem into a series of finite-horizon optimal control problems. In each horizon, a closed-loop tracking control policy is successively updated using a class of approximate dynamic programming algorithms called finite-horizon dual heuristic programming (DHP). The convergence property of the proposed method is analyzed and it is shown that the tracking control system based on RHDHP is asymptotically stable by using the Lyapunov approach. Simulation results on three tracking control problems demonstrate that the proposed method has improved control performance when compared with conventional model predictive control (MPC) and DHP. It is also illustrated that the proposed method has lower computational burden than conventional MPC, which is very beneficial for real-time tracking control.

Optimization-Based Image Reconstruction with Artifact Reduction in C-Arm CBCT

PubMed Central

Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan

2016-01-01

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility. PMID:27694700

Optimization-based image reconstruction with artifact reduction in C-arm CBCT

NASA Astrophysics Data System (ADS)

Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan

2016-10-01

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g. data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

The Michigan Surgical Home and Optimization Program is a scalable model to improve care and reduce costs.

PubMed

Englesbe, Michael J; Grenda, Dane R; Sullivan, June A; Derstine, Brian A; Kenney, Brooke N; Sheetz, Kyle H; Palazzolo, William C; Wang, Nicholas C; Goulson, Rebecca L; Lee, Jay S; Wang, Stewart C

2017-06-01

The Michigan Surgical Home and Optimization Program is a structured, home-based, preoperative training program targeting physical, nutritional, and psychological guidance. The purpose of this study was to determine if participation in this program was associated with reduced hospital duration of stay and health care costs. We conducted a retrospective, single center, cohort study evaluating patients who participated in the Michigan Surgical Home and Optimization Program and subsequently underwent major elective general and thoracic operative care between June 2014 and December 2015. Propensity score matching was used to match program participants to a control group who underwent operative care prior to program implementation. Primary outcome measures were hospital duration of stay and payer costs. Multivariate regression was used to determine the covariate-adjusted effect of program participation. A total of 641 patients participated in the program; 82% were actively engaged in the program, recording physical activity at least 3 times per week for the majority of the program; 182 patients were propensity matched to patients who underwent operative care prior to program implementation. Multivariate analysis demonstrated that participation in the Michigan Surgical Home and Optimization Program was associated with a 31% reduction in hospital duration of stay (P < .001) and 28% lower total costs (P < .001) after adjusting for covariates. A home-based, preoperative training program decreased hospital duration of stay, lowered costs of care, and was well accepted by patients. Further efforts will focus on broader implementation and linking participation to postoperative complications and rigorous patient-reported outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

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

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

Optimizing the creation of base populations for aquaculture breeding programs using phenotypic and genomic data and its consequences on genetic progress.

PubMed

Fernández, Jesús; Toro, Miguel Á; Sonesson, Anna K; Villanueva, Beatriz

2014-01-01

The success of an aquaculture breeding program critically depends on the way in which the base population of breeders is constructed since all the genetic variability for the traits included originally in the breeding goal as well as those to be included in the future is contained in the initial founders. Traditionally, base populations were created from a number of wild strains by sampling equal numbers from each strain. However, for some aquaculture species improved strains are already available and, therefore, mean phenotypic values for economically important traits can be used as a criterion to optimize the sampling when creating base populations. Also, the increasing availability of genome-wide genotype information in aquaculture species could help to refine the estimation of relationships within and between candidate strains and, thus, to optimize the percentage of individuals to be sampled from each strain. This study explores the advantages of using phenotypic and genome-wide information when constructing base populations for aquaculture breeding programs in terms of initial and subsequent trait performance and genetic diversity level. Results show that a compromise solution between diversity and performance can be found when creating base populations. Up to 6% higher levels of phenotypic performance can be achieved at the same level of global diversity in the base population by optimizing the selection of breeders instead of sampling equal numbers from each strain. The higher performance observed in the base population persisted during 10 generations of phenotypic selection applied in the subsequent breeding program.

A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao

2013-07-01

This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic-pessimistic index. The iterative nature of the authors' model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors' optimization method, which is very effective as compared to the standard PSO algorithm.

An Optimal Algorithm towards Successive Location Privacy in Sensor Networks with Dynamic Programming

NASA Astrophysics Data System (ADS)

Zhao, Baokang; Wang, Dan; Shao, Zili; Cao, Jiannong; Chan, Keith C. C.; Su, Jinshu

In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.

Modifications of ORNL's computer programs MSF-21 and VTE-21 for the evaluation and rapid optimization of multistage flash and vertical tube evaporators

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

Glueckstern, P.; Wilson, J.V.; Reed, S.A.

1976-06-01

Design and cost modifications were made to ORNL's Computer Programs MSF-21 and VTE-21 originally developed for the rapid calculation and design optimization of multistage flash (MSF) and multieffect vertical tube evaporator (VTE) desalination plants. The modifications include additional design options to make possible the evaluation of desalting plants based on current technology (the original programs were based on conceptual designs applying advanced and not yet proven technological developments and design features) and new materials and equipment costs updated to mid-1975.

Rapid design and optimization of low-thrust rendezvous/interception trajectory for asteroid deflection missions

NASA Astrophysics Data System (ADS)

Li, Shuang; Zhu, Yongsheng; Wang, Yukai

2014-02-01

Asteroid deflection techniques are essential in order to protect the Earth from catastrophic impacts by hazardous asteroids. Rapid design and optimization of low-thrust rendezvous/interception trajectories is considered as one of the key technologies to successfully deflect potentially hazardous asteroids. In this paper, we address a general framework for the rapid design and optimization of low-thrust rendezvous/interception trajectories for future asteroid deflection missions. The design and optimization process includes three closely associated steps. Firstly, shape-based approaches and genetic algorithm (GA) are adopted to perform preliminary design, which provides a reasonable initial guess for subsequent accurate optimization. Secondly, Radau pseudospectral method is utilized to transcribe the low-thrust trajectory optimization problem into a discrete nonlinear programming (NLP) problem. Finally, sequential quadratic programming (SQP) is used to efficiently solve the nonlinear programming problem and obtain the optimal low-thrust rendezvous/interception trajectories. The rapid design and optimization algorithms developed in this paper are validated by three simulation cases with different performance indexes and boundary constraints.

Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

NASA Astrophysics Data System (ADS)

Kmet', Tibor; Kmet'ová, Mária

2009-09-01

A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Optimization technique of wavefront coding system based on ZEMAX externally compiled programs

NASA Astrophysics Data System (ADS)

Han, Libo; Dong, Liquan; Liu, Ming; Zhao, Yuejin; Liu, Xiaohua

2016-10-01

Wavefront coding technique as a means of athermalization applied to infrared imaging system, the design of phase plate is the key to system performance. This paper apply the externally compiled programs of ZEMAX to the optimization of phase mask in the normal optical design process, namely defining the evaluation function of wavefront coding system based on the consistency of modulation transfer function (MTF) and improving the speed of optimization by means of the introduction of the mathematical software. User write an external program which computes the evaluation function on account of the powerful computing feature of the mathematical software in order to find the optimal parameters of phase mask, and accelerate convergence through generic algorithm (GA), then use dynamic data exchange (DDE) interface between ZEMAX and mathematical software to realize high-speed data exchanging. The optimization of the rotational symmetric phase mask and the cubic phase mask have been completed by this method, the depth of focus increases nearly 3 times by inserting the rotational symmetric phase mask, while the other system with cubic phase mask can be increased to 10 times, the consistency of MTF decrease obviously, the maximum operating temperature of optimized system range between -40°-60°. Results show that this optimization method can be more convenient to define some unconventional optimization goals and fleetly to optimize optical system with special properties due to its externally compiled function and DDE, there will be greater significance for the optimization of unconventional optical system.

An approach of traffic signal control based on NLRSQP algorithm

NASA Astrophysics Data System (ADS)

Zou, Yuan-Yang; Hu, Yu

2017-11-01

This paper presents a linear program model with linear complementarity constraints (LPLCC) to solve traffic signal optimization problem. The objective function of the model is to obtain the minimization of total queue length with weight factors at the end of each cycle. Then, a combination algorithm based on the nonlinear least regression and sequence quadratic program (NLRSQP) is proposed, by which the local optimal solution can be obtained. Furthermore, four numerical experiments are proposed to study how to set the initial solution of the algorithm that can get a better local optimal solution more quickly. In particular, the results of numerical experiments show that: The model is effective for different arrival rates and weight factors; and the lower bound of the initial solution is, the better optimal solution can be obtained.

Development of industry-based strategies for motivating seat-belt usage

DOT National Transportation Integrated Search

1983-03-01

A variety of incentive-based programs to motivate safety belt use were tested during the 18-month grant period in order to define optimal incentive strategies for particular corporate settings. Initial programs provoked important research questions w...

The Social Construction of "Evidence-Based" Drug Prevention Programs: A Reanalysis of Data from the Drug Abuse Resistance Education (DARE) Program

ERIC Educational Resources Information Center

Gorman, Dennis M.; Huber, J. Charles, Jr.

2009-01-01

This study explores the possibility that any drug prevention program might be considered "evidence-based" given the use of data analysis procedures that optimize the chance of producing statistically significant results by reanalyzing data from a Drug Abuse Resistance Education (DARE) program evaluation. The analysis produced a number of…

Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method

DOE PAGES

Huang, Kuo -Ling; Mehrotra, Sanjay

2016-11-08

We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 6.0.0.106), iOptimize solves convex quadraticmore » programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. In addition, we also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).« less

Optimizing use of library technology.

PubMed

Wink, Diane M; Killingsworth, Elizabeth K

2011-01-01

In this bimonthly series, the author examines how nurse educators can use the Internet and Web-based computer technologies such as search, communication, collaborative writing tools; social networking and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. This article describes optimizing the use of library technology.

Method of optimization onboard communication network

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Scott Horowitz | NREL

Science.gov Websites

area, which includes work on whole building energy modeling, cost-based optimization, model accuracy optimization tool used to provide support for the Building America program's teams and energy efficiency goals Colorado graduate student exploring enhancements to building optimization in terms of robustness and speed

Variable Complexity Structural Optimization of Shells

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.; Venkataraman, Satchi

1999-01-01

Structural designers today face both opportunities and challenges in a vast array of available analysis and optimization programs. Some programs such as NASTRAN, are very general, permitting the designer to model any structure, to any degree of accuracy, but often at a higher computational cost. Additionally, such general procedures often do not allow easy implementation of all constraints of interest to the designer. Other programs, based on algebraic expressions used by designers one generation ago, have limited applicability for general structures with modem materials. However, when applicable, they provide easy understanding of design decisions trade-off. Finally, designers can also use specialized programs suitable for designing efficiently a subset of structural problems. For example, PASCO and PANDA2 are panel design codes, which calculate response and estimate failure much more efficiently than general-purpose codes, but are narrowly applicable in terms of geometry and loading. Therefore, the problem of optimizing structures based on simultaneous use of several models and computer programs is a subject of considerable interest. The problem of using several levels of models in optimization has been dubbed variable complexity modeling. Work under NASA grant NAG1-2110 has been concerned with the development of variable complexity modeling strategies with special emphasis on response surface techniques. In addition, several modeling issues for the design of shells of revolution were studied.

Variable Complexity Structural Optimization of Shells

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.; Venkataraman, Satchi

1998-01-01

Structural designers today face both opportunities and challenges in a vast array of available analysis and optimization programs. Some programs such as NASTRAN, are very general, permitting the designer to model any structure, to any degree of accuracy, but often at a higher computational cost. Additionally, such general procedures often do not allow easy implementation of all constraints of interest to the designer. Other programs, based on algebraic expressions used by designers one generation ago, have limited applicability for general structures with modem materials. However, when applicable, they provide easy understanding of design decisions trade-off. Finally, designers can also use specialized programs suitable for designing efficiently a subset of structural problems. For example, PASCO and PANDA2 are panel design codes, which calculate response and estimate failure much more efficiently than general-purpose codes, but are narrowly applicable in terms of geometry and loading. Therefore, the problem of optimizing structures based on simultaneous use of several models and computer programs is a subject of considerable interest. The problem of using several levels of models in optimization has been dubbed variable complexity modeling. Work under NASA grant NAG1-1808 has been concerned with the development of variable complexity modeling strategies with special emphasis on response surface techniques. In addition several modeling issues for the design of shells of revolution were studied.

Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

PubMed

Adhikari, Shyam Prasad; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk

2018-01-10

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.

Constraint programming based biomarker optimization.

PubMed

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

2015-01-01

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

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy.

PubMed

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-05

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy

NASA Astrophysics Data System (ADS)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-01

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

Formation of the portfolio of high-rise construction projects on the basis of optimization of «risk-return» rate

NASA Astrophysics Data System (ADS)

Uvarova, Svetlana; Kutsygina, Olga; Smorodina, Elena; Gumba, Khuta

2018-03-01

The effectiveness and sustainability of an enterprise are based on the effectiveness and sustainability of its portfolio of projects. When creating a production program for a construction company based on a portfolio of projects and related to the planning and implementation of initiated organizational and economic changes, the problem of finding the optimal "risk-return" ratio of the program (portfolio of projects) is solved. The article proposes and approves the methodology of forming a portfolio of enterprise projects on the basis of the correspondence principle. Optimization of the portfolio of projects on the criterion of "risk-return" also contributes to the company's sustainability.

Integer Linear Programming in Computational Biology

NASA Astrophysics Data System (ADS)

Althaus, Ernst; Klau, Gunnar W.; Kohlbacher, Oliver; Lenhof, Hans-Peter; Reinert, Knut

Computational molecular biology (bioinformatics) is a young research field that is rich in NP-hard optimization problems. The problem instances encountered are often huge and comprise thousands of variables. Since their introduction into the field of bioinformatics in 1997, integer linear programming (ILP) techniques have been successfully applied to many optimization problems. These approaches have added much momentum to development and progress in related areas. In particular, ILP-based approaches have become a standard optimization technique in bioinformatics. In this review, we present applications of ILP-based techniques developed by members and former members of Kurt Mehlhorn’s group. These techniques were introduced to bioinformatics in a series of papers and popularized by demonstration of their effectiveness and potential.

Accounting for Test Variability through Sizing Local Domains in Sequential Design Optimization with Concurrent Calibration-Based Model Validation

DTIC Science & Technology

2013-08-01

in Sequential Design Optimization with Concurrent Calibration-Based Model Validation Dorin Drignei 1 Mathematics and Statistics Department...Validation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Dorin Drignei; Zissimos Mourelatos; Vijitashwa Pandey

Optimization applications in aircraft engine design and test

NASA Technical Reports Server (NTRS)

Pratt, T. K.

1984-01-01

Starting with the NASA-sponsored STAEBL program, optimization methods based primarily upon the versatile program COPES/CONMIN were introduced over the past few years to a broad spectrum of engineering problems in structural optimization, engine design, engine test, and more recently, manufacturing processes. By automating design and testing processes, many repetitive and costly trade-off studies have been replaced by optimization procedures. Rather than taking engineers and designers out of the loop, optimization has, in fact, put them more in control by providing sophisticated search techniques. The ultimate decision whether to accept or reject an optimal feasible design still rests with the analyst. Feedback obtained from this decision process has been invaluable since it can be incorporated into the optimization procedure to make it more intelligent. On several occasions, optimization procedures have produced novel designs, such as the nonsymmetric placement of rotor case stiffener rings, not anticipated by engineering designers. In another case, a particularly difficult resonance contraint could not be satisfied using hand iterations for a compressor blade, when the STAEBL program was applied to the problem, a feasible solution was obtained in just two iterations.

Plate/shell structure topology optimization of orthotropic material for buckling problem based on independent continuous topological variables

NASA Astrophysics Data System (ADS)

Ye, Hong-Ling; Wang, Wei-Wei; Chen, Ning; Sui, Yun-Kang

2017-10-01

The purpose of the present work is to study the buckling problem with plate/shell topology optimization of orthotropic material. A model of buckling topology optimization is established based on the independent, continuous, and mapping method, which considers structural mass as objective and buckling critical loads as constraints. Firstly, composite exponential function (CEF) and power function (PF) as filter functions are introduced to recognize the element mass, the element stiffness matrix, and the element geometric stiffness matrix. The filter functions of the orthotropic material stiffness are deduced. Then these filter functions are put into buckling topology optimization of a differential equation to analyze the design sensitivity. Furthermore, the buckling constraints are approximately expressed as explicit functions with respect to the design variables based on the first-order Taylor expansion. The objective function is standardized based on the second-order Taylor expansion. Therefore, the optimization model is translated into a quadratic program. Finally, the dual sequence quadratic programming (DSQP) algorithm and the global convergence method of moving asymptotes algorithm with two different filter functions (CEF and PF) are applied to solve the optimal model. Three numerical results show that DSQP&CEF has the best performance in the view of structural mass and discretion.

Optimal design of structures with multiple design variables per group and multiple loading conditions on the personal computer

NASA Technical Reports Server (NTRS)

Nguyen, D. T.; Rogers, J. L., Jr.

1986-01-01

A finite element based programming system for minimum weight design of a truss-type structure subjected to displacement, stress, and lower and upper bounds on design variables is presented. The programming system consists of a number of independent processors, each performing a specific task. These processors, however, are interfaced through a well-organized data base, thus making the tasks of modifying, updating, or expanding the programming system much easier in a friendly environment provided by many inexpensive personal computers. The proposed software can be viewed as an important step in achieving a 'dummy' finite element for optimization. The programming system has been implemented on both large and small computers (such as VAX, CYBER, IBM-PC, and APPLE) although the focus is on the latter. Examples are presented to demonstrate the capabilities of the code. The present programming system can be used stand-alone or as part of the multilevel decomposition procedure to obtain optimum design for very large scale structural systems. Furthermore, other related research areas such as developing optimization algorithms (or in the larger level: a structural synthesis program) for future trends in using parallel computers may also benefit from this study.

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

Huang, Kuo -Ling; Mehrotra, Sanjay

We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 6.0.0.106), iOptimize solves convex quadraticmore » programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. In addition, we also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).« less

Optimization of Residual Stresses in MMC's Using Compensating/Compliant Interfacial Layers. Part 2: OPTCOMP User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.; Williams, Todd O.

1994-01-01

A user's guide for the computer program OPTCOMP is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in uni-directional metal matrix composites subjected to combined thermo-mechanical axisymmetric loading using compensating or compliant layers at the fiber/matrix interface. The user specifies the architecture and the initial material parameters of the interfacial region, which can be either elastic or elastoplastic, and defines the design variables, together with the objective function, the associated constraints and the loading history through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the elastoplastic response of an arbitrarily layered multiple concentric cylinder model that is coupled to the commercial optimization package DOT. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

Energy simulation and optimization for a small commercial building through Modelica

NASA Astrophysics Data System (ADS)

Rivas, Bryan

Small commercial buildings make up the majority of buildings in the United States. Energy consumed by these buildings is expected to drastically increase in the next few decades, with a large percentage of the energy consumed attributed to cooling systems. This work presents the simulation and optimization of a thermostat schedule to minimize energy consumption in a small commercial building test bed during the cooling season. The simulation occurs through the use of the multi-engineering domain Dymola environment based on the Modelica open source programming language and is optimized with the Java based optimization program GenOpt. The simulation uses both physically based modeling utilizing heat transfer principles for the building and regression analysis for energy consumption. GenOpt is dynamically coupled to Dymola through various interface files. There are very few studies that have coupled GenOpt to a building simulation program and even fewer studies have used Dymola for building simulation as extensively as the work presented here. The work presented proves Dymola as a viable alternative to other building simulation programs such as EnergyPlus and MatLab. The model developed is used to simulate the energy consumption of a test bed, a commissioned real world small commercial building, while maintaining indoor thermal comfort. Potential applications include smart or intelligent building systems, predictive simulation of small commercial buildings, and building diagnostics.

Assessment of utility side financial benefits of demand side management considering environmental impacts

NASA Astrophysics Data System (ADS)

Abeygunawardane, Saranga Kumudu

2018-02-01

Any electrical utility prefers to implement demand side management and change the shape of the demand curve in a beneficial manner. This paper aims to assess the financial gains (or losses) to the generating sector through the implementation of demand side management programs. An optimization algorithm is developed to find the optimal generation mix that minimizes the daily total generating cost. This daily total generating cost includes the daily generating cost as well as the environmental damage cost. The proposed optimization algorithm is used to find the daily total generating cost for the base case and for several demand side management programs using the data obtained from the Sri Lankan power system. Results obtained for DSM programs are compared with the results obtained for the base case to assess the financial benefits of demand side management to the generating sector.

Structural Optimization for Reliability Using Nonlinear Goal Programming

NASA Technical Reports Server (NTRS)

El-Sayed, Mohamed E.

1999-01-01

This report details the development of a reliability based multi-objective design tool for solving structural optimization problems. Based on two different optimization techniques, namely sequential unconstrained minimization and nonlinear goal programming, the developed design method has the capability to take into account the effects of variability on the proposed design through a user specified reliability design criterion. In its sequential unconstrained minimization mode, the developed design tool uses a composite objective function, in conjunction with weight ordered design objectives, in order to take into account conflicting and multiple design criteria. Multiple design criteria of interest including structural weight, load induced stress and deflection, and mechanical reliability. The nonlinear goal programming mode, on the other hand, provides for a design method that eliminates the difficulty of having to define an objective function and constraints, while at the same time has the capability of handling rank ordered design objectives or goals. For simulation purposes the design of a pressure vessel cover plate was undertaken as a test bed for the newly developed design tool. The formulation of this structural optimization problem into sequential unconstrained minimization and goal programming form is presented. The resulting optimization problem was solved using: (i) the linear extended interior penalty function method algorithm; and (ii) Powell's conjugate directions method. Both single and multi-objective numerical test cases are included demonstrating the design tool's capabilities as it applies to this design problem.

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

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

Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S.

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller thatmore » adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution« less

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

DOE PAGES

Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S.

2016-04-01

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller thatmore » adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution« less

Modeling human decision making behavior in supervisory control

NASA Technical Reports Server (NTRS)

Tulga, M. K.; Sheridan, T. B.

1977-01-01

An optimal decision control model was developed, which is based primarily on a dynamic programming algorithm which looks at all the available task possibilities, charts an optimal trajectory, and commits itself to do the first step (i.e., follow the optimal trajectory during the next time period), and then iterates the calculation. A Bayesian estimator was included which estimates the tasks which might occur in the immediate future and provides this information to the dynamic programming routine. Preliminary trials comparing the human subject's performance to that of the optimal model show a great similarity, but indicate that the human skips certain movements which require quick change in strategy.

'It's a logistical nightmare!' Recommendations for optimising human papillomavirus school-based vaccination experience.

PubMed

Robbins, Spring Chenoa Cooper; Bernard, Diana; McCaffery, Kirsten; Skinner, S Rachel

2010-09-01

To date, no published studies examine procedural factors of the school-based human papillomavirus (HPV) vaccination program from the perspective of those involved. This study examines the factors that were perceived to impact optimal vaccination experience. Schools across Sydney were selected to reflect a range of vaccination coverage at the school level and different school types to ensure a range of experiences. Semi-structured focus groups were conducted with girls; and one-on-one interviews were undertaken with parents, teachers and nurses until saturation of data in all emergent themes was reached. Focus groups and interviews explored participants' experiences in school-based HPV vaccination. Transcripts were analysed, letting themes emerge. Themes related to participants' experience of the organisational, logistical and procedural aspects of the vaccination program and their perceptions of an optimal process were organised into two categories: (1) preparation for the vaccination program and (2) vaccination day strategies. In (1), themes emerged regarding commitment to the process from those involved, planning time and space for vaccinations, communication within and between agencies, and flexibility. In (2), themes included vaccinating the most anxious girls first, facilitating peer support, use of distraction techniques, minimising waiting time girls, and support staff. A range of views exists on what constitutes an optimal school-based program. Several findings were identified that should be considered in the development of guidelines for implementing school-based programs. Future research should evaluate how different approaches to acquiring parental consent, and the use of anxiety and fear reduction strategies impact experience and uptake in the school-based setting.

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming.

PubMed

Tanner, Matthew W; Sattenspiel, Lisa; Ntaimo, Lewis

2008-10-01

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*)

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

NASA Technical Reports Server (NTRS)

Shameldin, Ramez Ahmed; Bowling, Shannon R.

2010-01-01

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Evolving binary classifiers through parallel computation of multiple fitness cases.

PubMed

Cagnoni, Stefano; Bergenti, Federico; Mordonini, Monica; Adorni, Giovanni

2005-06-01

This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

PubMed

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

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

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

Artificial Intelligence-Based Models for the Optimal and Sustainable Use of Groundwater in Coastal Aquifers

NASA Astrophysics Data System (ADS)

Sreekanth, J.; Datta, Bithin

2011-07-01

Overexploitation of the coastal aquifers results in saltwater intrusion. Once saltwater intrusion occurs, it involves huge cost and long-term remediation measures to remediate these contaminated aquifers. Hence, it is important to have strategies for the sustainable use of coastal aquifers. This study develops a methodology for the optimal management of saltwater intrusion prone aquifers. A linked simulation-optimization-based management strategy is developed. The methodology uses genetic-programming-based models for simulating the aquifer processes, which is then linked to a multi-objective genetic algorithm to obtain optimal management strategies in terms of groundwater extraction from potential well locations in the aquifer.

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

NASA Technical Reports Server (NTRS)

1971-01-01

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

Selection and Implementation of Single Building EMCS (Energy Monitoring and Control Systems).

DTIC Science & Technology

1983-08-01

Setpoint Night Setback 161 Figure 20: Dual Setpoint Night Setback/up 162 Figure 21: Centrifugal Chiller Reset 166 Figure 22: Centrifugal Chiller Capacity...Program outputs. Hot water temperature. Application notes. A dedicated local loop controller may be implemented. Chiller optimization . The chiller ... optimization program can be implemented in chilled water plants with multiple chillers . Based on chiller operating data and the energy input requirements

Translator for Optimizing Fluid-Handling Components

NASA Technical Reports Server (NTRS)

Landon, Mark; Perry, Ernest

2007-01-01

A software interface has been devised to facilitate optimization of the shapes of valves, elbows, fittings, and other components used to handle fluids under extreme conditions. This software interface translates data files generated by PLOT3D (a NASA grid-based plotting-and- data-display program) and by computational fluid dynamics (CFD) software into a format in which the files can be read by Sculptor, which is a shape-deformation- and-optimization program. Sculptor enables the user to interactively, smoothly, and arbitrarily deform the surfaces and volumes in two- and three-dimensional CFD models. Sculptor also includes design-optimization algorithms that can be used in conjunction with the arbitrary-shape-deformation components to perform automatic shape optimization. In the optimization process, the output of the CFD software is used as feedback while the optimizer strives to satisfy design criteria that could include, for example, improved values of pressure loss, velocity, flow quality, mass flow, etc.

Optimizing Web-Based Instruction: A Case Study Using Poultry Processing Unit Operations

ERIC Educational Resources Information Center

O' Bryan, Corliss A.; Crandall, Philip G.; Shores-Ellis, Katrina; Johnson, Donald M.; Ricke, Steven C.; Marcy, John

2009-01-01

Food companies and supporting industries need inexpensive, revisable training methods for large numbers of hourly employees due to continuing improvements in Hazard Analysis Critical Control Point (HACCP) programs, new processing equipment, and high employee turnover. HACCP-based food safety programs have demonstrated their value by reducing the…

A novel recurrent neural network with finite-time convergence for linear programming.

PubMed

Liu, Qingshan; Cao, Jinde; Chen, Guanrong

2010-11-01

In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.

Optimization of Thermal Object Nonlinear Control Systems by Energy Efficiency Criterion.

NASA Astrophysics Data System (ADS)

Velichkin, Vladimir A.; Zavyalov, Vladimir A.

2018-03-01

This article presents the results of thermal object functioning control analysis (heat exchanger, dryer, heat treatment chamber, etc.). The results were used to determine a mathematical model of the generalized thermal control object. The appropriate optimality criterion was chosen to make the control more energy-efficient. The mathematical programming task was formulated based on the chosen optimality criterion, control object mathematical model and technological constraints. The “maximum energy efficiency” criterion helped avoid solving a system of nonlinear differential equations and solve the formulated problem of mathematical programming in an analytical way. It should be noted that in the case under review the search for optimal control and optimal trajectory reduces to solving an algebraic system of equations. In addition, it is shown that the optimal trajectory does not depend on the dynamic characteristics of the control object.

Optimization of Residual Stresses in MMC's through Process Parameter Control and the use of Heterogeneous Compensating/Compliant Interfacial Layers. OPTCOMP2 User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.

1996-01-01

A user's guide for the computer program OPTCOMP2 is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in unidirectional metal matrix composites subjected to combined thermomechanical axisymmetric loading by altering the processing history, as well as through the microstructural design of interfacial fiber coatings. The user specifies the initial architecture of the composite and the load history, with the constituent materials being elastic, plastic, viscoplastic, or as defined by the 'user-defined' constitutive model, in addition to the objective function and constraints, through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the inelastic response of a fiber/interface layer(s)/matrix concentric cylinder model where the interface layers can be either homogeneous or heterogeneous. The response of heterogeneous layers is modeled using Aboudi's three-dimensional method of cells micromechanics model. The commercial optimization package DOT is used for the nonlinear optimization problem. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Engaging Undergraduate Math Majors in Geoscience Research using Interactive Simulations and Computer Art

NASA Astrophysics Data System (ADS)

Matott, L. S.; Hymiak, B.; Reslink, C. F.; Baxter, C.; Aziz, S.

2012-12-01

As part of the NSF-sponsored 'URGE (Undergraduate Research Group Experiences) to Compute' program, Dr. Matott has been collaborating with talented Math majors to explore the design of cost-effective systems to safeguard groundwater supplies from contaminated sites. Such activity is aided by a combination of groundwater modeling, simulation-based optimization, and high-performance computing - disciplines largely unfamiliar to the students at the outset of the program. To help train and engage the students, a number of interactive and graphical software packages were utilized. Examples include: (1) a tutorial for exploring the behavior of evolutionary algorithms and other heuristic optimizers commonly used in simulation-based optimization; (2) an interactive groundwater modeling package for exploring alternative pump-and-treat containment scenarios at a contaminated site in Billings, Montana; (3) the R software package for visualizing various concepts related to subsurface hydrology; and (4) a job visualization tool for exploring the behavior of numerical experiments run on a large distributed computing cluster. Further engagement and excitement in the program was fostered by entering (and winning) a computer art competition run by the Coalition for Academic Scientific Computation (CASC). The winning submission visualizes an exhaustively mapped optimization cost surface and dramatically illustrates the phenomena of artificial minima - valley locations that correspond to designs whose costs are only partially optimal.

Managing simulation-based training: A framework for optimizing learning, cost, and time

NASA Astrophysics Data System (ADS)

Richmond, Noah Joseph

This study provides a management framework for optimizing training programs for learning, cost, and time when using simulation based training (SBT) and reality based training (RBT) as resources. Simulation is shown to be an effective means for implementing activity substitution as a way to reduce risk. The risk profile of 22 US Air Force vehicles are calculated, and the potential risk reduction is calculated under the assumption of perfect substitutability of RBT and SBT. Methods are subsequently developed to relax the assumption of perfect substitutability. The transfer effectiveness ratio (TER) concept is defined and modeled as a function of the quality of the simulator used, and the requirements of the activity trained. The Navy F/A-18 is then analyzed in a case study illustrating how learning can be maximized subject to constraints in cost and time, and also subject to the decision maker's preferences for the proportional and absolute use of simulation. Solution methods for optimizing multiple activities across shared resources are next provided. Finally, a simulation strategy including an operations planning program (OPP), an implementation program (IP), an acquisition program (AP), and a pedagogical research program (PRP) is detailed. The study provides the theoretical tools to understand how to leverage SBT, a case study demonstrating these tools' efficacy, and a set of policy recommendations to enable the US military to better utilize SBT in the future.

R programming for parameters estimation of geographically weighted ordinal logistic regression (GWOLR) model based on Newton Raphson

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Saputro, Dewi Retno Sari

2017-03-01

GWOLR model used for represent relationship between dependent variable has categories and scale of category is ordinal with independent variable influenced the geographical location of the observation site. Parameters estimation of GWOLR model use maximum likelihood provide system of nonlinear equations and hard to be found the result in analytic resolution. By finishing it, it means determine the maximum completion, this thing associated with optimizing problem. The completion nonlinear system of equations optimize use numerical approximation, which one is Newton Raphson method. The purpose of this research is to make iteration algorithm Newton Raphson and program using R software to estimate GWOLR model. Based on the research obtained that program in R can be used to estimate the parameters of GWOLR model by forming a syntax program with command "while".

Hybrid Differential Dynamic Programming with Stochastic Search

NASA Technical Reports Server (NTRS)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Optimizing a mobile robot control system using GPU acceleration

NASA Astrophysics Data System (ADS)

Tuck, Nat; McGuinness, Michael; Martin, Fred

2012-01-01

This paper describes our attempt to optimize a robot control program for the Intelligent Ground Vehicle Competition (IGVC) by running computationally intensive portions of the system on a commodity graphics processing unit (GPU). The IGVC Autonomous Challenge requires a control program that performs a number of different computationally intensive tasks ranging from computer vision to path planning. For the 2011 competition our Robot Operating System (ROS) based control system would not run comfortably on the multicore CPU on our custom robot platform. The process of profiling the ROS control program and selecting appropriate modules for porting to run on a GPU is described. A GPU-targeting compiler, Bacon, is used to speed up development and help optimize the ported modules. The impact of the ported modules on overall performance is discussed. We conclude that GPU optimization can free a significant amount of CPU resources with minimal effort for expensive user-written code, but that replacing heavily-optimized library functions is more difficult, and a much less efficient use of time.

Development of a unified guidance system for geocentric transfer. [for solar electric propulsion spacecraft

NASA Technical Reports Server (NTRS)

Cake, J. E.; Regetz, J. D., Jr.

1975-01-01

A method is presented for open loop guidance of a solar electric propulsion spacecraft to geosynchronous orbit. The method consists of determining the thrust vector profiles on the ground with an optimization computer program, and performing updates based on the difference between the actual trajectory and that predicted with a precision simulation computer program. The motivation for performing the guidance analysis during the mission planning phase is discussed, and a spacecraft design option that employs attitude orientation constraints is presented. The improvements required in both the optimization program and simulation program are set forth, together with the efforts to integrate the programs into the ground support software for the guidance system.

Development of a unified guidance system for geocentric transfer. [solar electric propulsion spacecraft

NASA Technical Reports Server (NTRS)

Cake, J. E.; Regetz, J. D., Jr.

1975-01-01

A method is presented for open loop guidance of a solar electric propulsion spacecraft to geosynchronsus orbit. The method consists of determining the thrust vector profiles on the ground with an optimization computer program, and performing updates based on the difference between the actual trajectory and that predicted with a precision simulation computer program. The motivation for performing the guidance analysis during the mission planning phase is discussed, and a spacecraft design option that employs attitude orientation constraints is presented. The improvements required in both the optimization program and simulation program are set forth, together with the efforts to integrate the programs into the ground support software for the guidance system.

Exact and heuristic algorithms for Space Information Flow.

PubMed

Uwitonze, Alfred; Huang, Jiaqing; Ye, Yuanqing; Cheng, Wenqing; Li, Zongpeng

2018-01-01

Space Information Flow (SIF) is a new promising research area that studies network coding in geometric space, such as Euclidean space. The design of algorithms that compute the optimal SIF solutions remains one of the key open problems in SIF. This work proposes the first exact SIF algorithm and a heuristic SIF algorithm that compute min-cost multicast network coding for N (N ≥ 3) given terminal nodes in 2-D Euclidean space. Furthermore, we find that the Butterfly network in Euclidean space is the second example besides the Pentagram network where SIF is strictly better than Euclidean Steiner minimal tree. The exact algorithm design is based on two key techniques: Delaunay triangulation and linear programming. Delaunay triangulation technique helps to find practically good candidate relay nodes, after which a min-cost multicast linear programming model is solved over the terminal nodes and the candidate relay nodes, to compute the optimal multicast network topology, including the optimal relay nodes selected by linear programming from all the candidate relay nodes and the flow rates on the connection links. The heuristic algorithm design is also based on Delaunay triangulation and linear programming techniques. The exact algorithm can achieve the optimal SIF solution with an exponential computational complexity, while the heuristic algorithm can achieve the sub-optimal SIF solution with a polynomial computational complexity. We prove the correctness of the exact SIF algorithm. The simulation results show the effectiveness of the heuristic SIF algorithm.

