Multi-probe-based resonance-frequency electrical impedance spectroscopy for detection of suspicious breast lesions: improving performance using partial ROC optimization

NASA Astrophysics Data System (ADS)

Lederman, Dror; Zheng, Bin; Wang, Xingwei; Wang, Xiao Hui; Gur, David

2011-03-01

We have developed a multi-probe resonance-frequency electrical impedance spectroscope (REIS) system to detect breast abnormalities. Based on assessing asymmetry in REIS signals acquired between left and right breasts, we developed several machine learning classifiers to classify younger women (i.e., under 50YO) into two groups of having high and low risk for developing breast cancer. In this study, we investigated a new method to optimize performance based on the area under a selected partial receiver operating characteristic (ROC) curve when optimizing an artificial neural network (ANN), and tested whether it could improve classification performance. From an ongoing prospective study, we selected a dataset of 174 cases for whom we have both REIS signals and diagnostic status verification. The dataset includes 66 "positive" cases recommended for biopsy due to detection of highly suspicious breast lesions and 108 "negative" cases determined by imaging based examinations. A set of REIS-based feature differences, extracted from the two breasts using a mirror-matched approach, was computed and constituted an initial feature pool. Using a leave-one-case-out cross-validation method, we applied a genetic algorithm (GA) to train the ANN with an optimal subset of features. Two optimization criteria were separately used in GA optimization, namely the area under the entire ROC curve (AUC) and the partial area under the ROC curve, up to a predetermined threshold (i.e., 90% specificity). The results showed that although the ANN optimized using the entire AUC yielded higher overall performance (AUC = 0.83 versus 0.76), the ANN optimized using the partial ROC area criterion achieved substantially higher operational performance (i.e., increasing sensitivity level from 28% to 48% at 95% specificity and/ or from 48% to 58% at 90% specificity).

Design and Performance of Lift-Offset Rotorcraft for Short-Haul Missions

DTIC Science & Technology

2012-01-01

loading and blade loading were varied to optimize the designs, based on gross weight and fuel burn. The influence of technology is shown, in terms of...loading were varied to optimize the designs, based on gross weight and fuel burn. The influence of technology is shown, in terms of rotor hub drag and...distributions were optimized for these conditions (Fig. 4), and the rotor and aircraft cruise performance was calculated (Fig. 5). Based on comprehensive

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Design optimization of hydraulic turbine draft tube based on CFD and DOE method

NASA Astrophysics Data System (ADS)

Nam, Mun chol; Dechun, Ba; Xiangji, Yue; Mingri, Jin

2018-03-01

In order to improve performance of the hydraulic turbine draft tube in its design process, the optimization for draft tube is performed based on multi-disciplinary collaborative design optimization platform by combining the computation fluid dynamic (CFD) and the design of experiment (DOE) in this paper. The geometrical design variables are considered as the median section in the draft tube and the cross section in its exit diffuser and objective function is to maximize the pressure recovery factor (Cp). Sample matrixes required for the shape optimization of the draft tube are generated by optimal Latin hypercube (OLH) method of the DOE technique and their performances are evaluated through computational fluid dynamic (CFD) numerical simulation. Subsequently the main effect analysis and the sensitivity analysis of the geometrical parameters of the draft tube are accomplished. Then, the design optimization of the geometrical design variables is determined using the response surface method. The optimization result of the draft tube shows a marked performance improvement over the original.

Research on connection structure of aluminumbody bus using multi-objective topology optimization

NASA Astrophysics Data System (ADS)

Peng, Q.; Ni, X.; Han, F.; Rhaman, K.; Ulianov, C.; Fang, X.

2018-01-01

For connecting Aluminum Alloy bus body aluminum components often occur the problem of failure, a new aluminum alloy connection structure is designed based on multi-objective topology optimization method. Determining the shape of the outer contour of the connection structure with topography optimization, establishing a topology optimization model of connections based on SIMP density interpolation method, going on multi-objective topology optimization, and improving the design of the connecting piece according to the optimization results. The results show that the quality of the aluminum alloy connector after topology optimization is reduced by 18%, and the first six natural frequencies are improved and the strength performance and stiffness performance are obviously improved.

Performance index and meta-optimization of a direct search optimization method

NASA Astrophysics Data System (ADS)

Krus, P.; Ölvander, J.

2013-10-01

Design optimization is becoming an increasingly important tool for design, often using simulation as part of the evaluation of the objective function. A measure of the efficiency of an optimization algorithm is of great importance when comparing methods. The main contribution of this article is the introduction of a singular performance criterion, the entropy rate index based on Shannon's information theory, taking both reliability and rate of convergence into account. It can also be used to characterize the difficulty of different optimization problems. Such a performance criterion can also be used for optimization of the optimization algorithms itself. In this article the Complex-RF optimization method is described and its performance evaluated and optimized using the established performance criterion. Finally, in order to be able to predict the resources needed for optimization an objective function temperament factor is defined that indicates the degree of difficulty of the objective function.

Multidisciplinary design optimization of vehicle instrument panel based on multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Ping; Wu, Guangqiang

2013-03-01

Typical multidisciplinary design optimization(MDO) has gradually been proposed to balance performances of lightweight, noise, vibration and harshness(NVH) and safety for instrument panel(IP) structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene(PP) under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment(DOE), response surface model(RSM), and collaborative optimization(CO) are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm(MOGA), the optimal Pareto sets are completed to solve the multi-objective optimization(MOO) problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.

Multidisciplinary design optimization of the belt drive system considering both structure and vibration characteristics based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Yuan, Yongliang; Song, Xueguan; Sun, Wei; Wang, Xiaobang

2018-05-01

The dynamic performance of a belt drive system is composed of many factors, such as the efficiency, the vibration, and the optimal parameters. The conventional design only considers the basic performance of the belt drive system, while ignoring its overall performance. To address all these challenges, the study on vibration characteristics and optimization strategies could be a feasible way. This paper proposes a new optimization strategy and takes a belt drive design optimization as a case study based on the multidisciplinary design optimization (MDO). The MDO of the belt drive system is established and the corresponding sub-systems are analyzed. The multidisciplinary optimization is performed by using an improved genetic algorithm. Based on the optimal results obtained from the MDO, the three-dimension (3D) model of the belt drive system is established for dynamics simulation by virtual prototyping. From the comparison of the results with respect to different velocities and loads, the MDO method can effectively reduce the transverse vibration amplitude. The law of the vibration displacement, the vibration frequency, and the influence of velocities on the transverse vibrations has been obtained. Results show that the MDO method is of great help to obtain the optimal structural parameters. Furthermore, the kinematics principle of the belt drive has been obtained. The belt drive design case indicates that the proposed method in this paper can also be used to solve other engineering optimization problems efficiently.

Cost effective simulation-based multiobjective optimization in the performance of an internal combustion engine

NASA Astrophysics Data System (ADS)

Aittokoski, Timo; Miettinen, Kaisa

2008-07-01

Solving real-life engineering problems can be difficult because they often have multiple conflicting objectives, the objective functions involved are highly nonlinear and they contain multiple local minima. Furthermore, function values are often produced via a time-consuming simulation process. These facts suggest the need for an automated optimization tool that is efficient (in terms of number of objective function evaluations) and capable of solving global and multiobjective optimization problems. In this article, the requirements on a general simulation-based optimization system are discussed and such a system is applied to optimize the performance of a two-stroke combustion engine. In the example of a simulation-based optimization problem, the dimensions and shape of the exhaust pipe of a two-stroke engine are altered, and values of three conflicting objective functions are optimized. These values are derived from power output characteristics of the engine. The optimization approach involves interactive multiobjective optimization and provides a convenient tool to balance between conflicting objectives and to find good solutions.

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

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.

Task constraints and minimization of muscle effort result in a small number of muscle synergies during gait.

PubMed

De Groote, Friedl; Jonkers, Ilse; Duysens, Jacques

2014-01-01

Finding muscle activity generating a given motion is a redundant problem, since there are many more muscles than degrees of freedom. The control strategies determining muscle recruitment from a redundant set are still poorly understood. One theory of motor control suggests that motion is produced through activating a small number of muscle synergies, i.e., muscle groups that are activated in a fixed ratio by a single input signal. Because of the reduced number of input signals, synergy-based control is low dimensional. But a major criticism on the theory of synergy-based control of muscles is that muscle synergies might reflect task constraints rather than a neural control strategy. Another theory of motor control suggests that muscles are recruited by optimizing performance. Optimization of performance has been widely used to calculate muscle recruitment underlying a given motion while assuming independent recruitment of muscles. If synergies indeed determine muscle recruitment underlying a given motion, optimization approaches that do not model synergy-based control could result in muscle activations that do not show the synergistic muscle action observed through electromyography (EMG). If, however, synergistic muscle action results from performance optimization and task constraints (joint kinematics and external forces), such optimization approaches are expected to result in low-dimensional synergistic muscle activations that are similar to EMG-based synergies. We calculated muscle recruitment underlying experimentally measured gait patterns by optimizing performance assuming independent recruitment of muscles. We found that the muscle activations calculated without any reference to synergies can be accurately explained by on average four synergies. These synergies are similar to EMG-based synergies. We therefore conclude that task constraints and performance optimization explain synergistic muscle recruitment from a redundant set of muscles.

OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms

DOE PAGES

Meng, Zhaoyi; Koniges, Alice; He, Yun Helen; ...

2016-09-21

In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelizemore » the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.« less

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

Speedup for quantum optimal control from automatic differentiation based on graphics processing units

NASA Astrophysics Data System (ADS)

Leung, Nelson; Abdelhafez, Mohamed; Koch, Jens; Schuster, David

2017-04-01

We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speedup calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.

Surrogate based wind farm layout optimization using manifold mapping

NASA Astrophysics Data System (ADS)

Kaja Kamaludeen, Shaafi M.; van Zuijle, Alexander; Bijl, Hester

2016-09-01

High computational cost associated with the high fidelity wake models such as RANS or LES serves as a primary bottleneck to perform a direct high fidelity wind farm layout optimization (WFLO) using accurate CFD based wake models. Therefore, a surrogate based multi-fidelity WFLO methodology (SWFLO) is proposed. The surrogate model is built using an SBO method referred as manifold mapping (MM). As a verification, optimization of spacing between two staggered wind turbines was performed using the proposed surrogate based methodology and the performance was compared with that of direct optimization using high fidelity model. Significant reduction in computational cost was achieved using MM: a maximum computational cost reduction of 65%, while arriving at the same optima as that of direct high fidelity optimization. The similarity between the response of models, the number of mapping points and its position, highly influences the computational efficiency of the proposed method. As a proof of concept, realistic WFLO of a small 7-turbine wind farm is performed using the proposed surrogate based methodology. Two variants of Jensen wake model with different decay coefficients were used as the fine and coarse model. The proposed SWFLO method arrived at the same optima as that of the fine model with very less number of fine model simulations.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

NASA Astrophysics Data System (ADS)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

A survey of compiler optimization techniques

NASA Technical Reports Server (NTRS)

Schneck, P. B.

1972-01-01

Major optimization techniques of compilers are described and grouped into three categories: machine dependent, architecture dependent, and architecture independent. Machine-dependent optimizations tend to be local and are performed upon short spans of generated code by using particular properties of an instruction set to reduce the time or space required by a program. Architecture-dependent optimizations are global and are performed while generating code. These optimizations consider the structure of a computer, but not its detailed instruction set. Architecture independent optimizations are also global but are based on analysis of the program flow graph and the dependencies among statements of source program. A conceptual review of a universal optimizer that performs architecture-independent optimizations at source-code level is also presented.

Optimization of locations of diffusion spots in indoor optical wireless local area networks

NASA Astrophysics Data System (ADS)

Eltokhey, Mahmoud W.; Mahmoud, K. R.; Ghassemlooy, Zabih; Obayya, Salah S. A.

2018-03-01

In this paper, we present a novel optimization of the locations of the diffusion spots in indoor optical wireless local area networks, based on the central force optimization (CFO) scheme. The users' performance uniformity is addressed by using the CFO algorithm, and adopting different objective function's configurations, while considering maximization and minimization of the signal to noise ratio and the delay spread, respectively. We also investigate the effect of varying the objective function's weights on the system and the users' performance as part of the adaptation process. The results show that the proposed objective function configuration-based optimization procedure offers an improvement of 65% in the standard deviation of individual receivers' performance.

Optimization and Validation of Rotating Current Excitation with GMR Array Sensors for Riveted

DTIC Science & Technology

2016-09-16

distribution. Simulation results, using both an optimized coil and a conventional coil, are generated using the finite element method (FEM) model...optimized coil and a conventional coil, are generated using the finite element method (FEM) model. The signal magnitude for an optimized coil is seen to be...optimized coil. 4. Model Based Performance Analysis A 3D finite element model (FEM) is used to analyze the performance of the optimized coil and

High-performance slow light photonic crystal waveguides with topology optimized or circular-hole based material layouts

NASA Astrophysics Data System (ADS)

Wang, Fengwen; Jensen, Jakob S.; Sigmund, Ole

2012-10-01

Photonic crystal waveguides are optimized for modal confinement and loss related to slow light with high group index. A detailed comparison between optimized circular-hole based waveguides and optimized waveguides with free topology is performed. Design robustness with respect to manufacturing imperfections is enforced by considering different design realizations generated from under-, standard- and over-etching processes in the optimization procedure. A constraint ensures a certain modal confinement, and loss related to slow light with high group index is indirectly treated by penalizing field energy located in air regions. It is demonstrated that slow light with a group index up to ng = 278 can be achieved by topology optimized waveguides with promising modal confinement and restricted group-velocity-dispersion. All the topology optimized waveguides achieve a normalized group-index bandwidth of 0.48 or above. The comparisons between circular-hole based designs and topology optimized designs illustrate that the former can be efficient for dispersion engineering but that larger improvements are possible if irregular geometries are allowed.

Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm.

PubMed

Ahmed, Ashik; Al-Amin, Rasheduzzaman; Amin, Ruhul

2014-01-01

This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

Decoupled CFD-based optimization of efficiency and cavitation performance of a double-suction pump

NASA Astrophysics Data System (ADS)

Škerlavaj, A.; Morgut, M.; Jošt, D.; Nobile, E.

2017-04-01

In this study the impeller geometry of a double-suction pump ensuring the best performances in terms of hydraulic efficiency and reluctance of cavitation is determined using an optimization strategy, which was driven by means of the modeFRONTIER optimization platform. The different impeller shapes (designs) are modified according to the optimization parameters and tested with a computational fluid dynamics (CFD) software, namely ANSYS CFX. The simulations are performed using a decoupled approach, where only the impeller domain region is numerically investigated for computational convenience. The flow losses in the volute are estimated on the base of the velocity distribution at the impeller outlet. The best designs are then validated considering the computationally more expensive full geometry CFD model. The overall results show that the proposed approach is suitable for quick impeller shape optimization.

Construction Performance Optimization toward Green Building Premium Cost Based on Greenship Rating Tools Assessment with Value Engineering Method

NASA Astrophysics Data System (ADS)

Latief, Yusuf; Berawi, Mohammed Ali; Basten, Van; Riswanto; Budiman, Rachmat

2017-07-01

Green building concept becomes important in current building life cycle to mitigate environment issues. The purpose of this paper is to optimize building construction performance towards green building premium cost, achieving green building rating tools with optimizing life cycle cost. Therefore, this study helps building stakeholder determining building fixture to achieve green building certification target. Empirically the paper collects data of green building in the Indonesian construction industry such as green building fixture, initial cost, operational and maintenance cost, and certification score achievement. After that, using value engineering method optimized green building fixture based on building function and cost aspects. Findings indicate that construction performance optimization affected green building achievement with increasing energy and water efficiency factors and life cycle cost effectively especially chosen green building fixture.

Concurrently examining unrealistic absolute and comparative optimism: Temporal shifts, individual-difference and event-specific correlates, and behavioural outcomes.

PubMed

Ruthig, Joelle C; Gamblin, Bradlee W; Jones, Kelly; Vanderzanden, Karen; Kehn, Andre

2017-02-01

Researchers have spent considerable effort examining unrealistic absolute optimism and unrealistic comparative optimism, yet there is a lack of research exploring them concurrently. This longitudinal study repeatedly assessed unrealistic absolute and comparative optimism within a performance context over several months to identify the degree to which they shift as a function of proximity to performance and performance feedback, their associations with global individual difference and event-specific factors, and their link to subsequent behavioural outcomes. Results showed similar shifts in unrealistic absolute and comparative optimism based on proximity to performance and performance feedback. Moreover, increases in both types of unrealistic optimism were associated with better subsequent performance beyond the effect of prior performance. However, several differences were found between the two forms of unrealistic optimism in their associations with global individual difference factors and event-specific factors, highlighting the distinctiveness of the two constructs. © 2016 The British Psychological Society.

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

PubMed

Zhang, Baqun; Zhang, Min

2017-12-11

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.

Moderate temperature control technology for a lunar base

NASA Technical Reports Server (NTRS)

Swanson, Theodore D.; Sridhar, K. R.; Gottmann, Matthias

1993-01-01

A parametric analysis is performed to compare different heat pump based thermal control systems for a Lunar Base. Rankine cycle and absorption cycle heat pumps are compared and optimized for a 100 kW cooling load. Variables include the use or lack of an interface heat exchanger, and different operating fluids. Optimization of system mass to radiator rejection temperature is performed. The results indicate a relatively small sensitivity of Rankine cycle system mass to these variables, with optimized system masses of about 6000 kg for the 100 kW thermal load. It is quantitaively demonstrated that absorption based systems are not mass competitive with Rankine systems.

Jig-Shape Optimization of a Low-Boom Supersonic Aircraft

NASA Technical Reports Server (NTRS)

Pak, Chan-gi

2018-01-01

A simple approach for optimizing the jig-shape is proposed in this study. This simple approach is based on an unconstrained optimization problem and applied to a low-boom supersonic aircraft. In this study, the jig-shape optimization is performed using the two-step approach. First, starting design variables are computed using the least squares surface fitting technique. Next, the jig-shape is further tuned using a numerical optimization procedure based on in-house object-oriented optimization tool.

Optimization of wastewater treatment plant operation for greenhouse gas mitigation.

PubMed

Kim, Dongwook; Bowen, James D; Ozelkan, Ertunga C

2015-11-01

This study deals with the determination of optimal operation of a wastewater treatment system for minimizing greenhouse gas emissions, operating costs, and pollution loads in the effluent. To do this, an integrated performance index that includes three objectives was established to assess system performance. The ASMN_G model was used to perform system optimization aimed at determining a set of operational parameters that can satisfy three different objectives. The complex nonlinear optimization problem was simulated using the Nelder-Mead Simplex optimization algorithm. A sensitivity analysis was performed to identify influential operational parameters on system performance. The results obtained from the optimization simulations for six scenarios demonstrated that there are apparent trade-offs among the three conflicting objectives. The best optimized system simultaneously reduced greenhouse gas emissions by 31%, reduced operating cost by 11%, and improved effluent quality by 2% compared to the base case operation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Rai, Man Mohan; Huber, Frank W.

1998-01-01

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique

NASA Astrophysics Data System (ADS)

Panda, Satyasen

2018-05-01

This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.

A new rational-based optimal design strategy of ship structure based on multi-level analysis and super-element modeling method

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Deyu

2011-09-01

A new multi-level analysis method of introducing the super-element modeling method, derived from the multi-level analysis method first proposed by O. F. Hughes, has been proposed in this paper to solve the problem of high time cost in adopting a rational-based optimal design method for ship structural design. Furthermore, the method was verified by its effective application in optimization of the mid-ship section of a container ship. A full 3-D FEM model of a ship, suffering static and quasi-static loads, was used as the analyzing object for evaluating the structural performance of the mid-ship module, including static strength and buckling performance. Research results reveal that this new method could substantially reduce the computational cost of the rational-based optimization problem without decreasing its accuracy, which increases the feasibility and economic efficiency of using a rational-based optimal design method in ship structural design.

Optimal diabatic dynamics of Majorana-based quantum gates

NASA Astrophysics Data System (ADS)

Rahmani, Armin; Seradjeh, Babak; Franz, Marcel

2017-08-01

In topological quantum computing, unitary operations on qubits are performed by adiabatic braiding of non-Abelian quasiparticles, such as Majorana zero modes, and are protected from local environmental perturbations. In the adiabatic regime, with timescales set by the inverse gap of the system, the errors can be made arbitrarily small by performing the process more slowly. To enhance the performance of quantum information processing with Majorana zero modes, we apply the theory of optimal control to the diabatic dynamics of Majorana-based qubits. While we sacrifice complete topological protection, we impose constraints on the optimal protocol to take advantage of the nonlocal nature of topological information and increase the robustness of our gates. By using the Pontryagin's maximum principle, we show that robust equivalent gates to perfect adiabatic braiding can be implemented in finite times through optimal pulses. In our implementation, modifications to the device Hamiltonian are avoided. Focusing on thermally isolated systems, we study the effects of calibration errors and external white and 1 /f (pink) noise on Majorana-based gates. While a noise-induced antiadiabatic behavior, where a slower process creates more diabatic excitations, prohibits indefinite enhancement of the robustness of the adiabatic scheme, our fast optimal protocols exhibit remarkable stability to noise and have the potential to significantly enhance the practical performance of Majorana-based information processing.

Measuring the performance of telephone-based disease surveillance systems in local health departments.

PubMed

Dausey, David J; Chandra, Anita; Schaefer, Agnes G; Bahney, Ben; Haviland, Amelia; Zakowski, Sarah; Lurie, Nicole

2008-09-01

We tested telephone-based disease surveillance systems in local health departments to identify system characteristics associated with consistent and timely responses to urgent case reports. We identified a stratified random sample of 74 health departments and conducted a series of unannounced tests of their telephone-based surveillance systems. We used regression analyses to identify system characteristics that predicted fast connection with an action officer (an appropriate public health professional). Optimal performance in consistently connecting callers with an action officer in 30 minutes or less was achieved by 31% of participating health departments. Reaching a live person upon dialing, regardless of who that person was, was the strongest predictor of optimal performance both in being connected with an action officer and in consistency of connection times. Health departments can achieve optimal performance in consistently connecting a caller with an action officer in 30 minutes or less and may improve performance by using a telephone-based disease surveillance system in which the phone is answered by a live person at all times.

The optimal design of the bed structure of bedstand based on ABAQUS

NASA Astrophysics Data System (ADS)

Yang, Xudong; Dong, Yu; Ge, Qingkuan; Wang, Song

2017-12-01

Hydraulic transmission bedstand is one kind of the most commonly used in engineering machinery companies, and the bed structure is the most important part. Based on the original hydraulic transmission bedstand bed structure and the CAE technology, the original bed structure is improved. The optimized bed greatly saves the material of the production bed and improves the seismic performance of the bed. In the end, the performance of the optimized bed was compared with the original bed.

Lightweight design of automobile frame based on magnesium alloy

NASA Astrophysics Data System (ADS)

Lyu, R.; Jiang, X.; Minoru, O.; Ju, D. Y.

2018-06-01

The structural performance and lightweighting of car base frame design is a challenging task due to all the performance targets that must be satisfied. In this paper, three kinds of materials (iron, aluminum and magnesium alloy) replacement along with section design optimization strategy is proposed to develop a lightweight car frame structure to satisfy the tensile and safety while reducing weight. Two kinds of cross-sections are considered as the design variables. Using Ansys static structure, the design optimization problem is solved, comparing the results of each step, structure of the base flame is optimized for lightweight.

Simplex-method based transmission performance optimization for 100G PDM-QPSK systems with non-identical spans

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Gao, Guanjun; Zhang, Jie; Zhang, Kai; Chen, Sai; Yu, Xiaosong; Gu, Wanyi

2015-06-01

A simplex-method based optimizing (SMO) strategy is proposed to improve the transmission performance for dispersion uncompensated (DU) coherent optical systems with non-identical spans. Through analytical expression of quality of transmission (QoT), this strategy improves the Q factors effectively, while minimizing the number of erbium-doped optical fiber amplifier (EDFA) that needs to be optimized. Numerical simulations are performed for 100 Gb/s polarization-division multiplexed quadrature phase shift keying (PDM-QPSK) channels over 10-span standard single mode fiber (SSMF) with randomly distributed span-lengths. Compared to the EDFA configurations with complete span loss compensation, the Q factor of the SMO strategy is improved by approximately 1 dB at the optimal transmitter launch power. Moreover, instead of adjusting the gains of all the EDFAs to their optimal value, the number of EDFA that needs to be adjusted for SMO is reduced from 8 to 2, showing much less tuning costs and almost negligible performance degradation.

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

NASA Astrophysics Data System (ADS)

Martowicz, Adam; Uhl, Tadeusz

2012-10-01

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

Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems.

PubMed

Liu, Yan-Jun; Tong, Shaocheng

2016-11-01

In this paper, we propose an optimal control scheme-based adaptive neural network design for a class of unknown nonlinear discrete-time systems. The controlled systems are in a block-triangular multi-input-multi-output pure-feedback structure, i.e., there are both state and input couplings and nonaffine functions to be included in every equation of each subsystem. The design objective is to provide a control scheme, which not only guarantees the stability of the systems, but also achieves optimal control performance. The main contribution of this paper is that it is for the first time to achieve the optimal performance for such a class of systems. Owing to the interactions among subsystems, making an optimal control signal is a difficult task. The design ideas are that: 1) the systems are transformed into an output predictor form; 2) for the output predictor, the ideal control signal and the strategic utility function can be approximated by using an action network and a critic network, respectively; and 3) an optimal control signal is constructed with the weight update rules to be designed based on a gradient descent method. The stability of the systems can be proved based on the difference Lyapunov method. Finally, a numerical simulation is given to illustrate the performance of the proposed scheme.

A seismic optimization procedure for reinforced concrete framed buildings based on eigenfrequency optimization

NASA Astrophysics Data System (ADS)

Arroyo, Orlando; Gutiérrez, Sergio

2017-07-01

Several seismic optimization methods have been proposed to improve the performance of reinforced concrete framed (RCF) buildings; however, they have not been widely adopted among practising engineers because they require complex nonlinear models and are computationally expensive. This article presents a procedure to improve the seismic performance of RCF buildings based on eigenfrequency optimization, which is effective, simple to implement and efficient. The method is used to optimize a 10-storey regular building, and its effectiveness is demonstrated by nonlinear time history analyses, which show important reductions in storey drifts and lateral displacements compared to a non-optimized building. A second example for an irregular six-storey building demonstrates that the method provides benefits to a wide range of RCF structures and supports the applicability of the proposed method.

Implementation and on-sky results of an optimal wavefront controller for the MMT NGS adaptive optics system

NASA Astrophysics Data System (ADS)

Powell, Keith B.; Vaitheeswaran, Vidhya

2010-07-01

The MMT observatory has recently implemented and tested an optimal wavefront controller for the NGS adaptive optics system. Open loop atmospheric data collected at the telescope is used as the input to a MATLAB based analytical model. The model uses nonlinear constrained minimization to determine controller gains and optimize the system performance. The real-time controller performing the adaptive optics close loop operation is implemented on a dedicated high performance PC based quad core server. The controller algorithm is written in C and uses the GNU scientific library for linear algebra. Tests at the MMT confirmed the optimal controller significantly reduced the residual RMS wavefront compared with the previous controller. Significant reductions in image FWHM and increased peak intensities were obtained in J, H and K-bands. The optimal PID controller is now operating as the baseline wavefront controller for the MMT NGS-AO system.

A systematic optimization for graphene-based supercapacitors

NASA Astrophysics Data System (ADS)

Deuk Lee, Sung; Lee, Han Sung; Kim, Jin Young; Jeong, Jaesik; Kahng, Yung Ho

2017-08-01

Increasing the energy-storage density for supercapacitors is critical for their applications. Many researchers have attempted to identify optimal candidate component materials to achieve this goal, but investigations into systematically optimizing their mixing rate for maximizing the performance of each candidate material have been insufficient, which hinders the progress in their technology. In this study, we employ a statistically systematic method to determine the optimum mixing ratio of three components that constitute graphene-based supercapacitor electrodes: reduced graphene oxide (rGO), acetylene black (AB), and polyvinylidene fluoride (PVDF). By using the extreme-vertices design, the optimized proportion is determined to be (rGO: AB: PVDF  =  0.95: 0.00: 0.05). The corresponding energy-storage density increases by a factor of 2 compared with that of non-optimized electrodes. Electrochemical and microscopic analyses are performed to determine the reason for the performance improvements.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Global optimization method based on ray tracing to achieve optimum figure error compensation

NASA Astrophysics Data System (ADS)

Liu, Xiaolin; Guo, Xuejia; Tang, Tianjin

2017-02-01

Figure error would degrade the performance of optical system. When predicting the performance and performing system assembly, compensation by clocking of optical components around the optical axis is a conventional but user-dependent method. Commercial optical software cannot optimize this clocking. Meanwhile existing automatic figure-error balancing methods can introduce approximate calculation error and the build process of optimization model is complex and time-consuming. To overcome these limitations, an accurate and automatic global optimization method of figure error balancing is proposed. This method is based on precise ray tracing to calculate the wavefront error, not approximate calculation, under a given elements' rotation angles combination. The composite wavefront error root-mean-square (RMS) acts as the cost function. Simulated annealing algorithm is used to seek the optimal combination of rotation angles of each optical element. This method can be applied to all rotational symmetric optics. Optimization results show that this method is 49% better than previous approximate analytical method.

Performance optimization of the power user electric energy data acquire system based on MOEA/D evolutionary algorithm

NASA Astrophysics Data System (ADS)

Ding, Zhongan; Gao, Chen; Yan, Shengteng; Yang, Canrong

2017-10-01

The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi-objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi-objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi-objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.

Virtually optimized insoles for offloading the diabetic foot: A randomized crossover study.

PubMed

Telfer, S; Woodburn, J; Collier, A; Cavanagh, P R

2017-07-26

Integration of objective biomechanical measures of foot function into the design process for insoles has been shown to provide enhanced plantar tissue protection for individuals at-risk of plantar ulceration. The use of virtual simulations utilizing numerical modeling techniques offers a potential approach to further optimize these devices. In a patient population at-risk of foot ulceration, we aimed to compare the pressure offloading performance of insoles that were optimized via numerical simulation techniques against shape-based devices. Twenty participants with diabetes and at-risk feet were enrolled in this study. Three pairs of personalized insoles: one based on shape data and subsequently manufactured via direct milling; and two were based on a design derived from shape, pressure, and ultrasound data which underwent a finite element analysis-based virtual optimization procedure. For the latter set of insole designs, one pair was manufactured via direct milling, and a second pair was manufactured through 3D printing. The offloading performance of the insoles was analyzed for forefoot regions identified as having elevated plantar pressures. In 88% of the regions of interest, the use of virtually optimized insoles resulted in lower peak plantar pressures compared to the shape-based devices. Overall, the virtually optimized insoles significantly reduced peak pressures by a mean of 41.3kPa (p<0.001, 95% CI [31.1, 51.5]) for milled and 40.5kPa (p<0.001, 95% CI [26.4, 54.5]) for printed devices compared to shape-based insoles. The integration of virtual optimization into the insole design process resulted in improved offloading performance compared to standard, shape-based devices. ISRCTN19805071, www.ISRCTN.org. Copyright © 2017 Elsevier Ltd. All rights reserved.

Particle Swarm Optimization Toolbox

NASA Technical Reports Server (NTRS)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Time domain topology optimization of 3D nanophotonic devices

NASA Astrophysics Data System (ADS)

Elesin, Y.; Lazarov, B. S.; Jensen, J. S.; Sigmund, O.

2014-02-01

We present an efficient parallel topology optimization framework for design of large scale 3D nanophotonic devices. The code shows excellent scalability and is demonstrated for optimization of broadband frequency splitter, waveguide intersection, photonic crystal-based waveguide and nanowire-based waveguide. The obtained results are compared to simplified 2D studies and we demonstrate that 3D topology optimization may lead to significant performance improvements.

Optimization of brushless direct current motor design using an intelligent technique.

PubMed

Shabanian, Alireza; Tousiwas, Armin Amini Poustchi; Pourmandi, Massoud; Khormali, Aminollah; Ataei, Abdolhay

2015-07-01

This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using an improved bee algorithm (IBA). The characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. This method is based on the capability of swarm-based algorithms in finding the optimal solution. One sample case is used to illustrate the performance of the design approach and optimization technique. The IBA has a better performance and speed of convergence compared with bee algorithm (BA). Simulation results show that the proposed method has a very high/efficient performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Improving the Hydrodynamic Performance of Diffuser Vanes via Shape Optimization

NASA Technical Reports Server (NTRS)

Goel, Tushar; Dorney, Daniel J.; Haftka, Raphael T.; Shyy, Wei

2007-01-01

The performance of a diffuser in a pump stage depends on its configuration and placement within the stage. The influence of vane shape on the hydrodynamic performance of a diffuser has been studied. The goal of this effort has been to improve the performance of a pump stage by optimizing the shape of the diffuser vanes. The shape of the vanes was defined using Bezier curves and circular arcs. Surrogate model based tools were used to identify regions of the vane that have a strong influence on its performance. Optimization of the vane shape, in the absence of manufacturing, and stress constraints, led to a nearly nine percent reduction in the total pressure losses compared to the baseline design by reducing the extent of the base separation.

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

PubMed

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

Perturbing engine performance measurements to determine optimal engine control settings

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

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan

Methods and systems for optimizing a performance of a vehicle engine are provided. The method includes determining an initial value for a first engine control parameter based on one or more detected operating conditions of the vehicle engine, determining a value of an engine performance variable, and artificially perturbing the determined value of the engine performance variable. The initial value for the first engine control parameter is then adjusted based on the perturbed engine performance variable causing the engine performance variable to approach a target engine performance variable. Operation of the vehicle engine is controlled based on the adjusted initialmore » value for the first engine control parameter. These acts are repeated until the engine performance variable approaches the target engine performance variable.« less

The effect of statistical noise on IMRT plan quality and convergence for MC-based and MC-correction-based optimized treatment plans.

PubMed

Siebers, Jeffrey V

2008-04-04

Monte Carlo (MC) is rarely used for IMRT plan optimization outside of research centres due to the extensive computational resources or long computation times required to complete the process. Time can be reduced by degrading the statistical precision of the MC dose calculation used within the optimization loop. However, this eventually introduces optimization convergence errors (OCEs). This study determines the statistical noise levels tolerated during MC-IMRT optimization under the condition that the optimized plan has OCEs <100 cGy (1.5% of the prescription dose) for MC-optimized IMRT treatment plans.Seven-field prostate IMRT treatment plans for 10 prostate patients are used in this study. Pre-optimization is performed for deliverable beams with a pencil-beam (PB) dose algorithm. Further deliverable-based optimization proceeds using: (1) MC-based optimization, where dose is recomputed with MC after each intensity update or (2) a once-corrected (OC) MC-hybrid optimization, where a MC dose computation defines beam-by-beam dose correction matrices that are used during a PB-based optimization. Optimizations are performed with nominal per beam MC statistical precisions of 2, 5, 8, 10, 15, and 20%. Following optimizer convergence, beams are re-computed with MC using 2% per beam nominal statistical precision and the 2 PTV and 10 OAR dose indices used in the optimization objective function are tallied. For both the MC-optimization and OC-optimization methods, statistical equivalence tests found that OCEs are less than 1.5% of the prescription dose for plans optimized with nominal statistical uncertainties of up to 10% per beam. The achieved statistical uncertainty in the patient for the 10% per beam simulations from the combination of the 7 beams is ~3% with respect to maximum dose for voxels with D>0.5D(max). The MC dose computation time for the OC-optimization is only 6.2 minutes on a single 3 Ghz processor with results clinically equivalent to high precision MC computations.

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

Optimal design of a hybrid MR brake for haptic wrist application

NASA Astrophysics Data System (ADS)

Nguyen, Quoc Hung; Nguyen, Phuong Bac; Choi, Seung-Bok

2011-03-01

In this work, a new configuration of a magnetorheological (MR) brake is proposed and an optimal design of the proposed MR brake for haptic wrist application is performed considering the required braking torque, the zero-field friction torque, the size and mass of the brake. The proposed MR brake configuration is a combination of disc-type and drum-type which is referred as a hybrid configuration in this study. After the MR brake with the hybrid configuration is proposed, braking torque of the brake is analyzed based on Bingham rheological model of the MR fluid. The zero-field friction torque of the MR brake is also obtained. An optimization procedure based on finite element analysis integrated with an optimization tool is developed for the MR brake. The purpose of the optimal design is to find the optimal geometric dimensions of the MR brake structure that can produce the required braking torque and minimize the uncontrollable torque (passive torque) of the haptic wrist. Based on developed optimization procedure, optimal solution of the proposed MR brake is achieved. The proposed optimized hybrid brake is then compared with conventional types of MR brake and discussions on working performance of the proposed MR brake are described.

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis

PubMed Central

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization. PMID:29377956

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis.

PubMed

Zu, Xianghuan; Yang, Chuanlei; Wang, Hechun; Wang, Yinyan

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization.

Exquisite Moments: Achieving Optimal Flow in Three Activity-Based Groups Regardless of Early-Childhood Adversity

ERIC Educational Resources Information Center

Thomson, Paula; Jaque, S. Victoria

2016-01-01

Flow experiences (also known as optimal performance) occur when people engage in activities they enjoy. The authors discuss such events in their study that examined a number of healthy, active individuals (performing artists, athletes, and others engaged in a range of recreational activities) and divided these into three groups based on adverse…

Bare-Bones Teaching-Learning-Based Optimization

PubMed Central

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. PMID:25013844

Bare-bones teaching-learning-based optimization.

PubMed

Zou, Feng; Wang, Lei; Hei, Xinhong; Chen, Debao; Jiang, Qiaoyong; Li, Hongye

2014-01-01

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

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

PubMed

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

2016-01-01

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

Formal optimization of hovering performance using free wake lifting surface theory

NASA Technical Reports Server (NTRS)

Chung, S. Y.

1986-01-01

Free wake techniques for performance prediction and optimization of hovering rotor are discussed. The influence functions due to vortex ring, vortex cylinder, and source or vortex sheets are presented. The vortex core sizes of rotor wake vortices are calculated and their importance is discussed. Lifting body theory for finite thickness body is developed for pressure calculation, and hence performance prediction of hovering rotors. Numerical optimization technique based on free wake lifting line theory is presented and discussed. It is demonstrated that formal optimization can be used with the implicit and nonlinear objective or cost function such as the performance of hovering rotors as used in this report.

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.

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

PubMed

Prakash, Jaya; Yalavarthy, Phaneendra K

2013-03-01

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

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang’an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application. PMID:28403224

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

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

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees ofmore » freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.« less

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy.

PubMed

Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2017-01-07

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6  ±  15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy

NASA Astrophysics Data System (ADS)

Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2017-01-01

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6  ±  15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.

A New Approach to Integrate GPU-based Monte Carlo Simulation into Inverse Treatment Plan Optimization for Proton Therapy

PubMed Central

Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun

2016-01-01

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6±15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size. PMID:27991456

An enhanced performance through agent-based secure approach for mobile ad hoc networks

NASA Astrophysics Data System (ADS)

Bisen, Dhananjay; Sharma, Sanjeev

2018-01-01

This paper proposes an agent-based secure enhanced performance approach (AB-SEP) for mobile ad hoc network. In this approach, agent nodes are selected through optimal node reliability as a factor. This factor is calculated on the basis of node performance features such as degree difference, normalised distance value, energy level, mobility and optimal hello interval of node. After selection of agent nodes, a procedure of malicious behaviour detection is performed using fuzzy-based secure architecture (FBSA). To evaluate the performance of the proposed approach, comparative analysis is done with conventional schemes using performance parameters such as packet delivery ratio, throughput, total packet forwarding, network overhead, end-to-end delay and percentage of malicious detection.

An Integrated Method Based on PSO and EDA for the Max-Cut Problem.

PubMed

Lin, Geng; Guan, Jian

2016-01-01

The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution algorithm (PSO-EDA) for solving the max-cut problem. The integrated algorithm overcomes the shortcomings of particle swarm optimization and estimation of distribution algorithm. To enhance the performance of the PSO-EDA, a fast local search procedure is applied. In addition, a path relinking procedure is developed to intensify the search. To evaluate the performance of PSO-EDA, extensive experiments were carried out on two sets of benchmark instances with 800 to 20,000 vertices from the literature. Computational results and comparisons show that PSO-EDA significantly outperforms the existing PSO-based and EDA-based algorithms for the max-cut problem. Compared with other best performing algorithms, PSO-EDA is able to find very competitive results in terms of solution quality.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

Integrating prediction, provenance, and optimization into high energy workflows

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

Schram, M.; Bansal, V.; Friese, R. D.

We propose a novel approach for efficient execution of workflows on distributed resources. The key components of this framework include: performance modeling to quantitatively predict workflow component behavior; optimization-based scheduling such as choosing an optimal subset of resources to meet demand and assignment of tasks to resources; distributed I/O optimizations such as prefetching; and provenance methods for collecting performance data. In preliminary results, these techniques improve throughput on a small Belle II workflow by 20%.

Aerodynamic Shape Optimization of a Dual-Stream Supersonic Plug Nozzle

NASA Technical Reports Server (NTRS)

Heath, Christopher M.; Gray, Justin S.; Park, Michael A.; Nielsen, Eric J.; Carlson, Jan-Renee

2015-01-01

Aerodynamic shape optimization was performed on an isolated axisymmetric plug nozzle sized for a supersonic business jet. The dual-stream concept was tailored to attenuate nearfield pressure disturbances without compromising nozzle performance. Adjoint-based anisotropic mesh refinement was applied to resolve nearfield compression and expansion features in the baseline viscous grid. Deformed versions of the adapted grid were used for subsequent adjoint-driven shape optimization. For design, a nonlinear gradient-based optimizer was coupled to the discrete adjoint formulation of the Reynolds-averaged Navier- Stokes equations. All nozzle surfaces were parameterized using 3rd order B-spline interpolants and perturbed axisymmetrically via free-form deformation. Geometry deformations were performed using 20 design variables shared between the outer cowl, shroud and centerbody nozzle surfaces. Interior volume grid deformation during design was accomplished using linear elastic mesh morphing. The nozzle optimization was performed at a design cruise speed of Mach 1.6, assuming core and bypass pressure ratios of 6.19 and 3.24, respectively. Ambient flight conditions at design were commensurate with 45,000-ft standard day atmosphere.