Dynamic programming and graph algorithms in computer vision.

PubMed

Felzenszwalb, Pedro F; Zabih, Ramin

2011-04-01

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.

Optimizing Violence Prevention Programs: An Examination of Program Effectiveness among Urban High School Students

ERIC Educational Resources Information Center

Thompkins, Amanda C.; Chauveron, Lisa M.; Harel, Ofer; Perkins, Daniel F.

2014-01-01

Background: While demand for youth violence prevention programs increases, the ability of the school-day schedule to accommodate their time requirements has diminished. Viable school-based prevention programs must strike a balance between brevity and effectiveness. This article reports results from an effectiveness trial of a 12-session…

47 CFR 1.2202 - Competitive bidding design options.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Section 1.2202 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Grants...) Procedures that utilize mathematical computer optimization software, such as integer programming, to evaluate... evaluating bids using a ranking based on specified factors. (B) Procedures that combine computer optimization...

Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization.

PubMed

Bauer, Markus; Klau, Gunnar W; Reinert, Knut

2007-07-27

The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account. We present a graph-based representation for sequence-structure alignments, which we model as an integer linear program (ILP). We sketch how we compute an optimal or near-optimal solution to the ILP using methods from combinatorial optimization, and present results on a recently published benchmark set for RNA alignments. The implementation of our algorithm yields better alignments in terms of two published scores than the other programs that we tested: This is especially the case with an increasing number of input sequences. Our program LARA is freely available for academic purposes from http://www.planet-lisa.net.

Sequential quadratic programming-based fast path planning algorithm subject to no-fly zone constraints

NASA Astrophysics Data System (ADS)

Liu, Wei; Ma, Shunjian; Sun, Mingwei; Yi, Haidong; Wang, Zenghui; Chen, Zengqiang

2016-08-01

Path planning plays an important role in aircraft guided systems. Multiple no-fly zones in the flight area make path planning a constrained nonlinear optimization problem. It is necessary to obtain a feasible optimal solution in real time. In this article, the flight path is specified to be composed of alternate line segments and circular arcs, in order to reformulate the problem into a static optimization one in terms of the waypoints. For the commonly used circular and polygonal no-fly zones, geometric conditions are established to determine whether or not the path intersects with them, and these can be readily programmed. Then, the original problem is transformed into a form that can be solved by the sequential quadratic programming method. The solution can be obtained quickly using the Sparse Nonlinear OPTimizer (SNOPT) package. Mathematical simulations are used to verify the effectiveness and rapidity of the proposed algorithm.

Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

PubMed

Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

2016-08-01

This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

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

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

CAN-DO, CFD-based Aerodynamic Nozzle Design and Optimization program for supersonic/hypersonic wind tunnels

NASA Technical Reports Server (NTRS)

Korte, John J.; Kumar, Ajay; Singh, D. J.; White, J. A.

1992-01-01

A design program is developed which incorporates a modern approach to the design of supersonic/hypersonic wind-tunnel nozzles. The approach is obtained by the coupling of computational fluid dynamics (CFD) with design optimization. The program can be used to design a 2D or axisymmetric, supersonic or hypersonic, wind-tunnel nozzles that can be modeled with a calorically perfect gas. The nozzle design is obtained by solving a nonlinear least-squares optimization problem (LSOP). The LSOP is solved using an iterative procedure which requires intermediate flowfield solutions. The nozzle flowfield is simulated by solving the Navier-Stokes equations for the subsonic and transonic flow regions and the parabolized Navier-Stokes equations for the supersonic flow regions. The advantages of this method are that the design is based on the solution of the viscous equations eliminating the need to make separate corrections to a design contour, and the flexibility of applying the procedure to different types of nozzle design problems.

Method for depleting BWRs using optimal control rod patterns

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

Taner, M.S.; Levine, S.H.; Hsiao, M.Y.

1991-01-01

Control rod (CR) programming is an essential core management activity for boiling water reactors (BWRs). After establishing a core reload design for a BWR, CR programming is performed to develop a sequence of exposure-dependent CR patterns that assure the safe and effective depletion of the core through a reactor cycle. A time-variant target power distribution approach has been assumed in this study. The authors have developed OCTOPUS to implement a new two-step method for designing semioptimal CR programs for BWRs. The optimization procedure of OCTOPUS is based on the method of approximation programming and uses the SIMULATE-E code for nucleonicsmore » calculations.« less

Nonlinear Curve-Fitting Program

NASA Technical Reports Server (NTRS)

Everhart, Joel L.; Badavi, Forooz F.

1989-01-01

Nonlinear optimization algorithm helps in finding best-fit curve. Nonlinear Curve Fitting Program, NLINEAR, interactive curve-fitting routine based on description of quadratic expansion of X(sup 2) statistic. Utilizes nonlinear optimization algorithm calculating best statistically weighted values of parameters of fitting function and X(sup 2) minimized. Provides user with such statistical information as goodness of fit and estimated values of parameters producing highest degree of correlation between experimental data and mathematical model. Written in FORTRAN 77.

Progress in the Production of JP-8 Based Hydrogen and Advanced Tactical Fuels for Military Applications

DTIC Science & Technology

2011-02-01

of a multi- year program to develop, optimize, and demonstrate the military viability of a technology for on-demand production of high...continuous reactor system used for kinetic rate data experiment 86 52 Schematic of a differential reactor. The catalyst bed is kept small , and...program to develop, optimize, and demonstrate the military viability of a technology for on-demand production of high-pressure hydrogen for fuel

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

PubMed

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

2002-01-01

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

Back to the Future: Lessons Learned in Modern Target-based and Whole-Cell Lead Optimization of Antimalarials

PubMed Central

Chatterjee, Arnab K; Yeung, Bryan KS

2012-01-01

Antimalarial drug discovery has historically benefited from the whole-cell (phenotypic) screening approach to identify lead molecules in the search for new drugs. However over the past two decades there has been a shift in the pharmaceutical industry to move away from whole-cell screening to target-based approaches. As part of a Wellcome Trust and Medicines for Malaria Venture (MMV) funded consortium to discover new blood-stage antimalarials, we used both approaches to identify new antimalarial chemotypes, two of which have progressed beyond the lead optimization phase and display excellent in vivo efficacy in mice. These two advanced series were identified through a cell-based optimization devoid of target information and in this review we summarize the advantages of this approach versus a target-based optimization. Although the each lead optimization required slightly different medicinal chemistry strategies, we observed some common issues across the different the scaffolds which could be applied to other cell based lead optimization programs. PMID:22242845

A System to Derive Optimal Tree Diameter Increment Models from the Eastwide Forest Inventory Data Base (EFIDB)

Treesearch

Don C. Bragg

2002-01-01

This article is an introduction to the computer software used by the Potential Relative Increment (PRI) approach to optimal tree diameter growth modeling. These DOS programs extract qualified tree and plot data from the Eastwide Forest Inventory Data Base (EFIDB), calculate relative tree increment, sort for the highest relative increments by diameter class, and...

Solving bi-level optimization problems in engineering design using kriging models

NASA Astrophysics Data System (ADS)

Xia, Yi; Liu, Xiaojie; Du, Gang

2018-05-01

Stackelberg game-theoretic approaches are applied extensively in engineering design to handle distributed collaboration decisions. Bi-level genetic algorithms (BLGAs) and response surfaces have been used to solve the corresponding bi-level programming models. However, the computational costs for BLGAs often increase rapidly with the complexity of lower-level programs, and optimal solution functions sometimes cannot be approximated by response surfaces. This article proposes a new method, namely the optimal solution function approximation by kriging model (OSFAKM), in which kriging models are used to approximate the optimal solution functions. A detailed example demonstrates that OSFAKM can obtain better solutions than BLGAs and response surface-based methods, and at the same time reduce the workload of computation remarkably. Five benchmark problems and a case study of the optimal design of a thin-walled pressure vessel are also presented to illustrate the feasibility and potential of the proposed method for bi-level optimization in engineering design.

Hybrid Differential Dynamic Programming with Stochastic Search

NASA Technical Reports Server (NTRS)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob A.

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASA's Dawn mission. The Dawn trajectory was designed with the DDP-based Static/Dynamic Optimal Control algorithm used in the Mystic software.1 Another recently developed method, Hybrid Differential Dynamic Programming (HDDP),2, 3 is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Optimization of land use of agricultural farms in Sumedang regency by using linear programming models

NASA Astrophysics Data System (ADS)

Zenis, F. M.; Supian, S.; Lesmana, E.

2018-03-01

Land is one of the most important assets for farmers in Sumedang Regency. Therefore, agricultural land should be used optimally. This study aims to obtain the optimal land use composition in order to obtain maximum income. The optimization method used in this research is Linear Programming Models. Based on the results of the analysis, the composition of land use for rice area of 135.314 hectares, corn area of 11.798 hectares, soy area of 2.290 hectares, and peanuts of 2.818 hectares with the value of farmers income of IDR 2.682.020.000.000,-/year. The results of this analysis can be used as a consideration in decisions making about cropping patterns by farmers.

HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.

PubMed

Juusola, Jessie L; Brandeau, Margaret L

2016-04-01

To create a simple model to help public health decision makers determine how to best invest limited resources in HIV treatment scale-up and prevention. A linear model was developed for determining the optimal mix of investment in HIV treatment and prevention, given a fixed budget. The model incorporates estimates of secondary health benefits accruing from HIV treatment and prevention and allows for diseconomies of scale in program costs and subadditive benefits from concurrent program implementation. Data sources were published literature. The target population was individuals infected with HIV or at risk of acquiring it. Illustrative examples of interventions include preexposure prophylaxis (PrEP), community-based education (CBE), and antiretroviral therapy (ART) for men who have sex with men (MSM) in the US. Outcome measures were incremental cost, quality-adjusted life-years gained, and HIV infections averted. Base case analysis indicated that it is optimal to invest in ART before PrEP and to invest in CBE before scaling up ART. Diseconomies of scale reduced the optimal investment level. Subadditivity of benefits did not affect the optimal allocation for relatively low implementation levels. The sensitivity analysis indicated that investment in ART before PrEP was optimal in all scenarios tested. Investment in ART before CBE became optimal when CBE reduced risky behavior by 4% or less. Limitations of the study are that dynamic effects are approximated with a static model. Our model provides a simple yet accurate means of determining optimal investment in HIV prevention and treatment. For MSM in the US, HIV control funds should be prioritized on inexpensive, effective programs like CBE, then on ART scale-up, with only minimal investment in PrEP. © The Author(s) 2015.

Optimisation des trajectoires d'un systeme de gestion de vol d'avions pour la reduction des couts de vol

NASA Astrophysics Data System (ADS)

Sidibe, Souleymane

The implementation and monitoring of operational flight plans is a major occupation for a crew of commercial flights. The purpose of this operation is to set the vertical and lateral trajectories followed by airplane during phases of flight: climb, cruise, descent, etc. These trajectories are subjected to conflicting economical constraints: minimization of flight time and minimization of fuel consumed and environmental constraints. In its task of mission planning, the crew is assisted by the Flight Management System (FMS) which is used to construct the path to follow and to predict the behaviour of the aircraft along the flight plan. The FMS considered in our research, particularly includes an optimization model of flight only by calculating the optimal speed profile that minimizes the overall cost of flight synthesized by a criterion of cost index following a steady cruising altitude. However, the model based solely on optimization of the speed profile is not sufficient. It is necessary to expand the current optimization for simultaneous optimization of the speed and altitude in order to determine an optimum cruise altitude that minimizes the overall cost when the path is flown with the optimal speed profile. Then, a new program was developed. The latter is based on the method of dynamic programming invented by Bellman to solve problems of optimal paths. In addition, the improvement passes through research new patterns of trajectories integrating ascendant cruises and using the lateral plane with the effect of the weather: wind and temperature. Finally, for better optimization, the program takes into account constraint of flight domain of aircrafts which utilize the FMS.

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks.

PubMed

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Leng, Bing; Li, Shuangquan

2018-05-21

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks

PubMed Central

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Leng, Bing; Li, Shuangquan

2018-01-01

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods. PMID:29883410

Dynamic Programming and Graph Algorithms in Computer Vision*

PubMed Central

Felzenszwalb, Pedro F.; Zabih, Ramin

2013-01-01

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting, since by carefully exploiting problem structure they often provide non-trivial guarantees concerning solution quality. In this paper we briefly review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo; the mid-level problem of interactive object segmentation; and the high-level problem of model-based recognition. PMID:20660950

XAFSmass: a program for calculating the optimal mass of XAFS samples

NASA Astrophysics Data System (ADS)

Klementiev, K.; Chernikov, R.

2016-05-01

We present a new implementation of the XAFSmass program that calculates the optimal mass of XAFS samples. It has several improvements as compared to the old Windows based program XAFSmass: 1) it is truly platform independent, as provided by Python language, 2) it has an improved parser of chemical formulas that enables parentheses and nested inclusion-to-matrix weight percentages. The program calculates the absorption edge height given the total optical thickness, operates with differently determined sample amounts (mass, pressure, density or sample area) depending on the aggregate state of the sample and solves the inverse problem of finding the elemental composition given the experimental absorption edge jump and the chemical formula.

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

Gaitsgory, Vladimir, E-mail: vladimir.gaitsgory@mq.edu.au; Rossomakhine, Sergey, E-mail: serguei.rossomakhine@flinders.edu.au

The paper aims at the development of an apparatus for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems (that is, problems of optimal control of SP systems) considered on the infinite time horizon. We mostly focus on problems with time discounting criteria but a possibility of the extension of results to periodic optimization problems is discussed as well. Our consideration is based on earlier results on averaging of SP control systems and on linear programming formulations of optimal control problems. The idea that we exploit is to first asymptotically approximate a given problem ofmore » optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with two numerical examples.« less

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

Optimal GENCO bidding strategy

NASA Astrophysics Data System (ADS)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

Parallel computing and first-principles calculations: Applications to complex ceramics and Vitamin B12

NASA Astrophysics Data System (ADS)

Ouyang, Lizhi

A systematic improvement and extension of the orthogonalized linear combinations of atomic orbitals method was carried out using a combined computational and theoretical approach. For high performance parallel computing, a Beowulf class personal computer cluster was constructed. It also served as a parallel program development platform that helped us to port the programs of the method to the national supercomputer facilities. The program, received a language upgrade from Fortran 77 to Fortran 90, and a dynamic memory allocation feature. A preliminary parallel High Performance Fortran version of the program has been developed as well. To be of more benefit though, scalability improvements are needed. In order to circumvent the difficulties of the analytical force calculation in the method, we developed a geometry optimization scheme using the finite difference approximation based on the total energy calculation. The implementation of this scheme was facilitated by the powerful general utility lattice program, which offers many desired features such as multiple optimization schemes and usage of space group symmetry. So far, many ceramic oxides have been tested with the geometry optimization program. Their optimized geometries were in excellent agreement with the experimental data. For nine ceramic oxide crystals, the optimized cell parameters differ from the experimental ones within 0.5%. Moreover, the geometry optimization was recently used to predict a new phase of TiNx. The method has also been used to investigate a complex Vitamin B12-derivative, the OHCbl crystals. In order to overcome the prohibitive disk I/O demand, an on-demand version of the method was developed. Based on the electronic structure calculation of the OHCbl crystal, a partial density of states analysis and a bond order analysis were carried out. The calculated bonding of the corrin ring of OHCbl model was coincident with the big open-ring pi bond. One interesting find of the calculation was that the Co-OH bond was weak. This, together with the ongoing projects studying different Vitamin B12 derivatives, might help us to answer questions about the Co-C cleavage of the B12 coenzyme, which is involved in many important B12 enzymatic reactions.

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

PubMed

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

Shuttle filter study. Volume 1: Characterization and optimization of filtration devices

NASA Technical Reports Server (NTRS)

1974-01-01

A program to develop a new technology base for filtration equipment and comprehensive fluid particulate contamination management techniques was conducted. The study has application to the systems used in the space shuttle and space station projects. The scope of the program is as follows: (1) characterization and optimization of filtration devices, (2) characterization of contaminant generation and contaminant sensitivity at the component level, and (3) development of a comprehensive particulate contamination management plane for space shuttle fluid systems.

Multiparadigm Design Environments

DTIC Science & Technology

1992-01-01

following results: 1. New methods for programming in terms of conceptual models 2. Design of object-oriented languages 3. Compiler optimization and...experimented with object-based methods for programming directly in terms of conceptual models, object-oriented language design, computer program...expect the3e results to have a strong influence on future ,,j :- ...... L ! . . • a mm ammmml ll Illlll • l I 1 Conceptual Programming Conceptual

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

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

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

Clinical implementation of stereotaxic brain implant optimization

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

Rosenow, U.F.; Wojcicka, J.B.

1991-03-01

This optimization method for stereotaxic brain implants is based on seed/strand configurations of the basic type developed for the National Cancer Institute (NCI) atlas of regular brain implants. Irregular target volume shapes are determined from delineation in a stack of contrast enhanced computed tomography scans. The neurosurgeon may then select up to ten directions, or entry points, of surgical approach of which the program finds the optimal one under the criterion of smallest target volume diameter. Target volume cross sections are then reconstructed in 5-mm-spaced planes perpendicular to the implantation direction defined by the entry point and the target volumemore » center. This information is used to define a closed line in an implant cross section along which peripheral seed strands are positioned and which has now an irregular shape. Optimization points are defined opposite peripheral seeds on the target volume surface to which the treatment dose rate is prescribed. Three different optimization algorithms are available: linear least-squares programming, quadratic programming with constraints, and a simplex method. The optimization routine is implemented into a commercial treatment planning system. It generates coordinate and source strength information of the optimized seed configurations for further dose rate distribution calculation with the treatment planning system, and also the coordinate settings for the stereotaxic Brown-Roberts-Wells (BRW) implantation device.« less

Fuzzy multiobjective models for optimal operation of a hydropower system

NASA Astrophysics Data System (ADS)

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Optimizing Implementation of Obesity Prevention Programs: A Qualitative Investigation Within a Large-Scale Randomized Controlled Trial.

PubMed

Kozica, Samantha L; Teede, Helena J; Harrison, Cheryce L; Klein, Ruth; Lombard, Catherine B

2016-01-01

The prevalence of obesity in rural and remote areas is elevated in comparison to urban populations, highlighting the need for interventions targeting obesity prevention in these settings. Implementing evidence-based obesity prevention programs is challenging. This study aimed to investigate factors influencing the implementation of obesity prevention programs, including adoption, program delivery, community uptake, and continuation, specifically within rural settings. Nested within a large-scale randomized controlled trial, a qualitative exploratory approach was adopted, with purposive sampling techniques utilized, to recruit stakeholders from 41 small rural towns in Australia. In-depth semistructured interviews were conducted with clinical health professionals, health service managers, and local government employees. Open coding was completed independently by 2 investigators and thematic analysis undertaken. In-depth interviews revealed that obesity prevention programs were valued by the rural workforce. Program implementation is influenced by interrelated factors across: (1) contextual factors and (2) organizational capacity. Key recommendations to manage the challenges of implementing evidence-based programs focused on reducing program delivery costs, aided by the provision of a suite of implementation and evaluation resources. Informing the scale-up of future prevention programs, stakeholders highlighted the need to build local rural capacity through developing supportive university partnerships, generating local program ownership and promoting active feedback to all program partners. We demonstrate that the rural workforce places a high value on obesity prevention programs. Our results inform the future scale-up of obesity prevention programs, providing an improved understanding of strategies to optimize implementation of evidence-based prevention programs. © 2015 National Rural Health Association.

A simple approach to optimal control of invasive species.

PubMed

Hastings, Alan; Hall, Richard J; Taylor, Caz M

2006-12-01

The problem of invasive species and their control is one of the most pressing applied issues in ecology today. We developed simple approaches based on linear programming for determining the optimal removal strategies of different stage or age classes for control of invasive species that are still in a density-independent phase of growth. We illustrate the application of this method to the specific example of invasive Spartina alterniflora in Willapa Bay, WA. For all such systems, linear programming shows in general that the optimal strategy in any time step is to prioritize removal of a single age or stage class. The optimal strategy adjusts which class is the focus of control through time and can be much more cost effective than prioritizing removal of the same stage class each year.

Topology optimization of induction heating model using sequential linear programming based on move limit with adaptive relaxation

NASA Astrophysics Data System (ADS)

Masuda, Hiroshi; Kanda, Yutaro; Okamoto, Yoshifumi; Hirono, Kazuki; Hoshino, Reona; Wakao, Shinji; Tsuburaya, Tomonori

2017-12-01

It is very important to design electrical machineries with high efficiency from the point of view of saving energy. Therefore, topology optimization (TO) is occasionally used as a design method for improving the performance of electrical machinery under the reasonable constraints. Because TO can achieve a design with much higher degree of freedom in terms of structure, there is a possibility for deriving the novel structure which would be quite different from the conventional structure. In this paper, topology optimization using sequential linear programming using move limit based on adaptive relaxation is applied to two models. The magnetic shielding, in which there are many local minima, is firstly employed as firstly benchmarking for the performance evaluation among several mathematical programming methods. Secondly, induction heating model is defined in 2-D axisymmetric field. In this model, the magnetic energy stored in the magnetic body is maximized under the constraint on the volume of magnetic body. Furthermore, the influence of the location of the design domain on the solutions is investigated.

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

2017-08-01

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

Policy tree optimization for adaptive management of water resources systems

NASA Astrophysics Data System (ADS)

Herman, Jonathan; Giuliani, Matteo

2017-04-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points" that suggest the need of updating the policy. However, there remains a need for a general method to optimize the choice of the signposts to be used and their threshold values. This work contributes a general framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. Given a set of feature variables (e.g., reservoir level, inflow observations, inflow forecasts), the resulting policy defines both the optimal reservoir operations and the conditions under which such operations should be triggered. We demonstrate the approach using Folsom Reservoir (California) as a case study, in which operating policies must balance the risk of both floods and droughts. Numerical results show that the tree-based policies outperform the ones designed via Dynamic Programming. In addition, they display good adaptive capacity to the changing climate, successfully adapting the reservoir operations across a large set of uncertain climate scenarios.

Heliocentric interplanetary low thrust trajectory optimization program, supplement 1, part 2

NASA Technical Reports Server (NTRS)

Mann, F. I.; Horsewood, J. L.

1978-01-01

The improvements made to the HILTOP electric propulsion trajectory computer program are described. A more realistic propulsion system model was implemented in which various thrust subsystem efficiencies and specific impulse are modeled as variable functions of power available to the propulsion system. The number of operating thrusters are staged, and the beam voltage is selected from a set of five (or less) constant voltages, based upon the application of variational calculus. The constant beam voltages may be optimized individually or collectively. The propulsion system logic is activated by a single program input key in such a manner as to preserve the HILTOP logic. An analysis describing these features, a complete description of program input quantities, and sample cases of computer output illustrating the program capabilities are presented.

Meta-Modeling-Based Groundwater Remediation Optimization under Flexibility in Environmental Standard.

PubMed

He, Li; Xu, Zongda; Fan, Xing; Li, Jing; Lu, Hongwei

2017-05-01

  This study develops a meta-modeling based mathematical programming approach with flexibility in environmental standards. It integrates numerical simulation, meta-modeling analysis, and fuzzy programming within a general framework. A set of models between remediation strategies and remediation performance can well guarantee the mitigation in computational efforts in the simulation and optimization process. In order to prevent the occurrence of over-optimistic and pessimistic optimization strategies, a high satisfaction level resulting from the implementation of a flexible standard can indicate the degree to which the environmental standard is satisfied. The proposed approach is applied to a naphthalene-contaminated site in China. Results show that a longer remediation period corresponds to a lower total pumping rate and a stringent risk standard implies a high total pumping rate. The wells located near or in the down-gradient direction to the contaminant sources have the most significant efficiency among all of remediation schemes.

Efficient fractal-based mutation in evolutionary algorithms from iterated function systems

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Aybar-Ruíz, A.; Camacho-Gómez, C.; Pereira, E.

2018-03-01

In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.

Topology optimization applied to the design of cooling channels for plastic injection

NASA Astrophysics Data System (ADS)

Muñoz, D. A.; Arango, J. P.; González, C.; Puerto, E.; Garzón, M.

2018-04-01

In this paper, topology optimization is applied to design cooling channels in a mold of structural steel. The problem was implemented in COMSOL multiphysics, where two physics were coupled, heat transfer and solid mechanics. The optimization objective is to maximize the conduction heat flux in the mold and minimize the deformations when the plastic is injected. In order to find an optimal geometry for this objective, a density-based method was implemented into the nonlinear program (NLP) for which feasible results were found.

Expert System for Automated Design Synthesis

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Barthelemy, Jean-Francois M.

1987-01-01

Expert-system computer program EXADS developed to aid users of Automated Design Synthesis (ADS) general-purpose optimization program. EXADS aids engineer in determining best combination based on knowledge of specific problem and expert knowledge stored in knowledge base. Available in two interactive machine versions. IBM PC version (LAR-13687) written in IQ-LISP. DEC VAX version (LAR-13688) written in Franz-LISP.

A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

NASA Astrophysics Data System (ADS)

Tian, Wenli; Cao, Chengxuan

2017-03-01

A generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.

MCMC-ODPR: primer design optimization using Markov Chain Monte Carlo sampling.

PubMed

Kitchen, James L; Moore, Jonathan D; Palmer, Sarah A; Allaby, Robin G

2012-11-05

Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly. We aimed to develop a system that would implement primer reuse to design degenerate primers that could be designed around SNPs, thus find the fewest necessary primers and the lowest cost whilst maintaining an acceptable coverage and provide a cost effective solution. We have implemented Metropolis-Hastings Markov Chain Monte Carlo for optimizing primer reuse. We call it the Markov Chain Monte Carlo Optimized Degenerate Primer Reuse (MCMC-ODPR) algorithm. After repeating the program 1020 times to assess the variance, an average of 17.14% fewer primers were found to be necessary using MCMC-ODPR for an equivalent coverage without implementing primer reuse. The algorithm was able to reuse primers up to five times. We compared MCMC-ODPR with single sequence primer design programs Primer3 and Primer-BLAST and achieved a lower primer cost per amplicon base covered of 0.21 and 0.19 and 0.18 primer nucleotides on three separate gene sequences, respectively. With multiple sequences, MCMC-ODPR achieved a lower cost per base covered of 0.19 than programs BatchPrimer3 and PAMPS, which achieved 0.25 and 0.64 primer nucleotides, respectively. MCMC-ODPR is a useful tool for designing primers at various melting temperatures at good target coverage. By combining degeneracy with optimal primer reuse the user may increase coverage of sequences amplified by the designed primers at significantly lower costs. Our analyses showed that overall MCMC-ODPR outperformed the other primer-design programs in our study in terms of cost per covered base.

MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling

PubMed Central

2012-01-01

Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly. We aimed to develop a system that would implement primer reuse to design degenerate primers that could be designed around SNPs, thus find the fewest necessary primers and the lowest cost whilst maintaining an acceptable coverage and provide a cost effective solution. We have implemented Metropolis-Hastings Markov Chain Monte Carlo for optimizing primer reuse. We call it the Markov Chain Monte Carlo Optimized Degenerate Primer Reuse (MCMC-ODPR) algorithm. Results After repeating the program 1020 times to assess the variance, an average of 17.14% fewer primers were found to be necessary using MCMC-ODPR for an equivalent coverage without implementing primer reuse. The algorithm was able to reuse primers up to five times. We compared MCMC-ODPR with single sequence primer design programs Primer3 and Primer-BLAST and achieved a lower primer cost per amplicon base covered of 0.21 and 0.19 and 0.18 primer nucleotides on three separate gene sequences, respectively. With multiple sequences, MCMC-ODPR achieved a lower cost per base covered of 0.19 than programs BatchPrimer3 and PAMPS, which achieved 0.25 and 0.64 primer nucleotides, respectively. Conclusions MCMC-ODPR is a useful tool for designing primers at various melting temperatures at good target coverage. By combining degeneracy with optimal primer reuse the user may increase coverage of sequences amplified by the designed primers at significantly lower costs. Our analyses showed that overall MCMC-ODPR outperformed the other primer-design programs in our study in terms of cost per covered base. PMID:23126469

Model-Based Optimal Experimental Design for Complex Physical Systems

DTIC Science & Technology

2015-12-03

for public release. magnitude reduction in estimator error required to make solving the exact optimal design problem tractable. Instead of using a naive...for designing a sequence of experiments uses suboptimal approaches: batch design that has no feedback, or greedy ( myopic ) design that optimally...approved for public release. Equation 1 is difficult to solve directly, but can be expressed in an equivalent form using the principle of dynamic programming

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw

2001-01-01

An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee

2015-08-01

This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted.

Reduction of shock induced noise in imperfectly expanded supersonic jets using convex optimization

NASA Astrophysics Data System (ADS)

Adhikari, Sam

2007-11-01

Imperfectly expanded jets generate screech noise. The imbalance between the backpressure and the exit pressure of the imperfectly expanded jets produce shock cells and expansion or compression waves from the nozzle. The instability waves and the shock cells interact to generate the screech sound. The mathematical model consists of cylindrical coordinate based full Navier-Stokes equations and large-eddy-simulation turbulence modeling. Analytical and computational analysis of the three-dimensional helical effects provide a model that relates several parameters with shock cell patterns, screech frequency and distribution of shock generation locations. Convex optimization techniques minimize the shock cell patterns and the instability waves. The objective functions are (convex) quadratic and the constraint functions are affine. In the quadratic optimization programs, minimization of the quadratic functions over a set of polyhedrons provides the optimal result. Various industry standard methods like regression analysis, distance between polyhedra, bounding variance, Markowitz optimization, and second order cone programming is used for Quadratic Optimization.

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach

PubMed Central

Duarte, Belmiro P. M.; Wong, Weng Kee

2014-01-01

Summary This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D-, A- or E-optimality. As an illustrative example, we demonstrate the approach using the power-logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D-optimal designs with two regressors for a logistic model and a two-variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted. PMID:26512159

Simulation-based planning for theater air warfare

NASA Astrophysics Data System (ADS)

Popken, Douglas A.; Cox, Louis A., Jr.

2004-08-01

Planning for Theatre Air Warfare can be represented as a hierarchy of decisions. At the top level, surviving airframes must be assigned to roles (e.g., Air Defense, Counter Air, Close Air Support, and AAF Suppression) in each time period in response to changing enemy air defense capabilities, remaining targets, and roles of opposing aircraft. At the middle level, aircraft are allocated to specific targets to support their assigned roles. At the lowest level, routing and engagement decisions are made for individual missions. The decisions at each level form a set of time-sequenced Courses of Action taken by opposing forces. This paper introduces a set of simulation-based optimization heuristics operating within this planning hierarchy to optimize allocations of aircraft. The algorithms estimate distributions for stochastic outcomes of the pairs of Red/Blue decisions. Rather than using traditional stochastic dynamic programming to determine optimal strategies, we use an innovative combination of heuristics, simulation-optimization, and mathematical programming. Blue decisions are guided by a stochastic hill-climbing search algorithm while Red decisions are found by optimizing over a continuous representation of the decision space. Stochastic outcomes are then provided by fast, Lanchester-type attrition simulations. This paper summarizes preliminary results from top and middle level models.

A Generalization of the Karush-Kuhn-Tucker Theorem for Approximate Solutions of Mathematical Programming Problems Based on Quadratic Approximation

NASA Astrophysics Data System (ADS)

Voloshinov, V. V.

2018-03-01

In computations related to mathematical programming problems, one often has to consider approximate, rather than exact, solutions satisfying the constraints of the problem and the optimality criterion with a certain error. For determining stopping rules for iterative procedures, in the stability analysis of solutions with respect to errors in the initial data, etc., a justified characteristic of such solutions that is independent of the numerical method used to obtain them is needed. A necessary δ-optimality condition in the smooth mathematical programming problem that generalizes the Karush-Kuhn-Tucker theorem for the case of approximate solutions is obtained. The Lagrange multipliers corresponding to the approximate solution are determined by solving an approximating quadratic programming problem.

The social construction of "evidence-based'' drug prevention programs: a reanalysis of data from the Drug Abuse Resistance Education (DARE) program.

PubMed

Gorman, Dennis M; Huber, J Charles

2009-08-01

This study explores the possibility that any drug prevention program might be considered ;;evidence-based'' given the use of data analysis procedures that optimize the chance of producing statistically significant results by reanalyzing data from a Drug Abuse Resistance Education (DARE) program evaluation. The analysis produced a number of statistically significant differences between the DARE and control conditions on alcohol and marijuana use measures. Many of these differences occurred at cutoff points on the assessment scales for which post hoc meaningful labels were created. Our results are compared to those from evaluations of programs that appear on evidence-based drug prevention lists.

Optimization of 200 MWth and 250 MWt Ship Based Small Long Life NPP

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

Fitriyani, Dian; Su'ud, Zaki

2010-06-22

Design optimization of ship-based 200 MWth and 250 MWt nuclear power reactors have been performed. The neutronic and thermo-hydraulic programs of the three-dimensional X-Y-Z geometry have been developed for the analysis of ship-based nuclear power plant. Quasi-static approach is adopted to treat seawater effect. The reactor are loop type lead bismuth cooled fast reactor with nitride fuel and with relatively large coolant pipe above reactor core, the heat from primary coolant system is directly transferred to watersteam loop through steam generators. Square core type are selected and optimized. As the optimization result, the core outlet temperature distribution is changing withmore » the elevation angle of the reactor system and the characteristics are discussed.« less

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

PubMed

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

Pavement maintenance optimization model using Markov Decision Processes

NASA Astrophysics Data System (ADS)

Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.

2017-09-01

This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

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

PubMed

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

2017-01-01

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

Artificial Bee Colony Optimization for Short-Term Hydrothermal Scheduling

NASA Astrophysics Data System (ADS)

Basu, M.

2014-12-01

Artificial bee colony optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The algorithm is tested on a multi-reservoir cascaded hydroelectric system having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. The algorithm is tested on two hydrothermal multi-reservoir cascaded hydroelectric test systems. The results of the proposed approach are compared with those of differential evolution, evolutionary programming and particle swarm optimization. From numerical results, it is found that the proposed artificial bee colony optimization based approach is able to provide better solution.

Leakage characterization of top select transistor for program disturbance optimization in 3D NAND flash

NASA Astrophysics Data System (ADS)

Zhang, Yu; Jin, Lei; Jiang, Dandan; Zou, Xingqi; Zhao, Zhiguo; Gao, Jing; Zeng, Ming; Zhou, Wenbin; Tang, Zhaoyun; Huo, Zongliang

2018-03-01

In order to optimize program disturbance characteristics effectively, a characterization approach that measures top select transistor (TSG) leakage from bit-line is proposed to quantify TSG leakage under program inhibit condition in 3D NAND flash memory. Based on this approach, the effect of Vth modulation of two-cell TSG on leakage is evaluated. By checking the dependence of leakage and corresponding program disturbance on upper and lower TSG Vth, this approach is validated. The optimal Vth pattern with high upper TSG Vth and low lower TSG Vth has been suggested for low leakage current and high boosted channel potential. It is found that upper TSG plays dominant role in preventing drain induced barrier lowering (DIBL) leakage from boosted channel to bit-line, while lower TSG assists to further suppress TSG leakage by providing smooth potential drop from dummy WL to edge of TSG, consequently suppressing trap assisted band-to-band tunneling current (BTBT) between dummy WL and TSG.

Development Of Educational Programs In Renewable And Alternative Energy Processing: The Case Of Russia

NASA Astrophysics Data System (ADS)

Svirina, Anna; Shindor, Olga; Tatmyshevsky, Konstantin

2014-12-01

The paper deals with the main problems of Russian energy system development that proves necessary to provide educational programs in the field of renewable and alternative energy. In the paper the process of curricula development and defining teaching techniques on the basis of expert opinion evaluation is defined, and the competence model for renewable and alternative energy processing master students is suggested. On the basis of a distributed questionnaire and in-depth interviews, the data for statistical analysis was obtained. On the basis of this data, an optimization of curricula structure was performed, and three models of a structure for optimizing teaching techniques were developed. The suggested educational program structure which was adopted by employers is presented in the paper. The findings include quantitatively estimated importance of systemic thinking and professional skills and knowledge as basic competences of a masters' program graduate; statistically estimated necessity of practice-based learning approach; and optimization models for structuring curricula in renewable and alternative energy processing. These findings allow the establishment of a platform for the development of educational programs.