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

PubMed Central

Svečko, Rajko

2014-01-01

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

On l(1): Optimal decentralized performance

NASA Technical Reports Server (NTRS)

Sourlas, Dennis; Manousiouthakis, Vasilios

1993-01-01

In this paper, the Manousiouthakis parametrization of all decentralized stabilizing controllers is employed in mathematically formulating the l(sup 1) optimal decentralized controller synthesis problem. The resulting optimization problem is infinite dimensional and therefore not directly amenable to computations. It is shown that finite dimensional optimization problems that have value arbitrarily close to the infinite dimensional one can be constructed. Based on this result, an algorithm that solves the l(sup 1) decentralized performance problems is presented. A global optimization approach to the solution of the infinite dimensional approximating problems is also discussed.

Optimal designs based on the maximum quasi-likelihood estimator

PubMed Central

Shen, Gang; Hyun, Seung Won; Wong, Weng Kee

2016-01-01

We use optimal design theory and construct locally optimal designs based on the maximum quasi-likelihood estimator (MqLE), which is derived under less stringent conditions than those required for the MLE method. We show that the proposed locally optimal designs are asymptotically as efficient as those based on the MLE when the error distribution is from an exponential family, and they perform just as well or better than optimal designs based on any other asymptotically linear unbiased estimators such as the least square estimator (LSE). In addition, we show current algorithms for finding optimal designs can be directly used to find optimal designs based on the MqLE. As an illustrative application, we construct a variety of locally optimal designs based on the MqLE for the 4-parameter logistic (4PL) model and study their robustness properties to misspecifications in the model using asymptotic relative efficiency. The results suggest that optimal designs based on the MqLE can be easily generated and they are quite robust to mis-specification in the probability distribution of the responses. PMID:28163359

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

PubMed

Zhang, Ming-Huan; Ma, Jun-Shan; Shen, Ying; Chen, Ying

2016-09-01

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images. T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy). Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively. The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Advanced Information Technology in Simulation Based Life Cycle Design

NASA Technical Reports Server (NTRS)

Renaud, John E.

2003-01-01

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

A holistic approach to movement education in sport and fitness: a systems based model.

PubMed

Polsgrove, Myles Jay

2012-01-01

The typical model used by movement professionals to enhance performance relies on the notion that a linear increase in load results in steady and progressive gains, whereby, the greater the effort, the greater the gains in performance. Traditional approaches to movement progression typically rely on the proper sequencing of extrinsically based activities to facilitate the individual in reaching performance objectives. However, physical rehabilitation or physical performance rarely progresses in such a linear fashion; instead they tend to evolve non-linearly and rather unpredictably. A dynamic system can be described as an entity that self-organizes into increasingly complex forms. Applying this view to the human body, practitioners could facilitate non-linear performance gains through a systems based programming approach. Utilizing a dynamic systems view, the Holistic Approach to Movement Education (HADME) is a model designed to optimize performance by accounting for non-linear and self-organizing traits associated with human movement. In this model, gains in performance occur through advancing individual perspectives and through optimizing sub-system performance. This inward shift of the focus of performance creates a sharper self-awareness and may lead to more optimal movements. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

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

Chavarría-Miranda, Daniel; Panyala, Ajay R.; Halappanavar, Mahantesh

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) {more » on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.« less

Three-Dimensional Viscous Alternating Direction Implicit Algorithm and Strategies for Shape Optimization

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Baysal, Oktay

1997-01-01

A gradient-based shape optimization based on quasi-analytical sensitivities has been extended for practical three-dimensional aerodynamic applications. The flow analysis has been rendered by a fully implicit, finite-volume formulation of the Euler and Thin-Layer Navier-Stokes (TLNS) equations. Initially, the viscous laminar flow analysis for a wing has been compared with an independent computational fluid dynamics (CFD) code which has been extensively validated. The new procedure has been demonstrated in the design of a cranked arrow wing at Mach 2.4 with coarse- and fine-grid based computations performed with Euler and TLNS equations. The influence of the initial constraints on the geometry and aerodynamics of the optimized shape has been explored. Various final shapes generated for an identical initial problem formulation but with different optimization path options (coarse or fine grid, Euler or TLNS), have been aerodynamically evaluated via a common fine-grid TLNS-based analysis. The initial constraint conditions show significant bearing on the optimization results. Also, the results demonstrate that to produce an aerodynamically efficient design, it is imperative to include the viscous physics in the optimization procedure with the proper resolution. Based upon the present results, to better utilize the scarce computational resources, it is recommended that, a number of viscous coarse grid cases using either a preconditioned bi-conjugate gradient (PbCG) or an alternating-direction-implicit (ADI) method, should initially be employed to improve the optimization problem definition, the design space and initial shape. Optimized shapes should subsequently be analyzed using a high fidelity (viscous with fine-grid resolution) flow analysis to evaluate their true performance potential. Finally, a viscous fine-grid-based shape optimization should be conducted, using an ADI method, to accurately obtain the final optimized shape.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PubMed

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

PubMed Central

Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

Optimal design of high damping force engine mount featuring MR valve structure with both annular and radial flow paths

NASA Astrophysics Data System (ADS)

Nguyen, Q. H.; Choi, S. B.; Lee, Y. S.; Han, M. S.

2013-11-01

This paper focuses on the optimal design of a compact and high damping force engine mount featuring magnetorheological fluid (MRF). In the mount, a MR valve structure with both annular and radial flows is employed to generate a high damping force. First, the configuration and working principle of the proposed MR mount is introduced. The MRF flows in the mount are then analyzed and the governing equations of the MR mount are derived based on the Bingham plastic behavior of the MRF. An optimal design of the MR mount is then performed to find the optimal structure of the MR valve to generate a maximum damping force with certain design constraints. In addition, the gap size of MRF ducts is empirically chosen considering the ‘lockup’ problem of the mount at high frequency. Performance of the optimized MR mount is then evaluated based on finite element analysis and discussions on performance results of the optimized MR mount are given. The effectiveness of the proposed MR engine mount is demonstrated via computer simulation by presenting damping force and power consumption.

Thickness optimization of the ZnO based TCO layer in a CZTSSe solar cell. Evolution of its performance with thickness when external temperature changes.

NASA Astrophysics Data System (ADS)

Chadel, Meriem; Moustafa Bouzaki, Mohammed; Chadel, Asma; Aillerie, Michel; Benyoucef, Boumediene

2017-07-01

The influence of the thickness of a Zinc Oxide (ZnO) transparent conductive oxide (TCO) layer on the performance of the CZTSSe solar cell is shown in detail. In a photovoltaic cell, the thickness of each layer largely influence the performance of the solar cell and optimization of each layer constitutes a complete work. Here, using the Solar Cell Capacitance Simulation (SCAPS) software, we present simulation results obtained in the analyze of the influence of the TCO layer thickness on the performance of a CZTSSe solar cell, starting from performance of a CZTSSe solar cell commercialized in 2014 with an initial efficiency equal to 12.6%. In simulation, the temperature was considered as a functioning parameter and the evolution of tthe performance of the cell for various thickness of the TCO layer when the external temperature changes is simulated and discussed. The best efficiency of the solar cell based in CZTSSe is obtained with a ZnO thickness equal to 50 nm and low temperature. Based on the considered marketed cell, we show a technological possible increase of the global efficiency achieving 13% by optimization of ZnO based TCO layer.

Optimization of the Upper Surface of Hypersonic Vehicle Based on CFD Analysis

NASA Astrophysics Data System (ADS)

Gao, T. Y.; Cui, K.; Hu, S. C.; Wang, X. P.; Yang, G. W.

2011-09-01

For the hypersonic vehicle, the aerodynamic performance becomes more intensive. Therefore, it is a significant event to optimize the shape of the hypersonic vehicle to achieve the project demands. It is a key technology to promote the performance of the hypersonic vehicle with the method of shape optimization. Based on the existing vehicle, the optimization to the upper surface of the Simplified hypersonic vehicle was done to obtain a shape which suits the project demand. At the cruising condition, the upper surface was parameterized with the B-Spline curve method. The incremental parametric method and the reconstruction technology of the local mesh were applied here. The whole flow field was been calculated and the aerodynamic performance of the craft were obtained by the computational fluid dynamic (CFD) technology. Then the vehicle shape was optimized to achieve the maximum lift-drag ratio at attack angle 3°, 4° and 5°. The results will provide the reference for the practical design.

Viscous Aerodynamic Shape Optimization with Installed Propulsion Effects

NASA Technical Reports Server (NTRS)

Heath, Christopher M.; Seidel, Jonathan A.; Rallabhandi, Sriram K.

2017-01-01

Aerodynamic shape optimization is demonstrated to tailor the under-track pressure signature of a conceptual low-boom supersonic aircraft. Primarily, the optimization reduces nearfield pressure waveforms induced by propulsion integration effects. For computational efficiency, gradient-based optimization is used and coupled to the discrete adjoint formulation of the Reynolds-averaged Navier Stokes equations. The engine outer nacelle, nozzle, and vertical tail fairing are axi-symmetrically parameterized, while the horizontal tail is shaped using a wing-based parameterization. Overall, 48 design variables are coupled to the geometry and used to deform the outer mold line. During the design process, an inequality drag constraint is enforced to avoid major compromise in aerodynamic performance. Linear elastic mesh morphing is used to deform volume grids between design iterations. The optimization is performed at Mach 1.6 cruise, assuming standard day altitude conditions at 51,707-ft. To reduce uncertainty, a coupled thermodynamic engine cycle model is employed that captures installed inlet performance effects on engine operation.

Processing time tolerance-based ACO algorithm for solving job-shop scheduling problem

NASA Astrophysics Data System (ADS)

Luo, Yabo; Waden, Yongo P.

2017-06-01

Ordinarily, Job Shop Scheduling Problem (JSSP) is known as NP-hard problem which has uncertainty and complexity that cannot be handled by a linear method. Thus, currently studies on JSSP are concentrated mainly on applying different methods of improving the heuristics for optimizing the JSSP. However, there still exist many problems for efficient optimization in the JSSP, namely, low efficiency and poor reliability, which can easily trap the optimization process of JSSP into local optima. Therefore, to solve this problem, a study on Ant Colony Optimization (ACO) algorithm combined with constraint handling tactics is carried out in this paper. Further, the problem is subdivided into three parts: (1) Analysis of processing time tolerance-based constraint features in the JSSP which is performed by the constraint satisfying model; (2) Satisfying the constraints by considering the consistency technology and the constraint spreading algorithm in order to improve the performance of ACO algorithm. Hence, the JSSP model based on the improved ACO algorithm is constructed; (3) The effectiveness of the proposed method based on reliability and efficiency is shown through comparative experiments which are performed on benchmark problems. Consequently, the results obtained by the proposed method are better, and the applied technique can be used in optimizing JSSP.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization.

PubMed

Hartenfeller, Markus; Proschak, Ewgenij; Schüller, Andreas; Schneider, Gisbert

2008-07-01

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

Toward Optimization of Gaze-Controlled Human-Computer Interaction: Application to Hindi Virtual Keyboard for Stroke Patients.

PubMed

Meena, Yogesh Kumar; Cecotti, Hubert; Wong-Lin, Kongfatt; Dutta, Ashish; Prasad, Girijesh

2018-04-01

Virtual keyboard applications and alternative communication devices provide new means of communication to assist disabled people. To date, virtual keyboard optimization schemes based on script-specific information, along with multimodal input access facility, are limited. In this paper, we propose a novel method for optimizing the position of the displayed items for gaze-controlled tree-based menu selection systems by considering a combination of letter frequency and command selection time. The optimized graphical user interface layout has been designed for a Hindi language virtual keyboard based on a menu wherein 10 commands provide access to type 88 different characters, along with additional text editing commands. The system can be controlled in two different modes: eye-tracking alone and eye-tracking with an access soft-switch. Five different keyboard layouts have been presented and evaluated with ten healthy participants. Furthermore, the two best performing keyboard layouts have been evaluated with eye-tracking alone on ten stroke patients. The overall performance analysis demonstrated significantly superior typing performance, high usability (87% SUS score), and low workload (NASA TLX with 17 scores) for the letter frequency and time-based organization with script specific arrangement design. This paper represents the first optimized gaze-controlled Hindi virtual keyboard, which can be extended to other languages.

Optimal placement and sizing of wind / solar based DG sources in distribution system

NASA Astrophysics Data System (ADS)

Guan, Wanlin; Guo, Niao; Yu, Chunlai; Chen, Xiaoguang; Yu, Haiyang; Liu, Zhipeng; Cui, Jiapeng

2017-06-01

Proper placement and sizing of Distributed Generation (DG) in distribution system can obtain maximum potential benefits. This paper proposes quantum particle swarm algorithm (QPSO) based wind turbine generation unit (WTGU) and photovoltaic (PV) array placement and sizing approach for real power loss reduction and voltage stability improvement of distribution system. Performance modeling of wind and solar generation system are described and classified into PQ\\PQ (V)\\PI type models in power flow. Considering the WTGU and PV based DGs in distribution system is geographical restrictive, the optimal area and DG capacity limits of each bus in the setting area need to be set before optimization, the area optimization method is proposed . The method has been tested on IEEE 33-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method.

Estimated Benefits of Variable-Geometry Wing Camber Control for Transport Aircraft

NASA Technical Reports Server (NTRS)

Bolonkin, Alexander; Gilyard, Glenn B.

1999-01-01

Analytical benefits of variable-camber capability on subsonic transport aircraft are explored. Using aerodynamic performance models, including drag as a function of deflection angle for control surfaces of interest, optimal performance benefits of variable camber are calculated. Results demonstrate that if all wing trailing-edge surfaces are available for optimization, drag can be significantly reduced at most points within the flight envelope. The optimization approach developed and illustrated for flight uses variable camber for optimization of aerodynamic efficiency (maximizing the lift-to-drag ratio). Most transport aircraft have significant latent capability in this area. Wing camber control that can affect performance optimization for transport aircraft includes symmetric use of ailerons and flaps. In this paper, drag characteristics for aileron and flap deflections are computed based on analytical and wind-tunnel data. All calculations based on predictions for the subject aircraft and the optimal surface deflection are obtained by simple interpolation for given conditions. An algorithm is also presented for computation of optimal surface deflection for given conditions. Benefits of variable camber for a transport configuration using a simple trailing-edge control surface system can approach more than 10 percent, especially for nonstandard flight conditions. In the cruise regime, the benefit is 1-3 percent.

High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil

NASA Technical Reports Server (NTRS)

Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)

1998-01-01

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Optimal fractional order PID design via Tabu Search based algorithm.

PubMed

Ateş, Abdullah; Yeroglu, Celaleddin

2016-01-01

This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

NASA Astrophysics Data System (ADS)

Gang, Grace J.; Siewerdsen, Jeffrey H.; Webster Stayman, J.

2017-06-01

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)—each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d‧) of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d‧ of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

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

PubMed

Kiumarsi, Bahare; Lewis, Frank L

2015-01-01

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

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform.

PubMed

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-08-14

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS(®); then, to analyze the system's kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB(®) SIMULINK(®) controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform

PubMed Central

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-01-01

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS®; then, to analyze the system’s kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB® SIMULINK® controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance. PMID:26287210

Optimal control and optimal trajectories of regional macroeconomic dynamics based on the Pontryagin maximum principle

NASA Astrophysics Data System (ADS)

Bulgakov, V. K.; Strigunov, V. V.

2009-05-01

The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.

Electrochemical model based charge optimization for lithium-ion batteries

NASA Astrophysics Data System (ADS)

Pramanik, Sourav; Anwar, Sohel

2016-05-01

In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.

Modified Shuffled Frog Leaping Optimization Algorithm Based Distributed Generation Rescheduling for Loss Minimization

NASA Astrophysics Data System (ADS)

Arya, L. D.; Koshti, Atul

2018-05-01

This paper investigates the Distributed Generation (DG) capacity optimization at location based on the incremental voltage sensitivity criteria for sub-transmission network. The Modified Shuffled Frog Leaping optimization Algorithm (MSFLA) has been used to optimize the DG capacity. Induction generator model of DG (wind based generating units) has been considered for study. Standard test system IEEE-30 bus has been considered for the above study. The obtained results are also validated by shuffled frog leaping algorithm and modified version of bare bones particle swarm optimization (BBExp). The performance of MSFLA has been found more efficient than the other two algorithms for real power loss minimization problem.

Load Frequency Control of AC Microgrid Interconnected Thermal Power System

NASA Astrophysics Data System (ADS)

Lal, Deepak Kumar; Barisal, Ajit Kumar

2017-08-01

In this paper, a microgrid (MG) power generation system is interconnected with a single area reheat thermal power system for load frequency control study. A new meta-heuristic optimization algorithm i.e. Moth-Flame Optimization (MFO) algorithm is applied to evaluate optimal gains of the fuzzy based proportional, integral and derivative (PID) controllers. The system dynamic performance is studied by comparing the results with MFO optimized classical PI/PID controllers. Also the system performance is investigated with fuzzy PID controller optimized by recently developed grey wolf optimizer (GWO) algorithm, which has proven its superiority over other previously developed algorithm in many interconnected power systems.

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

Spatio-temporal modeling and optimization of a deformable-grating compressor for short high-energy laser pulses

DOE PAGES

Qiao, Jie; Papa, J.; Liu, X.

2015-09-24

Monolithic large-scale diffraction gratings are desired to improve the performance of high-energy laser systems and scale them to higher energy, but the surface deformation of these diffraction gratings induce spatio-temporal coupling that is detrimental to the focusability and compressibility of the output pulse. A new deformable-grating-based pulse compressor architecture with optimized actuator positions has been designed to correct the spatial and temporal aberrations induced by grating wavefront errors. An integrated optical model has been built to analyze the effect of grating wavefront errors on the spatio-temporal performance of a compressor based on four deformable gratings. Moreover, a 1.5-meter deformable gratingmore » has been optimized using an integrated finite-element-analysis and genetic-optimization model, leading to spatio-temporal performance similar to the baseline design with ideal gratings.« less

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.

Topology Optimization using the Level Set and eXtended Finite Element Methods: Theory and Applications

NASA Astrophysics Data System (ADS)

Villanueva Perez, Carlos Hernan

Computational design optimization provides designers with automated techniques to develop novel and non-intuitive optimal designs. Topology optimization is a design optimization technique that allows for the evolution of a broad variety of geometries in the optimization process. Traditional density-based topology optimization methods often lack a sufficient resolution of the geometry and physical response, which prevents direct use of the optimized design in manufacturing and the accurate modeling of the physical response of boundary conditions. The goal of this thesis is to introduce a unified topology optimization framework that uses the Level Set Method (LSM) to describe the design geometry and the eXtended Finite Element Method (XFEM) to solve the governing equations and measure the performance of the design. The methodology is presented as an alternative to density-based optimization approaches, and is able to accommodate a broad range of engineering design problems. The framework presents state-of-the-art methods for immersed boundary techniques to stabilize the systems of equations and enforce the boundary conditions, and is studied with applications in 2D and 3D linear elastic structures, incompressible flow, and energy and species transport problems to test the robustness and the characteristics of the method. A comparison of the framework against density-based topology optimization approaches is studied with regards to convergence, performance, and the capability to manufacture the designs. Furthermore, the ability to control the shape of the design to operate within manufacturing constraints is developed and studied. The analysis capability of the framework is validated quantitatively through comparison against previous benchmark studies, and qualitatively through its application to topology optimization problems. The design optimization problems converge to intuitive designs and resembled well the results from previous 2D or density-based studies.

Improving scanner wafer alignment performance by target optimization

NASA Astrophysics Data System (ADS)

Leray, Philippe; Jehoul, Christiane; Socha, Robert; Menchtchikov, Boris; Raghunathan, Sudhar; Kent, Eric; Schoonewelle, Hielke; Tinnemans, Patrick; Tuffy, Paul; Belen, Jun; Wise, Rich

2016-03-01

In the process nodes of 10nm and below, the patterning complexity along with the processing and materials required has resulted in a need to optimize alignment targets in order to achieve the required precision, accuracy and throughput performance. Recent industry publications on the metrology target optimization process have shown a move from the expensive and time consuming empirical methodologies, towards a faster computational approach. ASML's Design for Control (D4C) application, which is currently used to optimize YieldStar diffraction based overlay (DBO) metrology targets, has been extended to support the optimization of scanner wafer alignment targets. This allows the necessary process information and design methodology, used for DBO target designs, to be leveraged for the optimization of alignment targets. In this paper, we show how we applied this computational approach to wafer alignment target design. We verify the correlation between predictions and measurements for the key alignment performance metrics and finally show the potential alignment and overlay performance improvements that an optimized alignment target could achieve.

Topology optimization under stochastic stiffness

NASA Astrophysics Data System (ADS)

Asadpoure, Alireza

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

Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

PubMed Central

Daza, Iván G.; Bergasa, Luis M.; Bronte, Sebastián; Yebes, J. Javier; Almazán, Javier; Arroyo, Roberto

2014-01-01

This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study. PMID:24412904

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

A systematic FPGA acceleration design for applications based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Dong, Hao; Jiang, Li; Li, Tianjian; Liang, Xiaoyao

2018-04-01

Most FPGA accelerators for convolutional neural network are designed to optimize the inner acceleration and are ignored of the optimization for the data path between the inner accelerator and the outer system. This could lead to poor performance in applications like real time video object detection. We propose a brand new systematic FPFA acceleration design to solve this problem. This design takes the data path optimization between the inner accelerator and the outer system into consideration and optimizes the data path using techniques like hardware format transformation, frame compression. It also takes fixed-point, new pipeline technique to optimize the inner accelerator. All these make the final system's performance very good, reaching about 10 times the performance comparing with the original system.

Development and optimization of an energy-regenerative suspension system under stochastic road excitation

NASA Astrophysics Data System (ADS)

Huang, Bo; Hsieh, Chen-Yu; Golnaraghi, Farid; Moallem, Mehrdad

2015-11-01

In this paper a vehicle suspension system with energy harvesting capability is developed, and an analytical methodology for the optimal design of the system is proposed. The optimization technique provides design guidelines for determining the stiffness and damping coefficients aimed at the optimal performance in terms of ride comfort and energy regeneration. The corresponding performance metrics are selected as root-mean-square (RMS) of sprung mass acceleration and expectation of generated power. The actual road roughness is considered as the stochastic excitation defined by ISO 8608:1995 standard road profiles and used in deriving the optimization method. An electronic circuit is proposed to provide variable damping in the real-time based on the optimization rule. A test-bed is utilized and the experiments under different driving conditions are conducted to verify the effectiveness of the proposed method. The test results suggest that the analytical approach is credible in determining the optimality of system performance.

Gamut Volume Index: a color preference metric based on meta-analysis and optimized colour samples.

PubMed

Liu, Qiang; Huang, Zheng; Xiao, Kaida; Pointer, Michael R; Westland, Stephen; Luo, M Ronnier

2017-07-10

A novel metric named Gamut Volume Index (GVI) is proposed for evaluating the colour preference of lighting. This metric is based on the absolute gamut volume of optimized colour samples. The optimal colour set of the proposed metric was obtained by optimizing the weighted average correlation between the metric predictions and the subjective ratings for 8 psychophysical studies. The performance of 20 typical colour metrics was also investigated, which included colour difference based metrics, gamut based metrics, memory based metrics as well as combined metrics. It was found that the proposed GVI outperformed the existing counterparts, especially for the conditions where correlated colour temperatures differed.

Multidisciplinary Shape Optimization of a Composite Blended Wing Body Aircraft

NASA Astrophysics Data System (ADS)

Boozer, Charles Maxwell

A multidisciplinary shape optimization tool coupling aerodynamics, structure, and performance was developed for battery powered aircraft. Utilizing high-fidelity computational fluid dynamics analysis tools and a structural wing weight tool, coupled based on the multidisciplinary feasible optimization architecture; aircraft geometry is modified in the optimization of the aircraft's range or endurance. The developed tool is applied to three geometries: a hybrid blended wing body, delta wing UAS, the ONERA M6 wing, and a modified ONERA M6 wing. First, the optimization problem is presented with the objective function, constraints, and design vector. Next, the tool's architecture and the analysis tools that are utilized are described. Finally, various optimizations are described and their results analyzed for all test subjects. Results show that less computationally expensive inviscid optimizations yield positive performance improvements using planform, airfoil, and three-dimensional degrees of freedom. From the results obtained through a series of optimizations, it is concluded that the newly developed tool is both effective at improving performance and serves as a platform ready to receive additional performance modules, further improving its computational design support potential.

Mass Optimization of Battery/Supercapacitors Hybrid Systems Based on a Linear Programming Approach

NASA Astrophysics Data System (ADS)

Fleury, Benoit; Labbe, Julien

2014-08-01

The objective of this paper is to show that, on a specific launcher-type mission profile, a 40% gain of mass is expected using a battery/supercapacitors active hybridization instead of a single battery solution. This result is based on the use of a linear programming optimization approach to perform the mass optimization of the hybrid power supply solution.

An optimization-based integrated controls-structures design methodology for flexible space structures

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Joshi, Suresh M.; Armstrong, Ernest S.

1993-01-01

An approach for an optimization-based integrated controls-structures design is presented for a class of flexible spacecraft that require fine attitude pointing and vibration suppression. The integrated design problem is posed in the form of simultaneous optimization of both structural and control design variables. The approach is demonstrated by application to the integrated design of a generic space platform and to a model of a ground-based flexible structure. The numerical results obtained indicate that the integrated design approach can yield spacecraft designs that have substantially superior performance over a conventional design wherein the structural and control designs are performed sequentially. For example, a 40-percent reduction in the pointing error is observed along with a slight reduction in mass, or an almost twofold increase in the controlled performance is indicated with more than a 5-percent reduction in the overall mass of the spacecraft (a reduction of hundreds of kilograms).

FPGA-based protein sequence alignment : A review

NASA Astrophysics Data System (ADS)

Isa, Mohd. Nazrin Md.; Muhsen, Ku Noor Dhaniah Ku; Saiful Nurdin, Dayana; Ahmad, Muhammad Imran; Anuar Zainol Murad, Sohiful; Nizam Mohyar, Shaiful; Harun, Azizi; Hussin, Razaidi

2017-11-01

Sequence alignment have been optimized using several techniques in order to accelerate the computation time to obtain the optimal score by implementing DP-based algorithm into hardware such as FPGA-based platform. During hardware implementation, there will be performance challenges such as the frequent memory access and highly data dependent in computation process. Therefore, investigation in processing element (PE) configuration where involves more on memory access in load or access the data (substitution matrix, query sequence character) and the PE configuration time will be the main focus in this paper. There are various approaches to enhance the PE configuration performance that have been done in previous works such as by using serial configuration chain and parallel configuration chain i.e. the configuration data will be loaded into each PEs sequentially and simultaneously respectively. Some researchers have proven that the performance using parallel configuration chain has optimized both the configuration time and area.

New approaches to optimization in aerospace conceptual design

NASA Technical Reports Server (NTRS)

Gage, Peter J.

1995-01-01

Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.

Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach.

PubMed

Zhang, Huaguang; Feng, Tao; Yang, Guang-Hong; Liang, Hongjing

2015-07-01

In this paper, the inverse optimal approach is employed to design distributed consensus protocols that guarantee consensus and global optimality with respect to some quadratic performance indexes for identical linear systems on a directed graph. The inverse optimal theory is developed by introducing the notion of partial stability. As a result, the necessary and sufficient conditions for inverse optimality are proposed. By means of the developed inverse optimal theory, the necessary and sufficient conditions are established for globally optimal cooperative control problems on directed graphs. Basic optimal cooperative design procedures are given based on asymptotic properties of the resulting optimal distributed consensus protocols, and the multiagent systems can reach desired consensus performance (convergence rate and damping rate) asymptotically. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

NASA Astrophysics Data System (ADS)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.

PubMed

Lu, Baofeng; Wang, Qiuying; Yu, Chunmei; Gao, Wei

2015-06-25

Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.

Optimal block cosine transform image coding for noisy channels

NASA Technical Reports Server (NTRS)

Vaishampayan, V.; Farvardin, N.

1986-01-01

The two dimensional block transform coding scheme based on the discrete cosine transform was studied extensively for image coding applications. While this scheme has proven to be efficient in the absence of channel errors, its performance degrades rapidly over noisy channels. A method is presented for the joint source channel coding optimization of a scheme based on the 2-D block cosine transform when the output of the encoder is to be transmitted via a memoryless design of the quantizers used for encoding the transform coefficients. This algorithm produces a set of locally optimum quantizers and the corresponding binary code assignment for the assumed transform coefficient statistics. To determine the optimum bit assignment among the transform coefficients, an algorithm was used based on the steepest descent method, which under certain convexity conditions on the performance of the channel optimized quantizers, yields the optimal bit allocation. Comprehensive simulation results for the performance of this locally optimum system over noisy channels were obtained and appropriate comparisons against a reference system designed for no channel error were rendered.

Design of optimal buffer layers for CuInGaSe2 thin-film solar cells(Conference Presentation)

NASA Astrophysics Data System (ADS)

Lordi, Vincenzo; Varley, Joel B.; He, Xiaoqing; Rockett, Angus A.; Bailey, Jeff; Zapalac, Geordie H.; Mackie, Neil; Poplavskyy, Dmitry; Bayman, Atiye

2016-09-01

Optimizing the buffer layer in manufactured thin-film PV is essential to maximize device efficiency. Here, we describe a combined synthesis, characterization, and theory effort to design optimal buffers based on the (Cd,Zn)(O,S) alloy system for CIGS devices. Optimization of buffer composition and absorber/buffer interface properties in light of several competing requirements for maximum device efficiency were performed, along with process variations to control the film and interface quality. The most relevant buffer properties controlling performance include band gap, conduction band offset with absorber, dopability, interface quality, and film crystallinity. Control of an all-PVD deposition process enabled variation of buffer composition, crystallinity, doping, and quality of the absorber/buffer interface. Analytical electron microscopy was used to characterize the film composition and morphology, while hybrid density functional theory was used to predict optimal compositions and growth parameters based on computed material properties. Process variations were developed to produce layers with controlled crystallinity, varying from amorphous to fully epitaxial, depending primarily on oxygen content. Elemental intermixing between buffer and absorber, particularly involving Cd and Cu, also is controlled and significantly affects device performance. Secondary phase formation at the interface is observed for some conditions and may be detrimental depending on the morphology. Theoretical calculations suggest optimal composition ranges for the buffer based on a suite of computed properties and drive process optimizations connected with observed film properties. Prepared by LLNL under Contract DE-AC52-07NA27344.

Market-Based and System-Wide Fuel Cycle Optimization

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

Wilson, Paul Philip Hood; Scopatz, Anthony; Gidden, Matthew

This work introduces automated optimization into fuel cycle simulations in the Cyclus platform. This includes system-level optimizations, seeking a deployment plan that optimizes the performance over the entire transition, and market-level optimization, seeking an optimal set of material trades at each time step. These concepts were introduced in a way that preserves the flexibility of the Cyclus fuel cycle framework, one of its most important design principles.

Performance evaluation of matrix gradient coils.

PubMed

Jia, Feng; Schultz, Gerrit; Testud, Frederik; Welz, Anna Masako; Weber, Hans; Littin, Sebastian; Yu, Huijun; Hennig, Jürgen; Zaitsev, Maxim

2016-02-01

In this paper, we present a new performance measure of a matrix coil (also known as multi-coil) from the perspective of efficient, local, non-linear encoding without explicitly considering target encoding fields. An optimization problem based on a joint optimization for the non-linear encoding fields is formulated. Based on the derived objective function, a figure of merit of a matrix coil is defined, which is a generalization of a previously known resistive figure of merit for traditional gradient coils. A cylindrical matrix coil design with a high number of elements is used to illustrate the proposed performance measure. The results are analyzed to reveal novel features of matrix coil designs, which allowed us to optimize coil parameters, such as number of coil elements. A comparison to a scaled, existing multi-coil is also provided to demonstrate the use of the proposed performance parameter. The assessment of a matrix gradient coil profits from using a single performance parameter that takes the local encoding performance of the coil into account in relation to the dissipated power.

Sensor fusion display evaluation using information integration models in enhanced/synthetic vision applications

NASA Technical Reports Server (NTRS)

Foyle, David C.

1993-01-01

Based on existing integration models in the psychological literature, an evaluation framework is developed to assess sensor fusion displays as might be implemented in an enhanced/synthetic vision system. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The pilot's performance with the sensor fusion image is compared to models' predictions based on the pilot's performance when viewing the original component sensor images prior to fusion. This allows for the determination as to when a sensor fusion system leads to: poorer performance than one of the original sensor displays, clearly an undesirable system in which the fused sensor system causes some distortion or interference; better performance than with either single sensor system alone, but at a sub-optimal level compared to model predictions; optimal performance compared to model predictions; or, super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays.

On the use of controls for subsonic transport performance improvement: Overview and future directions

NASA Technical Reports Server (NTRS)

Gilyard, Glenn; Espana, Martin

1994-01-01

Increasing competition among airline manufacturers and operators has highlighted the issue of aircraft efficiency. Fewer aircraft orders have led to an all-out efficiency improvement effort among the manufacturers to maintain if not increase their share of the shrinking number of aircraft sales. Aircraft efficiency is important in airline profitability and is key if fuel prices increase from their current low. In a continuing effort to improve aircraft efficiency and develop an optimal performance technology base, NASA Dryden Flight Research Center developed and flight tested an adaptive performance seeking control system to optimize the quasi-steady-state performance of the F-15 aircraft. The demonstrated technology is equally applicable to transport aircraft although with less improvement. NASA Dryden, in transitioning this technology to transport aircraft, is specifically exploring the feasibility of applying adaptive optimal control techniques to performance optimization of redundant control effectors. A simulation evaluation of a preliminary control law optimizes wing-aileron camber for minimum net aircraft drag. Two submodes are evaluated: one to minimize fuel and the other to maximize velocity. This paper covers the status of performance optimization of the current fleet of subsonic transports. Available integrated controls technologies are reviewed to define approaches using active controls. A candidate control law for adaptive performance optimization is presented along with examples of algorithm operation.

A novel channel selection method for optimal classification in different motor imagery BCI paradigms.

PubMed

Shan, Haijun; Xu, Haojie; Zhu, Shanan; He, Bin

2015-10-21

For sensorimotor rhythms based brain-computer interface (BCI) systems, classification of different motor imageries (MIs) remains a crucial problem. An important aspect is how many scalp electrodes (channels) should be used in order to reach optimal performance classifying motor imaginations. While the previous researches on channel selection mainly focus on MI tasks paradigms without feedback, the present work aims to investigate the optimal channel selection in MI tasks paradigms with real-time feedback (two-class control and four-class control paradigms). In the present study, three datasets respectively recorded from MI tasks experiment, two-class control and four-class control experiments were analyzed offline. Multiple frequency-spatial synthesized features were comprehensively extracted from every channel, and a new enhanced method IterRelCen was proposed to perform channel selection. IterRelCen was constructed based on Relief algorithm, but was enhanced from two aspects: change of target sample selection strategy and adoption of the idea of iterative computation, and thus performed more robust in feature selection. Finally, a multiclass support vector machine was applied as the classifier. The least number of channels that yield the best classification accuracy were considered as the optimal channels. One-way ANOVA was employed to test the significance of performance improvement among using optimal channels, all the channels and three typical MI channels (C3, C4, Cz). The results show that the proposed method outperformed other channel selection methods by achieving average classification accuracies of 85.2, 94.1, and 83.2 % for the three datasets, respectively. Moreover, the channel selection results reveal that the average numbers of optimal channels were significantly different among the three MI paradigms. It is demonstrated that IterRelCen has a strong ability for feature selection. In addition, the results have shown that the numbers of optimal channels in the three different motor imagery BCI paradigms are distinct. From a MI task paradigm, to a two-class control paradigm, and to a four-class control paradigm, the number of required channels for optimizing the classification accuracy increased. These findings may provide useful information to optimize EEG based BCI systems, and further improve the performance of noninvasive BCI.

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

A solution quality assessment method for swarm intelligence optimization algorithms.

PubMed

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.

Nozzle Mounting Method Optimization Based on Robot Kinematic Analysis

NASA Astrophysics Data System (ADS)

Chen, Chaoyue; Liao, Hanlin; Montavon, Ghislain; Deng, Sihao

2016-08-01

Nowadays, the application of industrial robots in thermal spray is gaining more and more importance. A desired coating quality depends on factors such as a balanced robot performance, a uniform scanning trajectory and stable parameters (e.g. nozzle speed, scanning step, spray angle, standoff distance). These factors also affect the mass and heat transfer as well as the coating formation. Thus, the kinematic optimization of all these aspects plays a key role in order to obtain an optimal coating quality. In this study, the robot performance was optimized from the aspect of nozzle mounting on the robot. An optimized nozzle mounting for a type F4 nozzle was designed, based on the conventional mounting method from the point of view of robot kinematics validated on a virtual robot. Robot kinematic parameters were obtained from the simulation by offline programming software and analyzed by statistical methods. The energy consumptions of different nozzle mounting methods were also compared. The results showed that it was possible to reasonably assign the amount of robot motion to each axis during the process, so achieving a constant nozzle speed. Thus, it is possible optimize robot performance and to economize robot energy.

The Topology Optimization Design Research for Aluminum Inner Panel of Automobile Engine Hood

NASA Astrophysics Data System (ADS)

Li, Minhao; Hu, Dongqing; Liu, Xiangzheng; Yuan, Huanquan

2017-11-01

This article discusses the topology optimization methods for automobile engine hood design. The aluminum inner panel of engine hood and mucilage glue regions are set as design areas, and the static performances of engine hood included modal frequency, lateral stiffness, torsional stiffness and indentation stiffness are set as the optimization objectives. The topology optimization results about different objective functions are contrasted for analysis. And based on the reasonable topology optimization result, a suited automobile engine hood designs are raised to further study. Finally, an automobile engine hood that good at all of static performances is designed, and a favorable topology optimization method is put forward for discussion.

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.

Topology-optimized metasurfaces: impact of initial geometric layout.

PubMed

Yang, Jianji; Fan, Jonathan A

2017-08-15

Topology optimization is a powerful iterative inverse design technique in metasurface engineering and can transform an initial layout into a high-performance device. With this method, devices are optimized within a local design phase space, making the identification of suitable initial geometries essential. In this Letter, we examine the impact of initial geometric layout on the performance of large-angle (75 deg) topology-optimized metagrating deflectors. We find that when conventional metasurface designs based on dielectric nanoposts are used as initial layouts for topology optimization, the final devices have efficiencies around 65%. In contrast, when random initial layouts are used, the final devices have ultra-high efficiencies that can reach 94%. Our numerical experiments suggest that device topologies based on conventional metasurface designs may not be suitable to produce ultra-high-efficiency, large-angle metasurfaces. Rather, initial geometric layouts with non-trivial topologies and shapes are required.

Multidisciplinary Aerospace Systems Optimization: Computational AeroSciences (CAS) Project

NASA Technical Reports Server (NTRS)

Kodiyalam, S.; Sobieski, Jaroslaw S. (Technical Monitor)

2001-01-01

The report describes a method for performing optimization of a system whose analysis is so expensive that it is impractical to let the optimization code invoke it directly because excessive computational cost and elapsed time might result. In such situation it is imperative to have user control the number of times the analysis is invoked. The reported method achieves that by two techniques in the Design of Experiment category: a uniform dispersal of the trial design points over a n-dimensional hypersphere and a response surface fitting, and the technique of krigging. Analyses of all the trial designs whose number may be set by the user are performed before activation of the optimization code and the results are stored as a data base. That code is then executed and referred to the above data base. Two applications, one of the airborne laser system, and one of an aircraft optimization illustrate the method application.

Simulation Research on Vehicle Active Suspension Controller Based on G1 Method

NASA Astrophysics Data System (ADS)

Li, Gen; Li, Hang; Zhang, Shuaiyang; Luo, Qiuhui

2017-09-01

Based on the order relation analysis method (G1 method), the optimal linear controller of vehicle active suspension is designed. The system of the main and passive suspension of the single wheel vehicle is modeled and the system input signal model is determined. Secondly, the system motion state space equation is established by the kinetic knowledge and the optimal linear controller design is completed with the optimal control theory. The weighting coefficient of the performance index coefficients of the main passive suspension is determined by the relational analysis method. Finally, the model is simulated in Simulink. The simulation results show that: the optimal weight value is determined by using the sequence relation analysis method under the condition of given road conditions, and the vehicle acceleration, suspension stroke and tire motion displacement are optimized to improve the comprehensive performance of the vehicle, and the active control is controlled within the requirements.

Near-Optimal Re-Entry Trajectories for Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Chou, H.-C.; Ardema, M. D.; Bowles, J. V.

1997-01-01

A near-optimal guidance law for the descent trajectory for earth orbit re-entry of a fully reusable single-stage-to-orbit pure rocket launch vehicle is derived. A methodology is developed to investigate using both bank angle and altitude as control variables and selecting parameters that maximize various performance functions. The method is based on the energy-state model of the aircraft equations of motion. The major task of this paper is to obtain optimal re-entry trajectories under a variety of performance goals: minimum time, minimum surface temperature, minimum heating, and maximum heading change; four classes of trajectories were investigated: no banking, optimal left turn banking, optimal right turn banking, and optimal bank chattering. The cost function is in general a weighted sum of all performance goals. In particular, the trade-off between minimizing heat load into the vehicle and maximizing cross range distance is investigated. The results show that the optimization methodology can be used to derive a wide variety of near-optimal trajectories.