Representing and comparing protein structures as paths in three-dimensional space

PubMed Central

Zhi, Degui; Krishna, S Sri; Cao, Haibo; Pevzner, Pavel; Godzik, Adam

2006-01-01

Background Most existing formulations of protein structure comparison are based on detailed atomic level descriptions of protein structures and bypass potential insights that arise from a higher-level abstraction. Results We propose a structure comparison approach based on a simplified representation of proteins that describes its three-dimensional path by local curvature along the generalized backbone of the polypeptide. We have implemented a dynamic programming procedure that aligns curvatures of proteins by optimizing a defined sum turning angle deviation measure. Conclusion Although our procedure does not directly optimize global structural similarity as measured by RMSD, our benchmarking results indicate that it can surprisingly well recover the structural similarity defined by structure classification databases and traditional structure alignment programs. In addition, our program can recognize similarities between structures with extensive conformation changes that are beyond the ability of traditional structure alignment programs. We demonstrate the applications of procedure to several contexts of structure comparison. An implementation of our procedure, CURVE, is available as a public webserver. PMID:17052359

Site Selection and Resource Allocation of Oil Spill Emergency Base for Offshore Oil Facilities

NASA Astrophysics Data System (ADS)

Li, Yunbin; Liu, Jingxian; Wei, Lei; Wu, Weihuang

2018-02-01

Based on the analysis of the historical data about oil spill accidents in the Bohai Sea, this paper discretizes oil spilled source into a limited number of spill points. According to the probability of oil spill risk, the demand for salvage forces at each oil spill point is evaluated. Aiming at the specific location of the rescue base around the Bohai Sea, a cost-benefit analysis is conducted to determine the total cost of disasters for each rescue base. Based on the relationship between the oil spill point and the rescue site, a multi-objective optimization location model for the oil spill rescue base in the Bohai Sea region is established. And the genetic algorithm is used to solve the optimization problem, and determine the emergency rescue base optimization program and emergency resources allocation ratio.

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

PubMed Central

Zhang, Zheng; Han, Xiao; Pearson, Erik; Pelizzari, Charles; Sidky, Emil Y; Pan, Xiaochuan

2017-01-01

Increasing interest in optimization-based reconstruction in research on, and applications of, cone-beam computed tomography (CBCT) exists because it has been shown to have to potential to reduce artifacts observed in reconstructions obtained with the Feldkamp–Davis–Kress (FDK) algorithm (or its variants), which is used extensively for image reconstruction in current CBCT applications. In this work, we carried out a study on optimization-based reconstruction for possible reduction of artifacts in FDK reconstruction specifically from short-scan CBCT data. The investigation includes a set of optimization programs such as the image-total-variation (TV)-constrained data-divergency minimization, data-weighting matrices such as the Parker weighting matrix, and objects of practical interest for demonstrating and assessing the degree of artifact reduction. Results of investigative work reveal that appropriately designed optimization-based reconstruction, including the image-TV-constrained reconstruction, can reduce significant artifacts observed in FDK reconstruction in CBCT with a short-scan configuration. PMID:27046218

Computer optimization of cutting yield from multiple ripped boards

Treesearch

A.R. Stern; K.A. McDonald

1978-01-01

RIPYLD is a computer program that optimizes the cutting yield from multiple-ripped boards. Decisions are based on automatically collected defect information, cutting bill requirements, and sawing variables. The yield of clear cuttings from a board is calculated for every possible permutation of specified rip widths and both the maximum and minimum percent yield...

Optimal Stratification of Item Pools in a-Stratified Computerized Adaptive Testing.

ERIC Educational Resources Information Center

Chang, Hua-Hua; van der Linden, Wim J.

2003-01-01

Developed a method based on 0-1 linear programming to stratify an item pool optimally for use in alpha-stratified adaptive testing. Applied the method to a previous item pool from the computerized adaptive test of the Graduate Record Examinations. Results show the new method performs well in practical situations. (SLD)

Shape optimization of three-dimensional stamped and solid automotive components

NASA Technical Reports Server (NTRS)

Botkin, M. E.; Yang, R.-J.; Bennett, J. A.

1987-01-01

The shape optimization of realistic, 3-D automotive components is discussed. The integration of the major parts of the total process: modeling, mesh generation, finite element and sensitivity analysis, and optimization are stressed. Stamped components and solid components are treated separately. For stamped parts a highly automated capability was developed. The problem description is based upon a parameterized boundary design element concept for the definition of the geometry. Automatic triangulation and adaptive mesh refinement are used to provide an automated analysis capability which requires only boundary data and takes into account sensitivity of the solution accuracy to boundary shape. For solid components a general extension of the 2-D boundary design element concept has not been achieved. In this case, the parameterized surface shape is provided using a generic modeling concept based upon isoparametric mapping patches which also serves as the mesh generator. Emphasis is placed upon the coupling of optimization with a commercially available finite element program. To do this it is necessary to modularize the program architecture and obtain shape design sensitivities using the material derivative approach so that only boundary solution data is needed.

New numerical methods for open-loop and feedback solutions to dynamic optimization problems

NASA Astrophysics Data System (ADS)

Ghosh, Pradipto

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.

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

NASA Astrophysics Data System (ADS)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

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

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

PubMed

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

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

A duality approach for solving bounded linear programming problems with fuzzy variables based on ranking functions and its application in bounded transportation problems

NASA Astrophysics Data System (ADS)

Ebrahimnejad, Ali

2015-08-01

There are several methods, in the literature, for solving fuzzy variable linear programming problems (fuzzy linear programming in which the right-hand-side vectors and decision variables are represented by trapezoidal fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings a new method based on the bounded dual simplex method is proposed to determine the fuzzy optimal solution of that kind of fuzzy variable linear programming problems in which some or all variables are restricted to lie within lower and upper bounds. To illustrate the proposed method, an application example is solved and the obtained results are given. The advantages of the proposed method over existing methods are discussed. Also, one application of this algorithm in solving bounded transportation problems with fuzzy supplies and demands is dealt with. The proposed method is easy to understand and to apply for determining the fuzzy optimal solution of bounded fuzzy variable linear programming problems occurring in real-life situations.

Optimizing the performance and structure of the D0 Collie confidence limit evaluator

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

Fishchler, Mark; /Fermilab

2010-07-01

D0 Collie is a program used to perform limit calculations based on ensembles of pseudo-experiments ('PEs'). Since the application of this program to the crucial Higgs mass limit is quite CPU intensive, it has been deemed important to carefully review this program, with an eye toward identifying and implementing potential performance improvements. At the same time, we identify any coding errors or opportunities for potential structural (or algorithm) improvement discovered in the course of gaining sufficient understanding of the workings of Collie to sensibly explore for optimizations. Based on a careful analysis of the program, a series of code changesmore » with potential for improving performance has been identified. The implementation and evaluation of the most important parts of this series has been done, with gratifying speedup results. The bottom line: We have identified and implemented changes leading to a factor of 2.19 speedup in the example program provided, and expected to translate to a factor of roughly 4 speedup in typical realistic usage.« less

Derivative Trade Optimizing Model Utilizing GP Based on Behavioral Finance Theory

NASA Astrophysics Data System (ADS)

Matsumura, Koki; Kawamoto, Masaru

This paper proposed a new technique which makes the strategy trees for the derivative (option) trading investment decision based on the behavioral finance theory and optimizes it using evolutionary computation, in order to achieve high profitability. The strategy tree uses a technical analysis based on a statistical, experienced technique for the investment decision. The trading model is represented by various technical indexes, and the strategy tree is optimized by the genetic programming(GP) which is one of the evolutionary computations. Moreover, this paper proposed a method using the prospect theory based on the behavioral finance theory to set psychological bias for profit and deficit and attempted to select the appropriate strike price of option for the higher investment efficiency. As a result, this technique produced a good result and found the effectiveness of this trading model by the optimized dealings strategy.

Construction of pore network models for Berea and Fontainebleau sandstones using non-linear programing and optimization techniques

NASA Astrophysics Data System (ADS)

Sharqawy, Mostafa H.

2016-12-01

Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder-Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available.

Image compression system and method having optimized quantization tables

NASA Technical Reports Server (NTRS)

Ratnakar, Viresh (Inventor); Livny, Miron (Inventor)

1998-01-01

A digital image compression preprocessor for use in a discrete cosine transform-based digital image compression device is provided. The preprocessor includes a gathering mechanism for determining discrete cosine transform statistics from input digital image data. A computing mechanism is operatively coupled to the gathering mechanism to calculate a image distortion array and a rate of image compression array based upon the discrete cosine transform statistics for each possible quantization value. A dynamic programming mechanism is operatively coupled to the computing mechanism to optimize the rate of image compression array against the image distortion array such that a rate-distortion-optimal quantization table is derived. In addition, a discrete cosine transform-based digital image compression device and a discrete cosine transform-based digital image compression and decompression system are provided. Also, a method for generating a rate-distortion-optimal quantization table, using discrete cosine transform-based digital image compression, and operating a discrete cosine transform-based digital image compression and decompression system are provided.

Program Aids Analysis And Optimization Of Design

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1994-01-01

NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.

Optimal fabrication processes for unidirectional metal-matrix composites: A computational simulation

NASA Technical Reports Server (NTRS)

Saravanos, D. A.; Murthy, P. L. N.; Morel, M.

1990-01-01

A method is proposed for optimizing the fabrication process of unidirectional metal matrix composites. The temperature and pressure histories are optimized such that the residual microstresses of the composite at the end of the fabrication process are minimized and the material integrity throughout the process is ensured. The response of the composite during the fabrication is simulated based on a nonlinear micromechanics theory. The optimal fabrication problem is formulated and solved with non-linear programming. Application cases regarding the optimization of the fabrication cool-down phases of unidirectional ultra-high modulus graphite/copper and silicon carbide/titanium composites are presented.

Optimal fabrication processes for unidirectional metal-matrix composites - A computational simulation

NASA Technical Reports Server (NTRS)

Saravanos, D. A.; Murthy, P. L. N.; Morel, M.

1990-01-01

A method is proposed for optimizing the fabrication process of unidirectional metal matrix composites. The temperature and pressure histories are optimized such that the residual microstresses of the composite at the end of the fabrication process are minimized and the material integrity throughout the process is ensured. The response of the composite during the fabrication is simulated based on a nonlinear micromechanics theory. The optimal fabrication problem is formulated and solved with nonlinear programming. Application cases regarding the optimization of the fabrication cool-down phases of unidirectional ultra-high modulus graphite/copper and silicon carbide/titanium composites are presented.

Enhancements on the Convex Programming Based Powered Descent Guidance Algorithm for Mars Landing

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Blackmore, Lars; Scharf, Daniel P.; Wolf, Aron

2008-01-01

In this paper, we present enhancements on the powered descent guidance algorithm developed for Mars pinpoint landing. The guidance algorithm solves the powered descent minimum fuel trajectory optimization problem via a direct numerical method. Our main contribution is to formulate the trajectory optimization problem, which has nonconvex control constraints, as a finite dimensional convex optimization problem, specifically as a finite dimensional second order cone programming (SOCP) problem. SOCP is a subclass of convex programming, and there are efficient SOCP solvers with deterministic convergence properties. Hence, the resulting guidance algorithm can potentially be implemented onboard a spacecraft for real-time applications. Particularly, this paper discusses the algorithmic improvements obtained by: (i) Using an efficient approach to choose the optimal time-of-flight; (ii) Using a computationally inexpensive way to detect the feasibility/ infeasibility of the problem due to the thrust-to-weight constraint; (iii) Incorporating the rotation rate of the planet into the problem formulation; (iv) Developing additional constraints on the position and velocity to guarantee no-subsurface flight between the time samples of the temporal discretization; (v) Developing a fuel-limited targeting algorithm; (vi) Initial result on developing an onboard table lookup method to obtain almost fuel optimal solutions in real-time.

ALPS yield optimization cutting program

Treesearch

P. Klinkhachorn; J.P. Franklin; Charles W. McMillin; H.A. Huber

1989-01-01

This paper reports ongoing work on a series of computer programs developed to automate hardwood lumber processing in a furniture roughmill. The program computes the placement of cuttings on lumber, based on a description of each board in terms of shape and defect location, and a cutting bill. These results are suitable for use with a high-power laser to cut the parts...

Generation of structural topologies using efficient technique based on sorted compliances

NASA Astrophysics Data System (ADS)

Mazur, Monika; Tajs-Zielińska, Katarzyna; Bochenek, Bogdan

2018-01-01

Topology optimization, although well recognized is still widely developed. It has gained recently more attention since large computational ability become available for designers. This process is stimulated simultaneously by variety of emerging, innovative optimization methods. It is observed that traditional gradient-based mathematical programming algorithms, in many cases, are replaced by novel and e cient heuristic methods inspired by biological, chemical or physical phenomena. These methods become useful tools for structural optimization because of their versatility and easy numerical implementation. In this paper engineering implementation of a novel heuristic algorithm for minimum compliance topology optimization is discussed. The performance of the topology generator is based on implementation of a special function utilizing information of compliance distribution within the design space. With a view to cope with engineering problems the algorithm has been combined with structural analysis system Ansys.

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

PubMed Central

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571

Optimization of space manufacturing systems

NASA Technical Reports Server (NTRS)

Akin, D. L.

1979-01-01

Four separate analyses are detailed: transportation to low earth orbit, orbit-to-orbit optimization, parametric analysis of SPS logistics based on earth and lunar source locations, and an overall program option optimization implemented with linear programming. It is found that smaller vehicles are favored for earth launch, with the current Space Shuttle being right at optimum payload size. Fully reusable launch vehicles represent a savings of 50% over the Space Shuttle; increased reliability with less maintenance could further double the savings. An optimization of orbit-to-orbit propulsion systems using lunar oxygen for propellants shows that ion propulsion is preferable by a 3:1 cost margin over a mass driver reaction engine at optimum values; however, ion engines cannot yet operate in the lower exhaust velocity range where the optimum lies, and total program costs between the two systems are ambiguous. Heavier payloads favor the use of a MDRE. A parametric model of a space manufacturing facility is proposed, and used to analyze recurring costs, total costs, and net present value discounted cash flows. Parameters studied include productivity, effects of discounting, materials source tradeoffs, economic viability of closed-cycle habitats, and effects of varying degrees of nonterrestrial SPS materials needed from earth. Finally, candidate optimal scenarios are chosen, and implemented in a linear program with external constraints in order to arrive at an optimum blend of SPS production strategies in order to maximize returns.

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

PubMed

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

2018-06-01

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

Research on crude oil storage and transportation based on optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Xuhua

2018-04-01

At present, the optimization theory and method have been widely used in the optimization scheduling and optimal operation scheme of complex production systems. Based on C++Builder 6 program development platform, the theoretical research results are implemented by computer. The simulation and intelligent decision system of crude oil storage and transportation inventory scheduling are designed. The system includes modules of project management, data management, graphics processing, simulation of oil depot operation scheme. It can realize the optimization of the scheduling scheme of crude oil storage and transportation system. A multi-point temperature measuring system for monitoring the temperature field of floating roof oil storage tank is developed. The results show that by optimizing operating parameters such as tank operating mode and temperature, the total transportation scheduling costs of the storage and transportation system can be reduced by 9.1%. Therefore, this method can realize safe and stable operation of crude oil storage and transportation system.

Reliability-based design optimization using a generalized subset simulation method and posterior approximation

NASA Astrophysics Data System (ADS)

Ma, Yuan-Zhuo; Li, Hong-Shuang; Yao, Wei-Xing

2018-05-01

The evaluation of the probabilistic constraints in reliability-based design optimization (RBDO) problems has always been significant and challenging work, which strongly affects the performance of RBDO methods. This article deals with RBDO problems using a recently developed generalized subset simulation (GSS) method and a posterior approximation approach. The posterior approximation approach is used to transform all the probabilistic constraints into ordinary constraints as in deterministic optimization. The assessment of multiple failure probabilities required by the posterior approximation approach is achieved by GSS in a single run at all supporting points, which are selected by a proper experimental design scheme combining Sobol' sequences and Bucher's design. Sequentially, the transformed deterministic design optimization problem can be solved by optimization algorithms, for example, the sequential quadratic programming method. Three optimization problems are used to demonstrate the efficiency and accuracy of the proposed method.

An expert system for choosing the best combination of options in a general purpose program for automated design synthesis

NASA Technical Reports Server (NTRS)

Rogers, J. L.; Barthelemy, J.-F. M.

1986-01-01

An expert system called EXADS has been developed to aid users of the Automated Design Synthesis (ADS) general purpose optimization program. ADS has approximately 100 combinations of strategy, optimizer, and one-dimensional search options from which to choose. It is difficult for a nonexpert to make this choice. This expert system aids the user in choosing the best combination of options based on the users knowledge of the problem and the expert knowledge stored in the knowledge base. The knowledge base is divided into three categories; constrained problems, unconstrained problems, and constrained problems being treated as unconstrained problems. The inference engine and rules are written in LISP, contains about 200 rules, and executes on DEC-VAX (with Franz-LISP) and IBM PC (with IQ-LISP) computers.

OPTIMAL AIRCRAFT TRAJECTORIES FOR SPECIFIED RANGE

NASA Technical Reports Server (NTRS)

Lee, H.

1994-01-01

For an aircraft operating over a fixed range, the operating costs are basically a sum of fuel cost and time cost. While minimum fuel and minimum time trajectories are relatively easy to calculate, the determination of a minimum cost trajectory can be a complex undertaking. This computer program was developed to optimize trajectories with respect to a cost function based on a weighted sum of fuel cost and time cost. As a research tool, the program could be used to study various characteristics of optimum trajectories and their comparison to standard trajectories. It might also be used to generate a model for the development of an airborne trajectory optimization system. The program could be incorporated into an airline flight planning system, with optimum flight plans determined at takeoff time for the prevailing flight conditions. The use of trajectory optimization could significantly reduce the cost for a given aircraft mission. The algorithm incorporated in the program assumes that a trajectory consists of climb, cruise, and descent segments. The optimization of each segment is not done independently, as in classical procedures, but is performed in a manner which accounts for interaction between the segments. This is accomplished by the application of optimal control theory. The climb and descent profiles are generated by integrating a set of kinematic and dynamic equations, where the total energy of the aircraft is the independent variable. At each energy level of the climb and descent profiles, the air speed and power setting necessary for an optimal trajectory are determined. The variational Hamiltonian of the problem consists of the rate of change of cost with respect to total energy and a term dependent on the adjoint variable, which is identical to the optimum cruise cost at a specified altitude. This variable uniquely specifies the optimal cruise energy, cruise altitude, cruise Mach number, and, indirectly, the climb and descent profiles. If the optimum cruise cost is specified, an optimum trajectory can easily be generated; however, the range obtained for a particular optimum cruise cost is not known a priori. For short range flights, the program iteratively varies the optimum cruise cost until the computed range converges to the specified range. For long-range flights, iteration is unnecessary since the specified range can be divided into a cruise segment distance and full climb and descent distances. The user must supply the program with engine fuel flow rate coefficients and an aircraft aerodynamic model. The program currently includes coefficients for the Pratt-Whitney JT8D-7 engine and an aerodynamic model for the Boeing 727. Input to the program consists of the flight range to be covered and the prevailing flight conditions including pressure, temperature, and wind profiles. Information output by the program includes: optimum cruise tables at selected weights, optimal cruise quantities as a function of cruise weight and cruise distance, climb and descent profiles, and a summary of the complete synthesized optimal trajectory. This program is written in FORTRAN IV for batch execution and has been implemented on a CDC 6000 series computer with a central memory requirement of approximately 100K (octal) of 60 bit words. This aircraft trajectory optimization program was developed in 1979.

Shape optimization of the modular press body

NASA Astrophysics Data System (ADS)

Pabiszczak, Stanisław

2016-12-01

A paper contains an optimization algorithm of cross-sectional dimensions of a modular press body for the minimum mass criterion. Parameters of the wall thickness and the angle of their inclination relative to the base of section are assumed as the decision variables. The overall dimensions are treated as a constant. The optimal values of parameters were calculated using numerical method of the tool Solver in the program Microsoft Excel. The results of the optimization procedure helped reduce body weight by 27% while maintaining the required rigidity of the body.

A design optimization process for Space Station Freedom

NASA Technical Reports Server (NTRS)

Chamberlain, Robert G.; Fox, George; Duquette, William H.

1990-01-01

The Space Station Freedom Program is used to develop and implement a process for design optimization. Because the relative worth of arbitrary design concepts cannot be assessed directly, comparisons must be based on designs that provide the same performance from the point of view of station users; such designs can be compared in terms of life cycle cost. Since the technology required to produce a space station is widely dispersed, a decentralized optimization process is essential. A formulation of the optimization process is provided and the mathematical models designed to facilitate its implementation are described.

Process modeling for carbon-phenolic nozzle materials

NASA Technical Reports Server (NTRS)

Letson, Mischell A.; Bunker, Robert C.; Remus, Walter M., III; Clinton, R. G.

1989-01-01

A thermochemical model based on the SINDA heat transfer program is developed for carbon-phenolic nozzle material processes. The model can be used to optimize cure cycles and to predict material properties based on the types of materials and the process by which these materials are used to make nozzle components. Chemical kinetic constants for Fiberite MX4926 were determined so that optimization of cure cycles for the current Space Shuttle Solid Rocket Motor nozzle rings can be determined.

Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease.

PubMed

Heldman, Dustin A; Pulliam, Christopher L; Urrea Mendoza, Enrique; Gartner, Maureen; Giuffrida, Joseph P; Montgomery, Erwin B; Espay, Alberto J; Revilla, Fredy J

2016-02-01

Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments. Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage. Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01). Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers. © 2015 International Neuromodulation Society.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

PubMed

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

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

Liu, Cong; Botterud, Audun; Zhou, Zhi

In this study, we propose a fuzzy-based energy and reserve co-optimization model with consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzymore » mathematical programming formulation into traditional mixed integer linear programming problems.« less

Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

DOE PAGES

Liu, Cong; Botterud, Audun; Zhou, Zhi; ...

2016-10-21

In this study, we propose a fuzzy-based energy and reserve co-optimization model with consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzymore » mathematical programming formulation into traditional mixed integer linear programming problems.« less

The extension of the thermal-vacuum test optimization program to multiple flights

NASA Technical Reports Server (NTRS)

Williams, R. E.; Byrd, J.

1981-01-01

The thermal vacuum test optimization model developed to provide an approach to the optimization of a test program based on prediction of flight performance with a single flight option in mind is extended to consider reflight as in space shuttle missions. The concept of 'utility', developed under the name of 'availability', is used to follow performance through the various options encountered when the capabilities of reflight and retrievability of space shuttle are available. Also, a 'lost value' model is modified to produce a measure of the probability of a mission's success, achieving a desired utility using a minimal cost test strategy. The resulting matrix of probabilities and their associated costs provides a means for project management to evaluate various test and reflight strategies.

Optimal design application on the advanced aeroelastic rotor blade

NASA Technical Reports Server (NTRS)

Wei, F. S.; Jones, R.

1985-01-01

The vibration and performance optimization procedure using regression analysis was successfully applied to an advanced aeroelastic blade design study. The major advantage of this regression technique is that multiple optimizations can be performed to evaluate the effects of various objective functions and constraint functions. The data bases obtained from the rotorcraft flight simulation program C81 and Myklestad mode shape program are analytically determined as a function of each design variable. This approach has been verified for various blade radial ballast weight locations and blade planforms. This method can also be utilized to ascertain the effect of a particular cost function which is composed of several objective functions with different weighting factors for various mission requirements without any additional effort.

Optimal approach to quantum communication using dynamic programming.

PubMed

Jiang, Liang; Taylor, Jacob M; Khaneja, Navin; Lukin, Mikhail D

2007-10-30

Reliable preparation of entanglement between distant systems is an outstanding problem in quantum information science and quantum communication. In practice, this has to be accomplished by noisy channels (such as optical fibers) that generally result in exponential attenuation of quantum signals at large distances. A special class of quantum error correction protocols, quantum repeater protocols, can be used to overcome such losses. In this work, we introduce a method for systematically optimizing existing protocols and developing more efficient protocols. Our approach makes use of a dynamic programming-based searching algorithm, the complexity of which scales only polynomially with the communication distance, letting us efficiently determine near-optimal solutions. We find significant improvements in both the speed and the final-state fidelity for preparing long-distance entangled states.

Blended Learning

ERIC Educational Resources Information Center

Halan, Deepak

2005-01-01

Blended learning basically refers to using several methods for teaching. It can be thought to be a learning program where more than one delivery mode is being used with the ultimate goal of optimizing the learning result and cost of program delivery. Examples of blended learning could be the combination of technology-based resources and…

Facilitating Facilitators: Enhancing PBL through a Structured Facilitator Development Program

ERIC Educational Resources Information Center

Salinitri, Francine D.; Wilhelm, Sheila M.; Crabtree, Brian L.

2015-01-01

With increasing adoption of the problem-based learning (PBL) model, creative approaches to enhancing facilitator training and optimizing resources to maintain effective learning in small groups is essential. We describe a theoretical framework for the development of a PBL facilitator training program that uses the constructivist approach as the…

Multi-Objective Programming for Lot-Sizing with Quantity Discount

NASA Astrophysics Data System (ADS)

Kang, He-Yau; Lee, Amy H. I.; Lai, Chun-Mei; Kang, Mei-Sung

2011-11-01

Multi-objective programming (MOP) is one of the popular methods for decision making in a complex environment. In a MOP, decision makers try to optimize two or more objectives simultaneously under various constraints. A complete optimal solution seldom exists, and a Pareto-optimal solution is usually used. Some methods, such as the weighting method which assigns priorities to the objectives and sets aspiration levels for the objectives, are used to derive a compromise solution. The ɛ-constraint method is a modified weight method. One of the objective functions is optimized while the other objective functions are treated as constraints and are incorporated in the constraint part of the model. This research considers a stochastic lot-sizing problem with multi-suppliers and quantity discounts. The model is transformed into a mixed integer programming (MIP) model next based on the ɛ-constraint method. An illustrative example is used to illustrate the practicality of the proposed model. The results demonstrate that the model is an effective and accurate tool for determining the replenishment of a manufacturer from multiple suppliers for multi-periods.

Optimal Energy Consumption Analysis of Natural Gas Pipeline

PubMed Central

Liu, Enbin; Li, Changjun; Yang, Yi

2014-01-01

There are many compressor stations along long-distance natural gas pipelines. Natural gas can be transported using different boot programs and import pressures, combined with temperature control parameters. Moreover, different transport methods have correspondingly different energy consumptions. At present, the operating parameters of many pipelines are determined empirically by dispatchers, resulting in high energy consumption. This practice does not abide by energy reduction policies. Therefore, based on a full understanding of the actual needs of pipeline companies, we introduce production unit consumption indicators to establish an objective function for achieving the goal of lowering energy consumption. By using a dynamic programming method for solving the model and preparing calculation software, we can ensure that the solution process is quick and efficient. Using established optimization methods, we analyzed the energy savings for the XQ gas pipeline. By optimizing the boot program, the import station pressure, and the temperature parameters, we achieved the optimal energy consumption. By comparison with the measured energy consumption, the pipeline now has the potential to reduce energy consumption by 11 to 16 percent. PMID:24955410

Optimization algorithms for large-scale multireservoir hydropower systems

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

Hiew, K.L.

Five optimization algorithms were vigorously evaluated based on applications on a hypothetical five-reservoir hydropower system. These algorithms are incremental dynamic programming (IDP), successive linear programing (SLP), feasible direction method (FDM), optimal control theory (OCT) and objective-space dynamic programming (OSDP). The performance of these algorithms were comparatively evaluated using unbiased, objective criteria which include accuracy of results, rate of convergence, smoothness of resulting storage and release trajectories, computer time and memory requirements, robustness and other pertinent secondary considerations. Results have shown that all the algorithms, with the exception of OSDP converge to optimum objective values within 1.0% difference from one another.more » The highest objective value is obtained by IDP, followed closely by OCT. Computer time required by these algorithms, however, differ by more than two orders of magnitude, ranging from 10 seconds in the case of OCT to a maximum of about 2000 seconds for IDP. With a well-designed penalty scheme to deal with state-space constraints, OCT proves to be the most-efficient algorithm based on its overall performance. SLP, FDM, and OCT were applied to the case study of Mahaweli project, a ten-powerplant system in Sri Lanka.« less

Workplace wellness recognition for optimizing workplace health: a presidential advisory from the American Heart Association.

PubMed

Fonarow, Gregg C; Calitz, Chris; Arena, Ross; Baase, Catherine; Isaac, Fikry W; Lloyd-Jones, Donald; Peterson, Eric D; Pronk, Nico; Sanchez, Eduardo; Terry, Paul E; Volpp, Kevin G; Antman, Elliott M

2015-05-19

The workplace is an important setting for promoting cardiovascular health and cardiovascular disease and stroke prevention in the United States. Well-designed, comprehensive workplace wellness programs have the potential to improve cardiovascular health and to reduce mortality, morbidity, and disability resulting from cardiovascular disease and stroke. Nevertheless, widespread implementation of comprehensive workplace wellness programs is lacking, and program composition and quality vary. Several organizations provide worksite wellness recognition programs; however, there is variation in recognition criteria, and they do not specifically focus on cardiovascular disease and stroke prevention. Although there is limited evidence to suggest that company performance on employer health management scorecards is associated with favorable healthcare cost trends, these data are not currently robust, and further evaluation is needed. As a recognized national leader in evidence-based guidelines, care systems, and quality programs, the American Heart Association/American Stroke Association is uniquely positioned and committed to promoting the adoption of comprehensive workplace wellness programs, as well as improving program quality and workforce health outcomes. As part of its commitment to improve the cardiovascular health of all Americans, the American Heart Association/American Stroke Association will promote science-based best practices for comprehensive workplace wellness programs and establish benchmarks for a national workplace wellness recognition program to assist employers in applying the best systems and strategies for optimal programming. The recognition program will integrate identification of a workplace culture of health and achievement of rigorous standards for cardiovascular health based on Life's Simple 7 metrics. In addition, the American Heart Association/American Stroke Association will develop resources that assist employers in meeting these rigorous standards, facilitating access to high-quality comprehensive workplace wellness programs for both employees and dependents, and fostering innovation and additional research. © 2015 American Heart Association, Inc.

Factors associated with the implementation of community-based peer-led health promotion programs: A scoping review.

PubMed

Lorthios-Guilledroit, Agathe; Richard, Lucie; Filiatrault, Johanne

2018-06-01

Peer education is growing in popularity as a useful health promotion strategy. However, optimal conditions for implementing peer-led health promotion programs (HPPs) remain unclear. This scoping review aimed to describe factors that can influence implementation of peer-led HPPs targeting adult populations. Five databases were searched using the keywords "health promotion/prevention", "implementation", "peers", and related terms. Studies were included if they reported at least one factor associated with the implementation of community-based peer-led HPPs. Fifty-five studies were selected for the analysis. The method known as "best fit framework synthesis" was used to analyze the factors identified in the selected papers. Many factors included in existing implementation conceptual frameworks were deemed applicable to peer-led HPPs. However, other factors related to individuals, programs, and implementation context also emerged from the analysis. Based on this synthesis, an adapted theoretical framework was elaborated, grounded in a complex adaptive system perspective and specifying potential mechanisms through which factors may influence implementation of community-based peer-led HPPs. Further research is needed to test the theoretical framework against empirical data. Findings from this scoping review increase our knowledge of the optimal conditions for implementing peer-led HPPs and thereby maximizing the benefits of such programs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

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

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, themore » proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.« less

Human motion planning based on recursive dynamics and optimal control techniques

NASA Technical Reports Server (NTRS)

Lo, Janzen; Huang, Gang; Metaxas, Dimitris

2002-01-01

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.

Optimal satisfaction degree in energy harvesting cognitive radio networks

NASA Astrophysics Data System (ADS)

Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui

2015-12-01

A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).

TH-B-207B-00: Pediatric Image Quality Optimization

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

NONE

This imaging educational program will focus on solutions to common pediatric image quality optimization challenges. The speakers will present collective knowledge on best practices in pediatric imaging from their experience at dedicated children’s hospitals. One of the most commonly encountered pediatric imaging requirements for the non-specialist hospital is pediatric CT in the emergency room setting. Thus, this educational program will begin with optimization of pediatric CT in the emergency department. Though pediatric cardiovascular MRI may be less common in the non-specialist hospitals, low pediatric volumes and unique cardiovascular anatomy make optimization of these techniques difficult. Therefore, our second speaker willmore » review best practices in pediatric cardiovascular MRI based on experiences from a children’s hospital with a large volume of cardiac patients. Learning Objectives: To learn techniques for optimizing radiation dose and image quality for CT of children in the emergency room setting. To learn solutions for consistently high quality cardiovascular MRI of children.« less

TH-B-207B-01: Optimizing Pediatric CT in the Emergency Department

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

Dodge, C.

This imaging educational program will focus on solutions to common pediatric image quality optimization challenges. The speakers will present collective knowledge on best practices in pediatric imaging from their experience at dedicated children’s hospitals. One of the most commonly encountered pediatric imaging requirements for the non-specialist hospital is pediatric CT in the emergency room setting. Thus, this educational program will begin with optimization of pediatric CT in the emergency department. Though pediatric cardiovascular MRI may be less common in the non-specialist hospitals, low pediatric volumes and unique cardiovascular anatomy make optimization of these techniques difficult. Therefore, our second speaker willmore » review best practices in pediatric cardiovascular MRI based on experiences from a children’s hospital with a large volume of cardiac patients. Learning Objectives: To learn techniques for optimizing radiation dose and image quality for CT of children in the emergency room setting. To learn solutions for consistently high quality cardiovascular MRI of children.« less

Performance seeking control program overview

NASA Technical Reports Server (NTRS)

Orme, John S.

1995-01-01

The Performance Seeking Control (PSC) program evolved from a series of integrated propulsion-flight control research programs flown at NASA Dryden Flight Research Center (DFRC) on an F-15. The first of these was the Digital Electronic Engine Control (DEEC) program and provided digital engine controls suitable for integration. The DEEC and digital electronic flight control system of the NASA F-15 were ideally suited for integrated controls research. The Advanced Engine Control System (ADECS) program proved that integrated engine and aircraft control could improve overall system performance. The objective of the PSC program was to advance the technology for a fully integrated propulsion flight control system. Whereas ADECS provided single variable control for an average engine, PSC controlled multiple propulsion system variables while adapting to the measured engine performance. PSC was developed as a model-based, adaptive control algorithm and included four optimization modes: minimum fuel flow at constant thrust, minimum turbine temperature at constant thrust, maximum thrust, and minimum thrust. Subsonic and supersonic flight testing were conducted at NASA Dryden covering the four PSC optimization modes and over the full throttle range. Flight testing of the PSC algorithm, conducted in a series of five flight test phases, has been concluded at NASA Dryden covering all four of the PSC optimization modes. Over a three year period and five flight test phases 72 research flights were conducted. The primary objective of flight testing was to exercise each PSC optimization mode and quantify the resulting performance improvements.

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

Deng, J.

This imaging educational program will focus on solutions to common pediatric image quality optimization challenges. The speakers will present collective knowledge on best practices in pediatric imaging from their experience at dedicated children’s hospitals. One of the most commonly encountered pediatric imaging requirements for the non-specialist hospital is pediatric CT in the emergency room setting. Thus, this educational program will begin with optimization of pediatric CT in the emergency department. Though pediatric cardiovascular MRI may be less common in the non-specialist hospitals, low pediatric volumes and unique cardiovascular anatomy make optimization of these techniques difficult. Therefore, our second speaker willmore » review best practices in pediatric cardiovascular MRI based on experiences from a children’s hospital with a large volume of cardiac patients. Learning Objectives: To learn techniques for optimizing radiation dose and image quality for CT of children in the emergency room setting. To learn solutions for consistently high quality cardiovascular MRI of children.« less

[Diagnosis and the technology for optimizing the medical support of a troop unit].