Development of Regional Excel-Based Stormwater/Nutrient BMP Optimization Tool (Opti-Tool)

EPA Science Inventory

During 2014, EPA Region 1 contracted with Tetra Tech, Inc. to work with a regional technical Advisory Committee to develop an Excel-based stormwater/nutrient BMP optimization tool (Opti-Tool) using regional precipitation data and regionally calibrated BMP performance data from UN...

Optimal Base Station Density of Dense Network: From the Viewpoint of Interference and Load.

PubMed

Feng, Jianyuan; Feng, Zhiyong

2017-09-11

Network densification is attracting increasing attention recently due to its ability to improve network capacity by spatial reuse and relieve congestion by offloading. However, excessive densification and aggressive offloading can also cause the degradation of network performance due to problems of interference and load. In this paper, with consideration of load issues, we study the optimal base station density that maximizes the throughput of the network. The expected link rate and the utilization ratio of the contention-based channel are derived as the functions of base station density using the Poisson Point Process (PPP) and Markov Chain. They reveal the rules of deployment. Based on these results, we obtain the throughput of the network and indicate the optimal deployment density under different network conditions. Extensive simulations are conducted to validate our analysis and show the substantial performance gain obtained by the proposed deployment scheme. These results can provide guidance for the network densification.

Fog computing job scheduling optimization based on bees swarm

NASA Astrophysics Data System (ADS)

Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

2018-04-01

Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography.

PubMed

Gang, Grace J; Siewerdsen, Jeffrey H; Stayman, J Webster

2017-12-01

This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

Methodology for designing and manufacturing complex biologically inspired soft robotic fluidic actuators: prosthetic hand case study.

PubMed

Thompson-Bean, E; Das, R; McDaid, A

2016-10-31

We present a novel methodology for the design and manufacture of complex biologically inspired soft robotic fluidic actuators. The methodology is applied to the design and manufacture of a prosthetic for the hand. Real human hands are scanned to produce a 3D model of a finger, and pneumatic networks are implemented within it to produce a biomimetic bending motion. The finger is then partitioned into material sections, and a genetic algorithm based optimization, using finite element analysis, is employed to discover the optimal material for each section. This is based on two biomimetic performance criteria. Two sets of optimizations using two material sets are performed. Promising optimized material arrangements are fabricated using two techniques to validate the optimization routine, and the fabricated and simulated results are compared. We find that the optimization is successful in producing biomimetic soft robotic fingers and that fabrication of the fingers is possible. Limitations and paths for development are discussed. This methodology can be applied for other fluidic soft robotic devices.

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

PubMed

Cheng, Xianfu; Lin, Yuqun

2014-01-01

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

Multi-disciplinary optimization of aeroservoelastic systems

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1990-01-01

Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process which allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum State aerodynamic approximations and dynamic residualization methods are used to construct a high accuracy, low order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins and control effectiveness with respect to structural and control design variables are presented. The performance requirements are utilized by equality constraints which affect the sensitivity derivatives. A gradient-based optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.

Multidisciplinary optimization of aeroservoelastic systems using reduced-size models

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1992-01-01

Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process which allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum State aerodynamic approximations and dynamic residualization methods are used to construct a high accuracy, low order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins and control effectiveness with respect to structural and control design variables are presented. The performance requirements are utilized by equality constraints which affect the sensitivity derivatives. A gradient-based optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.

Mass-based design and optimization of wave rotors for gas turbine engine enhancement

NASA Astrophysics Data System (ADS)

Chan, S.; Liu, H.

2017-03-01

An analytic method aiming at mass properties was developed for the preliminary design and optimization of wave rotors. In the present method, we introduce the mass balance principle into the design and thus can predict and optimize the mass qualities as well as the performance of wave rotors. A dedicated least-square method with artificial weighting coefficients was developed to solve the over-constrained system in the mass-based design. This method and the adoption of the coefficients were validated by numerical simulation. Moreover, the problem of fresh air exhaustion (FAE) was put forward and analyzed, and exhaust gas recirculation (EGR) was investigated. Parameter analyses and optimization elucidated which designs would not only achieve the best performance, but also operate with minimum EGR and no FAE.

Direct Optimal Control of Duffing Dynamics

NASA Technical Reports Server (NTRS)

Oz, Hayrani; Ramsey, John K.

2002-01-01

The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.

Optimization of motion control laws for tether crawler or elevator systems

NASA Technical Reports Server (NTRS)

Swenson, Frank R.; Von Tiesenhausen, Georg

1988-01-01

Based on the proposal of a motion control law by Lorenzini (1987), a method is developed for optimizing motion control laws for tether crawler or elevator systems in terms of the performance measures of travel time, the smoothness of acceleration and deceleration, and the maximum values of velocity and acceleration. The Lorenzini motion control law, based on powers of the hyperbolic tangent function, is modified by the addition of a constant-velocity section, and this modified function is then optimized by parameter selections to minimize the peak acceleration value for a selected travel time or to minimize travel time for the selected peak values of velocity and acceleration. It is shown that the addition of a constant-velocity segment permits further optimization of the motion control law performance.

Displacement based multilevel structural optimization

NASA Technical Reports Server (NTRS)

Striz, Alfred G.

1995-01-01

Multidisciplinary design optimization (MDO) is expected to play a major role in the competitive transportation industries of tomorrow, i.e., in the design of aircraft and spacecraft, of high speed trains, boats, and automobiles. All of these vehicles require maximum performance at minimum weight to keep fuel consumption low and conserve resources. Here, MDO can deliver mathematically based design tools to create systems with optimum performance subject to the constraints of disciplines such as structures, aerodynamics, controls, etc. Although some applications of MDO are beginning to surface, the key to a widespread use of this technology lies in the improvement of its efficiency. This aspect is investigated here for the MDO subset of structural optimization, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures (here, statically indeterminate trusses and beams for proof of concept) is performed. In the system level optimization, the design variables are the coefficients of assumed displacement functions, and the load unbalance resulting from the solution of the stiffness equations is minimized. Constraints are placed on the deflection amplitudes and the weight of the structure. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. This approach is expected to prove very efficient, especially for complex structures, since the design task is broken down into a large number of small and efficiently handled subtasks, each with only a small number of variables. This partitioning will also allow for the use of parallel computing, first, by sending the system and subsystems level computations to two different processors, ultimately, by performing all subsystems level optimizations in a massively parallel manner on separate processors. It is expected that the subsystems level optimizations can be further improved through the use of controlled growth, a method which reduces an optimization to a more efficient analysis with only a slight degradation in accuracy. The efficiency of all proposed techniques is being evaluated relative to the performance of the standard single level optimization approach where the complete structure is weight minimized under the action of all given constraints by one processor and to the performance of simultaneous analysis and design which combines analysis and optimization into a single step. It is expected that the present approach can be expanded to include additional structural constraints (buckling, free and forced vibration, etc.) or other disciplines (passive and active controls, aerodynamics, etc.) for true MDO.

Real-time CT-video registration for continuous endoscopic guidance

NASA Astrophysics Data System (ADS)

Merritt, Scott A.; Rai, Lav; Higgins, William E.

2006-03-01

Previous research has shown that CT-image-based guidance could be useful for the bronchoscopic assessment of lung cancer. This research drew upon the registration of bronchoscopic video images to CT-based endoluminal renderings of the airway tree. The proposed methods either were restricted to discrete single-frame registration, which took several seconds to complete, or required non-real-time buffering and processing of video sequences. We have devised a fast 2D/3D image registration method that performs single-frame CT-Video registration in under 1/15th of a second. This allows the method to be used for real-time registration at full video frame rates without significantly altering the physician's behavior. The method achieves its speed through a gradient-based optimization method that allows most of the computation to be performed off-line. During live registration, the optimization iteratively steps toward the locally optimal viewpoint at which a CT-based endoluminal view is most similar to a current bronchoscopic video frame. After an initial registration to begin the process (generally done in the trachea for bronchoscopy), subsequent registrations are performed in real-time on each incoming video frame. As each new bronchoscopic video frame becomes available, the current optimization is initialized using the previous frame's optimization result, allowing continuous guidance to proceed without manual re-initialization. Tests were performed using both synthetic and pre-recorded bronchoscopic video. The results show that the method is robust to initialization errors, that registration accuracy is high, and that continuous registration can proceed on real-time video at >15 frames per sec. with minimal user-intervention.

On the Efficacy of Source Code Optimizations for Cache-Based Systems

NASA Technical Reports Server (NTRS)

VanderWijngaart, Rob F.; Saphir, William C.

1998-01-01

Obtaining high performance without machine-specific tuning is an important goal of scientific application programmers. Since most scientific processing is done on commodity microprocessors with hierarchical memory systems, this goal of "portable performance" can be achieved if a common set of optimization principles is effective for all such systems. It is widely believed, or at least hoped, that portable performance can be realized. The rule of thumb for optimization on hierarchical memory systems is to maximize temporal and spatial locality of memory references by reusing data and minimizing memory access stride. We investigate the effects of a number of optimizations on the performance of three related kernels taken from a computational fluid dynamics application. Timing the kernels on a range of processors, we observe an inconsistent and often counterintuitive impact of the optimizations on performance. In particular, code variations that have a positive impact on one architecture can have a negative impact on another, and variations expected to be unimportant can produce large effects. Moreover, we find that cache miss rates - as reported by a cache simulation tool, and confirmed by hardware counters - only partially explain the results. By contrast, the compiler-generated assembly code provides more insight by revealing the importance of processor-specific instructions and of compiler maturity, both of which strongly, and sometimes unexpectedly, influence performance. We conclude that it is difficult to obtain performance portability on modern cache-based computers, and comment on the implications of this result.

On the Efficacy of Source Code Optimizations for Cache-Based Systems

NASA Technical Reports Server (NTRS)

VanderWijngaart, Rob F.; Saphir, William C.; Saini, Subhash (Technical Monitor)

1998-01-01

Obtaining high performance without machine-specific tuning is an important goal of scientific application programmers. Since most scientific processing is done on commodity microprocessors with hierarchical memory systems, this goal of "portable performance" can be achieved if a common set of optimization principles is effective for all such systems. It is widely believed, or at least hoped, that portable performance can be realized. The rule of thumb for optimization on hierarchical memory systems is to maximize temporal and spatial locality of memory references by reusing data and minimizing memory access stride. We investigate the effects of a number of optimizations on the performance of three related kernels taken from a computational fluid dynamics application. Timing the kernels on a range of processors, we observe an inconsistent and often counterintuitive impact of the optimizations on performance. In particular, code variations that have a positive impact on one architecture can have a negative impact on another, and variations expected to be unimportant can produce large effects. Moreover, we find that cache miss rates-as reported by a cache simulation tool, and confirmed by hardware counters-only partially explain the results. By contrast, the compiler-generated assembly code provides more insight by revealing the importance of processor-specific instructions and of compiler maturity, both of which strongly, and sometimes unexpectedly, influence performance. We conclude that it is difficult to obtain performance portability on modern cache-based computers, and comment on the implications of this result.

Reduction of wafer-edge overlay errors using advanced correction models, optimized for minimal metrology requirements

NASA Astrophysics Data System (ADS)

Kim, Min-Suk; Won, Hwa-Yeon; Jeong, Jong-Mun; Böcker, Paul; Vergaij-Huizer, Lydia; Kupers, Michiel; Jovanović, Milenko; Sochal, Inez; Ryan, Kevin; Sun, Kyu-Tae; Lim, Young-Wan; Byun, Jin-Moo; Kim, Gwang-Gon; Suh, Jung-Joon

2016-03-01

In order to optimize yield in DRAM semiconductor manufacturing for 2x nodes and beyond, the (processing induced) overlay fingerprint towards the edge of the wafer needs to be reduced. Traditionally, this is achieved by acquiring denser overlay metrology at the edge of the wafer, to feed field-by-field corrections. Although field-by-field corrections can be effective in reducing localized overlay errors, the requirement for dense metrology to determine the corrections can become a limiting factor due to a significant increase of metrology time and cost. In this study, a more cost-effective solution has been found in extending the regular correction model with an edge-specific component. This new overlay correction model can be driven by an optimized, sparser sampling especially at the wafer edge area, and also allows for a reduction of noise propagation. Lithography correction potential has been maximized, with significantly less metrology needs. Evaluations have been performed, demonstrating the benefit of edge models in terms of on-product overlay performance, as well as cell based overlay performance based on metrology-to-cell matching improvements. Performance can be increased compared to POR modeling and sampling, which can contribute to (overlay based) yield improvement. Based on advanced modeling including edge components, metrology requirements have been optimized, enabling integrated metrology which drives down overall metrology fab footprint and lithography cycle time.

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Multiobjective Optimization Using a Pareto Differential Evolution Approach

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Reliability-based structural optimization: A proposed analytical-experimental study

NASA Technical Reports Server (NTRS)

Stroud, W. Jefferson; Nikolaidis, Efstratios

1993-01-01

An analytical and experimental study for assessing the potential of reliability-based structural optimization is proposed and described. In the study, competing designs obtained by deterministic and reliability-based optimization are compared. The experimental portion of the study is practical because the structure selected is a modular, actively and passively controlled truss that consists of many identical members, and because the competing designs are compared in terms of their dynamic performance and are not destroyed if failure occurs. The analytical portion of this study is illustrated on a 10-bar truss example. In the illustrative example, it is shown that reliability-based optimization can yield a design that is superior to an alternative design obtained by deterministic optimization. These analytical results provide motivation for the proposed study, which is underway.

Design Tool Using a New Optimization Method Based on a Stochastic Process

NASA Astrophysics Data System (ADS)

Yoshida, Hiroaki; Yamaguchi, Katsuhito; Ishikawa, Yoshio

Conventional optimization methods are based on a deterministic approach since their purpose is to find out an exact solution. However, such methods have initial condition dependence and the risk of falling into local solution. In this paper, we propose a new optimization method based on the concept of path integrals used in quantum mechanics. The method obtains a solution as an expected value (stochastic average) using a stochastic process. The advantages of this method are that it is not affected by initial conditions and does not require techniques based on experiences. We applied the new optimization method to a hang glider design. In this problem, both the hang glider design and its flight trajectory were optimized. The numerical calculation results prove that performance of the method is sufficient for practical use.

A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

PubMed

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

PubMed Central

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA. PMID:24892059

The effect of Fisher information matrix approximation methods in population optimal design calculations.

PubMed

Strömberg, Eric A; Nyberg, Joakim; Hooker, Andrew C

2016-12-01

With the increasing popularity of optimal design in drug development it is important to understand how the approximations and implementations of the Fisher information matrix (FIM) affect the resulting optimal designs. The aim of this work was to investigate the impact on design performance when using two common approximations to the population model and the full or block-diagonal FIM implementations for optimization of sampling points. Sampling schedules for two example experiments based on population models were optimized using the FO and FOCE approximations and the full and block-diagonal FIM implementations. The number of support points was compared between the designs for each example experiment. The performance of these designs based on simulation/estimations was investigated by computing bias of the parameters as well as through the use of an empirical D-criterion confidence interval. Simulations were performed when the design was computed with the true parameter values as well as with misspecified parameter values. The FOCE approximation and the Full FIM implementation yielded designs with more support points and less clustering of sample points than designs optimized with the FO approximation and the block-diagonal implementation. The D-criterion confidence intervals showed no performance differences between the full and block diagonal FIM optimal designs when assuming true parameter values. However, the FO approximated block-reduced FIM designs had higher bias than the other designs. When assuming parameter misspecification in the design evaluation, the FO Full FIM optimal design was superior to the FO block-diagonal FIM design in both of the examples.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Routing performance analysis and optimization within a massively parallel computer

DOEpatents

Archer, Charles Jens; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen

2013-04-16

An apparatus, program product and method optimize the operation of a massively parallel computer system by, in part, receiving actual performance data concerning an application executed by the plurality of interconnected nodes, and analyzing the actual performance data to identify an actual performance pattern. A desired performance pattern may be determined for the application, and an algorithm may be selected from among a plurality of algorithms stored within a memory, the algorithm being configured to achieve the desired performance pattern based on the actual performance data.

A Simplified GCS-DCSK Modulation and Its Performance Optimization

NASA Astrophysics Data System (ADS)

Xu, Weikai; Wang, Lin; Chi, Chong-Yung

2016-12-01

In this paper, a simplified Generalized Code-Shifted Differential Chaos Shift Keying (GCS-DCSK) whose transmitter never needs any delay circuits, is proposed. However, its performance is deteriorated because the orthogonality between substreams cannot be guaranteed. In order to optimize its performance, the system model of the proposed GCS-DCSK with power allocations on substreams is presented. An approximate bit error rate (BER) expression of the proposed model, which is a function of substreams’ power, is derived using Gaussian Approximation. Based on the BER expression, an optimal power allocation strategy between information substreams and reference substream is obtained. Simulation results show that the BER performance of the proposed GCS-DCSK with the optimal power allocation can be significantly improved when the number of substreams M is large.

An Integrated Framework for Parameter-based Optimization of Scientific Workflows.

PubMed

Kumar, Vijay S; Sadayappan, P; Mehta, Gaurang; Vahi, Karan; Deelman, Ewa; Ratnakar, Varun; Kim, Jihie; Gil, Yolanda; Hall, Mary; Kurc, Tahsin; Saltz, Joel

2009-01-01

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

Task-Driven Tube Current Modulation and Regularization Design in Computed Tomography with Penalized-Likelihood Reconstruction.

PubMed

Gang, G J; Siewerdsen, J H; Stayman, J W

2016-02-01

This work applies task-driven optimization to design CT tube current modulation and directional regularization in penalized-likelihood (PL) reconstruction. The relative performance of modulation schemes commonly adopted for filtered-backprojection (FBP) reconstruction were also evaluated for PL in comparison. We adopt a task-driven imaging framework that utilizes a patient-specific anatomical model and information of the imaging task to optimize imaging performance in terms of detectability index ( d' ). This framework leverages a theoretical model based on implicit function theorem and Fourier approximations to predict local spatial resolution and noise characteristics of PL reconstruction as a function of the imaging parameters to be optimized. Tube current modulation was parameterized as a linear combination of Gaussian basis functions, and regularization was based on the design of (directional) pairwise penalty weights for the 8 in-plane neighboring voxels. Detectability was optimized using a covariance matrix adaptation evolutionary strategy algorithm. Task-driven designs were compared to conventional tube current modulation strategies for a Gaussian detection task in an abdomen phantom. The task-driven design yielded the best performance, improving d' by ~20% over an unmodulated acquisition. Contrary to FBP, PL reconstruction using automatic exposure control and modulation based on minimum variance (in FBP) performed worse than the unmodulated case, decreasing d' by 16% and 9%, respectively. This work shows that conventional tube current modulation schemes suitable for FBP can be suboptimal for PL reconstruction. Thus, the proposed task-driven optimization provides additional opportunities for improved imaging performance and dose reduction beyond that achievable with conventional acquisition and reconstruction.

Comparative molecular field analysis of artemisinin derivatives: Ab initio versus semiempirical optimized structures

NASA Astrophysics Data System (ADS)

Tonmunphean, Somsak; Kokpol, Sirirat; Parasuk, Vudhichai; Wolschann, Peter; Winger, Rudolf H.; Liedl, Klaus R.; Rode, Bernd M.

1998-07-01

Based on the belief that structural optimization methods, producing structures more closely to the experimental ones, should give better, i.e. more relevant, steric fields and hence more predictive CoMFA models, comparative molecular field analyses of artemisinin derivatives were performed based on semiempirical AM1 and HF/3-21G optimized geometries. Using these optimized geometries, the CoMFA results derived from the HF/3-21G method are found to be usually but not drastically better than those from AM1. Additional calculations were performed to investigate the electrostatic field difference using the Gasteiger and Marsili charges, the electrostatic potential fit charges at the AM1 level, and the natural population analysis charges at the HF/3-21G level of theory. For the HF/3-21G optimized structures no difference in predictability was observed, whereas for AM1 optimized structures such differences were found. Interestingly, if ionic compounds are omitted, differences between the various HF/3-21G optimized structure models using these electrostatic fields were found.

Optimization study on inductive-resistive circuit for broadband piezoelectric energy harvesters

NASA Astrophysics Data System (ADS)

Tan, Ting; Yan, Zhimiao

2017-03-01

The performance of cantilever-beam piezoelectric energy harvester is usually analyzed with pure resistive circuit. The optimal performance of such a vibration-based energy harvesting system is limited by narrow bandwidth around its modified natural frequency. For broadband piezoelectric energy harvesting, series and parallel inductive-resistive circuits are introduced. The electromechanical coupled distributed parameter models for such systems under harmonic base excitations are decoupled with modified natural frequency and electrical damping to consider the coupling effect. Analytical solutions of the harvested power and tip displacement for the electromechanical decoupled model are confirmed with numerical solutions for the coupled model. The optimal performance of piezoelectric energy harvesting with inductive-resistive circuits is revealed theoretically as constant maximal power at any excitation frequency. This is achieved by the scenarios of matching the modified natural frequency with the excitation frequency and equating the electrical damping to the mechanical damping. The inductance and load resistance should be simultaneously tuned to their optimal values, which may not be applicable for very high electromechanical coupling systems when the excitation frequency is higher than their natural frequencies. With identical optimal performance, the series inductive-resistive circuit is recommended for relatively small load resistance, while the parallel inductive-resistive circuit is suggested for relatively large load resistance. This study provides a simplified optimization method for broadband piezoelectric energy harvesters with inductive-resistive circuits.