PubMed

Korshever, N G; Polkovov, S V; Lavrinenko, O V; Krupnov, P A; Anastasov, K N

2000-05-01

The work is devoted to investigation of the system of military unit medical support with the use of principles and states of organizational diagnosis; development of the method allowing to assess its functional activity; and determination of optimization trends. Basing on the conducted organizational diagnosis and expert inquiry the informative criteria were determined which characterize the stages of functioning of the military unit medical support system. To evaluate the success of military unit medical support the complex multi-criteria pattern was developed and algorithm of this process optimization was substantiated. Using the results obtained, particularly realization of principles and states of decision taking theory in machine program it is possible to solve more complex problem of comparison between any number of military units: to dispose them according to priority decrease; to select the programmed number of the best and worst; to determine the trends of activity optimization in corresponding medical service personnel.

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

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

PubMed

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

2011-12-01

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

Optimizing Interactive Development of Data-Intensive Applications

PubMed Central

Interlandi, Matteo; Tetali, Sai Deep; Gulzar, Muhammad Ali; Noor, Joseph; Condie, Tyson; Kim, Miryung; Millstein, Todd

2017-01-01

Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch jobs that execute programs (e.g., queries) compiled from a high-level language. These programs are often developed interactively by posing ad-hoc queries over the base data until a desired result is generated. We observe that there can be significant overlap in the structure of these queries used to derive the final program. Yet, each successive execution of a slightly modified query is performed anew, which can significantly increase the development cycle. Vega is an Apache Spark framework that we have implemented for optimizing a series of similar Spark programs, likely originating from a development or exploratory data analysis session. Spark developers (e.g., data scientists) can leverage Vega to significantly reduce the amount of time it takes to re-execute a modified Spark program, reducing the overall time to market for their Big Data applications. PMID:28405637

Clinical feasibility of exercise-based A-V interval optimization for cardiac resynchronization: a pilot study.

PubMed

Choudhuri, Indrajit; MacCarter, Dean; Shaw, Rachael; Anderson, Steve; St Cyr, John; Niazi, Imran

2014-11-01

One-third of eligible patients fail to respond to cardiac resynchronization therapy (CRT). Current methods to "optimize" the atrio-ventricular (A-V) interval are performed at rest, which may limit its efficacy during daily activities. We hypothesized that low-intensity cardiopulmonary exercise testing (CPX) could identify the most favorable physiologic combination of specific gas exchange parameters reflecting pulmonary blood flow or cardiac output, stroke volume, and left atrial pressure to guide determination of the optimal A-V interval. We assessed relative feasibility of determining the optimal A-V interval by three methods in 17 patients who underwent optimization of CRT: (1) resting echocardiographic optimization (the Ritter method), (2) resting electrical optimization (intrinsic A-V interval and QRS duration), and (3) during low-intensity, steady-state CPX. Five sequential, incremental A-V intervals were programmed in each method. Assessment of cardiopulmonary stability and potential influence on the CPX-based method were assessed. CPX and determination of a physiological optimal A-V interval was successfully completed in 94.1% of patients, slightly higher than the resting echo-based approach (88.2%). There was a wide variation in the optimal A-V delay determined by each method. There was no observed cardiopulmonary instability or impact of the implant procedure that affected determination of the CPX-based optimized A-V interval. Determining optimized A-V intervals by CPX is feasible. Proposed mechanisms explaining this finding and long-term impact require further study. ©2014 Wiley Periodicals, Inc.

Bell-Curve Based Evolutionary Strategies for Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

2001-01-01

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity. However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold. One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumbersome binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back and Dasgupta and Michalesicz. We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

Optimal design of geodesically stiffened composite cylindrical shells

NASA Technical Reports Server (NTRS)

Gendron, G.; Guerdal, Z.

1992-01-01

An optimization system based on the finite element code Computations Structural Mechanics (CSM) Testbed and the optimization program, Automated Design Synthesis (ADS), is described. The optimization system can be used to obtain minimum-weight designs of composite stiffened structures. Ply thickness, ply orientations, and stiffener heights can be used as design variables. Buckling, displacement, and material failure constraints can be imposed on the design. The system is used to conduct a design study of geodesically stiffened shells. For comparison purposes, optimal designs of unstiffened shells and shells stiffened by rings and stingers are also obtained. Trends in the design of geodesically stiffened shells are identified. An approach to include local stress concentrations during the design optimization process is then presented. The method is based on a global/local analysis technique. It employs spline interpolation functions to determine displacements and rotations from a global model which are used as 'boundary conditions' for the local model. The organization of the strategy in the context of an optimization process is described. The method is validated with an example.

Trajectory optimization for lunar rover performing vertical takeoff vertical landing maneuvers in the presence of terrain

NASA Astrophysics Data System (ADS)

Ma, Lin; Wang, Kexin; Xu, Zuhua; Shao, Zhijiang; Song, Zhengyu; Biegler, Lorenz T.

2018-05-01

This study presents a trajectory optimization framework for lunar rover performing vertical takeoff vertical landing (VTVL) maneuvers in the presence of terrain using variable-thrust propulsion. First, a VTVL trajectory optimization problem with three-dimensional kinematics and dynamics model, boundary conditions, and path constraints is formulated. Then, a finite-element approach transcribes the formulated trajectory optimization problem into a nonlinear programming (NLP) problem solved by a highly efficient NLP solver. A homotopy-based backtracking strategy is applied to enhance the convergence in solving the formulated VTVL trajectory optimization problem. The optimal thrust solution typically has a "bang-bang" profile considering that bounds are imposed on the magnitude of engine thrust. An adaptive mesh refinement strategy based on a constant Hamiltonian profile is designed to address the difficulty in locating the breakpoints in the thrust profile. Four scenarios are simulated. Simulation results indicate that the proposed trajectory optimization framework has sufficient adaptability to handle VTVL missions efficiently.

Outreach Science Education: Evidence-Based Studies in a Gene Technology Lab

ERIC Educational Resources Information Center

Scharfenberg, Franz-Josef; Bogner, Franz X.

2014-01-01

Nowadays, outreach labs are important informal learning environments in science education. After summarizing research to goals outreach labs focus on, we describe our evidence-based gene technology lab as a model of a research-driven outreach program. Evaluation-based optimizations of hands-on teaching based on cognitive load theory (additional…

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

PubMed

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

PubMed Central

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-01-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

Mutually unbiased bases and semi-definite programming

NASA Astrophysics Data System (ADS)

Brierley, Stephen; Weigert, Stefan

2010-11-01

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Gröbner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.

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

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning

PubMed Central

Starek, Joseph A.; Gomez, Javier V.; Schmerling, Edward; Janson, Lucas; Moreno, Luis; Pavone, Marco

2015-01-01

Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bidirectional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners. PMID:27004130

Analytical Model-Based Design Optimization of a Transverse Flux Machine

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

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

NASA Astrophysics Data System (ADS)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

The use of optimization techniques to design controlled diffusion compressor blading

NASA Technical Reports Server (NTRS)

Sanger, N. L.

1982-01-01

A method for automating compressor blade design using numerical optimization, and applied to the design of a controlled diffusion stator blade row is presented. A general purpose optimization procedure is employed, based on conjugate directions for locally unconstrained problems and on feasible directions for locally constrained problems. Coupled to the optimizer is an analysis package consisting of three analysis programs which calculate blade geometry, inviscid flow, and blade surface boundary layers. The optimizing concepts and selection of design objective and constraints are described. The procedure for automating the design of a two dimensional blade section is discussed, and design results are presented.

An enhanced export coefficient based optimization model for supporting agricultural nonpoint source pollution mitigation under uncertainty.

PubMed

Rong, Qiangqiang; Cai, Yanpeng; Chen, Bing; Yue, Wencong; Yin, Xin'an; Tan, Qian

2017-02-15

In this research, an export coefficient based dual inexact two-stage stochastic credibility constrained programming (ECDITSCCP) model was developed through integrating an improved export coefficient model (ECM), interval linear programming (ILP), fuzzy credibility constrained programming (FCCP) and a fuzzy expected value equation within a general two stage programming (TSP) framework. The proposed ECDITSCCP model can effectively address multiple uncertainties expressed as random variables, fuzzy numbers, pure and dual intervals. Also, the model can provide a direct linkage between pre-regulated management policies and the associated economic implications. Moreover, the solutions under multiple credibility levels can be obtained for providing potential decision alternatives for decision makers. The proposed model was then applied to identify optimal land use structures for agricultural NPS pollution mitigation in a representative upstream subcatchment of the Miyun Reservoir watershed in north China. Optimal solutions of the model were successfully obtained, indicating desired land use patterns and nutrient discharge schemes to get a maximum agricultural system benefits under a limited discharge permit. Also, numerous results under multiple credibility levels could provide policy makers with several options, which could help get an appropriate balance between system benefits and pollution mitigation. The developed ECDITSCCP model can be effectively applied to addressing the uncertain information in agricultural systems and shows great applicability to the land use adjustment for agricultural NPS pollution mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of dynamic programming to control khuzestan water resources system

USGS Publications Warehouse

Jamshidi, M.; Heidari, M.

1977-01-01

An approximate optimization technique based on discrete dynamic programming called discrete differential dynamic programming (DDDP), is employed to obtain the near optimal operation policies of a water resources system in the Khuzestan Province of Iran. The technique makes use of an initial nominal state trajectory for each state variable, and forms corridors around the trajectories. These corridors represent a set of subdomains of the entire feasible domain. Starting with such a set of nominal state trajectories, improvements in objective function are sought within the corridors formed around them. This leads to a set of new nominal trajectories upon which more improvements may be sought. Since optimization is confined to a set of subdomains, considerable savings in memory and computer time are achieved over that of conventional dynamic programming. The Kuzestan water resources system considered in this study is located in southwest Iran, and consists of two rivers, three reservoirs, three hydropower plants, and three irrigable areas. Data and cost benefit functions for the analysis were obtained either from the historical records or from similar studies. ?? 1977.

A model for managing sources of groundwater pollution

USGS Publications Warehouse

Gorelick, Steven M.

1982-01-01

The waste disposal capacity of a groundwater system can be maximized while maintaining water quality at specified locations by using a groundwater pollutant source management model that is based upon linear programing and numerical simulation. The decision variables of the management model are solute waste disposal rates at various facilities distributed over space. A concentration response matrix is used in the management model to describe transient solute transport and is developed using the U.S. Geological Survey solute transport simulation model. The management model was applied to a complex hypothetical groundwater system. Large-scale management models were formulated as dual linear programing problems to reduce numerical difficulties and computation time. Linear programing problems were solved using a numerically stable, available code. Optimal solutions to problems with successively longer management time horizons indicated that disposal schedules at some sites are relatively independent of the number of disposal periods. Optimal waste disposal schedules exhibited pulsing rather than constant disposal rates. Sensitivity analysis using parametric linear programing showed that a sharp reduction in total waste disposal potential occurs if disposal rates at any site are increased beyond their optimal values.

Poster — Thur Eve — 69: Computational Study of DVH-guided Cancer Treatment Planning Optimization Methods

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

Ghomi, Pooyan Shirvani; Zinchenko, Yuriy

2014-08-15

Purpose: To compare methods to incorporate the Dose Volume Histogram (DVH) curves into the treatment planning optimization. Method: The performance of three methods, namely, the conventional Mixed Integer Programming (MIP) model, a convex moment-based constrained optimization approach, and an unconstrained convex moment-based penalty approach, is compared using anonymized data of a prostate cancer patient. Three plans we generated using the corresponding optimization models. Four Organs at Risk (OARs) and one Tumor were involved in the treatment planning. The OARs and Tumor were discretized into total of 50,221 voxels. The number of beamlets was 943. We used commercially available optimization softwaremore » Gurobi and Matlab to solve the models. Plan comparison was done by recording the model runtime followed by visual inspection of the resulting dose volume histograms. Conclusion: We demonstrate the effectiveness of the moment-based approaches to replicate the set of prescribed DVH curves. The unconstrained convex moment-based penalty approach is concluded to have the greatest potential to reduce the computational effort and holds a promise of substantial computational speed up.« less

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Optimizing Biorefinery Design and Operations via Linear Programming Models

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

Talmadge, Michael; Batan, Liaw; Lamers, Patrick

The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LPmore » models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for maximizing the potential benefits of biomass utilization for production of fuels, chemicals and power.« less

On unified modeling, theory, and method for solving multi-scale global optimization problems

NASA Astrophysics Data System (ADS)

Gao, David Yang

2016-10-01

A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming.

PubMed

Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun

2015-01-01

The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.

A VLBI variance-covariance analysis interactive computer program. M.S. Thesis

NASA Technical Reports Server (NTRS)

Bock, Y.

1980-01-01

An interactive computer program (in FORTRAN) for the variance covariance analysis of VLBI experiments is presented for use in experiment planning, simulation studies and optimal design problems. The interactive mode is especially suited to these types of analyses providing ease of operation as well as savings in time and cost. The geodetic parameters include baseline vector parameters and variations in polar motion and Earth rotation. A discussion of the theroy on which the program is based provides an overview of the VLBI process emphasizing the areas of interest to geodesy. Special emphasis is placed on the problem of determining correlations between simultaneous observations from a network of stations. A model suitable for covariance analyses is presented. Suggestions towards developing optimal observation schedules are included.

Switching Patients to Home-Based Subcutaneous Immunoglobulin: an Economic Evaluation of an Interprofessional Drug Therapy Management Program.

PubMed

Perraudin, Clemence; Bourdin, Aline; Spertini, Francois; Berger, Jérôme; Bugnon, Olivier

2016-07-01

Home-based subcutaneous immunoglobulin (SCIg) therapy is an alternative to hospital-based intravenous infusions (IVIg). However, SCIg requires patient training and long-term support to ensure proper adherence, optimal efficacy and safety. We evaluated if switching patients to home-based SCIg including an interprofessional drug therapy management program (physician, community pharmacist and nurse) would be cost-effective within the Swiss healthcare system. A 3-year cost-minimization analysis was performed from a societal perspective comparing monthly IVIg in an outpatient clinic and home-based weekly SCIg including an interprofessional program. Healthcare costs (immunoglobulin, professional time, infusion pump and disposables) were derived from administrative data. Transportation and productivity loss were estimated by expert opinion. The results were expressed in Swiss francs (CHF) and converted to Euros and US dollars (1 CHF = 0.92€, 1 CHF = $1.02; www.xe.com , 12/14/2015). Under base case assumptions, SCIg was estimated to cost 35,862 CHF (33,134€; $36,595) per patient during the first year and 30,309 CHF (28,004€; $30,929) in subsequent years versus 35,370 CHF (32,679€; $36,095) per year for IVIg. The total savings from switching to SCIg with the interprofessional program were 9630 CHF (8897€; $9828) per patient over 3 years. The results were relatively sensitive to the cost per gram of IgG, the cost of equipment and the annual number of infusions. Home-based SCIg including an interprofessional therapy management program may be an efficient alternative for patients. The program provides long-term support from self-administration training to the responsible use of therapy (proper adherence, optimal efficacy and safety). Over the short term, additional costs from purchasing equipment and the drug therapy management program were offset by avoiding hospital costs.

VAXELN Experimentation: Programming a Real-Time Periodic Task Dispatcher Using VAXELN Ada 1.1

DTIC Science & Technology

1987-11-01

synchronization to the SQM and VAXELN semaphores. Based on real-time scheduling theory, the optimal rate-monotonic scheduling algorithm [Lui 73...schedulability test based on the rate-monotonic algorithm , namely task-lumping [Sha 871, was necessary to cal- culate the theoretically expected schedulability...8217 Guide Digital Equipment Corporation, Maynard, MA, 1986. [Lui 73] Liu, C.L., Layland, J.W. Scheduling Algorithms for Multi-programming in a Hard-Real-Time

A technique for locating function roots and for satisfying equality constraints in optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1991-01-01

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.

A technique for locating function roots and for satisfying equality constraints in optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.

1992-01-01

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.

A Passion for Learning: The Theory and Practice of Optimal Match at the University of Washington

ERIC Educational Resources Information Center

Noble, Kathleen D.; Childers, Sarah A.

2008-01-01

Early entrance from secondary school to university, based on the principle of optimal match, is a rare but highly effective educational strategy for many gifted students. The University of Washington offers two early entrance options for gifted adolescents: the Early Entrance Program for students prior to age 15, and the UW Academy for Young…

Nonlinear optimization with linear constraints using a projection method

NASA Technical Reports Server (NTRS)

Fox, T.

1982-01-01

Nonlinear optimization problems that are encountered in science and industry are examined. A method of projecting the gradient vector onto a set of linear contraints is developed, and a program that uses this method is presented. The algorithm that generates this projection matrix is based on the Gram-Schmidt method and overcomes some of the objections to the Rosen projection method.

Optimal Path Planning Program for Autonomous Speed Sprayer in Orchard Using Order-Picking Algorithm

NASA Astrophysics Data System (ADS)

Park, T. S.; Park, S. J.; Hwang, K. Y.; Cho, S. I.

This study was conducted to develop a software program which computes optimal path for autonomous navigation in orchard, especially for speed sprayer. Possibilities of autonomous navigation in orchard were shown by other researches which have minimized distance error between planned path and performed path. But, research of planning an optimal path for speed sprayer in orchard is hardly founded. In this study, a digital map and a database for orchard which contains GPS coordinate information (coordinates of trees and boundary of orchard) and entity information (heights and widths of trees, radius of main stem of trees, disease of trees) was designed. An orderpicking algorithm which has been used for management of warehouse was used to calculate optimum path based on the digital map. Database for digital map was created by using Microsoft Access and graphic interface for database was made by using Microsoft Visual C++ 6.0. It was possible to search and display information about boundary of an orchard, locations of trees, daily plan for scattering chemicals and plan optimal path on different orchard based on digital map, on each circumstance (starting speed sprayer in different location, scattering chemicals for only selected trees).

Optimal multi-floor plant layout based on the mathematical programming and particle swarm optimization.

PubMed

Lee, Chang Jun

2015-01-01

In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study.

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

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-09-01

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

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.

PubMed

Castelli, Mauro; Trujillo, Leonardo; Vanneschi, Leonardo

2015-01-01

Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

A Review of the Impacts of Different Approaches for Diabetes Prevention and a Framework for Making Investment Decisions

PubMed Central

2018-01-01

This paper selectively reviews the economic research on individual (i.e., diabetes prevention programs and financial rewards for weight loss) and population-wide based diabetes prevention interventions (such as food taxes, nutritional labeling, and worksite wellness programs) that demonstrate a direct reduction in diabetes incidence or improvements in diabetes risk factors such as weight, glucose or glycated hemoglobin. The paper suggests a framework to guide decision makers on how to use the available evidence to determine the optimal allocation of resources across population-wide and individual-based interventions. This framework should also assist in the discussion of what parameters are needed from research to inform decision-making on what might be the optimal mix of strategies to reduce diabetes prevalence. PMID:29543711

A Review of the Impacts of Different Approaches for Diabetes Prevention and a Framework for Making Investment Decisions.

PubMed

Alva, Maria L

2018-03-15

This paper selectively reviews the economic research on individual (i.e., diabetes prevention programs and financial rewards for weight loss) and population-wide based diabetes prevention interventions (such as food taxes, nutritional labeling, and worksite wellness programs) that demonstrate a direct reduction in diabetes incidence or improvements in diabetes risk factors such as weight, glucose or glycated hemoglobin. The paper suggests a framework to guide decision makers on how to use the available evidence to determine the optimal allocation of resources across population-wide and individual-based interventions. This framework should also assist in the discussion of what parameters are needed from research to inform decision-making on what might be the optimal mix of strategies to reduce diabetes prevalence.

A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking*

PubMed Central

Nan, Feng; Moghadasi, Mohammad; Vakili, Pirooz; Vajda, Sandor; Kozakov, Dima; Ch. Paschalidis, Ioannis

2015-01-01

We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods. PMID:25914440

PCSYS: The optimal design integration system picture drawing system with hidden line algorithm capability for aerospace vehicle configurations

NASA Technical Reports Server (NTRS)

Hague, D. S.; Vanderburg, J. D.

1977-01-01

A vehicle geometric definition based upon quadrilateral surface elements to produce realistic pictures of an aerospace vehicle. The PCSYS programs can be used to visually check geometric data input, monitor geometric perturbations, and to visualize the complex spatial inter-relationships between the internal and external vehicle components. PCSYS has two major component programs. The between program, IMAGE, draws a complex aerospace vehicle pictorial representation based on either an approximate but rapid hidden line algorithm or without any hidden line algorithm. The second program, HIDDEN, draws a vehicle representation using an accurate but time consuming hidden line algorithm.

SNR-based queue observations at CFHT

NASA Astrophysics Data System (ADS)

Devost, Daniel; Moutou, Claire; Manset, Nadine; Mahoney, Billy; Burdullis, Todd; Cuillandre, Jean-Charles; Racine, René

2016-07-01

In an effort to optimize the night time utilizing the exquisite weather on Maunakea, CFHT has equipped its dome with vents and is now moving its Queued Scheduled Observing (QSO)1 based operations toward Signal to Noise Ratio (SNR) observing. In this new mode, individual exposure times for a science program are estimated using a model that uses measurements of the weather conditions as input and the science program is considered completed when the depth required by the scientific requirements are reached. These changes allow CFHT to make better use of the excellent seeing conditions provided by Maunakea, allowing us to complete programs in a shorter time than allocated to the science programs.

Programs for analysis and resizing of complex structures. [computerized minimum weight design

NASA Technical Reports Server (NTRS)

Haftka, R. T.; Prasad, B.

1978-01-01

The paper describes the PARS (Programs for Analysis and Resizing of Structures) system. PARS is a user oriented system of programs for the minimum weight design of structures modeled by finite elements and subject to stress, displacement, flutter and thermal constraints. The system is built around SPAR - an efficient and modular general purpose finite element program, and consists of a series of processors that communicate through the use of a data base. An efficient optimizer based on the Sequence of Unconstrained Minimization Technique (SUMT) with an extended interior penalty function and Newton's method is used. Several problems are presented for demonstration of the system capabilities.

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

PubMed

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

2017-01-25

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

Mathematical programming models for the economic design and assessment of wind energy conversion systems

NASA Astrophysics Data System (ADS)

Reinert, K. A.

The use of linear decision rules (LDR) and chance constrained programming (CCP) to optimize the performance of wind energy conversion clusters coupled to storage systems is described. Storage is modelled by LDR and output by CCP. The linear allocation rule and linear release rule prescribe the size and optimize a storage facility with a bypass. Chance constraints are introduced to explicitly treat reliability in terms of an appropriate value from an inverse cumulative distribution function. Details of deterministic programming structure and a sample problem involving a 500 kW and a 1.5 MW WECS are provided, considering an installed cost of $1/kW. Four demand patterns and three levels of reliability are analyzed for optimizing the generator choice and the storage configuration for base load and peak operating conditions. Deficiencies in ability to predict reliability and to account for serial correlations are noted in the model, which is concluded useful for narrowing WECS design options.

Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

PubMed

Legendre, Audrey; Angel, Eric; Tahi, Fariza

2018-01-15

RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

Generating moment matching scenarios using optimization techniques

DOE PAGES

Mehrotra, Sanjay; Papp, Dávid

2013-05-16

An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the distribution can be defined over an arbitrary set. This allows for a reduction in the number of scenarios and allows the scenarios to be better tailored to the problem at hand. The method is based on a semi-infinite linear programming formulation of the problem thatmore » is shown to be solvable with polynomial iteration complexity. A practical column generation method is implemented. The column generation subproblems are polynomial optimization problems; however, they need not be solved to optimality. It is found that the columns in the column generation approach can be efficiently generated by random sampling. The number of scenarios generated matches a lower bound of Tchakaloff's. The rate of convergence of the approximation error is established for continuous integrands, and an improved bound is given for smooth integrands. Extensive numerical experiments are presented in which variants of the proposed method are compared to Monte Carlo and quasi-Monte Carlo methods on both numerical integration problems and stochastic optimization problems. The benefits of being able to match any prescribed set of moments, rather than all moments up to a certain order, is also demonstrated using optimization problems with 100-dimensional random vectors. Here, empirical results show that the proposed approach outperforms Monte Carlo and quasi-Monte Carlo based approaches on the tested problems.« less

Conjugate gradient optimization programs for shuttle reentry

NASA Technical Reports Server (NTRS)

Powers, W. F.; Jacobson, R. A.; Leonard, D. A.

1972-01-01

Two computer programs for shuttle reentry trajectory optimization are listed and described. Both programs use the conjugate gradient method as the optimization procedure. The Phase 1 Program is developed in cartesian coordinates for a rotating spherical earth, and crossrange, downrange, maximum deceleration, total heating, and terminal speed, altitude, and flight path angle are included in the performance index. The programs make extensive use of subroutines so that they may be easily adapted to other atmospheric trajectory optimization problems.

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Study on Web-Based Tool for Regional Agriculture Industry Structure Optimization Using Ajax

NASA Astrophysics Data System (ADS)

Huang, Xiaodong; Zhu, Yeping

According to the research status of regional agriculture industry structure adjustment information system and the current development of information technology, this paper takes web-based regional agriculture industry structure optimization tool as research target. This paper introduces Ajax technology and related application frameworks to build an auxiliary toolkit of decision support system for agricultural policy maker and economy researcher. The toolkit includes a “one page” style component of regional agriculture industry structure optimization which provides agile arguments setting method that enables applying sensitivity analysis and usage of data and comparative advantage analysis result, and a component that can solve the linear programming model and its dual problem by simplex method.

Bell-Curve Based Evolutionary Strategies for Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

2000-01-01

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity (Reeves 1997). However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold (Glover 1998). One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumber-some binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back (1996) and Dasgupta and Michalesicz (1997). We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

An Optimization Model for the Allocation of University Based Merit Aid

ERIC Educational Resources Information Center

Sugrue, Paul K.

2010-01-01

The allocation of merit-based financial aid during the college admissions process presents postsecondary institutions with complex and financially expensive decisions. This article describes the application of linear programming as a decision tool in merit based financial aid decisions at a medium size private university. The objective defined for…

Optimal investment in a portfolio of HIV prevention programs.

PubMed

Zaric, G S; Brandeau, M L

2001-01-01

In this article, the authors determine the optimal allocation of HIV prevention funds and investigate the impact of different allocation methods on health outcomes. The authors present a resource allocation model that can be used to determine the allocation of HIV prevention funds that maximizes quality-adjusted life years (or life years) gained or HIV infections averted in a population over a specified time horizon. They apply the model to determine the allocation of a limited budget among 3 types of HIV prevention programs in a population of injection drug users and nonusers: needle exchange programs, methadone maintenance treatment, and condom availability programs. For each prevention program, the authors estimate a production function that relates the amount invested to the associated change in risky behavior. The authors determine the optimal allocation of funds for both objective functions for a high-prevalence population and a low-prevalence population. They also consider the allocation of funds under several common rules of thumb that are used to allocate HIV prevention resources. It is shown that simpler allocation methods (e.g., allocation based on HIV incidence or notions of equity among population groups) may lead to alloctions that do not yield the maximum health benefit. The optimal allocation of HIV prevention funds in a population depends on HIV prevalence and incidence, the objective function, the production functions for the prevention programs, and other factors. Consideration of cost, equity, and social and political norms may be important when allocating HIV prevention funds. The model presented in this article can help decision makers determine the health consequences of different allocations of funds.

Surgical simulation: Current practices and future perspectives for technical skills training.

PubMed

Bjerrum, Flemming; Thomsen, Ann Sofia Skou; Nayahangan, Leizl Joy; Konge, Lars

2018-06-17

Simulation-based training (SBT) has become a standard component of modern surgical education, yet successful implementation of evidence-based training programs remains challenging. In this narrative review, we use Kern's framework for curriculum development to describe where we are now and what lies ahead for SBT within surgery with a focus on technical skills in operative procedures. Despite principles for optimal SBT (proficiency-based, distributed, and deliberate practice) having been identified, massed training with fixed time intervals or a fixed number of repetitions is still being extensively used, and simulators are generally underutilized. SBT should be part of surgical training curricula, including theoretical, technical, and non-technical skills, and be based on relevant needs assessments. Furthermore, training should follow evidence-based theoretical principles for optimal training, and the effect of training needs to be evaluated using relevant outcomes. There is a larger, still unrealized potential of surgical SBT, which may be realized in the near future as simulator technologies evolve, more evidence-based training programs are implemented, and cost-effectiveness and impact on patient safety is clearly demonstrated.

Can hydro-economic river basin models simulate water shadow prices under asymmetric access?

PubMed

Kuhn, A; Britz, W

2012-01-01

Hydro-economic river basin models (HERBM) based on mathematical programming are conventionally formulated as explicit 'aggregate optimization' problems with a single, aggregate objective function. Often unintended, this format implicitly assumes that decisions on water allocation are made via central planning or functioning markets such as to maximize social welfare. In the absence of perfect water markets, however, individually optimal decisions by water users will differ from the social optimum. Classical aggregate HERBMs cannot simulate that situation and thus might be unable to describe existing institutions governing access to water and might produce biased results for alternative ones. We propose a new solution format for HERBMs, based on the format of the mixed complementarity problem (MCP), where modified shadow price relations express spatial externalities resulting from asymmetric access to water use. This new problem format, as opposed to commonly used linear (LP) or non-linear programming (NLP) approaches, enables the simultaneous simulation of numerous 'independent optimization' decisions by multiple water users while maintaining physical interdependences based on water use and flow in the river basin. We show that the alternative problem format allows the formulation HERBMs that yield more realistic results when comparing different water management institutions.

Component-based integration of chemistry and optimization software.

PubMed

Kenny, Joseph P; Benson, Steven J; Alexeev, Yuri; Sarich, Jason; Janssen, Curtis L; McInnes, Lois Curfman; Krishnan, Manojkumar; Nieplocha, Jarek; Jurrus, Elizabeth; Fahlstrom, Carl; Windus, Theresa L

2004-11-15

Typical scientific software designs make rigid assumptions regarding programming language and data structures, frustrating software interoperability and scientific collaboration. Component-based software engineering is an emerging approach to managing the increasing complexity of scientific software. Component technology facilitates code interoperability and reuse. Through the adoption of methodology and tools developed by the Common Component Architecture Forum, we have developed a component architecture for molecular structure optimization. Using the NWChem and Massively Parallel Quantum Chemistry packages, we have produced chemistry components that provide capacity for energy and energy derivative evaluation. We have constructed geometry optimization applications by integrating the Toolkit for Advanced Optimization, Portable Extensible Toolkit for Scientific Computation, and Global Arrays packages, which provide optimization and linear algebra capabilities. We present a brief overview of the component development process and a description of abstract interfaces for chemical optimizations. The components conforming to these abstract interfaces allow the construction of applications using different chemistry and mathematics packages interchangeably. Initial numerical results for the component software demonstrate good performance, and highlight potential research enabled by this platform.

Optimization of spatiotemporally fractionated radiotherapy treatments with bounds on the achievable benefit

NASA Astrophysics Data System (ADS)

Gaddy, Melissa R.; Yıldız, Sercan; Unkelbach, Jan; Papp, Dávid

2018-01-01

Spatiotemporal fractionation schemes, that is, treatments delivering different dose distributions in different fractions, can potentially lower treatment side effects without compromising tumor control. This can be achieved by hypofractionating parts of the tumor while delivering approximately uniformly fractionated doses to the surrounding tissue. Plan optimization for such treatments is based on biologically effective dose (BED); however, this leads to computationally challenging nonconvex optimization problems. Optimization methods that are in current use yield only locally optimal solutions, and it has hitherto been unclear whether these plans are close to the global optimum. We present an optimization framework to compute rigorous bounds on the maximum achievable normal tissue BED reduction for spatiotemporal plans. The approach is demonstrated on liver tumors, where the primary goal is to reduce mean liver BED without compromising any other treatment objective. The BED-based treatment plan optimization problems are formulated as quadratically constrained quadratic programming (QCQP) problems. First, a conventional, uniformly fractionated reference plan is computed using convex optimization. Then, a second, nonconvex, QCQP model is solved to local optimality to compute a spatiotemporally fractionated plan that minimizes mean liver BED, subject to the constraints that the plan is no worse than the reference plan with respect to all other planning goals. Finally, we derive a convex relaxation of the second model in the form of a semidefinite programming problem, which provides a rigorous lower bound on the lowest achievable mean liver BED. The method is presented on five cases with distinct geometries. The computed spatiotemporal plans achieve 12-35% mean liver BED reduction over the optimal uniformly fractionated plans. This reduction corresponds to 79-97% of the gap between the mean liver BED of the uniform reference plans and our lower bounds on the lowest achievable mean liver BED. The results indicate that spatiotemporal treatments can achieve substantial reductions in normal tissue dose and BED, and that local optimization techniques provide high-quality plans that are close to realizing the maximum potential normal tissue dose reduction.

Runway Scheduling Using Generalized Dynamic Programming

NASA Technical Reports Server (NTRS)

Montoya, Justin; Wood, Zachary; Rathinam, Sivakumar

2011-01-01

A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway fight sequences that are optimal for both runway throughput and delay. Realistic time-based operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour ad can be 100x faster.

Structural design using equilibrium programming formulations

NASA Technical Reports Server (NTRS)

Scotti, Stephen J.

1995-01-01

Solutions to increasingly larger structural optimization problems are desired. However, computational resources are strained to meet this need. New methods will be required to solve increasingly larger problems. The present approaches to solving large-scale problems involve approximations for the constraints of structural optimization problems and/or decomposition of the problem into multiple subproblems that can be solved in parallel. An area of game theory, equilibrium programming (also known as noncooperative game theory), can be used to unify these existing approaches from a theoretical point of view (considering the existence and optimality of solutions), and be used as a framework for the development of new methods for solving large-scale optimization problems. Equilibrium programming theory is described, and existing design techniques such as fully stressed design and constraint approximations are shown to fit within its framework. Two new structural design formulations are also derived. The first new formulation is another approximation technique which is a general updating scheme for the sensitivity derivatives of design constraints. The second new formulation uses a substructure-based decomposition of the structure for analysis and sensitivity calculations. Significant computational benefits of the new formulations compared with a conventional method are demonstrated.

Optimal reactive planning with security constraints

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

Thomas, W.R.; Cheng, D.T.Y.; Dixon, A.M.

1995-12-31

The National Grid Company (NGC) of England and Wales has developed a computer program, SCORPION, to help system planners optimize the location and size of new reactive compensation plant on the transmission system. The reactive power requirements of the NGC system have risen as a result of increased power flows and the shorter timescale on which power stations are commissioned and withdrawn from service. In view of the high costs involved, it is important that reactive compensation be installed as economically as possible, without compromising security. Traditional methods based on iterative use of a load flow program are labor intensivemore » and subjective. SCORPION determines a near-optimal pattern of new reactive sources which are required to satisfy voltage constraints for normal and contingent states of operation of the transmission system. The algorithm processes the system states sequentially, instead of optimizing all of them simultaneously. This allows a large number of system states to be considered with an acceptable run time and computer memory requirement. Installed reactive sources are treated as continuous, rather than discrete, variables. However, the program has a restart facility which enables the user to add realistically sized reactive sources explicitly and thereby work towards a realizable solution to the planning problem.« less

An optimization framework for workplace charging strategies

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

Huang, Yongxi; Zhou, Yan

2015-03-01

The workplace charging (WPC) has been recently recognized as the most important secondary charging point next to residential charging for plug-in electric vehicles (PEVs). The current WPC practice is spontaneous and grants every PEV a designated charger, which may not be practical or economic when there are a large number of PEVs present at workplace. This study is the first research undertaken that develops an optimization framework for WPC strategies to satisfy all charging demand while explicitly addressing different eligible levels of charging technology and employees’ demographic distributions. The optimization model is to minimize the lifetime cost of equipment, installations,more » and operations, and is formulated as an integer program. We demonstrate the applicability of the model using numerical examples based on national average data. The results indicate that the proposed optimization model can reduce the total cost of running a WPC system by up to 70% compared to the current practice. The WPC strategies are sensitive to the time windows and installation costs, and dominated by the PEV population size. The WPC has also been identified as an alternative sustainable transportation program to the public transit subsidy programs for both economic and environmental advantages.« less

Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming.

PubMed

Glocker, Ben; Paragios, Nikos; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir

2007-01-01

In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.

Performance seeking control: Program overview and future directions

NASA Technical Reports Server (NTRS)

Gilyard, Glenn B.; Orme, John S.

1993-01-01

A flight test evaluation of the performance-seeking control (PSC) algorithm on the NASA F-15 highly integrated digital electronic control research aircraft was conducted for single-engine operation at subsonic and supersonic speeds. The model-based PSC system was developed with three optimization modes: minimum fuel flow at constant thrust, minimum turbine temperature at constant thrust, and maximum thrust at maximum dry and full afterburner throttle settings. Subsonic and supersonic flight testing were conducted at the NASA Dryden Flight Research Facility covering the three PSC optimization modes and over the full throttle range. Flight results show substantial benefits. In the maximum thrust mode, thrust increased up to 15 percent at subsonic and 10 percent at supersonic flight conditions. The minimum fan turbine inlet temperature mode reduced temperatures by more than 100 F at high altitudes. The minimum fuel flow mode results decreased fuel consumption up to 2 percent in the subsonic regime and almost 10 percent supersonically. These results demonstrate that PSC technology can benefit the next generation of fighter or transport aircraft. NASA Dryden is developing an adaptive aircraft performance technology system that is measurement based and uses feedback to ensure optimality. This program will address the technical weaknesses identified in the PSC program and will increase performance gains.

YAPPA: a Compiler-Based Parallelization Framework for Irregular Applications on MPSoCs

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

Lovergine, Silvia; Tumeo, Antonino; Villa, Oreste

Modern embedded systems include hundreds of cores. Because of the difficulty in providing a fast, coherent memory architecture, these systems usually rely on non-coherent, non-uniform memory architectures with private memories for each core. However, programming these systems poses significant challenges. The developer must extract large amounts of parallelism, while orchestrating communication among cores to optimize application performance. These issues become even more significant with irregular applications, which present data sets difficult to partition, unpredictable memory accesses, unbalanced control flow and fine grained communication. Hand-optimizing every single aspect is hard and time-consuming, and it often does not lead to the expectedmore » performance. There is a growing gap between such complex and highly-parallel architectures and the high level languages used to describe the specification, which were designed for simpler systems and do not consider these new issues. In this paper we introduce YAPPA (Yet Another Parallel Programming Approach), a compilation framework for the automatic parallelization of irregular applications on modern MPSoCs based on LLVM. We start by considering an efficient parallel programming approach for irregular applications on distributed memory systems. We then propose a set of transformations that can reduce the development and optimization effort. The results of our initial prototype confirm the correctness of the proposed approach.« less

Metamodeling and the Critic-based approach to multi-level optimization.

PubMed

Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J

2012-08-01

Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. Copyright © 2012 Elsevier Ltd. All rights reserved.

Uplink Packet-Data Scheduling in DS-CDMA Systems

NASA Astrophysics Data System (ADS)

Choi, Young Woo; Kim, Seong-Lyun

In this letter, we consider the uplink packet scheduling for non-real-time data users in a DS-CDMA system. As an effort to jointly optimize throughput and fairness, we formulate a time-span minimization problem incorporating the time-multiplexing of different simultaneous transmission schemes. Based on simple rules, we propose efficient scheduling algorithms and compare them with the optimal solution obtained by linear programming.

Health Optimizing Physical Education (HOPE): A New Curriculum for School Programs--Part 2: Teacher Knowledge and Collaboration

ERIC Educational Resources Information Center

Metzler, Michael W.; McKenzie, Thomas L.; van der Mars, Hans; Barrett-Williams, Shannon L.; Ellis, Rebecca

2013-01-01

Part 1 of this article, which appeared in the April 2013 of JOPERD, presented the theoretical foundation and evidence-based needs for a main-theme curriculum model called Health Optimizing Physical Education (HOPE) for schools. It also described eight strands that could be used to plan, implement, and assess this version of a comprehensive school…

Cost Overrun Optimism: Fact or Fiction

DTIC Science & Technology

2016-02-29

Base, OH. Homgren, C. T. (1990). In G. Foster (Ed.), Cost accounting : A managerial emphasis (7th ed.). Englewood Cliffs, NJ: Prentice Hall. Morrison... Accounting Office. Gansler, J. S. (1989). Affording defense. Cambridge, MA: The MIT Press. Heise, S. R. (1991). A review of cost performance index...Image designed by Diane Fleischer Cost Overrun Optimism: FACT or FICTION? Maj David D. Christensen, USAF Program managers are advocates by

Numerical optimization methods for controlled systems with parameters

NASA Astrophysics Data System (ADS)

Tyatyushkin, A. I.

2017-10-01

First- and second-order numerical methods for optimizing controlled dynamical systems with parameters are discussed. In unconstrained-parameter problems, the control parameters are optimized by applying the conjugate gradient method. A more accurate numerical solution in these problems is produced by Newton's method based on a second-order functional increment formula. Next, a general optimal control problem with state constraints and parameters involved on the righthand sides of the controlled system and in the initial conditions is considered. This complicated problem is reduced to a mathematical programming one, followed by the search for optimal parameter values and control functions by applying a multimethod algorithm. The performance of the proposed technique is demonstrated by solving application problems.

Whole-School Approaches to Incorporating Mindfulness-Based Interventions: Supporting the Capacity for Optimal Functioning in School Settings

ERIC Educational Resources Information Center

Kielty, Michele L.; Gilligan, Tammy D.; Staton, A. Renee

2017-01-01

With any intervention program, involving all stakeholders in a joint effort toward implementation is most likely to lead to success. Whole-school approaches that involve school personnel, students, families, and local communities have been associated with positive, sustained outcomes. For mindfulness training programs to generate the most…

E-Learning Technologies: Employing Matlab Web Server to Facilitate the Education of Mathematical Programming

ERIC Educational Resources Information Center

Karagiannis, P.; Markelis, I.; Paparrizos, K.; Samaras, N.; Sifaleras, A.

2006-01-01

This paper presents new web-based educational software (webNetPro) for "Linear Network Programming." It includes many algorithms for "Network Optimization" problems, such as shortest path problems, minimum spanning tree problems, maximum flow problems and other search algorithms. Therefore, webNetPro can assist the teaching process of courses such…

Factors influencing law enforcement decisions to adopt an evidence-based robbery prevention program.

PubMed

Cabell, A; Casteel, C; Chronister, T; Nocera, M; Vladutiu, C J; Peek-Asa, C

2013-12-01

Homicide is the leading cause of workplace death among small retail and service businesses in the United States. Evidence-based programs have been shown to reduce robbery and robbery-related crimes in small retail businesses; however, reaching small businesses with programs has been difficult. As small businesses typically have no corporate backing or trade affiliation, police departments have been identified as potential vehicles for program dissemination. A national sample of 300 law enforcement agencies was surveyed to identify facilitators and barriers to adoption and sustainability of an evidence-based program. The questionnaire was developed using behavioral theory concepts and administered via telephone. Preliminary findings suggest the primary facilitators to program adoption included organizational capacity factors such as staff buy-in, dedicated personnel and financial support. Competing responsibilities was the primary barrier identified by agencies. Agency size and program complexity were identified as potential predictors of program adoption. Identifying agency and program-specific characteristics that influence program adoption by law enforcement agencies will be valuable for marketing programs to agencies that have the infrastructure to support and sustain program dissemination. Understanding these factors will optimize the reach of evidence-based strategies to small businesses.

Interactive Land-Use Optimization Using Laguerre Voronoi Diagram with Dynamic Generating Point Allocation

NASA Astrophysics Data System (ADS)

Chaidee, S.; Pakawanwong, P.; Suppakitpaisarn, V.; Teerasawat, P.

2017-09-01

In this work, we devise an efficient method for the land-use optimization problem based on Laguerre Voronoi diagram. Previous Voronoi diagram-based methods are more efficient and more suitable for interactive design than discrete optimization-based method, but, in many cases, their outputs do not satisfy area constraints. To cope with the problem, we propose a force-directed graph drawing algorithm, which automatically allocates generating points of Voronoi diagram to appropriate positions. Then, we construct a Laguerre Voronoi diagram based on these generating points, use linear programs to adjust each cell, and reconstruct the diagram based on the adjustment. We adopt the proposed method to the practical case study of Chiang Mai University's allocated land for a mixed-use complex. For this case study, compared to other Voronoi diagram-based method, we decrease the land allocation error by 62.557 %. Although our computation time is larger than the previous Voronoi-diagram-based method, it is still suitable for interactive design.

Three Program Architecture for Design Optimization

NASA Technical Reports Server (NTRS)

Miura, Hirokazu; Olson, Lawrence E. (Technical Monitor)

1998-01-01

In this presentation, I would like to review historical perspective on the program architecture used to build design optimization capabilities based on mathematical programming and other numerical search techniques. It is rather straightforward to classify the program architecture in three categories as shown above. However, the relative importance of each of the three approaches has not been static, instead dynamically changing as the capabilities of available computational resource increases. For example, we considered that the direct coupling architecture would never be used for practical problems, but availability of such computer systems as multi-processor. In this presentation, I would like to review the roles of three architecture from historical as well as current and future perspective. There may also be some possibility for emergence of hybrid architecture. I hope to provide some seeds for active discussion where we are heading to in the very dynamic environment for high speed computing and communication.

Data compression of discrete sequence: A tree based approach using dynamic programming

NASA Technical Reports Server (NTRS)

Shivaram, Gurusrasad; Seetharaman, Guna; Rao, T. R. N.

1994-01-01

A dynamic programming based approach for data compression of a ID sequence is presented. The compression of an input sequence of size N to that of a smaller size k is achieved by dividing the input sequence into k subsequences and replacing the subsequences by their respective average values. The partitioning of the input sequence is carried with the intention of reducing the mean squared error in the reconstructed sequence. The complexity involved in finding the partitions which would result in such an optimal compressed sequence is reduced by using the dynamic programming approach, which is presented.

Partial discharge localization in power transformers based on the sequential quadratic programming-genetic algorithm adopting acoustic emission techniques

NASA Astrophysics Data System (ADS)

Liu, Hua-Long; Liu, Hua-Dong

2014-10-01

Partial discharge (PD) in power transformers is one of the prime reasons resulting in insulation degradation and power faults. Hence, it is of great importance to study the techniques of the detection and localization of PD in theory and practice. The detection and localization of PD employing acoustic emission (AE) techniques, as a kind of non-destructive testing, plus due to the advantages of powerful capability of locating and high precision, have been paid more and more attention. The localization algorithm is the key factor to decide the localization accuracy in AE localization of PD. Many kinds of localization algorithms exist for the PD source localization adopting AE techniques including intelligent and non-intelligent algorithms. However, the existed algorithms possess some defects such as the premature convergence phenomenon, poor local optimization ability and unsuitability for the field applications. To overcome the poor local optimization ability and easily caused premature convergence phenomenon of the fundamental genetic algorithm (GA), a new kind of improved GA is proposed, namely the sequence quadratic programming-genetic algorithm (SQP-GA). For the hybrid optimization algorithm, SQP-GA, the sequence quadratic programming (SQP) algorithm which is used as a basic operator is integrated into the fundamental GA, so the local searching ability of the fundamental GA is improved effectively and the premature convergence phenomenon is overcome. Experimental results of the numerical simulations of benchmark functions show that the hybrid optimization algorithm, SQP-GA, is better than the fundamental GA in the convergence speed and optimization precision, and the proposed algorithm in this paper has outstanding optimization effect. At the same time, the presented SQP-GA in the paper is applied to solve the ultrasonic localization problem of PD in transformers, then the ultrasonic localization method of PD in transformers based on the SQP-GA is proposed. And localization results based on the SQP-GA are compared with some algorithms such as the GA, some other intelligent and non-intelligent algorithms. The results of calculating examples both stimulated and spot experiments demonstrate that the localization method based on the SQP-GA can effectively prevent the results from getting trapped into the local optimum values, and the localization method is of great feasibility and very suitable for the field applications, and the precision of localization is enhanced, and the effectiveness of localization is ideal and satisfactory.

An algorithm for control system design via parameter optimization. M.S. Thesis

NASA Technical Reports Server (NTRS)

Sinha, P. K.

1972-01-01

An algorithm for design via parameter optimization has been developed for linear-time-invariant control systems based on the model reference adaptive control concept. A cost functional is defined to evaluate the system response relative to nominal, which involves in general the error between the system and nominal response, its derivatives and the control signals. A program for the practical implementation of this algorithm has been developed, with the computational scheme for the evaluation of the performance index based on Lyapunov's theorem for stability of linear invariant systems.

A Smoothed Eclipse Model for Solar Electric Propulsion Trajectory Optimization

NASA Technical Reports Server (NTRS)

Aziz, Jonathan D.; Scheeres, Daniel J.; Parker, Jeffrey S.; Englander, Jacob A.

2017-01-01

Solar electric propulsion (SEP) is the dominant design option for employing low-thrust propulsion on a space mission. Spacecraft solar arrays power the SEP system but are subject to blackout periods during solar eclipse conditions. Discontinuity in power available to the spacecraft must be accounted for in trajectory optimization, but gradient-based methods require a differentiable power model. This work presents a power model that smooths the eclipse transition from total eclipse to total sunlight with a logistic function. Example trajectories are computed with differential dynamic programming, a second-order gradient-based method.

Hormesis and the salk polio vaccine.

PubMed

Calabrese, Edward J

2012-01-01

The production of the Salk vaccine polio virus by monkey kidney cells was generated using the synthetic tissue culture medium, Mixture 199. In this paper's retrospective assessment of this process, it was discovered that Mixture 199 was modified by the addition of ethanol to optimize animal cell survival based on experimentation that revealed a hormetic-like biphasic response relationship. This hormesis-based optimization procedure was then applied to all uses of Mixture 199 and modifications of it, including its application to the Salk polio vaccine during preliminary testing and in its subsequent major societal treatment programs.

Active model-based balancing strategy for self-reconfigurable batteries

NASA Astrophysics Data System (ADS)

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

2016-08-01

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

Design enhancement tools in MSC/NASTRAN

NASA Technical Reports Server (NTRS)

Wallerstein, D. V.

1984-01-01

Design sensitivity is the calculation of derivatives of constraint functions with respect to design variables. While a knowledge of these derivatives is useful in its own right, the derivatives are required in many efficient optimization methods. Constraint derivatives are also required in some reanalysis methods. It is shown where the sensitivity coefficients fit into the scheme of a basic organization of an optimization procedure. The analyzer is to be taken as MSC/NASTRAN. The terminator program monitors the termination criteria and ends the optimization procedure when the criteria are satisfied. This program can reside in several plances: in the optimizer itself, in a user written code, or as part of the MSC/EOS (Engineering Operating System) MSC/EOS currently under development. Since several excellent optimization codes exist and since they require such very specialized technical knowledge, the optimizer under the new MSC/EOS is considered to be selected and supplied by the user to meet his specific needs and preferences. The one exception to this is a fully stressed design (FSD) based on simple scaling. The gradients are currently supplied by various design sensitivity options now existing in MSC/NASTRAN's design sensitivity analysis (DSA).

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

PubMed Central

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-01-01

In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

PubMed

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-07-09

In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

Optimal Power Control in Wireless Powered Sensor Networks: A Dynamic Game-Based Approach

PubMed Central

Xu, Haitao; Guo, Chao; Zhang, Long

2017-01-01

In wireless powered sensor networks (WPSN), it is essential to research uplink transmit power control in order to achieve throughput performance balancing and energy scheduling. Each sensor should have an optimal transmit power level for revenue maximization. In this paper, we discuss a dynamic game-based algorithm for optimal power control in WPSN. The main idea is to use the non-cooperative differential game to control the uplink transmit power of wireless sensors in WPSN, to extend their working hours and to meet QoS (Quality of Services) requirements. Subsequently, the Nash equilibrium solutions are obtained through Bellman dynamic programming. At the same time, an uplink power control algorithm is proposed in a distributed manner. Through numerical simulations, we demonstrate that our algorithm can obtain optimal power control and reach convergence for an infinite horizon. PMID:28282945

Analytical sizing methods for behind-the-meter battery storage

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

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

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

Quantum optimization for training support vector machines.

PubMed

Anguita, Davide; Ridella, Sandro; Rivieccio, Fabio; Zunino, Rodolfo

2003-01-01

Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered.

A new implementation of the programming system for structural synthesis (PROSSS-2)

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.

1984-01-01

This new implementation of the PROgramming System for Structural Synthesis (PROSSS-2) combines a general-purpose finite element computer program for structural analysis, a state-of-the-art optimization program, and several user-supplied, problem-dependent computer programs. The results are flexibility of the optimization procedure, organization, and versatility of the formulation of constraints and design variables. The analysis-optimization process results in a minimized objective function, typically the mass. The analysis and optimization programs are executed repeatedly by looping through the system until the process is stopped by a user-defined termination criterion. However, some of the analysis, such as model definition, need only be one time and the results are saved for future use. The user must write some small, simple FORTRAN programs to interface between the analysis and optimization programs. One of these programs, the front processor, converts the design variables output from the optimizer into the suitable format for input into the analyzer. Another, the end processor, retrieves the behavior variables and, optionally, their gradients from the analysis program and evaluates the objective function and constraints and optionally their gradients. These quantities are output in a format suitable for input into the optimizer. These user-supplied programs are problem-dependent because they depend primarily upon which finite elements are being used in the model. PROSSS-2 differs from the original PROSSS in that the optimizer and front and end processors have been integrated into the finite element computer program. This was done to reduce the complexity and increase portability of the system, and to take advantage of the data handling features found in the finite element program.

Application of optimization technique for flood damage modeling in river system

NASA Astrophysics Data System (ADS)

Barman, Sangita Deb; Choudhury, Parthasarathi

2018-04-01

A river system is defined as a network of channels that drains different parts of a basin uniting downstream to form a common outflow. An application of various models found in literatures, to a river system having multiple upstream flows is not always straight forward, involves a lengthy procedure; and with non-availability of data sets model calibration and applications may become difficult. In the case of a river system the flow modeling can be simplified to a large extent if the channel network is replaced by an equivalent single channel. In the present work optimization model formulations based on equivalent flow and applications of the mixed integer programming based pre-emptive goal programming model in evaluating flood control alternatives for a real life river system in India are proposed to be covered in the study.

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

Computer program for optimal BWR congtrol rod programming

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

Taner, M.S.; Levine, S.H.; Carmody, J.M.

1995-12-31

A fully automated computer program has been developed for designing optimal control rod (CR) patterns for boiling water reactors (BWRs). The new program, called OCTOPUS-3, is based on the OCTOPUS code and employs SIMULATE-3 (Ref. 2) for the analysis. There are three aspects of OCTOPUS-3 that make it successful for use at PECO Energy. It incorporates a new feasibility algorithm that makes the CR design meet all constraints, it has been coupled to a Bourne Shell program 3 to allow the user to run the code interactively without the need for a manual, and it develops a low axial peakmore » to extend the cycle. For PECO Energy Co.`s limericks it increased the energy output by 1 to 2% over the traditional PECO Energy design. The objective of the optimization in OCTOPUS-3 is to approximate a very low axial peaked target power distribution while maintaining criticality, keeping the nodal and assembly peaks below the allowed maximum, and meeting the other constraints. The user-specified input for each exposure point includes: CR groups allowed-to-move, target k{sub eff}, and amount of core flow. The OCTOPUS-3 code uses the CR pattern from the previous step as the initial guess unless indicated otherwise.« less

Stochastic Optimization for Unit Commitment-A Review

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

Zheng, Qipeng P.; Wang, Jianhui; Liu, Andrew L.

2015-07-01

Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC's birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave ismore » focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.« less

LQR-Based Optimal Distributed Cooperative Design for Linear Discrete-Time Multiagent Systems.

PubMed

Zhang, Huaguang; Feng, Tao; Liang, Hongjing; Luo, Yanhong

2017-03-01

In this paper, a novel linear quadratic regulator (LQR)-based optimal distributed cooperative design method is developed for synchronization control of general linear discrete-time multiagent systems on a fixed, directed graph. Sufficient conditions are derived for synchronization, which restrict the graph eigenvalues into a bounded circular region in the complex plane. The synchronizing speed issue is also considered, and it turns out that the synchronizing region reduces as the synchronizing speed becomes faster. To obtain more desirable synchronizing capacity, the weighting matrices are selected by sufficiently utilizing the guaranteed gain margin of the optimal regulators. Based on the developed LQR-based cooperative design framework, an approximate dynamic programming technique is successfully introduced to overcome the (partially or completely) model-free cooperative design for linear multiagent systems. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design methods.

Energy efficient LED layout optimization for near-uniform illumination

NASA Astrophysics Data System (ADS)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Evolutionary and biological metaphors for engineering design

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

Jakiela, M.

1994-12-31

Since computing became generally available, there has been strong interest in using computers to assist and automate engineering design processes. Specifically, for design optimization and automation, nonlinear programming and artificial intelligence techniques have been extensively studied. New computational techniques, based upon the natural processes of evolution, adaptation, and learing, are showing promise because of their generality and robustness. This presentation will describe the use of two such techniques, genetic algorithms and classifier systems, for a variety of engineering design problems. Structural topology optimization, meshing, and general engineering optimization are shown as example applications.

Optimal reconfiguration strategy for a degradable multimodule computing system

NASA Technical Reports Server (NTRS)

Lee, Yann-Hang; Shin, Kang G.

1987-01-01

The present quantitative approach to the problem of reconfiguring a degradable multimode system assigns some modules to computation and arranges others for reliability. By using expected total reward as the optimal criterion, there emerges an active reconfiguration strategy based not only on the occurrence of failure but the progression of the given mission. This reconfiguration strategy requires specification of the times at which the system should undergo reconfiguration, and the configurations to which the system should change. The optimal reconfiguration problem is converted to integer nonlinear knapsack and fractional programming problems.

Mars Mission Optimization Based on Collocation of Resources

NASA Technical Reports Server (NTRS)

Chamitoff, G. E.; James, G. H.; Barker, D. C.; Dershowitz, A. L.

2003-01-01

This paper presents a powerful approach for analyzing Martian data and for optimizing mission site selection based on resource collocation. This approach is implemented in a program called PROMT (Planetary Resource Optimization and Mapping Tool), which provides a wide range of analysis and display functions that can be applied to raw data or imagery. Thresholds, contours, custom algorithms, and graphical editing are some of the various methods that can be used to process data. Output maps can be created to identify surface regions on Mars that meet any specific criteria. The use of this tool for analyzing data, generating maps, and collocating features is demonstrated using data from the Mars Global Surveyor and the Odyssey spacecraft. The overall mission design objective is to maximize a combination of scientific return and self-sufficiency based on utilization of local materials. Landing site optimization involves maximizing accessibility to collocated science and resource features within a given mission radius. Mission types are categorized according to duration, energy resources, and in-situ resource utilization. Optimization results are shown for a number of mission scenarios.

Optimization for minimum sensitivity to uncertain parameters

NASA Technical Reports Server (NTRS)

Pritchard, Jocelyn I.; Adelman, Howard M.; Sobieszczanski-Sobieski, Jaroslaw

1994-01-01

A procedure to design a structure for minimum sensitivity to uncertainties in problem parameters is described. The approach is to minimize directly the sensitivity derivatives of the optimum design with respect to fixed design parameters using a nested optimization procedure. The procedure is demonstrated for the design of a bimetallic beam for minimum weight with insensitivity to uncertainties in structural properties. The beam is modeled with finite elements based on two dimensional beam analysis. A sequential quadratic programming procedure used as the optimizer supplies the Lagrange multipliers that are used to calculate the optimum sensitivity derivatives. The method was perceived to be successful from comparisons of the optimization results with parametric studies.

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Approximate labeling via graph cuts based on linear programming.

PubMed

Komodakis, Nikos; Tziritas, Georgios

2007-08-01

A new framework is presented for both understanding and developing graph-cut-based combinatorial algorithms suitable for the approximate optimization of a very wide class of Markov Random Fields (MRFs) that are frequently encountered in computer vision. The proposed framework utilizes tools from the duality theory of linear programming in order to provide an alternative and more general view of state-of-the-art techniques like the \\alpha-expansion algorithm, which is included merely as a special case. Moreover, contrary to \\alpha-expansion, the derived algorithms generate solutions with guaranteed optimality properties for a much wider class of problems, for example, even for MRFs with nonmetric potentials. In addition, they are capable of providing per-instance suboptimality bounds in all occasions, including discrete MRFs with an arbitrary potential function. These bounds prove to be very tight in practice (that is, very close to 1), which means that the resulting solutions are almost optimal. Our algorithms' effectiveness is demonstrated by presenting experimental results on a variety of low-level vision tasks, such as stereo matching, image restoration, image completion, and optical flow estimation, as well as on synthetic problems.

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Towards Implementation of a Generalized Architecture for High-Level Quantum Programming Language

NASA Astrophysics Data System (ADS)

Ameen, El-Mahdy M.; Ali, Hesham A.; Salem, Mofreh M.; Badawy, Mahmoud

2017-08-01

This paper investigates a novel architecture to the problem of quantum computer programming. A generalized architecture for a high-level quantum programming language has been proposed. Therefore, the programming evolution from the complicated quantum-based programming to the high-level quantum independent programming will be achieved. The proposed architecture receives the high-level source code and, automatically transforms it into the equivalent quantum representation. This architecture involves two layers which are the programmer layer and the compilation layer. These layers have been implemented in the state of the art of three main stages; pre-classification, classification, and post-classification stages respectively. The basic building block of each stage has been divided into subsequent phases. Each phase has been implemented to perform the required transformations from one representation to another. A verification process was exposed using a case study to investigate the ability of the compiler to perform all transformation processes. Experimental results showed that the efficacy of the proposed compiler achieves a correspondence correlation coefficient about R ≈ 1 between outputs and the targets. Also, an obvious achievement has been utilized with respect to the consumed time in the optimization process compared to other techniques. In the online optimization process, the consumed time has increased exponentially against the amount of accuracy needed. However, in the proposed offline optimization process has increased gradually.

Improved Evolutionary Programming with Various Crossover Techniques for Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Tangpatiphan, Kritsana; Yokoyama, Akihiko

This paper presents an Improved Evolutionary Programming (IEP) for solving the Optimal Power Flow (OPF) problem, which is considered as a non-linear, non-smooth, and multimodal optimization problem in power system operation. The total generator fuel cost is regarded as an objective function to be minimized. The proposed method is an Evolutionary Programming (EP)-based algorithm with making use of various crossover techniques, normally applied in Real Coded Genetic Algorithm (RCGA). The effectiveness of the proposed approach is investigated on the IEEE 30-bus system with three different types of fuel cost functions; namely the quadratic cost curve, the piecewise quadratic cost curve, and the quadratic cost curve superimposed by sine component. These three cost curves represent the generator fuel cost functions with a simplified model and more accurate models of a combined-cycle generating unit and a thermal unit with value-point loading effect respectively. The OPF solutions by the proposed method and Pure Evolutionary Programming (PEP) are observed and compared. The simulation results indicate that IEP requires less computing time than PEP with better solutions in some cases. Moreover, the influences of important IEP parameters on the OPF solution are described in details.

Layout design-based research on optimization and assessment method for shipbuilding workshop

NASA Astrophysics Data System (ADS)

Liu, Yang; Meng, Mei; Liu, Shuang

2013-06-01

The research study proposes to examine a three-dimensional visualization program, emphasizing on improving genetic algorithms through the optimization of a layout design-based standard and discrete shipbuilding workshop. By utilizing a steel processing workshop as an example, the principle of minimum logistic costs will be implemented to obtain an ideological equipment layout, and a mathematical model. The objectiveness is to minimize the total necessary distance traveled between machines. An improved control operator is implemented to improve the iterative efficiency of the genetic algorithm, and yield relevant parameters. The Computer Aided Tri-Dimensional Interface Application (CATIA) software is applied to establish the manufacturing resource base and parametric model of the steel processing workshop. Based on the results of optimized planar logistics, a visual parametric model of the steel processing workshop is constructed, and qualitative and quantitative adjustments then are applied to the model. The method for evaluating the results of the layout is subsequently established through the utilization of AHP. In order to provide a mode of reference to the optimization and layout of the digitalized production workshop, the optimized discrete production workshop will possess a certain level of practical significance.

The NASA/Industry Design Analysis Methods for Vibrations (DAMVIBS) Program - A government overview. [of rotorcraft technology development using finite element method

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.

1992-01-01

An overview is presented of government contributions to the program called Design Analysis Methods for Vibrations (DAMV) which attempted to develop finite-element-based analyses of rotorcraft vibrations. NASA initiated the program with a finite-element modeling program for the CH-47D tandem-rotor helicopter. The DAMV program emphasized four areas including: airframe finite-element modeling, difficult components studies, coupled rotor-airframe vibrations, and airframe structural optimization. Key accomplishments of the program include industrywide standards for modeling metal and composite airframes, improved industrial designs for vibrations, and the identification of critical structural contributors to airframe vibratory responses. The program also demonstrated the value of incorporating secondary modeling details to improving correlation, and the findings provide the basis for an improved finite-element-based dynamics design-analysis capability.

An Instructional Systems Technology Model for Institutional Change.

ERIC Educational Resources Information Center

Dudgeon, Paul J.

A program based on instructional systems technology was developed at Canadore College as a means of devising the optimal learning experience for each individual student. The systems approach is used to solve educational problems through a process of analysis, synthesis, modeling, and simulation, based on the LOGOS (Language for Optimizing…

Designing optimal food intake patterns to achieve nutritional goals for Japanese adults through the use of linear programming optimization models.

PubMed

Okubo, Hitomi; Sasaki, Satoshi; Murakami, Kentaro; Yokoyama, Tetsuji; Hirota, Naoko; Notsu, Akiko; Fukui, Mitsuru; Date, Chigusa

2015-06-06

Simultaneous dietary achievement of a full set of nutritional recommendations is difficult. Diet optimization model using linear programming is a useful mathematical means of translating nutrient-based recommendations into realistic nutritionally-optimal food combinations incorporating local and culture-specific foods. We used this approach to explore optimal food intake patterns that meet the nutrient recommendations of the Dietary Reference Intakes (DRIs) while incorporating typical Japanese food selections. As observed intake values, we used the food and nutrient intake data of 92 women aged 31-69 years and 82 men aged 32-69 years living in three regions of Japan. Dietary data were collected with semi-weighed dietary record on four non-consecutive days in each season of the year (16 days total). The linear programming models were constructed to minimize the differences between observed and optimized food intake patterns while also meeting the DRIs for a set of 28 nutrients, setting energy equal to estimated requirements, and not exceeding typical quantities of each food consumed by each age (30-49 or 50-69 years) and gender group. We successfully developed mathematically optimized food intake patterns that met the DRIs for all 28 nutrients studied in each sex and age group. Achieving nutritional goals required minor modifications of existing diets in older groups, particularly women, while major modifications were required to increase intake of fruit and vegetables in younger groups of both sexes. Across all sex and age groups, optimized food intake patterns demanded greatly increased intake of whole grains and reduced-fat dairy products in place of intake of refined grains and full-fat dairy products. Salt intake goals were the most difficult to achieve, requiring marked reduction of salt-containing seasoning (65-80%) in all sex and age groups. Using a linear programming model, we identified optimal food intake patterns providing practical food choices and meeting nutritional recommendations for Japanese populations. Dietary modifications from current eating habits required to fulfil nutritional goals differed by age: more marked increases in food volume were required in younger groups.

A multi-objective programming model for assessment the GHG emissions in MSW management

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

Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr; Skoulaxinou, Sotiria; Gakis, Nikos

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty yearsmore » they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.« less

Challenges in defining an optimal approach to formula-based allocations of public health funds in the United States.

PubMed

Buehler, James W; Holtgrave, David R

2007-03-29

Controversy and debate can arise whenever public health agencies determine how program funds should be allocated among constituent jurisdictions. Two common strategies for making such allocations are expert review of competitive applications and the use of funding formulas. Despite widespread use of funding formulas by public health agencies in the United States, formula allocation strategies in public health have been subject to relatively little formal scrutiny, with the notable exception of the attention focused on formula funding of HIV care programs. To inform debates and deliberations in the selection of a formula-based approach, we summarize key challenges to formula-based funding, based on prior reviews of federal programs in the United States. The primary challenge lies in identifying data sources and formula calculation methods that both reflect and serve program objectives, with or without adjustments for variations in the cost of delivering services, the availability of local resources, capacity, or performance. Simplicity and transparency are major advantages of formula-based allocations, but these advantages can be offset if formula-based allocations are perceived to under- or over-fund some jurisdictions, which may result from how guaranteed minimum funding levels are set or from "hold-harmless" provisions intended to blunt the effects of changes in formula design or random variations in source data. While fairness is considered an advantage of formula-based allocations, the design of a formula may implicitly reflect unquestioned values concerning equity versus equivalence in setting funding policies. Whether or how past or projected trends are taken into account can also have substantial impacts on allocations. Insufficient attention has been focused on how the approach to designing funding formulas in public health should differ for treatment or service versus prevention programs. Further evaluations of formula-based versus competitive allocation methods are needed to promote the optimal use of public health funds. In the meantime, those who use formula-based strategies to allocate funds should be familiar with the nuances of this approach.

SDU: A Semidefinite Programming-Based Underestimation Method for Stochastic Global Optimization in Protein Docking

DTIC Science & Technology

2007-04-01

optimization methodology we introduce. State-of-the-art protein - protein docking approaches start by identifying conformations with good surface /chemical com...side-chains on the interface ). The protein - protein docking literature (e.g., [8] and the references therein) is predominantly treating the docking...mations by various measures of surface complementarity which can be efficiently computed using fast Fourier correlation tech- niques (FFTs). However, when

HOMER® Micropower Optimization Model

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

Lilienthal, P.

2005-01-01

NREL has developed the HOMER micropower optimization model. The model can analyze all of the available small power technologies individually and in hybrid configurations to identify least-cost solutions to energy requirements. This capability is valuable to a diverse set of energy professionals and applications. NREL has actively supported its growing user base and developed training programs around the model. These activities are helping to grow the global market for solar technologies.

The Design and Evaluation of "CAPTools"--A Computer Aided Parallelization Toolkit

NASA Technical Reports Server (NTRS)

Yan, Jerry; Frumkin, Michael; Hribar, Michelle; Jin, Haoqiang; Waheed, Abdul; Johnson, Steve; Cross, Jark; Evans, Emyr; Ierotheou, Constantinos; Leggett, Pete;

Towards optimizing server performance in an educational MMORPG for teaching computer programming

NASA Astrophysics Data System (ADS)

Malliarakis, Christos; Satratzemi, Maya; Xinogalos, Stelios

2013-10-01

Web-based games have become significantly popular during the last few years. This is due to the gradual increase of internet speed, which has led to the ongoing multiplayer games development and more importantly the emergence of the Massive Multiplayer Online Role Playing Games (MMORPG) field. In parallel, similar technologies called educational games have started to be developed in order to be put into practice in various educational contexts, resulting in the field of Game Based Learning. However, these technologies require significant amounts of resources, such as bandwidth, RAM and CPU capacity etc. These amounts may be even larger in an educational MMORPG game that supports computer programming education, due to the usual inclusion of a compiler and the constant client/server data transmissions that occur during program coding, possibly leading to technical issues that could cause malfunctions during learning. Thus, the determination of the elements that affect the overall games resources' load is essential so that server administrators can configure them and ensure educational games' proper operation during computer programming education. In this paper, we propose a new methodology with which we can achieve monitoring and optimization of the load balancing, so that the essential resources for the creation and proper execution of an educational MMORPG for computer programming can be foreseen and bestowed without overloading the system.

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

NASA Astrophysics Data System (ADS)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

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

NASA Astrophysics Data System (ADS)

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

2009-11-01

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

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Multi-input multioutput orthogonal frequency division multiplexing radar waveform design for improving the detection performance of space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Hongyan

2017-04-01

This paper addresses the waveform optimization problem for improving the detection performance of multi-input multioutput (MIMO) orthogonal frequency division multiplexing (OFDM) radar-based space-time adaptive processing (STAP) in the complex environment. By maximizing the output signal-to-interference-and-noise-ratio (SINR) criterion, the waveform optimization problem for improving the detection performance of STAP, which is subjected to the constant modulus constraint, is derived. To tackle the resultant nonlinear and complicated optimization issue, a diagonal loading-based method is proposed to reformulate the issue as a semidefinite programming one; thereby, this problem can be solved very efficiently. In what follows, the optimized waveform can be obtained to maximize the output SINR of MIMO-OFDM such that the detection performance of STAP can be improved. The simulation results show that the proposed method can improve the output SINR detection performance considerably as compared with that of uncorrelated waveforms and the existing MIMO-based STAP method.