Optimization and performance comparison for galloping-based piezoelectric energy harvesters with alternating-current and direct-current interface circuits

NASA Astrophysics Data System (ADS)

Tan, Ting; Yan, Zhimiao; Lei, Hong

2017-07-01

Galloping-based piezoelectric energy harvesters scavenge small-scale wind energy and convert it into electrical energy. For piezoelectric energy harvesting with the same vibrational source (galloping) but different (alternating-current (AC) and direct-current (DC)) interfaces, general analytical solutions of the electromechanical coupled distributed parameter model are proposed. Galloping is theoretically proven to appear when the linear aerodynamic negative damping overcomes the electrical damping and mechanical damping. The harvested power is demonstrated as being done by the electrical damping force. Via tuning the load resistance to its optimal value for optimal or maximal electrical damping, the harvested power of the given structure with the AC/DC interface is maximized. The optimal load resistances and the corresponding performances of such two systems are compared. The optimal electrical damping are the same but with different optimal load resistances for the systems with the AC and DC interfaces. At small wind speeds where the optimal electrical damping can be realized by only tuning the load resistance, the performances of such two energy harvesting systems, including the minimal onset speeds to galloping, maximal harvested powers and corresponding tip displacements are almost the same. Smaller maximal electrical damping with larger optimal load resistance is found for the harvester with the DC interface when compared to those for the harvester with the AC interface. At large wind speeds when the maximal electrical damping rather than the optimal electrical damping can be reached by tuning the load resistance alone, the harvester with the AC interface circuit is recommended for a higher maximal harvested power with a smaller tip displacement. This study provides a method using the general electrical damping to connect and compare the performances of piezoelectric energy harvesters with same excitation source but different interfaces.

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

PSO-tuned PID controller for coupled tank system via priority-based fitness scheme

NASA Astrophysics Data System (ADS)

Jaafar, Hazriq Izzuan; Hussien, Sharifah Yuslinda Syed; Selamat, Nur Asmiza; Abidin, Amar Faiz Zainal; Aras, Mohd Shahrieel Mohd; Nasir, Mohamad Na'im Mohd; Bohari, Zul Hasrizal

2015-05-01

The industrial applications of Coupled Tank System (CTS) are widely used especially in chemical process industries. The overall process is require liquids to be pumped, stored in the tank and pumped again to another tank. Nevertheless, the level of liquid in tank need to be controlled and flow between two tanks must be regulated. This paper presents development of an optimal PID controller for controlling the desired liquid level of the CTS. Two method of Particle Swarm Optimization (PSO) algorithm will be tested in optimizing the PID controller parameters. These two methods of PSO are standard Particle Swarm Optimization (PSO) and Priority-based Fitness Scheme in Particle Swarm Optimization (PFPSO). Simulation is conducted within Matlab environment to verify the performance of the system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). It has been demonstrated that implementation of PSO via Priority-based Fitness Scheme (PFPSO) for this system is potential technique to control the desired liquid level and improve the system performances compared with standard PSO.

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

NASA Astrophysics Data System (ADS)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.

DSP code optimization based on cache

NASA Astrophysics Data System (ADS)

Xu, Chengfa; Li, Chengcheng; Tang, Bin

2013-03-01

DSP program's running efficiency on board is often lower than which via the software simulation during the program development, which is mainly resulted from the user's improper use and incomplete understanding of the cache-based memory. This paper took the TI TMS320C6455 DSP as an example, analyzed its two-level internal cache, and summarized the methods of code optimization. Processor can achieve its best performance when using these code optimization methods. At last, a specific algorithm application in radar signal processing is proposed. Experiment result shows that these optimization are efficient.

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

Creating A Data Base For Design Of An Impeller

NASA Technical Reports Server (NTRS)

Prueger, George H.; Chen, Wei-Chung

1993-01-01

Report describes use of Taguchi method of parametric design to create data base facilitating optimization of design of impeller in centrifugal pump. Data base enables systematic design analysis covering all significant design parameters. Reduces time and cost of parametric optimization of design: for particular impeller considered, one can cover 4,374 designs by computational simulations of performance for only 18 cases.

Parametric Study of Biconic Re-Entry Vehicles

NASA Technical Reports Server (NTRS)

Steele, Bryan; Banks, Daniel W.; Whitmore, Stephen A.

2007-01-01

An optimization based on hypersonic aerodynamic performance and volumetric efficiency was accomplished for a range of biconic configurations. Both axisymmetric and quasi-axisymmetric geometries (bent and flattened) were analyzed. The aerodynamic optimization wag based on hypersonic simple Incidence angle analysis tools. The range of configurations included those suitable for r lunar return trajectory with a lifting aerocapture at Earth and an overall volume that could support a nominal crew. The results yielded five configurations that had acceptable aerodynamic performance and met overall geometry and size limitations

Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances.

PubMed

Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Yao, Yang

2013-07-01

Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimal performance of single-column chromatography and simulated moving bed processes for the separation of optical isomers

NASA Astrophysics Data System (ADS)

Medi, Bijan; Kazi, Monzure-Khoda; Amanullah, Mohammad

2013-06-01

Chromatography has been established as the method of choice for the separation and purification of optically pure drugs which has a market size of about 250 billion USD. Single column chromatography (SCC) is commonly used in the development and testing phase of drug development while multi-column Simulated Moving Bed (SMB) chromatography is more suitable for large scale production due to its continuous nature. In this study, optimal performance of SCC and SMB processes for the separation of optical isomers under linear and overloaded separation conditions has been investigated. The performance indicators, namely productivity and desorbent requirement have been compared under geometric similarity for the separation of a mixture of guaifenesin, and Tröger's base enantiomers. SCC process has been analyzed under equilibrium assumption i.e., assuming infinite column efficiency, and zero dispersion, and its optimal performance parameters are compared with the optimal prediction of an SMB process by triangle theory. Simulation results obtained using actual experimental data indicate that SCC may compete with SMB in terms of productivity depending on the molecules to be separated. Besides, insights into the process performances in terms of degree of freedom and relationship between the optimal operating point and solubility limit of the optical isomers have been ascertained. This investigation enables appropriate selection of single or multi-column chromatographic processes based on column packing properties and isotherm parameters.

Parallel Aircraft Trajectory Optimization with Analytic Derivatives

NASA Technical Reports Server (NTRS)

Falck, Robert D.; Gray, Justin S.; Naylor, Bret

2016-01-01

Trajectory optimization is an integral component for the design of aerospace vehicles, but emerging aircraft technologies have introduced new demands on trajectory analysis that current tools are not well suited to address. Designing aircraft with technologies such as hybrid electric propulsion and morphing wings requires consideration of the operational behavior as well as the physical design characteristics of the aircraft. The addition of operational variables can dramatically increase the number of design variables which motivates the use of gradient based optimization with analytic derivatives to solve the larger optimization problems. In this work we develop an aircraft trajectory analysis tool using a Legendre-Gauss-Lobatto based collocation scheme, providing analytic derivatives via the OpenMDAO multidisciplinary optimization framework. This collocation method uses an implicit time integration scheme that provides a high degree of sparsity and thus several potential options for parallelization. The performance of the new implementation was investigated via a series of single and multi-trajectory optimizations using a combination of parallel computing and constraint aggregation. The computational performance results show that in order to take full advantage of the sparsity in the problem it is vital to parallelize both the non-linear analysis evaluations and the derivative computations themselves. The constraint aggregation results showed a significant numerical challenge due to difficulty in achieving tight convergence tolerances. Overall, the results demonstrate the value of applying analytic derivatives to trajectory optimization problems and lay the foundation for future application of this collocation based method to the design of aircraft with where operational scheduling of technologies is key to achieving good performance.

Optimizing Multiple QoS for Workflow Applications using PSO and Min-Max Strategy

NASA Astrophysics Data System (ADS)

Umar Ambursa, Faruku; Latip, Rohaya; Abdullah, Azizol; Subramaniam, Shamala

2017-08-01

Workflow scheduling under multiple QoS constraints is a complicated optimization problem. Metaheuristic techniques are excellent approaches used in dealing with such problem. Many metaheuristic based algorithms have been proposed, that considers various economic and trustworthy QoS dimensions. However, most of these approaches lead to high violation of user-defined QoS requirements in tight situation. Recently, a new Particle Swarm Optimization (PSO)-based QoS-aware workflow scheduling strategy (LAPSO) is proposed to improve performance in such situations. LAPSO algorithm is designed based on synergy between a violation handling method and a hybrid of PSO and min-max heuristic. Simulation results showed a great potential of LAPSO algorithm to handling user requirements even in tight situations. In this paper, the performance of the algorithm is anlysed further. Specifically, the impact of the min-max strategy on the performance of the algorithm is revealed. This is achieved by removing the violation handling from the operation of the algorithm. The results show that LAPSO based on only the min-max method still outperforms the benchmark, even though the LAPSO with the violation handling performs more significantly better.

Improving the Unsteady Aerodynamic Performance of Transonic Turbines using Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan; Madavan, Nateri K.; Huber, Frank W.

1999-01-01

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The procedure yielded a modified design that improves the aerodynamic performance through small changes to the reference design geometry. These results demonstrate the capabilities of the neural net-based design procedure, and also show the advantages of including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

A Modified Mean Gray Wolf Optimization Approach for Benchmark and Biomedical Problems.

PubMed

Singh, Narinder; Singh, S B

2017-01-01

A modified variant of gray wolf optimization algorithm, namely, mean gray wolf optimization algorithm has been developed by modifying the position update (encircling behavior) equations of gray wolf optimization algorithm. The proposed variant has been tested on 23 standard benchmark well-known test functions (unimodal, multimodal, and fixed-dimension multimodal), and the performance of modified variant has been compared with particle swarm optimization and gray wolf optimization. Proposed algorithm has also been applied to the classification of 5 data sets to check feasibility of the modified variant. The results obtained are compared with many other meta-heuristic approaches, ie, gray wolf optimization, particle swarm optimization, population-based incremental learning, ant colony optimization, etc. The results show that the performance of modified variant is able to find best solutions in terms of high level of accuracy in classification and improved local optima avoidance.

Orbit design and optimization based on global telecommunication performance metrics

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Lee, Charles H.; Kerridge, Stuart; Cheung, Kar-Ming; Edwards, Charles D.

2006-01-01

The orbit selection of telecommunications orbiters is one of the critical design processes and should be guided by global telecom performance metrics and mission-specific constraints. In order to aid the orbit selection, we have coupled the Telecom Orbit Analysis and Simulation Tool (TOAST) with genetic optimization algorithms. As a demonstration, we have applied the developed tool to select an optimal orbit for general Mars telecommunications orbiters with the constraint of being a frozen orbit. While a typical optimization goal is to minimize tele-communications down time, several relevant performance metrics are examined: 1) area-weighted average gap time, 2) global maximum of local maximum gap time, 3) global maximum of local minimum gap time. Optimal solutions are found with each of the metrics. Common and different features among the optimal solutions as well as the advantage and disadvantage of each metric are presented. The optimal solutions are compared with several candidate orbits that were considered during the development of Mars Telecommunications Orbiter.

Faculty Sense of Academic Optimism and Its Relationship to Students' Achievement in Well Performing High Schools

ERIC Educational Resources Information Center

Cromartie, Michael Tyrone

2013-01-01

The aim of this study was to determine the organizational characteristics and behaviors that contribute to sustaining a culture of academic optimism as a mechanism of student achievement. While there is a developing research base identifying both the individual elements of academic optimism as well as the academic optimism construct itself as…

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

Aerodynamic Design of Complex Configurations Using Cartesian Methods and CAD Geometry

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.; Pulliam, Thomas H.

2003-01-01

The objective for this paper is to present the development of an optimization capability for the Cartesian inviscid-flow analysis package of Aftosmis et al. We evaluate and characterize the following modules within the new optimization framework: (1) A component-based geometry parameterization approach using a CAD solid representation and the CAPRI interface. (2) The use of Cartesian methods in the development Optimization techniques using a genetic algorithm. The discussion and investigations focus on several real world problems of the optimization process. We examine the architectural issues associated with the deployment of a CAD-based design approach in a heterogeneous parallel computing environment that contains both CAD workstations and dedicated compute nodes. In addition, we study the influence of noise on the performance of optimization techniques, and the overall efficiency of the optimization process for aerodynamic design of complex three-dimensional configurations. of automated optimization tools. rithm and a gradient-based algorithm.

Context-sensitive trace inlining for Java.

PubMed

Häubl, Christian; Wimmer, Christian; Mössenböck, Hanspeter

2013-12-01

Method inlining is one of the most important optimizations in method-based just-in-time (JIT) compilers. It widens the compilation scope and therefore allows optimizing multiple methods as a whole, which increases the performance. However, if method inlining is used too frequently, the compilation time increases and too much machine code is generated. This has negative effects on the performance. Trace-based JIT compilers only compile frequently executed paths, so-called traces, instead of whole methods. This may result in faster compilation, less generated machine code, and better optimized machine code. In the previous work, we implemented a trace recording infrastructure and a trace-based compiler for [Formula: see text], by modifying the Java HotSpot VM. Based on this work, we evaluate the effect of trace inlining on the performance and the amount of generated machine code. Trace inlining has several major advantages when compared to method inlining. First, trace inlining is more selective than method inlining, because only frequently executed paths are inlined. Second, the recorded traces may capture information about virtual calls, which simplify inlining. A third advantage is that trace information is context sensitive so that different method parts can be inlined depending on the specific call site. These advantages allow more aggressive inlining while the amount of generated machine code is still reasonable. We evaluate several inlining heuristics on the benchmark suites DaCapo 9.12 Bach, SPECjbb2005, and SPECjvm2008 and show that our trace-based compiler achieves an up to 51% higher peak performance than the method-based Java HotSpot client compiler. Furthermore, we show that the large compilation scope of our trace-based compiler has a positive effect on other compiler optimizations such as constant folding or null check elimination.

A New Model for Optimal Mechanical and Thermal Performance of Cement-Based Partition Wall

PubMed Central

Huang, Shiping; Hu, Mengyu; Cui, Nannan; Wang, Weifeng

2018-01-01

The prefabricated cement-based partition wall has been widely used in assembled buildings because of its high manufacturing efficiency, high-quality surface, and simple and convenient construction process. In this paper, a general porous partition wall that is made from cement-based materials was proposed to meet the optimal mechanical and thermal performance during transportation, construction and its service life. The porosity of the proposed partition wall is formed by elliptic-cylinder-type cavities. The finite element method was used to investigate the mechanical and thermal behaviour, which shows that the proposed model has distinct advantages over the current partition wall that is used in the building industry. It is found that, by controlling the eccentricity of the elliptic-cylinder cavities, the proposed wall stiffness can be adjusted to respond to the imposed loads and to improve the thermal performance, which can be used for the optimum design. Finally, design guidance is provided to obtain the optimal mechanical and thermal performance. The proposed model could be used as a promising candidate for partition wall in the building industry. PMID:29673176

A New Model for Optimal Mechanical and Thermal Performance of Cement-Based Partition Wall.

PubMed

Huang, Shiping; Hu, Mengyu; Huang, Yonghui; Cui, Nannan; Wang, Weifeng

2018-04-17

The prefabricated cement-based partition wall has been widely used in assembled buildings because of its high manufacturing efficiency, high-quality surface, and simple and convenient construction process. In this paper, a general porous partition wall that is made from cement-based materials was proposed to meet the optimal mechanical and thermal performance during transportation, construction and its service life. The porosity of the proposed partition wall is formed by elliptic-cylinder-type cavities. The finite element method was used to investigate the mechanical and thermal behaviour, which shows that the proposed model has distinct advantages over the current partition wall that is used in the building industry. It is found that, by controlling the eccentricity of the elliptic-cylinder cavities, the proposed wall stiffness can be adjusted to respond to the imposed loads and to improve the thermal performance, which can be used for the optimum design. Finally, design guidance is provided to obtain the optimal mechanical and thermal performance. The proposed model could be used as a promising candidate for partition wall in the building industry.

Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

NASA Astrophysics Data System (ADS)

Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

2012-09-01

In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

PubMed

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

PubMed Central

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms. PMID:24764774

Sensitivity analysis of an optimization-based trajectory planner for autonomous vehicles in urban environments

NASA Astrophysics Data System (ADS)

Hardy, Jason; Campbell, Mark; Miller, Isaac; Schimpf, Brian

2008-10-01

The local path planner implemented on Cornell's 2007 DARPA Urban Challenge entry vehicle Skynet utilizes a novel mixture of discrete and continuous path planning steps to facilitate a safe, smooth, and human-like driving behavior. The planner first solves for a feasible path through the local obstacle map using a grid based search algorithm. The resulting path is then refined using a cost-based nonlinear optimization routine with both hard and soft constraints. The behavior of this optimization is influenced by tunable weighting parameters which govern the relative cost contributions assigned to different path characteristics. This paper studies the sensitivity of the vehicle's performance to these path planner weighting parameters using a data driven simulation based on logged data from the National Qualifying Event. The performance of the path planner in both the National Qualifying Event and in the Urban Challenge is also presented and analyzed.

Model-Based Design of Tree WSNs for Decentralized Detection.

PubMed

Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

2015-08-20

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.

Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks

NASA Technical Reports Server (NTRS)

Greenman, Roxana M.

1998-01-01

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule,' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs.

The Tool for Designing Engineering Systems Using a New Optimization Method Based on a Stochastic Process

NASA Astrophysics Data System (ADS)

Yoshida, Hiroaki; Yamaguchi, Katsuhito; Ishikawa, Yoshio

The conventional optimization methods were based on a deterministic approach, since their purpose is to find out an exact solution. However, these methods have initial condition dependence and risk of falling into local solution. In this paper, we propose a new optimization method based on a concept of path integral method used in quantum mechanics. The method obtains a solutions as an expected value (stochastic average) using a stochastic process. The advantages of this method are not to be affected by initial conditions and not to need techniques based on experiences. We applied the new optimization method to a design of the hang glider. In this problem, not only the hang glider design but also its flight trajectory were optimized. The numerical calculation results showed that the method has a sufficient performance.

Joint-layer encoder optimization for HEVC scalable extensions

NASA Astrophysics Data System (ADS)

Tsai, Chia-Ming; He, Yuwen; Dong, Jie; Ye, Yan; Xiu, Xiaoyu; He, Yong

2014-09-01

Scalable video coding provides an efficient solution to support video playback on heterogeneous devices with various channel conditions in heterogeneous networks. SHVC is the latest scalable video coding standard based on the HEVC standard. To improve enhancement layer coding efficiency, inter-layer prediction including texture and motion information generated from the base layer is used for enhancement layer coding. However, the overall performance of the SHVC reference encoder is not fully optimized because rate-distortion optimization (RDO) processes in the base and enhancement layers are independently considered. It is difficult to directly extend the existing joint-layer optimization methods to SHVC due to the complicated coding tree block splitting decisions and in-loop filtering process (e.g., deblocking and sample adaptive offset (SAO) filtering) in HEVC. To solve those problems, a joint-layer optimization method is proposed by adjusting the quantization parameter (QP) to optimally allocate the bit resource between layers. Furthermore, to make more proper resource allocation, the proposed method also considers the viewing probability of base and enhancement layers according to packet loss rate. Based on the viewing probability, a novel joint-layer RD cost function is proposed for joint-layer RDO encoding. The QP values of those coding tree units (CTUs) belonging to lower layers referenced by higher layers are decreased accordingly, and the QP values of those remaining CTUs are increased to keep total bits unchanged. Finally the QP values with minimal joint-layer RD cost are selected to match the viewing probability. The proposed method was applied to the third temporal level (TL-3) pictures in the Random Access configuration. Simulation results demonstrate that the proposed joint-layer optimization method can improve coding performance by 1.3% for these TL-3 pictures compared to the SHVC reference encoder without joint-layer optimization.

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.

Heuristic query optimization for query multiple table and multiple clausa on mobile finance application

NASA Astrophysics Data System (ADS)

Indrayana, I. N. E.; P, N. M. Wirasyanti D.; Sudiartha, I. KG

2018-01-01

Mobile application allow many users to access data from the application without being limited to space, space and time. Over time the data population of this application will increase. Data access time will cause problems if the data record has reached tens of thousands to millions of records.The objective of this research is to maintain the performance of data execution for large data records. One effort to maintain data access time performance is to apply query optimization method. The optimization used in this research is query heuristic optimization method. The built application is a mobile-based financial application using MySQL database with stored procedure therein. This application is used by more than one business entity in one database, thus enabling rapid data growth. In this stored procedure there is an optimized query using heuristic method. Query optimization is performed on a “Select” query that involves more than one table with multiple clausa. Evaluation is done by calculating the average access time using optimized and unoptimized queries. Access time calculation is also performed on the increase of population data in the database. The evaluation results shown the time of data execution with query heuristic optimization relatively faster than data execution time without using query optimization.