The Role of a Museum-Based Science Education Program in Promoting Content Knowledge and Science Motivation

ERIC Educational Resources Information Center

Martin, Andrew J.; Durksen, Tracy L.; Williamson, Derek; Kiss, Julia; Ginns, Paul

2016-01-01

Informal learning settings such as museums have been identified as opportunities to enhance students' knowledge and motivation in science and to optimize the connection between science and everyday life. The present study assessed the role of a self-paced science education program (situated in a medical science museum) in enhancing students'…

The National Clinical Skills Competition Promotes the Construction of a Comprehensive Training Program for Chinese Medical Students

ERIC Educational Resources Information Center

Yang, Jing; Huang, Si-min; Lu, Chun-ting; Feng, Lie

2018-01-01

To explore an efficient training program based on an analysis in organizing seven sessions for the National Clinical Skills Competition in China. Each year, 6-12 excellent medical students of our university are selected as an observation team. Comparisons of the teaching characteristics were performed in this study. An optimal curriculum…

CNN-coupled Humanoid Panoramic Annular Lens (PAL)-Optical System for Military Applications (Feasibility Study)

DTIC Science & Technology

2002-01-08

new PAL with a total viewing angle of around 80° and suitable for foveal vision, it turned out that the optical design program ZEMAX EE we intended to...use was not capable for optimization. The reason was that ZEMAX -EE and all present optical design programs are based on see-through-window (STW

OPTIMIZATION OF MUD HAMMER DRILLING PERFORMANCE--A PROGRAM TO BENCHMARK THE VIABILITY OF ADVANCED MUD HAMMER DRILLING

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

Arnis Judzis

2004-07-01

This document details the progress to date on the ''OPTIMIZATION OF MUD HAMMER DRILLING PERFORMANCE--A PROGRAM TO BENCHMARK THE VIABILITY OF ADVANCED MUD HAMMER DRILLING'' contract for the quarter starting April 2004 through June 2004. The DOE and TerraTek continue to wait for Novatek on the optimization portion of the testing program (they are completely rebuilding their fluid hammer). The latest indication is that the Novatek tool would be ready for retesting only 4Q 2004 or later. Smith International's hammer was tested in April of 2004 (2Q 2004 report). Accomplishments included the following: (1) TerraTek re-tested the ''optimized'' fluid hammermore » provided by Smith International during April 2004. Many improvements in mud hammer rates of penetration were noted over Phase 1 benchmark testing from November 2002. (2) Shell Exploration and Production in The Hague was briefed on various drilling performance projects including Task 8 ''Cutter Impact Testing''. Shell interest and willingness to assist in the test matrix as an Industry Advisor is appreciated. (3) TerraTek participated in a DOE/NETL Review meeting at Morgantown on April 15, 2004. The discussions were very helpful and a program related to the Mud Hammer optimization project was noted--Terralog modeling work on percussion tools. (4) Terralog's Dr. Gang Han witnessed some of the full-scale optimization testing of the Smith International hammer in order to familiarize him with downhole tools. TerraTek recommends that modeling first start with single cutters/inserts and progress in complexity. (5) The final equipment problem on the impact testing task was resolved through the acquisition of a high data rate laser based displacement instrument. (6) TerraTek provided Novatek much engineering support for the future re-testing of their optimized tool. Work was conducted on slip ring [electrical] specifications and tool collar sealing in the testing vessel with a reconfigured flow system on Novatek's collar.« less

Optimization Design of Minimum Total Resistance Hull Form Based on CFD Method

NASA Astrophysics Data System (ADS)

Zhang, Bao-ji; Zhang, Sheng-long; Zhang, Hui

2018-06-01

In order to reduce the resistance and improve the hydrodynamic performance of a ship, two hull form design methods are proposed based on the potential flow theory and viscous flow theory. The flow fields are meshed using body-fitted mesh and structured grids. The parameters of the hull modification function are the design variables. A three-dimensional modeling method is used to alter the geometry. The Non-Linear Programming (NLP) method is utilized to optimize a David Taylor Model Basin (DTMB) model 5415 ship under the constraints, including the displacement constraint. The optimization results show an effective reduction of the resistance. The two hull form design methods developed in this study can provide technical support and theoretical basis for designing green ships.

Study of self-compliance behaviors and internal filament characteristics in intrinsic SiO{sub x}-based resistive switching memory

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

Chang, Yao-Feng, E-mail: yfchang@utexas.edu; Zhou, Fei; Chen, Ying-Chen

2016-01-18

Self-compliance characteristics and reliability optimization are investigated in intrinsic unipolar silicon oxide (SiO{sub x})-based resistive switching (RS) memory using TiW/SiO{sub x}/TiW device structures. The program window (difference between SET voltage and RESET voltage) is dependent on external series resistance, demonstrating that the SET process is due to a voltage-triggered mechanism. The program window has been optimized for program/erase disturbance immunity and reliability for circuit-level applications. The SET and RESET transitions have also been characterized using a dynamic conductivity method, which distinguishes the self-compliance behavior due to an internal series resistance effect (filament) in SiO{sub x}-based RS memory. By using amore » conceptual “filament/resistive gap (GAP)” model of the conductive filament and a proton exchange model with appropriate assumptions, the internal filament resistance and GAP resistance can be estimated for high- and low-resistance states (HRS and LRS), and are found to be independent of external series resistance. Our experimental results not only provide insights into potential reliability issues but also help to clarify the switching mechanisms and device operating characteristics of SiO{sub x}-based RS memory.« less

Nouvelle methode d'optimisation du cout d'un vol par l'utilisation d'un systeme de gestion de vol et sa validation sur un avion Lockheed L-1011 TriStar

NASA Astrophysics Data System (ADS)

Gagne, Jocelyn

Usually, flights optimization and planning will take place before flight, on ground. However, it is not always feasible to do such optimization, or sometime unpredictable events may force pilots to change the flight path. In those circumstances, the pilots can only rely on charts or their Flight Management System (FMS) in order to maintain an economic flight. However, those FMS often rely on those same charts, which will not take into consideration different parameters, such as the cost index, the length on the flight or the weather. Even if some FMS take into consideration the weather, they may only rely on manually entered or limited data that could be outdated, insufficient or incomplete. The alleviate these problems, the function program's that was developed is mainly to determine the optimum flight profile for an aircraft, or more precisely, at the lowest overall cost, considering a take-off weight and weather conditions. The total cost is based on the value of time as well as the cost of fuel, resulting in the use of a ratio called the cost index. This index allows both to prioritize either the time or fuel consumption according to the costs related to a specific flight and/or airline. Thus, from a weight, the weather (wind, temperature, pressure), and the cost index, the program will calculate from the "Performance DataBase" (PDB) of a specific airplane an optimal flight profile over a given distance. The algorithm is based on linear interpolations in the performances tables using the Lagrange method. Moreover, in order to fully optimize the flight, the current program can, according to departure date and coordinates, download the latest available forecast from environment Canada website and calculate the optimum flight accordingly. The forecast data use by the program take the form of a 0.6 × 0.6 degrees grid in which the effects of wind, pressure and temperature are interpolated according to the aircraft geographical position and time. Using these tables, performances and forecasts, the program is therefore able to calculate the optimum profile from ground, but also in flight, if any change would occur on the path. Because all data is tabulated and not calculated, the required calculation power remains low, resulting in a short calculation time. Keywords: optimization, algorithm, simulation, cost.

Neural dynamic programming and its application to control systems

NASA Astrophysics Data System (ADS)

Seong, Chang-Yun

There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control problems that would be very difficult to solve with DP. NDP was demonstrated on several applications such as the lateral autopilot logic for a Boeing 747, the minimum fuel control of a double-integrator plant with bounded control, the backward steering of a two-trailer truck, and the set-point control of a two-link robot arm.

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

NASA Technical Reports Server (NTRS)

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

1975-01-01

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

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

Lee, Seyong; Kim, Jungwon; Vetter, Jeffrey S

This paper presents a directive-based, high-level programming framework for high-performance reconfigurable computing. It takes a standard, portable OpenACC C program as input and generates a hardware configuration file for execution on FPGAs. We implemented this prototype system using our open-source OpenARC compiler; it performs source-to-source translation and optimization of the input OpenACC program into an OpenCL code, which is further compiled into a FPGA program by the backend Altera Offline OpenCL compiler. Internally, the design of OpenARC uses a high- level intermediate representation that separates concerns of program representation from underlying architectures, which facilitates portability of OpenARC. In fact, thismore » design allowed us to create the OpenACC-to-FPGA translation framework with minimal extensions to our existing system. In addition, we show that our proposed FPGA-specific compiler optimizations and novel OpenACC pragma extensions assist the compiler in generating more efficient FPGA hardware configuration files. Our empirical evaluation on an Altera Stratix V FPGA with eight OpenACC benchmarks demonstrate the benefits of our strategy. To demonstrate the portability of OpenARC, we show results for the same benchmarks executing on other heterogeneous platforms, including NVIDIA GPUs, AMD GPUs, and Intel Xeon Phis. This initial evidence helps support the goal of using a directive-based, high-level programming strategy for performance portability across heterogeneous HPC architectures.« less

Optimal scheduling of micro grids based on single objective programming

NASA Astrophysics Data System (ADS)

Chen, Yue

2018-04-01

Faced with the growing demand for electricity and the shortage of fossil fuels, how to optimally optimize the micro-grid has become an important research topic to maximize the economic, technological and environmental benefits of the micro-grid. This paper considers the role of the battery and the micro-grid and power grid to allow the exchange of power not exceeding 150kW preconditions, the main study of the economy to load for the goal is to minimize the electricity cost (abandonment of wind), to establish an optimization model, and to solve the problem by genetic algorithm. The optimal scheduling scheme is obtained and the utilization of renewable energy and the impact of the battery involved in regulation are analyzed.

Cache Locality Optimization for Recursive Programs

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

Lifflander, Jonathan; Krishnamoorthy, Sriram

We present an approach to optimize the cache locality for recursive programs by dynamically splicing--recursively interleaving--the execution of distinct function invocations. By utilizing data effect annotations, we identify concurrency and data reuse opportunities across function invocations and interleave them to reduce reuse distance. We present algorithms that efficiently track effects in recursive programs, detect interference and dependencies, and interleave execution of function invocations using user-level (non-kernel) lightweight threads. To enable multi-core execution, a program is parallelized using a nested fork/join programming model. Our cache optimization strategy is designed to work in the context of a random work stealing scheduler. Wemore » present an implementation using the MIT Cilk framework that demonstrates significant improvements in sequential and parallel performance, competitive with a state-of-the-art compile-time optimizer for loop programs and a domain- specific optimizer for stencil programs.« less

Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

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

Kumar, Rajeeva; Kumar, Aditya; Dai, Dan

2012-12-31

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developedmore » will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.« less

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

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

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

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

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.« less

Optimization of planting pattern plan in Logung irrigation area using linear program

NASA Astrophysics Data System (ADS)

Wardoyo, Wasis; Setyono

2018-03-01

Logung irrigation area is located in Kudus Regency, Central Java Province, Indonesia. Irrigation area with 2810 Ha of extent is getting water supply from Logung dam. Yet, the utilization of water at Logung dam is not optimal and the distribution of water is still not evenly distributed. Therefore, this study will discuss about the optimization of irrigation water utilization based on the beginning of plant season. This optimization begins with the analysis of hydrology, climatology and river discharge in order to determine the irrigation water needs. After determining irrigation water needs, six alternatives of planting patterns with the different early planting periods, i.e. 1st November, 2nd November, 3rd November, 1st December, 2nd December, and 3rd December with the planting pattern of rice-secondary crop-sugarcane is introduced. It is continued by the analysis of water distribution conducted using linear program assisted by POM-Quantity method for Windows 3 with the reliable discharge limit and the available land area. Output of this calculation are to determine the land area that can be planted based on the type of plant and growing season, and to obtaine the profits of harvest yields. Based on the optimum area of each plant species with 6 alternatives, the most optimum area was obtained at the early planting periods on 3rd December with the production profit of Rp 113.397.338.854,- with the planting pattern of rice / beans / sugarcane-rice / beans / sugarcane-beans / sugarcane.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

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

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.« less

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

NASA Astrophysics Data System (ADS)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

NASA Technical Reports Server (NTRS)

Eun, Yeonju; Jeon, Daekeun; Lee, Hanbong; Jung, Yoon C.; Zhu, Zhifan; Jeong, Myeongsook; Kim, Hyounkong; Oh, Eunmi; Hong, Sungkwon

2017-01-01

This study aims to develop a controllers decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

NASA Technical Reports Server (NTRS)

Eun, Yeonju; Jeon, Daekeun; Lee, Hanbong; Jung, Yoon Chul; Zhu, Zhifan; Jeong, Myeong-Sook; Kim, Hyoun Kyoung; Oh, Eunmi; Hong, Sungkwon

2017-01-01

This study aims to develop a controllers' decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

An engineering approach to automatic programming

NASA Technical Reports Server (NTRS)

Rubin, Stuart H.

1990-01-01

An exploratory study of the automatic generation and optimization of symbolic programs using DECOM - a prototypical requirement specification model implemented in pure LISP was undertaken. It was concluded, on the basis of this study, that symbolic processing languages such as LISP can support a style of programming based upon formal transformation and dependent upon the expression of constraints in an object-oriented environment. Such languages can represent all aspects of the software generation process (including heuristic algorithms for effecting parallel search) as dynamic processes since data and program are represented in a uniform format.

Imaging Tasks Scheduling for High-Altitude Airship in Emergency Condition Based on Energy-Aware Strategy

PubMed Central

Zhimeng, Li; Chuan, He; Dishan, Qiu; Jin, Liu; Manhao, Ma

2013-01-01

Aiming to the imaging tasks scheduling problem on high-altitude airship in emergency condition, the programming models are constructed by analyzing the main constraints, which take the maximum task benefit and the minimum energy consumption as two optimization objectives. Firstly, the hierarchy architecture is adopted to convert this scheduling problem into three subproblems, that is, the task ranking, value task detecting, and energy conservation optimization. Then, the algorithms are designed for the sub-problems, and the solving results are corresponding to feasible solution, efficient solution, and optimization solution of original problem, respectively. This paper makes detailed introduction to the energy-aware optimization strategy, which can rationally adjust airship's cruising speed based on the distribution of task's deadline, so as to decrease the total energy consumption caused by cruising activities. Finally, the application results and comparison analysis show that the proposed strategy and algorithm are effective and feasible. PMID:23864822

Optimizing python-based ROOT I/O with PyPy's tracing just-in-time compiler

NASA Astrophysics Data System (ADS)

Tlp Lavrijsen, Wim

2012-12-01

The Python programming language allows objects and classes to respond dynamically to the execution environment. Most of this, however, is made possible through language hooks which by definition can not be optimized and thus tend to be slow. The PyPy implementation of Python includes a tracing just in time compiler (JIT), which allows similar dynamic responses but at the interpreter-, rather than the application-level. Therefore, it is possible to fully remove the hooks, leaving only the dynamic response, in the optimization stage for hot loops, if the types of interest are opened up to the JIT. A general opening up of types to the JIT, based on reflection information, has already been developed (cppyy). The work described in this paper takes it one step further by customizing access to ROOT I/O to the JIT, allowing for fully automatic optimizations.

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

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variablesmore » that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.« less

A trust region-based approach to optimize triple response systems

NASA Astrophysics Data System (ADS)

Fan, Shu-Kai S.; Fan, Chihhao; Huang, Chia-Fen

2014-05-01

This article presents a new computing procedure for the global optimization of the triple response system (TRS) where the response functions are non-convex quadratics and the input factors satisfy a radial constrained region of interest. The TRS arising from response surface modelling can be approximated using a nonlinear mathematical program that considers one primary objective function and two secondary constraint functions. An optimization algorithm named the triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the non-degenerate TRS. In TRSALG, the Lagrange multipliers of the secondary functions are determined using the Hooke-Jeeves search method and the Lagrange multiplier of the radial constraint is located using the trust region method within the global optimality space. The proposed algorithm is illustrated in terms of three examples appearing in the quality-control literature. The results of TRSALG compared to a gradient-based method are also presented.

A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

NASA Astrophysics Data System (ADS)

Niakan, F.; Vahdani, B.; Mohammadi, M.

2015-12-01

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

Split diversity in constrained conservation prioritization using integer linear programming.

PubMed

Chernomor, Olga; Minh, Bui Quang; Forest, Félix; Klaere, Steffen; Ingram, Travis; Henzinger, Monika; von Haeseler, Arndt

2015-01-01

Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization.Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator-prey interactions between the species in a community to define viability constraints.Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure.We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user-friendly software at http://www.cibiv.at/software/pda.

Performance Analysis and Design Synthesis (PADS) computer program. Volume 1: Formulation

NASA Technical Reports Server (NTRS)

1972-01-01

The program formulation for PADS computer program is presented. It can size launch vehicles in conjunction with calculus-of-variations optimal trajectories and can also be used as a general-purpose branched trajectory optimization program. In the former use, it has the Space Shuttle Synthesis Program as well as a simplified stage weight module for optimally sizing manned recoverable launch vehicles. For trajectory optimization alone or with sizing, PADS has two trajectory modules. The first trajectory module uses the method of steepest descent; the second employs the method of quasilinearization, which requires a starting solution from the first trajectory module.

Implementing a Coach-Delivered Dating Violence Prevention Program with High School Athletes.

PubMed

Jaime, Maria Catrina D; McCauley, Heather L; Tancredi, Daniel J; Decker, Michele R; Silverman, Jay G; O'Connor, Brian; Miller, Elizabeth

2018-05-10

Teen dating violence and sexual violence are severe public health problems. Abusive behaviors within the context of dating or romantic relationships are associated with adverse health outcomes. Promoting positive bystander intervention and increasing knowledge of abusive behaviors are promising strategies for preventing dating and sexual violence. Coaching Boys Into Men (CBIM) is an evidence-based, athletic coach-delivered dating violence prevention program that has been shown to increase positive bystander behaviors and reduce abuse perpetration among high school male athletes. Identifying specific barriers and facilitators based on the coaches' experiences with program delivery combined with the coaches' and athletes' program perceptions may help optimize future CBIM implementation and sustainability. Semi-structured interviews with coaches (n = 36) explored the implementers' perspectives on strategies that worked well and potential barriers to program implementation. Ten focus groups with male athletes (n = 39) assessed their experiences with CBIM and the suitability of having their coaches deliver this program. Coaches described using the CBIM training cards and integrating program delivery during practice. Athletes reported coaches routinely delivering the CBIM program and adding their own personal stories or examples to the discussions. Key facilitators to program implementation include support from the violence prevention advocate, the ease of integrating CBIM into the sports season, and using the program materials. Barriers to implementation included finding sufficient time for the program, dynamics of delivering sensitive program content, and participant constraints. Coaches and athletes alike found the program feasible and acceptable to implement within the sports setting. Both coaches and athletes offered insights on the implementation and the feasibility and acceptability of CBIM within school-based athletic programs. These experiences by implementers and recipients alike can inform future dissemination and implementation efforts of CBIM. Further, by pinpointing where and how coaches were successful in implementing the program and what resonated with athletes, can help better understand how CBIM is effective in promoting athletes to stop violence against women and girls. Coach and athlete reflections on CBIM implementation provide insights for optimizing future program delivery and dissemination.

Integrated Data Base Program: a status report

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

Notz, K.J.; Klein, J.A.

1984-06-01

The Integrated Data Base (IDB) Program provides official Department of Energy (DOE) data on spent fuel and radioactive waste inventories, projections, and characteristics. The accomplishments of FY 1983 are summarized for three broad areas: (1) upgrading and issuing of the annual report on spent fuel and radioactive waste inventories, projections, and characteristics, including ORIGEN2 applications and a quality assurance plan; (2) creation of a summary data file in user-friendly format for use on a personal computer and enhancing user access to program data; and (3) optimizing and documentation of the data handling methodology used by the IDB Program and providingmore » direct support to other DOE programs and sites in data handling. Plans for future work in these three areas are outlined. 23 references, 11 figures.« less

Stacking-sequence optimization for buckling of laminated plates by integer programming

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.; Walsh, Joanne L.

1991-01-01

Integer-programming formulations for the design of symmetric and balanced laminated plates under biaxial compression are presented. Both maximization of buckling load for a given total thickness and the minimization of total thickness subject to a buckling constraint are formulated. The design variables that define the stacking sequence of the laminate are zero-one integers. It is shown that the formulation results in a linear optimization problem that can be solved on readily available software. This is in contrast to the continuous case, where the design variables are the thicknesses of layers with specified ply orientations, and the optimization problem is nonlinear. Constraints on the stacking sequence such as a limit on the number of contiguous plies of the same orientation and limits on in-plane stiffnesses are easily accommodated. Examples are presented for graphite-epoxy plates under uniaxial and biaxial compression using a commercial software package based on the branch-and-bound algorithm.

Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming.

PubMed

Wang, Jun-Sheng; Yang, Guang-Hong

2017-07-25

This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where the terms depending on the innovation covariance matrix are available with the aid of the innovation covariance matrix identified beforehand. Therefore, by iterating the Bellman equation, the resulting value function can converge to the optimal one in the presence of the aforementioned noise, and the nearly optimal control laws are delivered. To show the effectiveness and the advantages of the proposed approach, a simulation example and a velocity control experiment on a dc machine are employed.

Further developments in the controlled growth approach for optimal structural synthesis

NASA Technical Reports Server (NTRS)

Hajela, P.

1982-01-01

It is pointed out that the use of nonlinear programming methods in conjunction with finite element and other discrete analysis techniques have provided a powerful tool in the domain of optimal structural synthesis. The present investigation is concerned with new strategies which comprise an extension to the controlled growth method considered by Hajela and Sobieski-Sobieszczanski (1981). This method proposed an approach wherein the standard nonlinear programming (NLP) methodology of working with a very large number of design variables was replaced by a sequence of smaller optimization cycles, each involving a single 'dominant' variable. The current investigation outlines some new features. Attention is given to a modified cumulative constraint representation which is defined in both the feasible and infeasible domain of the design space. Other new features are related to the evaluation of the 'effectiveness measure' on which the choice of the dominant variable and the linking strategy is based.

Sequence search on a supercomputer.

PubMed

Gotoh, O; Tagashira, Y

1986-01-10

A set of programs was developed for searching nucleic acid and protein sequence data bases for sequences similar to a given sequence. The programs, written in FORTRAN 77, were optimized for vector processing on a Hitachi S810-20 supercomputer. A search of a 500-residue protein sequence against the entire PIR data base Ver. 1.0 (1) (0.5 M residues) is carried out in a CPU time of 45 sec. About 4 min is required for an exhaustive search of a 1500-base nucleotide sequence against all mammalian sequences (1.2M bases) in Genbank Ver. 29.0. The CPU time is reduced to about a quarter with a faster version.

K-Minimax Stochastic Programming Problems

NASA Astrophysics Data System (ADS)

Nedeva, C.

2007-10-01

The purpose of this paper is a discussion of a numerical procedure based on the simplex method for stochastic optimization problems with partially known distribution functions. The convergence of this procedure is proved by the condition on dual problems.

Four-state virtual research peer exchange 2015 : final report.

DOT National Transportation Integrated Search

2016-01-01

The transportation research programs in Idaho, Nevada, South Dakota, and Wyoming each hosted a multi-state : online webinar-based peer exchange consisting of a series of four webinars addressing four topics: : - Research Quality: optimizing the value...

Solving large-scale fixed cost integer linear programming models for grid-based location problems with heuristic techniques

NASA Astrophysics Data System (ADS)

Noor-E-Alam, Md.; Doucette, John

2015-08-01

Grid-based location problems (GBLPs) can be used to solve location problems in business, engineering, resource exploitation, and even in the field of medical sciences. To solve these decision problems, an integer linear programming (ILP) model is designed and developed to provide the optimal solution for GBLPs considering fixed cost criteria. Preliminary results show that the ILP model is efficient in solving small to moderate-sized problems. However, this ILP model becomes intractable in solving large-scale instances. Therefore, a decomposition heuristic is proposed to solve these large-scale GBLPs, which demonstrates significant reduction of solution runtimes. To benchmark the proposed heuristic, results are compared with the exact solution via ILP. The experimental results show that the proposed method significantly outperforms the exact method in runtime with minimal (and in most cases, no) loss of optimality.

Development of an Algorithm for Automatic Analysis of the Impedance Spectrum Based on a Measurement Model

NASA Astrophysics Data System (ADS)

Kobayashi, Kiyoshi; Suzuki, Tohru S.

2018-03-01

A new algorithm for the automatic estimation of an equivalent circuit and the subsequent parameter optimization is developed by combining the data-mining concept and complex least-squares method. In this algorithm, the program generates an initial equivalent-circuit model based on the sampling data and then attempts to optimize the parameters. The basic hypothesis is that the measured impedance spectrum can be reproduced by the sum of the partial-impedance spectra presented by the resistor, inductor, resistor connected in parallel to a capacitor, and resistor connected in parallel to an inductor. The adequacy of the model is determined by using a simple artificial-intelligence function, which is applied to the output function of the Levenberg-Marquardt module. From the iteration of model modifications, the program finds an adequate equivalent-circuit model without any user input to the equivalent-circuit model.

TRANSP-based Trajectory Optimization of the Current Profile Evolution to Facilitate Robust Non-inductive Ramp-up in NSTX-U

NASA Astrophysics Data System (ADS)

Wehner, William; Schuster, Eugenio; Poli, Francesca

2016-10-01

Initial progress towards the design of non-inductive current ramp-up scenarios in the National Spherical Torus Experiment Upgrade (NSTX-U) has been made through the use of TRANSP predictive simulations. The strategy involves, first, ramping the plasma current with high harmonic fast waves (HHFW) to about 400 kA, and then further ramping to 900 kA with neutral beam injection (NBI). However, the early ramping of neutral beams and application of HHFW leads to an undesirably peaked current profile making the plasma unstable to ballooning modes. We present an optimization-based control approach to improve on the non-inductive ramp-up strategy. We combine the TRANSP code with an optimization algorithm based on sequential quadratic programming to search for time evolutions of the NBI powers, the HHFW powers, and the line averaged density that define an open-loop actuator strategy that maximizes the non-inductive current while satisfying constraints associated with the current profile evolution for MHD stable plasmas. This technique has the potential of playing a critical role in achieving robustly stable non-inductive ramp-up, which will ultimately be necessary to demonstrate applicability of the spherical torus concept to larger devices without sufficient room for a central coil. Supported by the US DOE under the SCGSR Program.

Parallel Computational Protein Design.

PubMed

Zhou, Yichao; Donald, Bruce R; Zeng, Jianyang

2017-01-01

Computational structure-based protein design (CSPD) is an important problem in computational biology, which aims to design or improve a prescribed protein function based on a protein structure template. It provides a practical tool for real-world protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational protein design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab (Gainza et al., Methods Enzymol 523:87, 2013) to implement a GPU-based massively parallel A* algorithm for improving protein design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large protein design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE (Gainza et al., PLoS Comput Biol 8:e1002335, 2012) and DEEPer (Hallen et al., Proteins 81:18-39, 2013) to also consider continuous backbone and side-chain flexibility.

Portfolio-Scale Optimization of Customer Energy Efficiency Incentive and Marketing: Cooperative Research and Development Final Report, CRADA Number CRD-13-535

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

Brackney, Larry J.

North East utility National Grid (NGrid) is developing a portfolio-scale application of OpenStudio designed to optimize incentive and marketing expenditures for their energy efficiency (EE) programs. NGrid wishes to leverage a combination of geographic information systems (GIS), public records, customer data, and content from the Building Component Library (BCL) to form a JavaScript Object Notation (JSON) input file that is consumed by an OpenStudio-based expert system for automated model generation. A baseline model for each customer building will be automatically tuned using electricity and gas consumption data, and a set of energy conservation measures (ECMs) associated with each NGrid incentivemore » program will be applied to the model. The simulated energy performance and return on investment (ROI) will be compared with customer hurdle rates and available incentives to A) optimize the incentive required to overcome the customer hurdle rate and B) determine if marketing activity associated with the specific ECM is warranted for that particular customer. Repeated across their portfolio, this process will enable NGrid to substantially optimize their marketing and incentive expenditures, targeting those customers that will likely adopt and benefit from specific EE programs.« less

Can Linear Superiorization Be Useful for Linear Optimization Problems?

PubMed Central

Censor, Yair

2017-01-01

Linear superiorization considers linear programming problems but instead of attempting to solve them with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward reduced (not necessarily minimal) target function values. The two questions that we set out to explore experimentally are (i) Does linear superiorization provide a feasible point whose linear target function value is lower than that obtained by running the same feasibility-seeking algorithm without superiorization under identical conditions? and (ii) How does linear superiorization fare in comparison with the Simplex method for solving linear programming problems? Based on our computational experiments presented here, the answers to these two questions are: “yes” and “very well”, respectively. PMID:29335660

Augmented Lagrange Programming Neural Network for Localization Using Time-Difference-of-Arrival Measurements.

PubMed

Han, Zifa; Leung, Chi Sing; So, Hing Cheung; Constantinides, Anthony George

2017-08-15

A commonly used measurement model for locating a mobile source is time-difference-of-arrival (TDOA). As each TDOA measurement defines a hyperbola, it is not straightforward to compute the mobile source position due to the nonlinear relationship in the measurements. This brief exploits the Lagrange programming neural network (LPNN), which provides a general framework to solve nonlinear constrained optimization problems, for the TDOA-based localization. The local stability of the proposed LPNN solution is also analyzed. Simulation results are included to evaluate the localization accuracy of the LPNN scheme by comparing with the state-of-the-art methods and the optimality benchmark of Cramér-Rao lower bound.

Can linear superiorization be useful for linear optimization problems?

NASA Astrophysics Data System (ADS)

Censor, Yair

2017-04-01

Linear superiorization (LinSup) considers linear programming problems but instead of attempting to solve them with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward reduced (not necessarily minimal) target function values. The two questions that we set out to explore experimentally are: (i) does LinSup provide a feasible point whose linear target function value is lower than that obtained by running the same feasibility-seeking algorithm without superiorization under identical conditions? (ii) How does LinSup fare in comparison with the Simplex method for solving linear programming problems? Based on our computational experiments presented here, the answers to these two questions are: ‘yes’ and ‘very well’, respectively.

An Approach for Dynamic Optimization of Prevention Program Implementation in Stochastic Environments

NASA Astrophysics Data System (ADS)

Kang, Yuncheol; Prabhu, Vittal

The science of preventing youth problems has significantly advanced in developing evidence-based prevention program (EBP) by using randomized clinical trials. Effective EBP can reduce delinquency, aggression, violence, bullying and substance abuse among youth. Unfortunately the outcomes of EBP implemented in natural settings usually tend to be lower than in clinical trials, which has motivated the need to study EBP implementations. In this paper we propose to model EBP implementations in natural settings as stochastic dynamic processes. Specifically, we propose Markov Decision Process (MDP) for modeling and dynamic optimization of such EBP implementations. We illustrate these concepts using simple numerical examples and discuss potential challenges in using such approaches in practice.

Optimization Model for Web Based Multimodal Interactive Simulations.

PubMed

Halic, Tansel; Ahn, Woojin; De, Suvranu

2015-07-15

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.

Optimization Model for Web Based Multimodal Interactive Simulations

PubMed Central

Halic, Tansel; Ahn, Woojin; De, Suvranu

2015-01-01

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update. In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach. PMID:26085713

Optimal therapies of a virus replication model with pharmacological delays based on reverse transcriptase inhibitors and protease inhibitors

NASA Astrophysics Data System (ADS)

Pei, Yongzhen; Li, Changguo; Liang, Xiyin

2017-11-01

A short delay in the pharmacological effect on account of the time required for drug absorption, distribution, and penetration into target cells after application of any anti-viral drug, is defined by the pharmacological delay (Herz et al 1996 Proc. Natl Acad. Sci. USA 93 7247-51). In this paper, a virus replication model with Beddington-DeAngelis incidence rate and the pharmacological and intracellular delays is presented to describe the treatment to cure the virus infection. The optimal controls represent the efficiency of reverse transcriptase inhibitors and protease inhibitors in suppressing viral production and prohibiting new infections. Due to the fact that both the control and state variables contain delays, we derive a necessary conditions for our optimal problem. Based on these results, numerical simulations are implemented not only to show the optimal therapeutic schedules for different infection and release rates, but also to compare the effective of three treatment programs. Furthermore, comparison of therapeutic effects under different maximum tolerable dosages is shown. Our research indicates that (1) the proper and specific treatment program should be determined according to the infection rates of different virus particles; (2) the optimal combined drug treatment is the most efficient; (3) the appropriate proportion of medicament must be formulated during the therapy due to the non-monotonic relationship between maximum tolerable dosages and therapeutic effects; (4) the therapeutic effect is advantageous when the pharmacological delay is considered.

Corner smoothing of 2D milling toolpath using b-spline curve by optimizing the contour error and the feedrate

NASA Astrophysics Data System (ADS)

Özcan, Abdullah; Rivière-Lorphèvre, Edouard; Ducobu, François

2018-05-01

In part manufacturing, efficient process should minimize the cycle time needed to reach the prescribed quality on the part. In order to optimize it, the machining time needs to be as low as possible and the quality needs to meet some requirements. For a 2D milling toolpath defined by sharp corners, the programmed feedrate is different from the reachable feedrate due to kinematic limits of the motor drives. This phenomena leads to a loss of productivity. Smoothing the toolpath allows to reduce significantly the machining time but the dimensional accuracy should not be neglected. Therefore, a way to address the problem of optimizing a toolpath in part manufacturing is to take into account the manufacturing time and the part quality. On one hand, maximizing the feedrate will minimize the manufacturing time and, on the other hand, the maximum of the contour error needs to be set under a threshold to meet the quality requirements. This paper presents a method to optimize sharp corner smoothing using b-spline curves by adjusting the control points defining the curve. The objective function used in the optimization process is based on the contour error and the difference between the programmed feedrate and an estimation of the reachable feedrate. The estimation of the reachable feedrate is based on geometrical information. Some simulation results are presented in the paper and the machining times are compared in each cases.

Tree value system: description and assumptions.

Treesearch

D.G. Briggs

1989-01-01

TREEVAL is a microcomputer model that calculates tree or stand values and volumes based on product prices, manufacturing costs, and predicted product recovery. It was designed as an aid in evaluating management regimes. TREEVAL calculates values in either of two ways, one based on optimized tree bucking using dynamic programming and one simulating the results of user-...

Optimization and Control of Agent-Based Models in Biology: A Perspective.

PubMed

An, G; Fitzpatrick, B G; Christley, S; Federico, P; Kanarek, A; Neilan, R Miller; Oremland, M; Salinas, R; Laubenbacher, R; Lenhart, S

2017-01-01

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.

Optimizing Retransmission Threshold in Wireless Sensor Networks

PubMed Central

Bi, Ran; Li, Yingshu; Tan, Guozhen; Sun, Liang

2016-01-01

The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is OnΔ·max1≤i≤n{ui}, where ui is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Δ is the given upper bound of the transmission delay of the delivery path. If Δ is greater than the polynomial, to reduce the time complexity, a linear programming-based (1+pmin)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance. PMID:27171092

User-oriented design strategies for a Lunar base

NASA Astrophysics Data System (ADS)

Jukola, Paivi

'Form follows function can be translated, among other, to communicate a desire to prioritize functional objectives for a particular design task. Thus it is less likely that a design program for a multi-functional habitat, for an all-purpose vehicle, or for a general community, will lead to most optimal, cost-effective and sustainable solutions. A power plant, a factory, a farm and a research center have over centuries had different logistical and functional requirements, despite of the local culture on various parts around the planet Earth. 'The same size fits all' concept is likely to lead to less user-friendly solutions. The paper proposes to rethink and to investigate alternative strategies to formulate objectives for a Lunar base. Diverse scientific experiments and potential future research programs for the Moon have a number of functional requirements that differ from each other. A crew of 4-6 may not be optimal for the most innovative research. The discussion is based on research of Human Factors and Design for visiting professor lectures for a Lunar base project with Howard University and NASA Marshall Space Center 2009-2010.