A study of the optimal transition temperatue of PCM (Phase Change Material) wallboard for solar energy storage

NASA Astrophysics Data System (ADS)

Drake, J. B.

1987-09-01

The performance of wallboard impregnated with phase change material (PCM) is considered. An ideal setting is assumed and several measures of performance discussed. With a definition of optimal performance given, the performance with respect to variation of transition temperature is studied. Results are based on computer simulations of PCM wallboard with a standard stud wall construction. The diurnal heat capacity was found to be to be overly sensitive to numerical errors for use in PCM applications. The other measures of performance, diurnal effectiveness, net collected to storage ratio, and absolute discharge flux, all indicate similar trends. It is shown that the optimal transition temperature of the PCM is strongly influenced by the amount of solar flux absorbed.

Parallel performance optimizations on unstructured mesh-based simulations

DOE PAGES

Sarje, Abhinav; Song, Sukhyun; Jacobsen, Douglas; ...

2015-06-01

This paper addresses two key parallelization challenges the unstructured mesh-based ocean modeling code, MPAS-Ocean, which uses a mesh based on Voronoi tessellations: (1) load imbalance across processes, and (2) unstructured data access patterns, that inhibit intra- and inter-node performance. Our work analyzes the load imbalance due to naive partitioning of the mesh, and develops methods to generate mesh partitioning with better load balance and reduced communication. Furthermore, we present methods that minimize both inter- and intranode data movement and maximize data reuse. Our techniques include predictive ordering of data elements for higher cache efficiency, as well as communication reduction approaches.more » We present detailed performance data when running on thousands of cores using the Cray XC30 supercomputer and show that our optimization strategies can exceed the original performance by over 2×. Additionally, many of these solutions can be broadly applied to a wide variety of unstructured grid-based computations.« less

A theoretical comparison of evolutionary algorithms and simulated annealing

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

Hart, W.E.

1995-08-28

This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less

Statistical model based iterative reconstruction in clinical CT systems. Part III. Task-based kV/mAs optimization for radiation dose reduction

PubMed Central

Li, Ke; Gomez-Cardona, Daniel; Hsieh, Jiang; Lubner, Meghan G.; Pickhardt, Perry J.; Chen, Guang-Hong

2015-01-01

Purpose: For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems. Methods: The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d′(kV,mAs) is not lower than the prescribed value of d℞′ for a given imaging task. Since it is difficult to analytically model the dependence of d′ on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d′(kV,mAs) at a variety of kV–mAs combinations, after which the overlay of the dose contours and d′ contours is used to graphically determine the optimal kV–mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, d′ for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50–700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d℞′=16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow. Results: For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d′ map and the iso-CNR map were the same for FBP; thus, the use of d′- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d′ and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA = 350, dose = 37.5 mGy; for MBIR, kV = 120, mA = 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments. Conclusions: A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance. PMID:26328971

Optimal Periodic Cooperative Spectrum Sensing Based on Weight Fusion in Cognitive Radio Networks

PubMed Central

Liu, Xin; Jia, Min; Gu, Xuemai; Tan, Xuezhi

2013-01-01

The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time. PMID:23604027

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

PubMed Central

Robinson, Y. Harold; Rajaram, M.

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

PubMed

Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

2018-02-01

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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

Optimization design of LED heat dissipation structure based on strip fins

NASA Astrophysics Data System (ADS)

Xue, Lingyun; Wan, Wenbin; Chen, Qingguang; Rao, Huanle; Xu, Ping

2018-03-01

To solve the heat dissipation problem of LED, a radiator structure based on strip fins is designed and the method to optimize the structure parameters of strip fins is proposed in this paper. The combination of RBF neural networks and particle swarm optimization (PSO) algorithm is used for modeling and optimization respectively. During the experiment, the 150 datasets of LED junction temperature when structure parameters of number of strip fins, length, width and height of the fins have different values are obtained by ANSYS software. Then RBF neural network is applied to build the non-linear regression model and the parameters optimization of structure based on particle swarm optimization algorithm is performed with this model. The experimental results show that the lowest LED junction temperature reaches 43.88 degrees when the number of hidden layer nodes in RBF neural network is 10, the two learning factors in particle swarm optimization algorithm are 0.5, 0.5 respectively, the inertia factor is 1 and the maximum number of iterations is 100, and now the number of fins is 64, the distribution structure is 8*8, and the length, width and height of fins are 4.3mm, 4.48mm and 55.3mm respectively. To compare the modeling and optimization results, LED junction temperature at the optimized structure parameters was simulated and the result is 43.592°C which approximately equals to the optimal result. Compared with the ordinary plate-fin-type radiator structure whose temperature is 56.38°C, the structure greatly enhances heat dissipation performance of the structure.

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

The Optimization of In-Memory Space Partitioning Trees for Cache Utilization

NASA Astrophysics Data System (ADS)

Yeo, Myung Ho; Min, Young Soo; Bok, Kyoung Soo; Yoo, Jae Soo

In this paper, a novel cache conscious indexing technique based on space partitioning trees is proposed. Many researchers investigated efficient cache conscious indexing techniques which improve retrieval performance of in-memory database management system recently. However, most studies considered data partitioning and targeted fast information retrieval. Existing data partitioning-based index structures significantly degrade performance due to the redundant accesses of overlapped spaces. Specially, R-tree-based index structures suffer from the propagation of MBR (Minimum Bounding Rectangle) information by updating data frequently. In this paper, we propose an in-memory space partitioning index structure for optimal cache utilization. The proposed index structure is compared with the existing index structures in terms of update performance, insertion performance and cache-utilization rate in a variety of environments. The results demonstrate that the proposed index structure offers better performance than existing index structures.

Investigating enhanced thermoelectric performance of graphene-based nano-structures.

PubMed

Hossain, Md Sharafat; Huynh, Duc Hau; Jiang, Liming; Rahman, Sharmin; Nguyen, Phuong Duc; Al-Dirini, Feras; Hossain, Faruque; Bahk, Je-Hyeong; Skafidas, Efstratios

2018-03-08

Recently, it has been demonstrated that graphene nano-ribbons (GNRs) exhibit superior thermoelectric performance compared to graphene sheets. However, the underlying mechanism behind this enhancement has not been systematically investigated and significant opportunity remains for further enhancement of the thermoelectric performance of GNRs by optimizing their charge carrier concentration. In this work, we modulate the carrier concentration of graphene-based nano-structures using a gate voltage and investigate the resulting carrier-concentration-dependent thermoelectric parameters using the Boltzmann transport equations. We investigate the effect of energy dependent scattering time and the role of substrate-induced charge carrier fluctuation in optimizing the Seebeck coefficient and power factor. Our approach predicts the scattering mechanism and the extent of the charge carrier fluctuation in different samples and explains the enhancement of thermoelectric performance of GNR samples. Subsequently, we propose a route towards the enhancement of thermoelectric performance of graphene-based devices which can also be applied to other two-dimensional materials.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms.

PubMed

Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M

2016-03-01

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Reference Strain Structure for Studying Dynamic Responses of Flexible Rockets

NASA Technical Reports Server (NTRS)

Tsushima, Natsuki; Su, Weihua; Wolf, Michael G.; Griffin, Edwin D.; Dumoulin, Marie P.

2017-01-01

In the proposed paper, the optimal design of reference strain structures (RSS) will be performed targeting for the accurate observation of the dynamic bending and torsion deformation of a flexible rocket. It will provide the detailed description of the finite-element (FE) model of a notional flexible rocket created in MSC.Patran. The RSS will be attached longitudinally along the side of the rocket and to track the deformation of the thin-walled structure under external loads. An integrated surrogate-based multi-objective optimization approach will be developed to find the optimal design of the RSS using the FE model. The Kriging method will be used to construct the surrogate model. For the data sampling and the performance evaluation, static/transient analyses will be performed with MSC.Natran/Patran. The multi-objective optimization will be solved with NSGA-II to minimize the difference between the strains of the launch vehicle and RSS. Finally, the performance of the optimal RSS will be evaluated by checking its strain-tracking capability in different numerical simulations of the flexible rocket.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Improving the performance of surgery-based clinical pathways: a simulation-optimization approach.

PubMed

Ozcan, Yasar A; Tànfani, Elena; Testi, Angela

2017-03-01

This paper aims to improve the performance of clinical processes using clinical pathways (CPs). The specific goal of this research is to develop a decision support tool, based on a simulation-optimization approach, which identify the proper adjustment and alignment of resources to achieve better performance for both the patients and the health-care facility. When multiple perspectives are present in a decision problem, critical issues arise and often require the balancing of goals. In our approach, meeting patients' clinical needs in a timely manner, and to avoid worsening of clinical conditions, we assess the level of appropriate resources. The simulation-optimization model seeks and evaluates alternative resource configurations aimed at balancing the two main objectives-meeting patient needs and optimal utilization of beds and operating rooms.Using primary data collected at a Department of Surgery of a public hospital located in Genoa, Italy. The simulation-optimization modelling approach in this study has been applied to evaluate the thyroid surgical treatment together with the other surgery-based CPs. The low rate of bed utilization and the long elective waiting lists of the specialty under study indicates that the wards were oversized while the operating room capacity was the bottleneck of the system. The model enables hospital managers determine which objective has to be given priority, as well as the corresponding opportunity costs.

A Mindfulness-Acceptance-Commitment-Based Approach to Athletic Performance Enhancement: Theoretical Considerations

ERIC Educational Resources Information Center

Gardner, Frank L.; Moore, Zella E.

2004-01-01

While traditional cognitive-behavioral skills-training-based approaches to athletic performance enhancement posit that negative thoughts and emotions must be controlled, eliminated, or replaced for athlete-clients to perform optimally, recent evidence suggests that efforts to control, eliminate, or suppress these internal states may actually have…

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

Meta-control of combustion performance with a data mining approach

NASA Astrophysics Data System (ADS)

Song, Zhe

Large scale combustion process is complex and proposes challenges of optimizing its performance. Traditional approaches based on thermal dynamics have limitations on finding optimal operational regions due to time-shift nature of the process. Recent advances in information technology enable people collect large volumes of process data easily and continuously. The collected process data contains rich information about the process and, to some extent, represents a digital copy of the process over time. Although large volumes of data exist in industrial combustion processes, they are not fully utilized to the level where the process can be optimized. Data mining is an emerging science which finds patterns or models from large data sets. It has found many successful applications in business marketing, medical and manufacturing domains The focus of this dissertation is on applying data mining to industrial combustion processes, and ultimately optimizing the combustion performance. However the philosophy, methods and frameworks discussed in this research can also be applied to other industrial processes. Optimizing an industrial combustion process has two major challenges. One is the underlying process model changes over time and obtaining an accurate process model is nontrivial. The other is that a process model with high fidelity is usually highly nonlinear, solving the optimization problem needs efficient heuristics. This dissertation is set to solve these two major challenges. The major contribution of this 4-year research is the data-driven solution to optimize the combustion process, where process model or knowledge is identified based on the process data, then optimization is executed by evolutionary algorithms to search for optimal operating regions.

Morphing Wings: A Study Using High-Fidelity Aerodynamic Shape Optimization

NASA Astrophysics Data System (ADS)

Curiale, Nathanael J.

With the aviation industry under pressure to reduce fuel consumption, morphing wings have the capacity to improve aircraft performance, thereby making a significant contribution to reversing climate change. Through high-fidelity aerodynamic shape optimization, various forms of morphing wings are assessed for a hypothetical regional-class aircraft. The framework used solves the Reynolds-averaged Navier-Stokes equations and utilizes a gradient-based optimization algorithm. Baseline geometries are developed through multipoint optimization, where the average drag coefficient is minimized over a range of flight conditions with additional dive constraints. Morphing optimizations are then performed, beginning with these baseline shapes. Five distinct types of morphing are investigated and compared. Overall, a theoretical fully adaptable wing produces roughly a 2% improvement in average performance, whereas trailing-edge morphing with a 27-point multipoint baseline results in just over a 1% improvement in average performance. Trailing-edge morphing proves to be more beneficial than leading-edge morphing, upper-surface morphing, and a conventional flap.

Optimizing the updated Goddard shortwave radiation Weather Research and Forecasting (WRF) scheme for Intel Many Integrated Core (MIC) architecture

NASA Astrophysics Data System (ADS)

Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.

2015-05-01

Intel Many Integrated Core (MIC) ushers in a new era of supercomputing speed, performance, and compatibility. It allows the developers to run code at trillions of calculations per second using the familiar programming model. In this paper, we present our results of optimizing the updated Goddard shortwave radiation Weather Research and Forecasting (WRF) scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The co-processor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of Xeon Phi will require using some novel optimization techniques. Those optimization techniques are discusses in this paper. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.3x.

Optimal Deployment of Sensor Nodes Based on Performance Surface of Underwater Acoustic Communication

PubMed Central

Choi, Jee Woong

2017-01-01

The underwater acoustic sensor network (UWASN) is a system that exchanges data between numerous sensor nodes deployed in the sea. The UWASN uses an underwater acoustic communication technique to exchange data. Therefore, it is important to design a robust system that will function even in severely fluctuating underwater communication conditions, along with variations in the ocean environment. In this paper, a new algorithm to find the optimal deployment positions of underwater sensor nodes is proposed. The algorithm uses the communication performance surface, which is a map showing the underwater acoustic communication performance of a targeted area. A virtual force-particle swarm optimization algorithm is then used as an optimization technique to find the optimal deployment positions of the sensor nodes, using the performance surface information to estimate the communication radii of the sensor nodes in each generation. The algorithm is evaluated by comparing simulation results between two different seasons (summer and winter) for an area located off the eastern coast of Korea as the selected targeted area. PMID:29053569

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

NASA Astrophysics Data System (ADS)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.

Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

NASA Technical Reports Server (NTRS)

Leong, Harrison Monfook

1988-01-01

General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT

NASA Astrophysics Data System (ADS)

Shimomura, Taizo; Fujimori, Yuji; Kaito, Kiyoyuki; Obama, Kengo; Kobayashi, Kiyoshi

In this paper, an optimal management model is formulated for the performance-based rehabilitation/maintenance contract for airport concrete pavement, whereby two types of life cycle cost risks, i.e., ground consolidation risk and concrete depreciation risk, are explicitly considered. The non-homogenous Markov chain model is formulated to represent the deterioration processes of concrete pavement which are conditional upon the ground consolidation processes. The optimal non-homogenous Markov decision model with multiple types of risk is presented to design the optimal rehabilitation/maintenance plans. And the methodology to revise the optimal rehabilitation/maintenance plans based upon the monitoring data by the Bayesian up-to-dating rules. The validity of the methodology presented in this paper is examined based upon the case studies carried out for the H airport.

A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

NASA Astrophysics Data System (ADS)

Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

2009-05-01

A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

Taguchi optimization of bismuth-telluride based thermoelectric cooler

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

PubMed

Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

2015-01-01

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Slot Optimization Design of Induction Motor for Electric Vehicle

NASA Astrophysics Data System (ADS)

Shen, Yiming; Zhu, Changqing; Wang, Xiuhe

2018-01-01

Slot design of induction motor has a great influence on its performance. The RMxprt module based on magnetic circuit method can be used to analyze the influence of rotor slot type on motor characteristics and optimize slot parameters. In this paper, the authors take an induction motor of electric vehicle for a typical example. The first step of the design is to optimize the rotor slot by RMxprt, and then compare the main performance of the motor before and after the optimization through Ansoft Maxwell 2D. After that, the combination of optimum slot type and the optimum parameters are obtained. The results show that the power factor and the starting torque of the optimized motor have been improved significantly. Furthermore, the electric vehicle works at a better running status after the optimization.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

PubMed

Bondi, Robert W; Igne, Benoît; Drennen, James K; Anderson, Carl A

2012-12-01

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

Risk-Based Sampling: I Don't Want to Weight in Vain.

PubMed

Powell, Mark R

2015-12-01

Recently, there has been considerable interest in developing risk-based sampling for food safety and animal and plant health for efficient allocation of inspection and surveillance resources. The problem of risk-based sampling allocation presents a challenge similar to financial portfolio analysis. Markowitz (1952) laid the foundation for modern portfolio theory based on mean-variance optimization. However, a persistent challenge in implementing portfolio optimization is the problem of estimation error, leading to false "optimal" portfolios and unstable asset weights. In some cases, portfolio diversification based on simple heuristics (e.g., equal allocation) has better out-of-sample performance than complex portfolio optimization methods due to estimation uncertainty. Even for portfolios with a modest number of assets, the estimation window required for true optimization may imply an implausibly long stationary period. The implications for risk-based sampling are illustrated by a simple simulation model of lot inspection for a small, heterogeneous group of producers. © 2015 Society for Risk Analysis.

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

On sustainable and efficient design of ground-source heat pump systems

NASA Astrophysics Data System (ADS)

Grassi, W.; Conti, P.; Schito, E.; Testi, D.

2015-11-01

This paper is mainly aimed at stressing some fundamental features of the GSHP design and is based on a broad research we are performing at the University of Pisa. In particular, we focus the discussion on an environmentally sustainable approach, based on performance optimization during the entire operational life. The proposed methodology aims at investigating design and management strategies to find the optimal level of exploitation of the ground source and refer to other technical means to cover the remaining energy requirements and modulate the power peaks. The method is holistic, considering the system as a whole, rather than focusing only on some components, usually considered as the most important ones. Each subsystem is modeled and coupled to the others in a full set of equations, which is used within an optimization routine to reproduce the operative performances of the overall GSHP system. As a matter of fact, the recommended methodology is a 4-in-1 activity, including sizing of components, lifecycle performance evaluation, optimization process, and feasibility analysis. The paper reviews also some previous works concerning possible applications of the proposed methodology. In conclusion, we describe undergoing research activities and objectives of future works.

Estimation of the optimal number of radiotherapy fractions for breast cancer: A review of the evidence.

PubMed

Wong, Karen; Delaney, Geoff P; Barton, Michael B

2015-08-01

There is variation in radiotherapy fractionation practice, however, there is no evidence-based benchmark for appropriate activity. An evidence-based model was constructed to estimate the optimal number of fractions for the first course of radiotherapy for breast cancer to aid in services planning and performance benchmarking. The published breast cancer radiotherapy utilisation model was adapted. Evidence-based number of fractions was added to each radiotherapy indication. The overall optimal number of fractions was calculated based on the frequency of specific clinical conditions where radiotherapy is indicated and the recommended number of fractions for each condition. Sensitivity analysis was performed to assess the impact of uncertainties on the model. For the entire Australian breast cancer patient population, the estimated optimal number of fractions per patient was 16.8, 14.6, 13.7 and 0.8 for ductal carcinoma in situ, early, advanced and metastatic breast cancer respectively. Overall, the optimal number of fractions per patient was 14.4 (range 14.4-18.7). These results allow comparison with actual practices, and workload prediction to aid in services planning. The model can be easily adapted to other countries by inserting population-specific epidemiological data, and to future changes in cancer incidence, stage distribution and fractionation recommendations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Rate and timing cues associated with the cochlear amplifier: level discrimination based on monaural cross-frequency coincidence detection.

PubMed

Heinz, M G; Colburn, H S; Carney, L H

2001-10-01

The perceptual significance of the cochlear amplifier was evaluated by predicting level-discrimination performance based on stochastic auditory-nerve (AN) activity. Performance was calculated for three models of processing: the optimal all-information processor (based on discharge times), the optimal rate-place processor (based on discharge counts), and a monaural coincidence-based processor that uses a non-optimal combination of rate and temporal information. An analytical AN model included compressive magnitude and level-dependent-phase responses associated with the cochlear amplifier, and high-, medium-, and low-spontaneous-rate (SR) fibers with characteristic frequencies (CFs) spanning the AN population. The relative contributions of nonlinear magnitude and nonlinear phase responses to level encoding were compared by using four versions of the model, which included and excluded the nonlinear gain and phase responses in all possible combinations. Nonlinear basilar-membrane (BM) phase responses are robustly encoded in near-CF AN fibers at low frequencies. Strongly compressive BM responses at high frequencies near CF interact with the high thresholds of low-SR AN fibers to produce large dynamic ranges. Coincidence performance based on a narrow range of AN CFs was robust across a wide dynamic range at both low and high frequencies, and matched human performance levels. Coincidence performance based on all CFs demonstrated the "near-miss" to Weber's law at low frequencies and the high-frequency "mid-level bump." Monaural coincidence detection is a physiologically realistic mechanism that is extremely general in that it can utilize AN information (average-rate, synchrony, and nonlinear-phase cues) from all SR groups.

Simulation-based robust optimization for signal timing and setting.

DOT National Transportation Integrated Search

2009-12-30

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

Information fusion based techniques for HEVC

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Performance optimization of dense-array concentrator photovoltaic system considering effects of circumsolar radiation and slope error.