Optimal Mixing On The Sphere

DTIC Science & Technology

2010-11-10

CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ...ORGANIZATION NAME(S) AND ADDRESS(ES) Woods Hole Oceanographic Institution,Woods Hole,MA,02543 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING...consider an alternate means of finding the minima of 〈|θ|2〉. We perform a two-part optimization process based on Matlab’s built-in nonlinear

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Hormesis and the Salk Polio Vaccine

PubMed Central

Calabrese, Edward J.

2011-01-01

The production of the Salk vaccine polio virus by monkey kidney cells was generated using the synthetic tissue culture medium, Mixture 199. In this paper’s retrospective assessment of this process, it was discovered that Mixture 199 was modified by the addition of ethanol to optimize animal cell survival based on experimentation that revealed a hormetic-like biphasic response relationship. This hormesis-based optimization procedure was then applied to all uses of Mixture 199 and modifications of it, including its application to the Salk polio vaccine during preliminary testing and in its subsequent major societal treatment programs. PMID:22423232

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

PubMed

Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio

2014-05-10

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

PubMed Central

2014-01-01

Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957

Development of a Platform for Simulating and Optimizing Thermoelectric Energy Systems

NASA Astrophysics Data System (ADS)

Kreuder, John J.

Thermoelectrics are solid state devices that can convert thermal energy directly into electrical energy. They have historically been used only in niche applications because of their relatively low efficiencies. With the advent of nanotechnology and improved manufacturing processes thermoelectric materials have become less costly and more efficient As next generation thermoelectric materials become available there is a need for industries to quickly and cost effectively seek out feasible applications for thermoelectric heat recovery platforms. Determining the technical and economic feasibility of such systems requires a model that predicts performance at the system level. Current models focus on specific system applications or neglect the rest of the system altogether, focusing on only module design and not an entire energy system. To assist in screening and optimizing entire energy systems using thermoelectrics, a novel software tool, Thermoelectric Power System Simulator (TEPSS), is developed for system level simulation and optimization of heat recovery systems. The platform is designed for use with a generic energy system so that most types of thermoelectric heat recovery applications can be modeled. TEPSS is based on object-oriented programming in MATLABRTM. A modular, shell based architecture is developed to carry out concept generation, system simulation and optimization. Systems are defined according to the components and interconnectivity specified by the user. An iterative solution process based on Newton's Method is employed to determine the system's steady state so that an objective function representing the cost of the system can be evaluated at the operating point. An optimization algorithm from MATLAB's Optimization Toolbox uses sequential quadratic programming to minimize this objective function with respect to a set of user specified design variables and constraints. During this iterative process many independent system simulations are executed and the optimal operating condition of the system is determined. A comprehensive guide to using the software platform is included. TEPSS is intended to be expandable so that users can add new types of components and implement component models with an adequate degree of complexity for a required application. Special steps are taken to ensure that the system of nonlinear algebraic equations presented in the system engineering model is square and that all equations are independent. In addition, the third party program FluidProp is leveraged to allow for simulations of systems with a range of fluids. Sequential unconstrained minimization techniques are used to prevent physical variables like pressure and temperature from trending to infinity during optimization. Two case studies are performed to verify and demonstrate the simulation and optimization routines employed by TEPSS. The first is of a simple combined cycle in which the size of the heat exchanger and fuel rate are optimized. The second case study is the optimization of geometric parameters of a thermoelectric heat recovery platform in a regenerative Brayton Cycle. A basic package of components and interconnections are verified and provided as well.

Design of underwater robot lines based on a hybrid automatic optimization strategy

NASA Astrophysics Data System (ADS)

Lyu, Wenjing; Luo, Weilin

2014-09-01

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Infinite horizon optimal impulsive control with applications to Internet congestion control

NASA Astrophysics Data System (ADS)

Avrachenkov, Konstantin; Habachi, Oussama; Piunovskiy, Alexey; Zhang, Yi

2015-04-01

We investigate infinite-horizon deterministic optimal control problems with both gradual and impulsive controls, where any finitely many impulses are allowed simultaneously. Both discounted and long-run time-average criteria are considered. We establish very general and at the same time natural conditions, under which the dynamic programming approach results in an optimal feedback policy. The established theoretical results are applied to the Internet congestion control, and by solving analytically and nontrivially the underlying optimal control problems, we obtain a simple threshold-based active queue management scheme, which takes into account the main parameters of the transmission control protocols, and improves the fairness among the connections in a given network.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Automating Embedded Analysis Capabilities and Managing Software Complexity in Multiphysics Simulation, Part II: Application to Partial Differential Equations

DOE PAGES

Pawlowski, Roger P.; Phipps, Eric T.; Salinger, Andrew G.; ...

2012-01-01

A template-based generic programming approach was presented in Part I of this series of papers [Sci. Program. 20 (2012), 197–219] that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded analysis algorithms. In this paper, we describe the implementation details for using the template-based generic programming approach for simulation and analysis of partial differential equations (PDEs). We detail several of the hurdles that we have encountered, and some of the software infrastructure developed to overcome them. We end with a demonstration where we present shape optimization and uncertaintymore » quantification results for a 3D PDE application.« less

78 FR 57845 - Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program Environmental...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-20

... (NOA) for Strategic Network Optimization (SNO) Program Environmental Assessment AGENCY: Defense Logistics Agency, DoD. ACTION: Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program... implement the SNO initiative for improvements to material distribution network for the Department of Defense...

Program to Optimize Simulated Trajectories (POST). Volume 1: Formulation manual

NASA Technical Reports Server (NTRS)

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

1975-01-01

A general purpose FORTRAN program for simulating and optimizing point mass trajectories (POST) of aerospace vehicles is described. The equations and the numerical techniques used in the program are documented. Topics discussed include: coordinate systems, planet model, trajectory simulation, auxiliary calculations, and targeting and optimization.

Deterministic Design Optimization of Structures in OpenMDAO Framework

NASA Technical Reports Server (NTRS)

Coroneos, Rula M.; Pai, Shantaram S.

2012-01-01

Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.

TRU Waste Management Program. Cost/schedule optimization analysis

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

Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.

This Current Year Work Plan presents in detail a description of the activities to be performed by the Joint Integration Office Rockwell International (JIO/RI) during FY86. It breaks down the activities into two major work areas: Program Management and Program Analysis. Program Management is performed by the JIO/RI by providing technical planning and guidance for the development of advanced TRU waste management capabilities. This includes equipment/facility design, engineering, construction, and operations. These functions are integrated to allow transition from interim storage to final disposition. JIO/RI tasks include program requirements identification, long-range technical planning, budget development, program planning document preparation, taskmore » guidance development, task monitoring, task progress information gathering and reporting to DOE, interfacing with other agencies and DOE lead programs, integrating public involvement with program efforts, and preparation of reports for DOE detailing program status. Program Analysis is performed by the JIO/RI to support identification and assessment of alternatives, and development of long-term TRU waste program capabilities. These analyses include short-term analyses in response to DOE information requests, along with performing an RH Cost/Schedule Optimization report. Systems models will be developed, updated, and upgraded as needed to enhance JIO/RI's capability to evaluate the adequacy of program efforts in various fields. A TRU program data base will be maintained and updated to provide DOE with timely responses to inventory related questions.« less

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Lauffenburger, Douglas A.; Alexopoulos, Leonidas G.

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms. PMID:23226239

Automatic design of synthetic gene circuits through mixed integer non-linear programming.

PubMed

Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

2012-01-01

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits.

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

PubMed

Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

Mars scouts: an overview

NASA Technical Reports Server (NTRS)

Matousek, S.

2001-01-01

The Mars program institutes the Mars Scout Missions in order to address science goals in the program not otherwise covered in the baseline Mars plan. Mars Scout Missions will be Principle-Investigator (PI) led science missions. Analogous to the Discovery Program, PI led investigations optimize the use of limited resources to accomplish the best focused science and allow the flexibility to quickly respond to discoveries at Mars. Scout missions also require unique investments in technology and reliance upon Mars-based infrastructure such as telecom relay orbiters.

CORSSTOL: Cylinder Optimization of Rings, Skin, and Stringers with Tolerance sensitivity

NASA Technical Reports Server (NTRS)

Finckenor, J.; Bevill, M.

1995-01-01

Cylinder Optimization of Rings, Skin, and Stringers with Tolerance (CORSSTOL) sensitivity is a design optimization program incorporating a method to examine the effects of user-provided manufacturing tolerances on weight and failure. CORSSTOL gives designers a tool to determine tolerances based on need. This is a decisive way to choose the best design among several manufacturing methods with differing capabilities and costs. CORSSTOL initially optimizes a stringer-stiffened cylinder for weight without tolerances. The skin and stringer geometry are varied, subject to stress and buckling constraints. Then the same analysis and optimization routines are used to minimize the maximum material condition weight subject to the least favorable combination of tolerances. The adjusted optimum dimensions are provided with the weight and constraint sensitivities of each design variable. The designer can immediately identify critical tolerances. The safety of parts made out of tolerance can also be determined. During design and development of weight-critical systems, design/analysis tools that provide product-oriented results are of vital significance. The development of this program and methodology provides designers with an effective cost- and weight-saving design tool. The tolerance sensitivity method can be applied to any system defined by a set of deterministic equations.

System design optimization for a Mars-roving vehicle and perturbed-optimal solutions in nonlinear programming

NASA Technical Reports Server (NTRS)

Pavarini, C.

1974-01-01

Work in two somewhat distinct areas is presented. First, the optimal system design problem for a Mars-roving vehicle is attacked by creating static system models and a system evaluation function and optimizing via nonlinear programming techniques. The second area concerns the problem of perturbed-optimal solutions. Given an initial perturbation in an element of the solution to a nonlinear programming problem, a linear method is determined to approximate the optimal readjustments of the other elements of the solution. Then, the sensitivity of the Mars rover designs is described by application of this method.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

SU-F-J-94: Development of a Plug-in Based Image Analysis Tool for Integration Into Treatment Planning

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

Owen, D; Anderson, C; Mayo, C

Purpose: To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image-based metrics within treatment plan optimization and (ii) evaluation of dose-functional volume relationships to assist in functional image adaptive radiotherapy. Methods: A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose-functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel-by-voxel basis through API extensions that can access registration transforms. A user interface was designed through WinFormsmore » to input parameters and display results. To test the performance of this program, image- and dose-based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared. Results: The integration of image analysis information was successfully implemented as a plug-in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image-based metrics as well as dose-intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image-based metrics, dose-intensity computations, and dose-intensity histograms were applied to analyze the image-dose profile. Conclusion: It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image-intensity-based metrics in the context of functional targeting and avoidance. In addition to providing dose-intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug-in optimization system, which can directly use the computed metrics for optimization of post-treatment tumor or normal tissue response models. Supported by NIH - P01 - CA059827.« less

TRU Waste Management Program cost/schedule optimization analysis

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

Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.

1985-10-01

The cost/schedule optimization task is a necessary function to insure that program goals and plans are optimized from a cost and schedule aspect. Results of this study will offer DOE information with which it can establish, within institutional constraints, the most efficient program for the long-term management and disposal of contact handled transuranic waste (CH-TRU). To this end, a comprehensive review of program cost/schedule tradeoffs has been made, to identify any major cost saving opportunities that may be realized by modification of current program plans. It was decided that all promising scenarios would be explored, and institutional limitations to implementationmore » would be described. Since a virtually limitless number of possible scenarios can be envisioned, it was necessary to distill these possibilities into a manageable number of alternatives. The resultant scenarios were described in the cost/schedule strategy and work plan document. Each scenario was compared with the base case: waste processing at the originating site; transport of CH-TRU wastes in TRUPACT; shipment of drums in 6-Packs; 25 year stored waste workoff; WIPP operational 10/88, with all sites shipping to WIPP beginning 10/88; and no processing at WIPP. Major savings were identified in two alternate scenarios: centralize waste processing at INEL and eliminate rail shipment of TRUPACT. No attempt was made to calculate savings due to combination of scenarios. 1 ref., 5 figs., 1 tab. (MHB)« less

Linear energy transfer incorporated intensity modulated proton therapy optimization

NASA Astrophysics Data System (ADS)

Cao, Wenhua; Khabazian, Azin; Yepes, Pablo P.; Lim, Gino; Poenisch, Falk; Grosshans, David R.; Mohan, Radhe

2018-01-01

The purpose of this study was to investigate the feasibility of incorporating linear energy transfer (LET) into the optimization of intensity modulated proton therapy (IMPT) plans. Because increased LET correlates with increased biological effectiveness of protons, high LETs in target volumes and low LETs in critical structures and normal tissues are preferred in an IMPT plan. However, if not explicitly incorporated into the optimization criteria, different IMPT plans may yield similar physical dose distributions but greatly different LET, specifically dose-averaged LET, distributions. Conventionally, the IMPT optimization criteria (or cost function) only includes dose-based objectives in which the relative biological effectiveness (RBE) is assumed to have a constant value of 1.1. In this study, we added LET-based objectives for maximizing LET in target volumes and minimizing LET in critical structures and normal tissues. Due to the fractional programming nature of the resulting model, we used a variable reformulation approach so that the optimization process is computationally equivalent to conventional IMPT optimization. In this study, five brain tumor patients who had been treated with proton therapy at our institution were selected. Two plans were created for each patient based on the proposed LET-incorporated optimization (LETOpt) and the conventional dose-based optimization (DoseOpt). The optimized plans were compared in terms of both dose (assuming a constant RBE of 1.1 as adopted in clinical practice) and LET. Both optimization approaches were able to generate comparable dose distributions. The LET-incorporated optimization achieved not only pronounced reduction of LET values in critical organs, such as brainstem and optic chiasm, but also increased LET in target volumes, compared to the conventional dose-based optimization. However, on occasion, there was a need to tradeoff the acceptability of dose and LET distributions. Our conclusion is that the inclusion of LET-dependent criteria in the IMPT optimization could lead to similar dose distributions as the conventional optimization but superior LET distributions in target volumes and normal tissues. This may have substantial advantages in improving tumor control and reducing normal tissue toxicities.

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

Parametric modeling and stagger angle optimization of an axial flow fan

NASA Astrophysics Data System (ADS)

Li, M. X.; Zhang, C. H.; Liu, Y.; Y Zheng, S.

2013-12-01

Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%.

Treatment Planning and Image Guidance for Radiofrequency Ablations of Large Tumors

PubMed Central

Ren, Hongliang; Campos-Nanez, Enrique; Yaniv, Ziv; Banovac, Filip; Abeledo, Hernan; Hata, Nobuhiko; Cleary, Kevin

2014-01-01

This article addresses the two key challenges in computer-assisted percutaneous tumor ablation: planning multiple overlapping ablations for large tumors while avoiding critical structures, and executing the prescribed plan. Towards semi-automatic treatment planning for image-guided surgical interventions, we develop a systematic approach to the needle-based ablation placement task, ranging from pre-operative planning algorithms to an intra-operative execution platform. The planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization formulation, which consists of optimal path selection and ablation coverage optimization based on integer programming. The system implementation is presented and validated in phantom studies and on an animal model. The presented system can potentially be further extended for other ablation techniques such as cryotherapy. PMID:24235279

The Optimization Based Dynamic and Cyclic Working Strategies for Rechargeable Wireless Sensor Networks with Multiple Base Stations and Wireless Energy Transfer Devices

PubMed Central

Ding, Xu; Han, Jianghong; Shi, Lei

2015-01-01

In this paper, the optimal working schemes for wireless sensor networks with multiple base stations and wireless energy transfer devices are proposed. The wireless energy transfer devices also work as data gatherers while charging sensor nodes. The wireless sensor network is firstly divided into sub networks according to the concept of Voronoi diagram. Then, the entire energy replenishing procedure is split into the pre-normal and normal energy replenishing stages. With the objective of maximizing the sojourn time ratio of the wireless energy transfer device, a continuous time optimization problem for the normal energy replenishing cycle is formed according to constraints with which sensor nodes and wireless energy transfer devices should comply. Later on, the continuous time optimization problem is reshaped into a discrete multi-phased optimization problem, which yields the identical optimality. After linearizing it, we obtain a linear programming problem that can be solved efficiently. The working strategies of both sensor nodes and wireless energy transfer devices in the pre-normal replenishing stage are also discussed in this paper. The intensive simulations exhibit the dynamic and cyclic working schemes for the entire energy replenishing procedure. Additionally, a way of eliminating “bottleneck” sensor nodes is also developed in this paper. PMID:25785305

The optimization based dynamic and cyclic working strategies for rechargeable wireless sensor networks with multiple base stations and wireless energy transfer devices.

PubMed

Ding, Xu; Han, Jianghong; Shi, Lei

2015-03-16

In this paper, the optimal working schemes for wireless sensor networks with multiple base stations and wireless energy transfer devices are proposed. The wireless energy transfer devices also work as data gatherers while charging sensor nodes. The wireless sensor network is firstly divided into sub networks according to the concept of Voronoi diagram. Then, the entire energy replenishing procedure is split into the pre-normal and normal energy replenishing stages. With the objective of maximizing the sojourn time ratio of the wireless energy transfer device, a continuous time optimization problem for the normal energy replenishing cycle is formed according to constraints with which sensor nodes and wireless energy transfer devices should comply. Later on, the continuous time optimization problem is reshaped into a discrete multi-phased optimization problem, which yields the identical optimality. After linearizing it, we obtain a linear programming problem that can be solved efficiently. The working strategies of both sensor nodes and wireless energy transfer devices in the pre-normal replenishing stage are also discussed in this paper. The intensive simulations exhibit the dynamic and cyclic working schemes for the entire energy replenishing procedure. Additionally, a way of eliminating "bottleneck" sensor nodes is also developed in this paper.

DORCA computer program. Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Wray, S. T., Jr.

1971-01-01

The Dynamic Operational Requirements and Cost Analysis Program (DORCA) was written to provide a top level analysis tool for NASA. DORCA relies on a man-machine interaction to optimize results based on external criteria. DORCA relies heavily on outside sources to provide cost information and vehicle performance parameters as the program does not determine these quantities but rather uses them. Given data describing missions, vehicles, payloads, containers, space facilities, schedules, cost values and costing procedures, the program computes flight schedules, cargo manifests, vehicle fleet requirements, acquisition schedules and cost summaries. The program is designed to consider the Earth Orbit, Lunar, Interplanetary and Automated Satellite Programs. A general outline of the capabilities of the program are provided.

The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis (Version 2.0.2).

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

Waddell, Lucas; Muldoon, Frank; Henry, Stephen Michael

In order to effectively plan the management and modernization of their large and diverse fleets of vehicles, Program Executive Office Ground Combat Systems (PEO GCS) and Program Executive Office Combat Support and Combat Service Support (PEO CS&CSS) commis- sioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thor-more » ough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation. This paper, which is an update to the original CPAT formulation document published in 2015 (SAND2015-3487), covers the formulation of important new CPAT features.« less

An iterative method for tri-level quadratic fractional programming problems using fuzzy goal programming approach

NASA Astrophysics Data System (ADS)

Kassa, Semu Mitiku; Tsegay, Teklay Hailay

2017-08-01

Tri-level optimization problems are optimization problems with three nested hierarchical structures, where in most cases conflicting objectives are set at each level of hierarchy. Such problems are common in management, engineering designs and in decision making situations in general, and are known to be strongly NP-hard. Existing solution methods lack universality in solving these types of problems. In this paper, we investigate a tri-level programming problem with quadratic fractional objective functions at each of the three levels. A solution algorithm has been proposed by applying fuzzy goal programming approach and by reformulating the fractional constraints to equivalent but non-fractional non-linear constraints. Based on the transformed formulation, an iterative procedure is developed that can yield a satisfactory solution to the tri-level problem. The numerical results on various illustrative examples demonstrated that the proposed algorithm is very much promising and it can also be used to solve larger-sized as well as n-level problems of similar structure.

DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization

NASA Technical Reports Server (NTRS)

Williams, C. H.; Spurlock, O. F.

2014-01-01

From the late 1960's through 1997, the leadership of NASA's Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRC's primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the code's operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960's is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the Atlas/Centaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (Atlas/Centaur, Titan/Centaur, and Shuttle/Centaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUP's many major impacts on launches are discussed including Intelsat, Voyager, Pioneer Venus, HEAO, Galileo, and Cassini.

DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization

NASA Technical Reports Server (NTRS)

Spurlock, O. Frank; Williams, Craig H.

2015-01-01

From the late 1960s through 1997, the leadership of NASAs Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRCs primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the codes operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960s is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the AtlasCentaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (AtlasCentaur, TitanCentaur, and ShuttleCentaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUPs many major impacts on launches are discussed including Intelsat, Voyager, Pioneer Venus, HEAO, Galileo, and Cassini.

Final Report A Multi-Language Environment For Programmable Code Optimization and Empirical Tuning

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

Yi, Qing; Whaley, Richard Clint; Qasem, Apan

This report summarizes our effort and results of building an integrated optimization environment to effectively combine the programmable control and the empirical tuning of source-to-source compiler optimizations within the framework of multiple existing languages, specifically C, C++, and Fortran. The environment contains two main components: the ROSE analysis engine, which is based on the ROSE C/C++/Fortran2003 source-to-source compiler developed by Co-PI Dr.Quinlan et. al at DOE/LLNL, and the POET transformation engine, which is based on an interpreted program transformation language developed by Dr. Yi at University of Texas at San Antonio (UTSA). The ROSE analysis engine performs advanced compiler analysis,more » identifies profitable code transformations, and then produces output in POET, a language designed to provide programmable control of compiler optimizations to application developers and to support the parameterization of architecture-sensitive optimizations so that their configurations can be empirically tuned later. This POET output can then be ported to different machines together with the user application, where a POET-based search engine empirically reconfigures the parameterized optimizations until satisfactory performance is found. Computational specialists can write POET scripts to directly control the optimization of their code. Application developers can interact with ROSE to obtain optimization feedback as well as provide domain-specific knowledge and high-level optimization strategies. The optimization environment is expected to support different levels of automation and programmer intervention, from fully-automated tuning to semi-automated development and to manual programmable control.« less

Exploring efficacy of residential energy efficiency programs in Florida

NASA Astrophysics Data System (ADS)

Taylor, Nicholas Wade

Electric utilities, government agencies, and private interests in the U.S. have committed and continue to invest substantial resources in the pursuit of energy efficiency and conservation through demand-side management (DSM) programs. Program investments, and the demand for impact evaluations that accompany them, are projected to grow in coming years due to increased pressure from state-level energy regulation, costs and challenges of building additional production capacity, fuel costs and potential carbon or renewable energy regulation. This dissertation provides detailed analyses of ex-post energy savings from energy efficiency programs in three key sectors of residential buildings: new, single-family, detached homes; retrofits to existing single-family, detached homes; and retrofits to existing multifamily housing units. Each of the energy efficiency programs analyzed resulted in statistically significant energy savings at the full program group level, yet savings for individual participants and participant subgroups were highly variable. Even though savings estimates were statistically greater than zero, those energy savings did not always meet expectations. Results also show that high variability in energy savings among participant groups or subgroups can negatively impact overall program performance and can undermine marketing efforts for future participation. Design, implementation, and continued support of conservation programs based solely on deemed or projected savings is inherently counter to the pursuit of meaningful energy conservation and reductions in greenhouse gas emissions. To fully understand and optimize program impacts, consistent and robust measurement and verification protocols must be instituted in the design phase and maintained over time. Furthermore, marketing for program participation must target those who have the greatest opportunity for savings. In most utility territories it is not possible to gain access to the type of large scale datasets that would facilitate robust program analysis. Along with measuring and optimizing energy conservation programs, utilities should provide public access to historical consumption data. Open access to data, program optimization, consistent measurement and verification and transparency in reported savings are essential to reducing energy use and its associated environmental impacts.

Evaluating the effects of real power losses in optimal power flow based storage integration

DOE PAGES

Castillo, Anya; Gayme, Dennice

2017-03-27

This study proposes a DC optimal power flow (DCOPF) with losses formulation (the `-DCOPF+S problem) and uses it to investigate the role of real power losses in OPF based grid-scale storage integration. We derive the `- DCOPF+S problem by augmenting a standard DCOPF with storage (DCOPF+S) problem to include quadratic real power loss approximations. This procedure leads to a multi-period nonconvex quadratically constrained quadratic program, which we prove can be solved to optimality using either a semidefinite or second order cone relaxation. Our approach has some important benefits over existing models. It is more computationally tractable than ACOPF with storagemore » (ACOPF+S) formulations and the provably exact convex relaxations guarantee that an optimal solution can be attained for a feasible problem. Adding loss approximations to a DCOPF+S model leads to a more accurate representation of locational marginal prices, which have been shown to be critical to determining optimal storage dispatch and siting in prior ACOPF+S based studies. Case studies demonstrate the improved accuracy of the `-DCOPF+S model over a DCOPF+S model and the computational advantages over an ACOPF+S formulation.« less

A novel approach based on preference-based index for interval bilevel linear programming problem.

PubMed

Ren, Aihong; Wang, Yuping; Xue, Xingsi

2017-01-01

This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation [Formula: see text]. Furthermore, the concept of a preference δ -optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

The Programming Optimization of Capacitorless 1T DRAM Based on the Dual-Gate TFET

NASA Astrophysics Data System (ADS)

Li, Wei; Liu, Hongxia; Wang, Shulong; Chen, Shupeng; Wang, Qianqiong

2017-09-01

The larger volume of capacitor and higher leakage current of transistor have become the inherent disadvantages for the traditional one transistor (1T)-one capacitor (1C) dynamic random access memory (DRAM). Recently, the tunneling FET (TFET) is applied in DRAM cell due to the low off-state current and high switching ratio. The dual-gate TFET (DG-TFET) DRAM cell with the capacitorless structure has the superior performance-higher retention time (RT) and weak temperature dependence. But the performance of TFET DRAM cell is sensitive to programming condition. In this paper, the guideline of programming optimization is discussed in detail by using simulation tool—Silvaco Atlas. Both the writing and reading operations of DG-TFET DRAM depend on the band-to-band tunneling (BTBT). During the writing operation, the holes coming from BTBT governed by Gate2 are stored in potential well under Gate2. A small negative voltage is applied at Gate2 to retain holes for a long time during holding "1". The BTBT governed by Gate1 mainly influences the reading current. Using the optimized programming condition, the DG-TFET DRAM obtains the higher current ratio of reading "1" to reading "0" (107) and RT of more than 2 s. The higher RT reduces the refresh rate and dynamic power consumption of DRAM.

An MILP-based cross-layer optimization for a multi-reader arbitration in the UHF RFID system.

PubMed

Choi, Jinchul; Lee, Chaewoo

2011-01-01

In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system performance depends on how to adapt the various reader arbitration metrics such as time, frequency, and output power to the system environment. However, complexity and difficulty of the optimization problem increase with respect to the variety of the arbitration metrics. Thus, most proposals in previous study have been suggested to primarily prevent the reader collision with consideration of one or two arbitration metrics. In this paper, we propose a novel cross-layer optimization design based on the concept of combining time division, frequency division, and power control not only to solve the reader interference problem, but also to achieve the multiple objectives such as minimum interrogation delay, maximum reader utilization, and energy efficiency. Based on the priority of the multiple objectives, our cross-layer design optimizes the system sequentially by means of the mixed-integer linear programming. In spite of the multi-stage optimization, the optimization design is formulated as a concise single mathematical form by properly assigning a weight to each objective. Numerical results demonstrate the effectiveness of the proposed optimization design.

An MILP-Based Cross-Layer Optimization for a Multi-Reader Arbitration in the UHF RFID System

PubMed Central

Choi, Jinchul; Lee, Chaewoo

2011-01-01

In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system performance depends on how to adapt the various reader arbitration metrics such as time, frequency, and output power to the system environment. However, complexity and difficulty of the optimization problem increase with respect to the variety of the arbitration metrics. Thus, most proposals in previous study have been suggested to primarily prevent the reader collision with consideration of one or two arbitration metrics. In this paper, we propose a novel cross-layer optimization design based on the concept of combining time division, frequency division, and power control not only to solve the reader interference problem, but also to achieve the multiple objectives such as minimum interrogation delay, maximum reader utilization, and energy efficiency. Based on the priority of the multiple objectives, our cross-layer design optimizes the system sequentially by means of the mixed-integer linear programming. In spite of the multi-stage optimization, the optimization design is formulated as a concise single mathematical form by properly assigning a weight to each objective. Numerical results demonstrate the effectiveness of the proposed optimization design. PMID:22163743

Solving iTOUGH2 simulation and optimization problems using the PEST protocol

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

Finsterle, S.A.; Zhang, Y.

2011-02-01

The PEST protocol has been implemented into the iTOUGH2 code, allowing the user to link any simulation program (with ASCII-based inputs and outputs) to iTOUGH2's sensitivity analysis, inverse modeling, and uncertainty quantification capabilities. These application models can be pre- or post-processors of the TOUGH2 non-isothermal multiphase flow and transport simulator, or programs that are unrelated to the TOUGH suite of codes. PEST-style template and instruction files are used, respectively, to pass input parameters updated by the iTOUGH2 optimization routines to the model, and to retrieve the model-calculated values that correspond to observable variables. We summarize the iTOUGH2 capabilities and demonstratemore » the flexibility added by the PEST protocol for the solution of a variety of simulation-optimization problems. In particular, the combination of loosely coupled and tightly integrated simulation and optimization routines provides both the flexibility and control needed to solve challenging inversion problems for the analysis of multiphase subsurface flow and transport systems.« less

Holistic irrigation water management approach based on stochastic soil water dynamics

NASA Astrophysics Data System (ADS)

Alizadeh, H.; Mousavi, S. J.

2012-04-01

Appreciating the essential gap between fundamental unsaturated zone transport processes and soil and water management due to low effectiveness of some of monitoring and modeling approaches, this study presents a mathematical programming model for irrigation management optimization based on stochastic soil water dynamics. The model is a nonlinear non-convex program with an economic objective function to address water productivity and profitability aspects in irrigation management through optimizing irrigation policy. Utilizing an optimization-simulation method, the model includes an eco-hydrological integrated simulation model consisting of an explicit stochastic module of soil moisture dynamics in the crop-root zone with shallow water table effects, a conceptual root-zone salt balance module, and the FAO crop yield module. Interdependent hydrology of soil unsaturated and saturated zones is treated in a semi-analytical approach in two steps. At first step analytical expressions are derived for the expected values of crop yield, total water requirement and soil water balance components assuming fixed level for shallow water table, while numerical Newton-Raphson procedure is employed at the second step to modify value of shallow water table level. Particle Swarm Optimization (PSO) algorithm, combined with the eco-hydrological simulation model, has been used to solve the non-convex program. Benefiting from semi-analytical framework of the simulation model, the optimization-simulation method with significantly better computational performance compared to a numerical Mote-Carlo simulation-based technique has led to an effective irrigation management tool that can contribute to bridging the gap between vadose zone theory and water management practice. In addition to precisely assessing the most influential processes at a growing season time scale, one can use the developed model in large scale systems such as irrigation districts and agricultural catchments. Accordingly, the model has been applied in Dasht-e-Abbas and Ein-khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. The area suffers from the water scarcity problem and therefore the trade-off between the level of deficit and economical profit should be assessed. Based on the results, while the maximum net benefit has been obtained for the stress-avoidance (SA) irrigation policy, the highest water profitability, defined by economical net benefit gained from unit irrigation water volume application, has been resulted when only about 60% of water used in the SA policy is applied.

Final Report from The University of Texas at Austin for DEGAS: Dynamic Global Address Space programming environments

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

Erez, Mattan; Yelick, Katherine; Sarkar, Vivek

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. Our approach is to provide an efficient and scalable programming model that can be adapted to application needs through the use of dynamic runtime features and domain-specific languages for computational kernels. We address the following technical challenges: Programmability: Rich set of programming constructs based on a Hierarchical Partitioned Global Address Space (HPGAS) model, demonstrated in UPC++. Scalability: Hierarchical locality control, lightweight communication (extended GASNet), and ef- ficient synchronization mechanisms (Phasers). Performance Portability:more » Just-in-time specialization (SEJITS) for generating hardware-specific code and scheduling libraries for domain-specific adaptive runtimes (Habanero). Energy Efficiency: Communication-optimal code generation to optimize energy efficiency by re- ducing data movement. Resilience: Containment Domains for flexible, domain-specific resilience, using state capture mechanisms and lightweight, asynchronous recovery mechanisms. Interoperability: Runtime and language interoperability with MPI and OpenMP to encourage broad adoption.« less

Physical activity and training in sarcoidosis: review and experience-based recommendations.

PubMed

Strookappe, Bert; Saketkoo, Lesley Ann; Elfferich, Marjon; Holland, Anne; De Vries, Jolanda; Knevel, Ton; Drent, Marjolein

2016-10-01

Sarcoidosis is a multisystemic inflammatory disorder with a great variety of symptoms, including fatigue, dyspnea, pain, reduced exercise tolerance and muscle strength. Physical training has the potential to improve exercise capacity and muscle strength, and reduce fatigue. The aim of this review and survey was to present information about the role of physical training in sarcoidosis and offer practical guidelines. A systematic literature review guided an international consensus effort among sarcoidosis experts to establish practice-basic recommendations for the implementation of exercise as treatment for patients with various manifestations of sarcoidosis. International sarcoidosis experts suggested considering physical training in symptomatic patients with sarcoidosis. Expert commentary: There is promising evidence of a positive effect of physical training. Recommendations were based on available data and expert consensus. However, the heterogeneity of these patients will require modification and program adjustment of the standard rehabilitation format for e.g. COPD or interstitial lung diseases. An optimal training program (types of exercise, intensities, frequency, duration) still needs to be defined to optimize training adjustments, especially reduction of fatigue. Further randomized controlled trials are needed to consolidate these findings and optimize the comprehensive care of sarcoidosis patients.

Human health risk constrained naphthalene-contaminated groundwater remediation management through an improved credibility method.

PubMed

Li, Jing; Lu, Hongwei; Fan, Xing; Chen, Yizhong

2017-07-01

In this study, a human health risk constrained groundwater remediation management program based on the improved credibility is developed for naphthalene contamination. The program integrates simulation, multivariate regression analysis, health risk assessment, uncertainty analysis, and nonlinear optimization into a general framework. The improved credibility-based optimization model for groundwater remediation management with consideration of human health risk (ICOM-HHR) is capable of not only effectively addressing parameter uncertainties and risk-exceeding possibility in human health risk but also providing a credibility level that indicates the satisfaction of the optimal groundwater remediation strategies with multiple contributions of possibility and necessity. The capabilities and effectiveness of ICOM-HHR are illustrated through a real-world case study in Anhui Province, China. Results indicate that the ICOM-HHR would generate double remediation cost yet reduce approximately 10 times of the naphthalene concentrations at monitoring wells, i.e., mostly less than 1 μg/L, which implies that the ICOM-HHR usually results in better environmental and health risk benefits. And it is acceptable to obtain a better environmental quality and a lower health risk level with sacrificing a certain economic benefit.

ReacKnock: Identifying Reaction Deletion Strategies for Microbial Strain Optimization Based on Genome-Scale Metabolic Network

PubMed Central

Xu, Zixiang; Zheng, Ping; Sun, Jibin; Ma, Yanhe

2013-01-01

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective. PMID:24348984

Programming scheme based optimization of hybrid 4T-2R OxRAM NVSRAM

NASA Astrophysics Data System (ADS)

Majumdar, Swatilekha; Kingra, Sandeep Kaur; Suri, Manan

2017-09-01

In this paper, we present a novel single-cycle programming scheme for 4T-2R NVSRAM, exploiting pulse engineered input signals. OxRAM devices based on 3 nm thick bi-layer active switching oxide and 90 nm CMOS technology node were used for all simulations. The cell design is implemented for real-time non-volatility rather than last-bit, or power-down non-volatility. Detailed analysis of the proposed single-cycle, parallel RRAM device programming scheme is presented in comparison to the two-cycle sequential RRAM programming used for similar 4T-2R NVSRAM bit-cells. The proposed single-cycle programming scheme coupled with the 4T-2R architecture leads to several benefits such as- possibility of unconventional transistor sizing, 50% lower latency, 20% improvement in SNM and ∼20× reduced energy requirements, when compared against two-cycle programming approach.

The T/R modules for phased-array antennas

NASA Astrophysics Data System (ADS)

Peignet, Colette; Mancuso, Yves; Resneau, J. Claude

1990-09-01

The concept of phased array radar is critically dependent on the availability of compact, reliable and low power consuming Transmitter/Receiver (T/R) modules. An overview is given on two major programs actually at development stage within the Thomson group and on three major development axis (electrical concept optimization, packaging, and size reduction). The technical feasibility of the concept was proven and the three major axis were enlightened, based on reliability, power added efficiency, and RF tests optimization.

DoD Lead System Integrator (LSI) Transformation - Creating a Model Based Acquisition Framework (MBAF)

DTIC Science & Technology

2014-04-30

cost to acquire systems as design maturity could be verified incrementally as the system was developed vice waiting for specific large “ big bang ...Framework (MBAF) be applied to simulate or optimize process variations on programs? LSI Roles and Responsibilities A review of the roles and...the model/process optimization process. It is the current intent that NAVAIR will use the model to run simulations on process changes in an attempt to

An export coefficient based inexact fuzzy bi-level multi-objective programming model for the management of agricultural nonpoint source pollution under uncertainty

NASA Astrophysics Data System (ADS)

Cai, Yanpeng; Rong, Qiangqiang; Yang, Zhifeng; Yue, Wencong; Tan, Qian

2018-02-01

In this research, an export coefficient based inexact fuzzy bi-level multi-objective programming (EC-IFBLMOP) model was developed through integrating export coefficient model (ECM), interval parameter programming (IPP) and fuzzy parameter programming (FPP) within a bi-level multi-objective programming framework. The proposed EC-IFBLMOP model can effectively deal with the multiple uncertainties expressed as discrete intervals and fuzzy membership functions. Also, the complexities in agricultural systems, such as the cooperation and gaming relationship between the decision makers at different levels, can be fully considered in the model. The developed model was then applied to identify the optimal land use patterns and BMP implementing levels for agricultural nonpoint source (NPS) pollution management in a subcatchment in the upper stream watershed of the Miyun Reservoir in north China. The results of the model showed that the desired optimal land use patterns and implementing levels of best management of practices (BMPs) would be obtained. It is the gaming result between the upper- and lower-level decision makers, when the allowable discharge amounts of NPS pollutants were limited. Moreover, results corresponding to different decision scenarios could provide a set of decision alternatives for the upper- and lower-level decision makers to identify the most appropriate management strategy. The model has a good applicability and can be effectively utilized for agricultural NPS pollution management.

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints

PubMed Central

2013-01-01

Background Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. Methods In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Results Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies. PMID:23368729

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints.

PubMed

Ren, Shaogang; Zeng, Bo; Qian, Xiaoning

2013-01-01

Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies.

Optimized and parallelized implementation of the electronegativity equalization method and the atom-bond electronegativity equalization method.

PubMed

Vareková, R Svobodová; Koca, J

2006-02-01

The most common way to calculate charge distribution in a molecule is ab initio quantum mechanics (QM). Some faster alternatives to QM have also been developed, the so-called "equalization methods" EEM and ABEEM, which are based on DFT. We have implemented and optimized the EEM and ABEEM methods and created the EEM SOLVER and ABEEM SOLVER programs. It has been found that the most time-consuming part of equalization methods is the reduction of the matrix belonging to the equation system generated by the method. Therefore, for both methods this part was replaced by the parallel algorithm WIRS and implemented within the PVM environment. The parallelized versions of the programs EEM SOLVER and ABEEM SOLVER showed promising results, especially on a single computer with several processors (compact PVM). The implemented programs are available through the Web page http://ncbr.chemi.muni.cz/~n19n/eem_abeem.

An interval programming model for continuous improvement in micro-manufacturing

NASA Astrophysics Data System (ADS)

Ouyang, Linhan; Ma, Yizhong; Wang, Jianjun; Tu, Yiliu; Byun, Jai-Hyun

2018-03-01

Continuous quality improvement in micro-manufacturing processes relies on optimization strategies that relate an output performance to a set of machining parameters. However, when determining the optimal machining parameters in a micro-manufacturing process, the economics of continuous quality improvement and decision makers' preference information are typically neglected. This article proposes an economic continuous improvement strategy based on an interval programming model. The proposed strategy differs from previous studies in two ways. First, an interval programming model is proposed to measure the quality level, where decision makers' preference information is considered in order to determine the weight of location and dispersion effects. Second, the proposed strategy is a more flexible approach since it considers the trade-off between the quality level and the associated costs, and leaves engineers a larger decision space through adjusting the quality level. The proposed strategy is compared with its conventional counterparts using an Nd:YLF laser beam micro-drilling process.

Influence of Sequential vs. Simultaneous Dual-Task Exercise Training on Cognitive Function in Older Adults.

PubMed

Tait, Jamie L; Duckham, Rachel L; Milte, Catherine M; Main, Luana C; Daly, Robin M

2017-01-01

Emerging research indicates that exercise combined with cognitive training may improve cognitive function in older adults. Typically these programs have incorporated sequential training, where exercise and cognitive training are undertaken separately. However, simultaneous or dual-task training, where cognitive and/or motor training are performed simultaneously with exercise, may offer greater benefits. This review summary provides an overview of the effects of combined simultaneous vs. sequential training on cognitive function in older adults. Based on the available evidence, there are inconsistent findings with regard to the cognitive benefits of sequential training in comparison to cognitive or exercise training alone. In contrast, simultaneous training interventions, particularly multimodal exercise programs in combination with secondary tasks regulated by sensory cues, have significantly improved cognition in both healthy older and clinical populations. However, further research is needed to determine the optimal characteristics of a successful simultaneous training program for optimizing cognitive function in older people.

HILTOP supplement: Heliocentric interplanetary low thrust trajectory optimization program, supplement 1

NASA Technical Reports Server (NTRS)

Mann, F. I.; Horsewood, J. L.

1974-01-01

Modifications and improvements are described that were made to the HILTOP electric propulsion trajectory optimization computer program during calendar years 1973 and 1974. New program features include the simulation of power degradation, housekeeping power, launch asymptote declination optimization, and powered and unpowered ballistic multiple swingby missions with an optional deep space burn.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

PubMed Central

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-01-01

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison. PMID:29614028

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

PubMed

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-04-03

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

Optimal Repair And Replacement Policy For A System With Multiple Components

DTIC Science & Technology

2016-06-17

Numerical Demonstration To implement the linear program, we use the Python Programming Language (PSF 2016) with the Pyomo optimization modeling language...opre.1040.0133. Hart, W.E., C. Laird, J. Watson, D.L. Woodruff. 2012. Pyomo–optimization modeling in python , vol. 67. Springer Science & Business...Media. Hart, W.E., J. Watson, D.L. Woodruff. 2011. Pyomo: modeling and solving mathematical programs in python . Mathematical Programming Computation 3(3

CometBoards Users Manual Release 1.0

NASA Technical Reports Server (NTRS)

Guptill, James D.; Coroneos, Rula M.; Patnaik, Surya N.; Hopkins, Dale A.; Berke, Lazlo

1996-01-01

Several nonlinear mathematical programming algorithms for structural design applications are available at present. These include the sequence of unconstrained minimizations technique, the method of feasible directions, and the sequential quadratic programming technique. The optimality criteria technique and the fully utilized design concept are two other structural design methods. A project was undertaken to bring all these design methods under a common computer environment so that a designer can select any one of these tools that may be suitable for his/her application. To facilitate selection of a design algorithm, to validate and check out the computer code, and to ascertain the relative merits of the design tools, modest finite element structural analysis programs based on the concept of stiffness and integrated force methods have been coupled to each design method. The code that contains both these design and analysis tools, by reading input information from analysis and design data files, can cast the design of a structure as a minimum-weight optimization problem. The code can then solve it with a user-specified optimization technique and a user-specified analysis method. This design code is called CometBoards, which is an acronym for Comparative Evaluation Test Bed of Optimization and Analysis Routines for the Design of Structures. This manual describes for the user a step-by-step procedure for setting up the input data files and executing CometBoards to solve a structural design problem. The manual includes the organization of CometBoards; instructions for preparing input data files; the procedure for submitting a problem; illustrative examples; and several demonstration problems. A set of 29 structural design problems have been solved by using all the optimization methods available in CometBoards. A summary of the optimum results obtained for these problems is appended to this users manual. CometBoards, at present, is available for Posix-based Cray and Convex computers, Iris and Sun workstations, and the VM/CMS system.

Automatic Phase Picker for Local and Teleseismic Events Using Wavelet Transform and Simulated Annealing

NASA Astrophysics Data System (ADS)

Gaillot, P.; Bardaine, T.; Lyon-Caen, H.

2004-12-01

Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro-seismicity data from glacier monitoring. The developed method is discussed and tested using our wavelet version of the standard STA-LTA algorithm.

Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach.

PubMed

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10(9) $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

NASA Astrophysics Data System (ADS)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 109 was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

AFLP Variation in Populations of Podisus maculiventris

USDA-ARS?s Scientific Manuscript database

We are developing methods to reduce costs of mass producing beneficial insect species for biological control programs. One of our methods entails selecting beneficials for optimal production traits. Currently we are selecting for increased fecundity. Selection protocols, whether based on phenotyp...

A programing system for research and applications in structural optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Rogers, J. L., Jr.

1981-01-01

The paper describes a computer programming system designed to be used for methodology research as well as applications in structural optimization. The flexibility necessary for such diverse utilizations is achieved by combining, in a modular manner, a state-of-the-art optimization program, a production level structural analysis program, and user supplied and problem dependent interface programs. Standard utility capabilities existing in modern computer operating systems are used to integrate these programs. This approach results in flexibility of the optimization procedure organization and versatility in the formulation of contraints and design variables. Features shown in numerical examples include: (1) variability of structural layout and overall shape geometry, (2) static strength and stiffness constraints, (3) local buckling failure, and (4) vibration constraints. The paper concludes with a review of the further development trends of this programing system.

IESIP - AN IMPROVED EXPLORATORY SEARCH TECHNIQUE FOR PURE INTEGER LINEAR PROGRAMMING PROBLEMS

NASA Technical Reports Server (NTRS)

Fogle, F. R.

1994-01-01

IESIP, an Improved Exploratory Search Technique for Pure Integer Linear Programming Problems, addresses the problem of optimizing an objective function of one or more variables subject to a set of confining functions or constraints by a method called discrete optimization or integer programming. Integer programming is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more difficult, integer programming is required for accuracy when modeling systems with small numbers of components such as the distribution of goods, machine scheduling, and production scheduling. IESIP establishes a new methodology for solving pure integer programming problems by utilizing a modified version of the univariate exploratory move developed by Robert Hooke and T.A. Jeeves. IESIP also takes some of its technique from the greedy procedure and the idea of unit neighborhoods. A rounding scheme uses the continuous solution found by traditional methods (simplex or other suitable technique) and creates a feasible integer starting point. The Hook and Jeeves exploratory search is modified to accommodate integers and constraints and is then employed to determine an optimal integer solution from the feasible starting solution. The user-friendly IESIP allows for rapid solution of problems up to 10 variables in size (limited by DOS allocation). Sample problems compare IESIP solutions with the traditional branch-and-bound approach. IESIP is written in Borland's TURBO Pascal for IBM PC series computers and compatibles running DOS. Source code and an executable are provided. The main memory requirement for execution is 25K. This program is available on a 5.25 inch 360K MS DOS format diskette. IESIP was developed in 1990. IBM is a trademark of International Business Machines. TURBO Pascal is registered by Borland International.

Evidence-based exercise prescription for balance and falls prevention: a current review of the literature.

PubMed

Shubert, Tiffany E

2011-01-01

Falls are the leading cause of emergency department visits, hospital admissions, and unintentional death for older adults. Balance and strength impairments are common falls risk factors for community-dwelling older adults. Though physical therapists commonly treat balance and strength, standardized falls screening has not been fully incorporated into physical therapy practice and there is much variation in the frequency, intensity, and duration of therapy prescribed to achieve optimal results. For community-dwelling older adults, a progressive exercise program that focuses on moderate to high-intensity balance exercises appears to be one of the most effective interventions to prevent falls. For more frail older adults in institutional settings, exercise programs in addition to multifactorial interventions appear to show promise as effective falls prevention interventions. The minimum dose of exercise to protect an older adult against falls is 50 hours. This article describes the current best practices for physical therapists to effectively improve balance and manage falls risk in patients. The unique challenges and opportunities for physical therapists to incorporate evidence-based fall-prevention strategies are discussed. Innovative practice models incorporating evidence-based fall-prevention programs and partnerships with public health and aging service providers to create a continuum of care and achieve the optimal dose of balance training are presented.

Performance Analysis and Design Synthesis (PADS) computer program. Volume 2: Program description, part 1

NASA Technical Reports Server (NTRS)

1972-01-01

The Performance Analysis and Design Synthesis (PADS) computer program has a two-fold purpose. It can size launch vehicles in conjunction with calculus-of-variations optimal trajectories and can also be used as a general-purpose branched trajectory optimization program. In the former use, it has the Space Shuttle Synthesis Program as well as a simplified stage weight module for optimally sizing manned recoverable launch vehicles. For trajectory optimization alone or with sizing, PADS has two trajectory modules. The first trajectory module uses the method of steepest descent; the second employs the method of quasilinearization, which requires a starting solution from the first trajectory module. For Volume 1 see N73-13199.

New reflective symmetry design capability in the JPL-IDEAS Structure Optimization Program

NASA Technical Reports Server (NTRS)

Strain, D.; Levy, R.

1986-01-01

The JPL-IDEAS antenna structure analysis and design optimization computer program was modified to process half structure models of symmetric structures subjected to arbitrary external static loads, synthesize the performance, and optimize the design of the full structure. Significant savings in computation time and cost (more than 50%) were achieved compared to the cost of full model computer runs. The addition of the new reflective symmetry analysis design capabilities to the IDEAS program allows processing of structure models whose size would otherwise prevent automated design optimization. The new program produced synthesized full model iterative design results identical to those of actual full model program executions at substantially reduced cost, time, and computer storage.

Block clustering based on difference of convex functions (DC) programming and DC algorithms.

PubMed

Le, Hoai Minh; Le Thi, Hoai An; Dinh, Tao Pham; Huynh, Van Ngai

2013-10-01

We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.

Finding theory- and evidence-based alternatives to fear appeals: Intervention Mapping

PubMed Central

Kok, Gerjo; Bartholomew, L Kay; Parcel, Guy S; Gottlieb, Nell H; Fernández, María E

2014-01-01

Fear arousal—vividly showing people the negative health consequences of life-endangering behaviors—is popular as a method to raise awareness of risk behaviors and to change them into health-promoting behaviors. However, most data suggest that, under conditions of low efficacy, the resulting reaction will be defensive. Instead of applying fear appeals, health promoters should identify effective alternatives to fear arousal by carefully developing theory- and evidence-based programs. The Intervention Mapping (IM) protocol helps program planners to optimize chances for effectiveness. IM describes the intervention development process in six steps: (1) assessing the problem and community capacities, (2) specifying program objectives, (3) selecting theory-based intervention methods and practical applications, (4) designing and organizing the program, (5) planning, adoption, and implementation, and (6) developing an evaluation plan. Authors who used IM indicated that it helped in bringing the development of interventions to a higher level. PMID:24811880

A neural network based implementation of an MPC algorithm applied in the control systems of electromechanical plants

NASA Astrophysics Data System (ADS)

Marusak, Piotr M.; Kuntanapreeda, Suwat

2018-01-01

The paper considers application of a neural network based implementation of a model predictive control (MPC) control algorithm to electromechanical plants. Properties of such control plants implicate that a relatively short sampling time should be used. However, in such a case, finding the control value numerically may be too time-consuming. Therefore, the current paper tests the solution based on transforming the MPC optimization problem into a set of differential equations whose solution is the same as that of the original optimization problem. This set of differential equations can be interpreted as a dynamic neural network. In such an approach, the constraints can be introduced into the optimization problem with relative ease. Moreover, the solution of the optimization problem can be obtained faster than when the standard numerical quadratic programming routine is used. However, a very careful tuning of the algorithm is needed to achieve this. A DC motor and an electrohydraulic actuator are taken as illustrative examples. The feasibility and effectiveness of the proposed approach are demonstrated through numerical simulations.

Optimizing legacy molecular dynamics software with directive-based offload

NASA Astrophysics Data System (ADS)

Michael Brown, W.; Carrillo, Jan-Michael Y.; Gavhane, Nitin; Thakkar, Foram M.; Plimpton, Steven J.

2015-10-01

Directive-based programming models are one solution for exploiting many-core coprocessors to increase simulation rates in molecular dynamics. They offer the potential to reduce code complexity with offload models that can selectively target computations to run on the CPU, the coprocessor, or both. In this paper, we describe modifications to the LAMMPS molecular dynamics code to enable concurrent calculations on a CPU and coprocessor. We demonstrate that standard molecular dynamics algorithms can run efficiently on both the CPU and an x86-based coprocessor using the same subroutines. As a consequence, we demonstrate that code optimizations for the coprocessor also result in speedups on the CPU; in extreme cases up to 4.7X. We provide results for LAMMPS benchmarks and for production molecular dynamics simulations using the Stampede hybrid supercomputer with both Intel® Xeon Phi™ coprocessors and NVIDIA GPUs. The optimizations presented have increased simulation rates by over 2X for organic molecules and over 7X for liquid crystals on Stampede. The optimizations are available as part of the "Intel package" supplied with LAMMPS.

Serenity: A subsystem quantum chemistry program.

PubMed

Unsleber, Jan P; Dresselhaus, Thomas; Klahr, Kevin; Schnieders, David; Böckers, Michael; Barton, Dennis; Neugebauer, Johannes

2018-05-15

We present the new quantum chemistry program Serenity. It implements a wide variety of functionalities with a focus on subsystem methodology. The modular code structure in combination with publicly available external tools and particular design concepts ensures extensibility and robustness with a focus on the needs of a subsystem program. Several important features of the program are exemplified with sample calculations with subsystem density-functional theory, potential reconstruction techniques, a projection-based embedding approach and combinations thereof with geometry optimization, semi-numerical frequency calculations and linear-response time-dependent density-functional theory. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

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

2011-05-31

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

New NAS Parallel Benchmarks Results

NASA Technical Reports Server (NTRS)

Yarrow, Maurice; Saphir, William; VanderWijngaart, Rob; Woo, Alex; Kutler, Paul (Technical Monitor)

1997-01-01

NPB2 (NAS (NASA Advanced Supercomputing) Parallel Benchmarks 2) is an implementation, based on Fortran and the MPI (message passing interface) message passing standard, of the original NAS Parallel Benchmark specifications. NPB2 programs are run with little or no tuning, in contrast to NPB vendor implementations, which are highly optimized for specific architectures. NPB2 results complement, rather than replace, NPB results. Because they have not been optimized by vendors, NPB2 implementations approximate the performance a typical user can expect for a portable parallel program on distributed memory parallel computers. Together these results provide an insightful comparison of the real-world performance of high-performance computers. New NPB2 features: New implementation (CG), new workstation class problem sizes, new serial sample versions, more performance statistics.

Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox

NASA Astrophysics Data System (ADS)

Li, R. N.; Liu, X.; Liu, S. J.

2013-12-01

In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.

UV-optical from space

NASA Technical Reports Server (NTRS)

Illingworth, Garth; Savage, Blair; Angel, J. Roger; Blandford, Roger D.; Boggess, Albert; Bowyer, C. Stuart; Carruthers, George R.; Cowie, Lennox L.; Doschek, George A.; Dupree, Andrea K.

1991-01-01

The following subject areas are covered: (1) the science program (star formation and origins of planetary systems; structure and evolution of the interstellar medium; stellar population; the galactic and extragalactic distance scale; nature of galaxy nuclei, AGNs, and QSOs; formation and evolution of galaxies at high redshifts; and cosmology); (2) implementation of the science program; (3) the observatory-class missions (HST; LST - the 6m successor to HST; and next-generation 16m telescope); (4) moderate and small missions (Delta-class Explorers; imaging astrometric interferometer; small Explorers; optics development and demonstrations; and supporting ground-based capabilities); (5) prerequisites - the current science program (Lyman-FUSE; HTS optimization; the near-term science program; data analysis, modeling, and theory funding; and archives); (6) technologies for the next century; and (7) lunar-based telescopes and instruments.

Mission Operations Planning with Preferences: An Empirical Study

NASA Technical Reports Server (NTRS)

Bresina, John L.; Khatib, Lina; McGann, Conor

2006-01-01

This paper presents an empirical study of some nonexhaustive approaches to optimizing preferences within the context of constraint-based, mixed-initiative planning for mission operations. This work is motivated by the experience of deploying and operating the MAPGEN (Mixed-initiative Activity Plan GENerator) system for the Mars Exploration Rover Mission. Responsiveness to the user is one of the important requirements for MAPGEN, hence, the additional computation time needed to optimize preferences must be kept within reasonabble bounds. This was the primary motivation for studying non-exhaustive optimization approaches. The specific goals of rhe empirical study are to assess the impact on solution quality of two greedy heuristics used in MAPGEN and to assess the improvement gained by applying a linear programming optimization technique to the final solution.

Optimizing Aspect-Oriented Mechanisms for Embedded Applications

NASA Astrophysics Data System (ADS)

Hundt, Christine; Stöhr, Daniel; Glesner, Sabine

As applications for small embedded mobile devices are getting larger and more complex, it becomes inevitable to adopt more advanced software engineering methods from the field of desktop application development. Aspect-oriented programming (AOP) is a promising approach due to its advanced modularization capabilities. However, existing AOP languages tend to add a substantial overhead in both execution time and code size which restricts their practicality for small devices with limited resources. In this paper, we present optimizations for aspect-oriented mechanisms at the level of the virtual machine. Our experiments show that these optimizations yield a considerable performance gain along with a reduction of the code size. Thus, our optimizations establish the base for using advanced aspect-oriented modularization techniques for developing Java applications on small embedded devices.

Optimization Program for Drinking Water Systems

EPA Pesticide Factsheets

The Area-Wide Optimization Program (AWOP) provides tools and approaches for drinking water systems to meet water quality optimization goals and provide an increased – and sustainable – level of public health protection to their consumers.

Control of water distribution networks with dynamic DMA topology using strictly feasible sequential convex programming

NASA Astrophysics Data System (ADS)

Wright, Robert; Abraham, Edo; Parpas, Panos; Stoianov, Ivan

2015-12-01

The operation of water distribution networks (WDN) with a dynamic topology is a recently pioneered approach for the advanced management of District Metered Areas (DMAs) that integrates novel developments in hydraulic modeling, monitoring, optimization, and control. A common practice for leakage management is the sectorization of WDNs into small zones, called DMAs, by permanently closing isolation valves. This facilitates water companies to identify bursts and estimate leakage levels by measuring the inlet flow for each DMA. However, by permanently closing valves, a number of problems have been created including reduced resilience to failure and suboptimal pressure management. By introducing a dynamic topology to these zones, these disadvantages can be eliminated while still retaining the DMA structure for leakage monitoring. In this paper, a novel optimization method based on sequential convex programming (SCP) is outlined for the control of a dynamic topology with the objective of reducing average zone pressure (AZP). A key attribute for control optimization is reliable convergence. To achieve this, the SCP method we propose guarantees that each optimization step is strictly feasible, resulting in improved convergence properties. By using a null space algorithm for hydraulic analyses, the computations required are also significantly reduced. The optimized control is actuated on a real WDN operated with a dynamic topology. This unique experimental program incorporates a number of technologies set up with the objective of investigating pioneering developments in WDN management. Preliminary results indicate AZP reductions for a dynamic topology of up to 6.5% over optimally controlled fixed topology DMAs. This article was corrected on 12 JAN 2016. See the end of the full text for details.

Comparison of penalty functions on a penalty approach to mixed-integer optimization

NASA Astrophysics Data System (ADS)

Francisco, Rogério B.; Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.

2016-06-01

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the `erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.

HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system.

PubMed

Majid, Abdul; Ali, Safdar

2015-01-01

We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches.

School-based internet obesity prevention programs for adolescents: a systematic literature review.

PubMed

Whittemore, Robin; Chao, Ariana; Popick, Rachel; Grey, Margaret

2013-03-01

In response to the childhood obesity epidemic, numerous studies on school-based Internet obesity prevention interventions have been conducted. The purpose of this systematic review is to describe, synthesize, and evaluate the research on school-based Internet obesity prevention programs for adolescents. Medline, CINAHL, and PsycInfo were searched from January 1995 to August 2012 to locate relevant studies. Ninety-one reports were initially identified, with 12 meeting the inclusion criteria. Studies had variable control groups, program content, and sample characteristics. Though few authors reported on implementation processes or body mass index (BMI) outcomes, the majority of studies were effective in improving health behaviors in the short term. Most studies were judged to have a high or unclear risk of bias in at least two domains, thus the quality of evidence for this body of literature is moderate. Further research is needed to examine programs of longer duration, optimal dose and timing of programs, cost-effectiveness, and mediators and moderators of intervention outcomes.

Application of a neural network to simulate analysis in an optimization process

NASA Technical Reports Server (NTRS)

Rogers, James L.; Lamarsh, William J., II

1992-01-01

A new experimental software package called NETS/PROSSS aimed at reducing the computing time required to solve a complex design problem is described. The software combines a neural network for simulating the analysis program with an optimization program. The neural network is applied to approximate results of a finite element analysis program to quickly obtain a near-optimal solution. Results of the NETS/PROSSS optimization process can also be used as an initial design in a normal optimization process and make it possible to converge to an optimum solution with significantly fewer iterations.

EXADS - EXPERT SYSTEM FOR AUTOMATED DESIGN SYNTHESIS

NASA Technical Reports Server (NTRS)

Rogers, J. L.

1994-01-01

The expert system called EXADS was developed to aid users of the Automated Design Synthesis (ADS) general purpose optimization program. Because of the general purpose nature of ADS, it is difficult for a nonexpert to select the best choice of strategy, optimizer, and one-dimensional search options from the one hundred or so combinations that are available. EXADS aids engineers in determining the best combination based on their knowledge of the problem and the expert knowledge previously stored by experts who developed ADS. EXADS is a customized application of the AESOP artificial intelligence program (the general version of AESOP is available separately from COSMIC. The ADS program is also available from COSMIC.) The expert system consists of two main components. The knowledge base contains about 200 rules and is divided into three categories: constrained, unconstrained, and constrained treated as unconstrained. The EXADS inference engine is rule-based and makes decisions about a particular situation using hypotheses (potential solutions), rules, and answers to questions drawn from the rule base. EXADS is backward-chaining, that is, it works from hypothesis to facts. The rule base was compiled from sources such as literature searches, ADS documentation, and engineer surveys. EXADS will accept answers such as yes, no, maybe, likely, and don't know, or a certainty factor ranging from 0 to 10. When any hypothesis reaches a confidence level of 90% or more, it is deemed as the best choice and displayed to the user. If no hypothesis is confirmed, the user can examine explanations of why the hypotheses failed to reach the 90% level. The IBM PC version of EXADS is written in IQ-LISP for execution under DOS 2.0 or higher with a central memory requirement of approximately 512K of 8 bit bytes. This program was developed in 1986.

Constraint Optimization Problem For The Cutting Of A Cobalt Chrome Refractory Material

NASA Astrophysics Data System (ADS)

Lebaal, Nadhir; Schlegel, Daniel; Folea, Milena

2011-05-01

This paper shows a complete approach to solve a given problem, from the experimentation to the optimization of different cutting parameters. In response to an industrial problem of slotting FSX 414, a Cobalt-based refractory material, we have implemented a design of experiment to determine the most influent parameters on the tool life, the surface roughness and the cutting forces. After theses trials, an optimization approach has been implemented to find the lowest manufacturing cost while respecting the roughness constraints and cutting force limitation constraints. The optimization approach is based on the Response Surface Method (RSM) using the Sequential Quadratic programming algorithm (SQP) for a constrained problem. To avoid a local optimum and to obtain an accurate solution at low cost, an efficient strategy, which allows improving the RSM accuracy in the vicinity of the global optimum, is presented. With these models and these trials, we could apply and compare our optimization methods in order to get the lowest cost for the best quality, i.e. a satisfying surface roughness and limited cutting forces.

On the preventive management of sediment-related sewer blockages: a combined maintenance and routing optimization approach.

PubMed

Fontecha, John E; Akhavan-Tabatabaei, Raha; Duque, Daniel; Medaglia, Andrés L; Torres, María N; Rodríguez, Juan Pablo

In this work we tackle the problem of planning and scheduling preventive maintenance (PM) of sediment-related sewer blockages in a set of geographically distributed sites that are subject to non-deterministic failures. To solve the problem, we extend a combined maintenance and routing (CMR) optimization approach which is a procedure based on two components: (a) first a maintenance model is used to determine the optimal time to perform PM operations for each site and second (b) a mixed integer program-based split procedure is proposed to route a set of crews (e.g., sewer cleaners, vehicles equipped with winches or rods and dump trucks) in order to perform PM operations at a near-optimal minimum expected cost. We applied the proposed CMR optimization approach to two (out of five) operative zones in the city of Bogotá (Colombia), where more than 100 maintenance operations per zone must be scheduled on a weekly basis. Comparing the CMR against the current maintenance plan, we obtained more than 50% of cost savings in 90% of the sites.

Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices.

PubMed

Qiao, Wei; Venayagamoorthy, Ganesh K; Harley, Ronald G

2008-01-01

Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area coordinating neurocontrol (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm and multiple flexible ac transmission system (FACTS) devices. An optimal wide-area monitor (OWAM), which is a radial basis function neural network (RBFNN), is designed to identify the input-output dynamics of the nonlinear power system. Its parameters are optimized through particle swarm optimization (PSO). Based on the OWAM, the WACNC is then designed by using the dual heuristic programming (DHP) method and RBFNNs, while considering the effect of signal transmission delays. The WACNC operates at a global level to coordinate the actions of local power system controllers. Each local controller communicates with the WACNC, receives remote control signals from the WACNC to enhance its dynamic performance and therefore helps improve system-wide dynamic and transient performance. The proposed control is verified by simulation studies on a multimachine power system.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Proposed Drill Sites

DOE Data Explorer

Lane, Michael

2013-06-28

Proposed drill sites for intermediate depth temperature gradient holes and/or deep resource confirmation wells. Temperature gradient contours based on shallow TG program and faults interpreted from seismic reflection survey are shown, as are two faults interpreted by seismic contractor Optim but not by Oski Energy, LLC.

ADS: A FORTRAN program for automated design synthesis: Version 1.10

NASA Technical Reports Server (NTRS)

Vanderplaats, G. N.

1985-01-01

A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis - Version 1.10) is a FORTRAN program for solution of nonlinear constrained optimization problems. The program is segmented into three levels: strategy, optimizer, and one-dimensional search. At each level, several options are available so that a total of over 100 possible combinations can be created. Examples of available strategies are sequential unconstrained minimization, the Augmented Lagrange Multiplier method, and Sequential Linear Programming. Available optimizers include variable metric methods and the Method of Feasible Directions as examples, and one-dimensional search options include polynomial interpolation and the Golden Section method as examples. Emphasis is placed on ease of use of the program. All information is transferred via a single parameter list. Default values are provided for all internal program parameters such as convergence criteria, and the user is given a simple means to over-ride these, if desired.

Electrostatic design of protein-protein association rates.

PubMed

Schreiber, Gideon; Shaul, Yossi; Gottschalk, Kay E

2006-01-01

De novo design and redesign of proteins and protein complexes have made promising progress in recent years. Here, we give an overview of how to use available computer-based tools to design proteins to bind faster and tighter to their protein-complex partner by electrostatic optimization between the two proteins. Electrostatic optimization is possible because of the simple relation between the Debye-Huckel energy of interaction between a pair of proteins and their rate of association. This can be used for rapid, structure-based calculations of the electrostatic attraction between the two proteins in the complex. Using these principles, we developed two computer programs that predict the change in k(on), and as such the affinity, on introducing charged mutations. The two programs have a web interface that is available at www.weizmann.ac.il/home/bcges/PARE.html and http://bip.weizmann.ac.il/hypare. When mutations leading to charge optimization are introduced outside the physical binding site, the rate of dissociation is unchanged and therefore the change in k(on) parallels that of the affinity. This design method was evaluated on a number of different protein complexes resulting in binding rates and affinities of hundreds of fold faster and tighter compared to wild type. In this chapter, we demonstrate the procedure and go step by step over the methodology of using these programs for protein-association design. Finally, the way to easily implement the principle of electrostatic design for any protein complex of choice is shown.

Optimization methods for decision making in disease prevention and epidemic control.

PubMed

Deng, Yan; Shen, Siqian; Vorobeychik, Yevgeniy

2013-11-01

This paper investigates problems of disease prevention and epidemic control (DPEC), in which we optimize two sets of decisions: (i) vaccinating individuals and (ii) closing locations, given respective budgets with the goal of minimizing the expected number of infected individuals after intervention. The spread of diseases is inherently stochastic due to the uncertainty about disease transmission and human interaction. We use a bipartite graph to represent individuals' propensities of visiting a set of location, and formulate two integer nonlinear programming models to optimize choices of individuals to vaccinate and locations to close. Our first model assumes that if a location is closed, its visitors stay in a safe location and will not visit other locations. Our second model incorporates compensatory behavior by assuming multiple behavioral groups, always visiting the most preferred locations that remain open. The paper develops algorithms based on a greedy strategy, dynamic programming, and integer programming, and compares the computational efficacy and solution quality. We test problem instances derived from daily behavior patterns of 100 randomly chosen individuals (corresponding to 195 locations) in Portland, Oregon, and provide policy insights regarding the use of the two DPEC models. Copyright © 2013 Elsevier Inc. All rights reserved.

Accelerating sino-atrium computer simulations with graphic processing units.

PubMed

Zhang, Hong; Xiao, Zheng; Lin, Shien-fong

2015-01-01

Sino-atrial node cells (SANCs) play a significant role in rhythmic firing. To investigate their role in arrhythmia and interactions with the atrium, computer simulations based on cellular dynamic mathematical models are generally used. However, the large-scale computation usually makes research difficult, given the limited computational power of Central Processing Units (CPUs). In this paper, an accelerating approach with Graphic Processing Units (GPUs) is proposed in a simulation consisting of the SAN tissue and the adjoining atrium. By using the operator splitting method, the computational task was made parallel. Three parallelization strategies were then put forward. The strategy with the shortest running time was further optimized by considering block size, data transfer and partition. The results showed that for a simulation with 500 SANCs and 30 atrial cells, the execution time taken by the non-optimized program decreased 62% with respect to a serial program running on CPU. The execution time decreased by 80% after the program was optimized. The larger the tissue was, the more significant the acceleration became. The results demonstrated the effectiveness of the proposed GPU-accelerating methods and their promising applications in more complicated biological simulations.

A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions

PubMed Central

Pan, Guang; Ye, Pengcheng; Yang, Zhidong

2014-01-01

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples. PMID:25133206

A Comparison of Trajectory Optimization Methods for the Impulsive Minimum Fuel Rendezvous Problem

NASA Technical Reports Server (NTRS)

Hughes, Steven P.; Mailhe, Laurie M.; Guzman, Jose J.

2002-01-01

In this paper we present a comparison of optimization approaches to the minimum fuel rendezvous problem. Both indirect and direct methods are compared for a variety of test cases. The indirect approach is based on primer vector theory. The direct approaches are implemented numerically and include Sequential Quadratic Programming (SQP), Quasi-Newton, Simplex, Genetic Algorithms, and Simulated Annealing. Each method is applied to a variety of test cases including, circular to circular coplanar orbits, LEO to GEO, and orbit phasing in highly elliptic orbits. We also compare different constrained optimization routines on complex orbit rendezvous problems with complicated, highly nonlinear constraints.

Optimal health insurance: the case of observable, severe illness.

PubMed

Chernew, M E; Encinosa, W E; Hirth, R A

2000-09-01

We explore optimal cost-sharing provisions for insurance contracts when individuals have observable, severe diseases with a discrete number of medically appropriate treatment options. Variation in preferences for alternative treatments is unobserved by the insurer and non-contractible. Interest in such situations is increasingly common, exemplified by disease carve-out programs and shared decision-making (SDM) tools. We demonstrate that optimal insurance charges a copay to patients choosing the high-cost treatment and provides consumers of the low-cost treatment a cash payment. A simulation of the effect of such a policy, based on prostate cancer, indicates a substantial reduction in moral hazard.