PubMed

Wong, Chee-Woon; Chong, Kok-Keong; Tan, Ming-Hui

2015-07-27

This paper presents an approach to optimize the electrical performance of dense-array concentrator photovoltaic system comprised of non-imaging dish concentrator by considering the circumsolar radiation and slope error effects. Based on the simulated flux distribution, a systematic methodology to optimize the layout configuration of solar cells interconnection circuit in dense array concentrator photovoltaic module has been proposed by minimizing the current mismatch caused by non-uniformity of concentrated sunlight. An optimized layout of interconnection solar cells circuit with minimum electrical power loss of 6.5% can be achieved by minimizing the effects of both circumsolar radiation and slope error.

Model-Based Design of Tree WSNs for Decentralized Detection †

PubMed Central

Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

2015-01-01

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches. PMID:26307989

Optimal nonlinear information processing capacity in delay-based reservoir computers

NASA Astrophysics Data System (ADS)

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-09-01

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Optimal nonlinear information processing capacity in delay-based reservoir computers.

PubMed

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-09-11

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Systematic Sensor Selection Strategy (S4) User Guide

NASA Technical Reports Server (NTRS)

Sowers, T. Shane

2012-01-01

This paper describes a User Guide for the Systematic Sensor Selection Strategy (S4). S4 was developed to optimally select a sensor suite from a larger pool of candidate sensors based on their performance in a diagnostic system. For aerospace systems, selecting the proper sensors is important for ensuring adequate measurement coverage to satisfy operational, maintenance, performance, and system diagnostic criteria. S4 optimizes the selection of sensors based on the system fault diagnostic approach while taking conflicting objectives such as cost, weight and reliability into consideration. S4 can be described as a general architecture structured to accommodate application-specific components and requirements. It performs combinational optimization with a user defined merit or cost function to identify optimum or near-optimum sensor suite solutions. The S4 User Guide describes the sensor selection procedure and presents an example problem using an open source turbofan engine simulation to demonstrate its application.

Optimal nonlinear information processing capacity in delay-based reservoir computers

PubMed Central

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-01-01

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature. PMID:26358528

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

PubMed

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Hybrid PSO-ASVR-based method for data fitting in the calibration of infrared radiometer

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

Yang, Sen; Li, Chengwei, E-mail: heikuanghit@163.com

2016-06-15

The present paper describes a hybrid particle swarm optimization-adaptive support vector regression (PSO-ASVR)-based method for data fitting in the calibration of infrared radiometer. The proposed hybrid PSO-ASVR-based method is based on PSO in combination with Adaptive Processing and Support Vector Regression (SVR). The optimization technique involves setting parameters in the ASVR fitting procedure, which significantly improves the fitting accuracy. However, its use in the calibration of infrared radiometer has not yet been widely explored. Bearing this in mind, the PSO-ASVR-based method, which is based on the statistical learning theory, is successfully used here to get the relationship between the radiationmore » of a standard source and the response of an infrared radiometer. Main advantages of this method are the flexible adjustment mechanism in data processing and the optimization mechanism in a kernel parameter setting of SVR. Numerical examples and applications to the calibration of infrared radiometer are performed to verify the performance of PSO-ASVR-based method compared to conventional data fitting methods.« less

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

PubMed Central

Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution. PMID:29186166

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment.

PubMed

Li, Jianjun; Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

An Integrated Optimization Design Method Based on Surrogate Modeling Applied to Diverging Duct Design

NASA Astrophysics Data System (ADS)

Hanan, Lu; Qiushi, Li; Shaobin, Li

2016-12-01

This paper presents an integrated optimization design method in which uniform design, response surface methodology and genetic algorithm are used in combination. In detail, uniform design is used to select the experimental sampling points in the experimental domain and the system performance is evaluated by means of computational fluid dynamics to construct a database. After that, response surface methodology is employed to generate a surrogate mathematical model relating the optimization objective and the design variables. Subsequently, genetic algorithm is adopted and applied to the surrogate model to acquire the optimal solution in the case of satisfying some constraints. The method has been applied to the optimization design of an axisymmetric diverging duct, dealing with three design variables including one qualitative variable and two quantitative variables. The method of modeling and optimization design performs well in improving the duct aerodynamic performance and can be also applied to wider fields of mechanical design and seen as a useful tool for engineering designers, by reducing the design time and computation consumption.

Support vector machine firefly algorithm based optimization of lens system.

PubMed

Shamshirband, Shahaboddin; Petković, Dalibor; Pavlović, Nenad T; Ch, Sudheer; Altameem, Torki A; Gani, Abdullah

2015-01-01

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, nonlinear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme support vector machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Wu, Baodong; Li, Shigang; Zhang, Yunquan; Nie, Ningming

2017-02-01

The parallel Kinetic Monte Carlo (KMC) algorithm based on domain decomposition has been widely used in large-scale physical simulations. However, the communication overhead of the parallel KMC algorithm is critical, and severely degrades the overall performance and scalability. In this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel KMC simulations. We first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. Then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. Finally, we optimize the communication scheduling using the neighborhood collective operations. We demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. Results show that the optimized KMC algorithm exhibits better performance and scalability than the well-known open-source library-SPPARKS. On 32-node Xeon E5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with SPPARKS.

Proposed evaluation framework for assessing operator performance with multisensor displays

NASA Technical Reports Server (NTRS)

Foyle, David C.

1992-01-01

Despite aggressive work on the development of sensor fusion algorithms and techniques, no formal evaluation procedures have been proposed. Based on existing integration models in the literature, an evaluation framework is developed to assess an operator's ability to use multisensor, or sensor fusion, displays. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The operator's performance with the sensor fusion display can be compared to the models' predictions based on the operator's performance when viewing the original sensor displays prior to fusion. This allows for the determination as to when a sensor fusion system leads to: 1) poorer performance than one of the original sensor displays (clearly an undesirable system in which the fused sensor system causes some distortion or interference); 2) better performance than with either single sensor system alone, but at a sub-optimal (compared to the model predictions) level; 3) optimal performance (compared to model predictions); or, 4) super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays. An experiment demonstrating the usefulness of the proposed evaluation framework is discussed.

Optimization and performance improvement of an electromagnetic-type energy harvester with consideration of human walking vibration

NASA Astrophysics Data System (ADS)

Seo, Jongho; Kim, Jin-Su; Jeong, Un-Chang; Kim, Yong-Dae; Kim, Young-Cheol; Lee, Hanmin; Oh, Jae-Eung

2016-02-01

In this study, we derived an equation of motion for an electromechanical system in view of the components and working mechanism of an electromagnetic-type energy harvester (ETEH). An electromechanical transduction factor (ETF) was calculated using a finite-element analysis (FEA) based on Maxwell's theory. The experimental ETF of the ETEH measured by means of sine wave excitation was compared with and FEA data. Design parameters for the stationary part of the energy harvester were optimized in terms of the power performance by using a response surface method (RSM). With optimized design parameters, the ETEH showed an improvement in performance. We experimented with the optimized ETEH (OETEH) with respect to changes in the external excitation frequency and the load resistance by taking human body vibration in to account. The OETEH achieved a performance improvement of about 30% compared to the initial model.

Evolutionary optimization methods for accelerator design

NASA Astrophysics Data System (ADS)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained optimization test problems for EA with a variety of different configurations and suggest optimal default parameter values based on the results. Then we study the performance of the REPA method on the same set of test problems and compare the obtained results with those of several commonly used constrained optimization methods with EA. Based on the obtained results, particularly on the outstanding performance of REPA on test problem that presents significant difficulty for other reviewed EAs, we conclude that the proposed method is useful and competitive. We discuss REPA parameter tuning for difficult problems and critically review some of the problems from the de-facto standard test problem set for the constrained optimization with EA. In order to demonstrate the practical usefulness of the developed method, we study several problems of accelerator design and demonstrate how they can be solved with EAs. These problems include a simple accelerator design problem (design a quadrupole triplet to be stigmatically imaging, find all possible solutions), a complex real-life accelerator design problem (an optimization of the front end section for the future neutrino factory), and a problem of the normal form defect function optimization which is used to rigorously estimate the stability of the beam dynamics in circular accelerators. The positive results we obtained suggest that the application of EAs to problems from accelerator theory can be very beneficial and has large potential. The developed optimization scenarios and tools can be used to approach similar problems.

Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

PubMed Central

Green, Paul

2015-01-01

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes. PMID:25874265

Conceptual comparison of population based metaheuristics for engineering problems.

PubMed

Adekanmbi, Oluwole; Green, Paul

2015-01-01

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.

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

Gang, G; Siewerdsen, J; Stayman, J

Purpose: There has been increasing interest in integrating fluence field modulation (FFM) devices with diagnostic CT scanners for dose reduction purposes. Conventional FFM strategies, however, are often either based on heuristics or the analysis of filtered-backprojection (FBP) performance. This work investigates a prospective task-driven optimization of FFM for model-based iterative reconstruction (MBIR) in order to improve imaging performance at the same total dose as conventional strategies. Methods: The task-driven optimization framework utilizes an ultra-low dose 3D scout as a patient-specific anatomical model and a mathematical formation of the imaging task. The MBIR method investigated is quadratically penalized-likelihood reconstruction. The FFMmore » objective function uses detectability index, d’, computed as a function of the predicted spatial resolution and noise in the image. To optimize performance throughout the object, a maxi-min objective was adopted where the minimum d’ over multiple locations is maximized. To reduce the dimensionality of the problem, FFM is parameterized as a linear combination of 2D Gaussian basis functions over horizontal detector pixels and projection angles. The coefficients of these bases are found using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. The task-driven design was compared with three other strategies proposed for FBP reconstruction for a calcification cluster discrimination task in an abdomen phantom. Results: The task-driven optimization yielded FFM that was significantly different from those designed for FBP. Comparing all four strategies, the task-based design achieved the highest minimum d’ with an 8–48% improvement, consistent with the maxi-min objective. In addition, d’ was improved to a greater extent over a larger area within the entire phantom. Conclusion: Results from this investigation suggests the need to re-evaluate conventional FFM strategies for MBIR. The task-based optimization framework provides a promising approach that maximizes imaging performance under the same total dose constraint.« less

Probabilistic Finite Element Analysis & Design Optimization for Structural Designs

NASA Astrophysics Data System (ADS)

Deivanayagam, Arumugam

This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that are performed to obtain probability of failure and reliability of structures. Next, decoupled RBDO procedure is proposed with a new reliability analysis formulation with sensitivity analysis, which is performed to remove the highly reliable constraints in the RBDO, thereby reducing the computational time and function evaluations. Followed by implementation of the reliability analysis concepts and RBDO in finite element 2D truss problems and a planar beam problem are presented and discussed.

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.

Phase-Division-Based Dynamic Optimization of Linkages for Drawing Servo Presses

NASA Astrophysics Data System (ADS)

Zhang, Zhi-Gang; Wang, Li-Ping; Cao, Yan-Ke

2017-11-01

Existing linkage-optimization methods are designed for mechanical presses; few can be directly used for servo presses, so development of the servo press is limited. Based on the complementarity of linkage optimization and motion planning, a phase-division-based linkage-optimization model for a drawing servo press is established. Considering the motion-planning principles of a drawing servo press, and taking account of work rating and efficiency, the constraints of the optimization model are constructed. Linkage is optimized in two modes: use of either constant eccentric speed or constant slide speed in the work segments. The performances of optimized linkages are compared with those of a mature linkage SL4-2000A, which is optimized by a traditional method. The results show that the work rating of a drawing servo press equipped with linkages optimized by this new method improved and the root-mean-square torque of the servo motors is reduced by more than 10%. This research provides a promising method for designing energy-saving drawing servo presses with high work ratings.

Genetic algorithm based task reordering to improve the performance of batch scheduled massively parallel scientific applications

DOE PAGES

Sankaran, Ramanan; Angel, Jordan; Brown, W. Michael

2015-04-08

The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on themore » performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.« less

Optimal control of a hybrid rhythmic-discrete task: the bouncing ball revisited.

PubMed

Ronsse, Renaud; Wei, Kunlin; Sternad, Dagmar

2010-05-01

Rhythmically bouncing a ball with a racket is a hybrid task that combines continuous rhythmic actuation of the racket with the control of discrete impact events between racket and ball. This study presents experimental data and a two-layered modeling framework that explicitly addresses the hybrid nature of control: a first discrete layer calculates the state to reach at impact and the second continuous layer smoothly drives the racket to this desired state, based on optimality principles. The testbed for this hybrid model is task performance at a range of increasingly slower tempos. When slowing the rhythm of the bouncing actions, the continuous cycles become separated into a sequence of discrete movements interspersed by dwell times and directed to achieve the desired impact. Analyses of human performance show increasing variability of performance measures with slower tempi, associated with a change in racket trajectories from approximately sinusoidal to less symmetrical velocity profiles. Matching results of model simulations give support to a hybrid control model based on optimality, and therefore suggest that optimality principles are applicable to the sensorimotor control of complex movements such as ball bouncing.

Optimal design of a main driving mechanism for servo punch press based on performance atlases

NASA Astrophysics Data System (ADS)

Zhou, Yanhua; Xie, Fugui; Liu, Xinjun

2013-09-01

The servomotor drive turret punch press is attracting more attentions and being developed more intensively due to the advantages of high speed, high accuracy, high flexibility, high productivity, low noise, cleaning and energy saving. To effectively improve the performance and lower the cost, it is necessary to develop new mechanisms and establish corresponding optimal design method with uniform performance indices. A new patented main driving mechanism and a new optimal design method are proposed. In the optimal design, the performance indices, i.e., the local motion/force transmission indices ITI, OTI, good transmission workspace good transmission workspace(GTW) and the global transmission indices GTIs are defined. The non-dimensional normalization method is used to get all feasible solutions in dimensional synthesis. Thereafter, the performance atlases, which can present all possible design solutions, are depicted. As a result, the feasible solution of the mechanism with good motion/force transmission performance is obtained. And the solution can be flexibly adjusted by designer according to the practical design requirements. The proposed mechanism is original, and the presented design method provides a feasible solution to the optimal design of the main driving mechanism for servo punch press.

Optimization of intra-voxel incoherent motion imaging at 3.0 Tesla for fast liver examination.

PubMed

Leporq, Benjamin; Saint-Jalmes, Hervé; Rabrait, Cecile; Pilleul, Frank; Guillaud, Olivier; Dumortier, Jérôme; Scoazec, Jean-Yves; Beuf, Olivier

2015-05-01

Optimization of multi b-values MR protocol for fast intra-voxel incoherent motion imaging of the liver at 3.0 Tesla. A comparison of four different acquisition protocols were carried out based on estimated IVIM (DSlow , DFast , and f) and ADC-parameters in 25 healthy volunteers. The effects of respiratory gating compared with free breathing acquisition then diffusion gradient scheme (simultaneous or sequential) and finally use of weighted averaging for different b-values were assessed. An optimization study based on Cramer-Rao lower bound theory was then performed to minimize the number of b-values required for a suitable quantification. The duration-optimized protocol was evaluated on 12 patients with chronic liver diseases No significant differences of IVIM parameters were observed between the assessed protocols. Only four b-values (0, 12, 82, and 1310 s.mm(-2) ) were found mandatory to perform a suitable quantification of IVIM parameters. DSlow and DFast significantly decreased between nonadvanced and advanced fibrosis (P < 0.05 and P < 0.01) whereas perfusion fraction and ADC variations were not found to be significant. Results showed that IVIM could be performed in free breathing, with a weighted-averaging procedure, a simultaneous diffusion gradient scheme and only four optimized b-values (0, 10, 80, and 800) reducing scan duration by a factor of nine compared with a nonoptimized protocol. Preliminary results have shown that parameters such as DSlow and DFast based on optimized IVIM protocol can be relevant biomarkers to distinguish between nonadvanced and advanced fibrosis. © 2014 Wiley Periodicals, Inc.

Optimization of mechanical performance of oxidative nano-particle electrode nitrile butadiene rubber conducting polymer actuator.

PubMed

Kim, Baek-Chul; Park, S J; Cho, M S; Lee, Y; Nam, J D; Choi, H R; Koo, J C

2009-12-01

Present work delivers a systematical evaluation of actuation efficiency of a nano-particle electrode conducting polymer actuator fabricated based on Nitrile Butadiene Rubber (NBR). Attempts are made for maximizing mechanical functionality of the nano-particle electrode conducting polymer actuator that can be driven in the air. As the conducting polymer polypyrrole of the actuator is to be fabricated through a chemical oxidation polymerization process that may impose certain limitations on both electrical and mechanical functionality of the actuator, a coordinated study for optimization process of the actuator is necessary for maximizing its performance. In this article actuation behaviors of the nano-particle electrode polypyrrole conducting polymer is studied and an optimization process for the mechanical performance maximization is performed.

IMPROVED ALGORITHMS FOR RADAR-BASED RECONSTRUCTION OF ASTEROID SHAPES

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

Greenberg, Adam H.; Margot, Jean-Luc

We describe our implementation of a global-parameter optimizer and Square Root Information Filter into the asteroid-modeling software shape. We compare the performance of our new optimizer with that of the existing sequential optimizer when operating on various forms of simulated data and actual asteroid radar data. In all cases, the new implementation performs substantially better than its predecessor: it converges faster, produces shape models that are more accurate, and solves for spin axis orientations more reliably. We discuss potential future changes to improve shape's fitting speed and accuracy.

Variable fidelity robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

NASA Astrophysics Data System (ADS)

Hou, Liqiang; Cai, Yuanli; Liu, Jin; Hou, Chongyuan

2016-04-01

A variable fidelity robust optimization method for pulsed laser orbital debris removal (LODR) under uncertainty is proposed. Dempster-shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used in the robust optimization. The robust optimization method optimizes the performance while at the same time maximizing its belief value. A population based multi-objective optimization (MOO) algorithm based on a steepest descent like strategy with proper orthogonal decomposition (POD) is used to search robust Pareto solutions. Analytical and numerical lifetime predictors are used to evaluate the debris lifetime after the laser pulses. Trust region based fidelity management is designed to reduce the computational cost caused by the expensive model. When the solutions fall into the trust region, the analytical model is used to reduce the computational cost. The proposed robust optimization method is first tested on a set of standard problems and then applied to the removal of Iridium 33 with pulsed lasers. It will be shown that the proposed approach can identify the most robust solutions with minimum lifetime under uncertainty.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

Simultaneous optimization of micro-heliostat geometry and field layout using a genetic algorithm

NASA Astrophysics Data System (ADS)

Lazardjani, Mani Yousefpour; Kronhardt, Valentina; Dikta, Gerhard; Göttsche, Joachim

2016-05-01

A new optimization tool for micro-heliostat (MH) geometry and field layout is presented. The method intends simultaneous performance improvement and cost reduction through iteration of heliostat geometry and field layout parameters. This tool was developed primarily for the optimization of a novel micro-heliostat concept, which was developed at Solar-Institut Jülich (SIJ). However, the underlying approach for the optimization can be used for any heliostat type. During the optimization the performance is calculated using the ray-tracing tool SolCal. The costs of the heliostats are calculated by use of a detailed cost function. A genetic algorithm is used to change heliostat geometry and field layout in an iterative process. Starting from an initial setup, the optimization tool generates several configurations of heliostat geometries and field layouts. For each configuration a cost-performance ratio is calculated. Based on that, the best geometry and field layout can be selected in each optimization step. In order to find the best configuration, this step is repeated until no significant improvement in the results is observed.

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

PubMed

Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

2016-01-01

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

PubMed Central

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

2016-01-01

Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc. PMID:27662651

Body-Cooling Paradigm in Sport: Maximizing Safety and Performance During Competition.

PubMed

Adams, William M; Hosokawa, Yuri; Casa, Douglas J

2016-12-01

Although body cooling has both performance and safety benefits, knowledge on optimizing cooling during specific sport competition is limited. To identify when, during sport competition, it is optimal for body cooling and to identify optimal body-cooling modalities to enhance safety and maximize sport performance. A comprehensive literature search was conducted to identify articles with specific context regarding body cooling, sport performance, and cooling modalities used during sport competition. A search of scientific peer-reviewed literature examining the effects of body cooling on exercise performance was done to examine the influence of body cooling on exercise performance. Subsequently, a literature search was done to identify effective cooling modalities that have been shown to improve exercise performance. The cooling modalities that are most effective in cooling the body during sport competition depend on the sport, timing of cooling, and feasibility based on the constraints of the sports rules and regulations. Factoring in the length of breaks (halftime substitutions, etc), the equipment worn during competition, and the cooling modalities that offer the greatest potential to cool must be considered in each individual sport. Scientific evidence supports using body cooling as a method of improving performance during sport competition. Developing a strategy to use cooling modalities that are scientifically evidence-based to improve performance while maximizing athlete's safety warrants further investigation.

Helium: lifting high-performance stencil kernels from stripped x86 binaries to halide DSL code

DOE PAGES

Mendis, Charith; Bosboom, Jeffrey; Wu, Kevin; ...

2015-06-03

Highly optimized programs are prone to bit rot, where performance quickly becomes suboptimal in the face of new hardware and compiler techniques. In this paper we show how to automatically lift performance-critical stencil kernels from a stripped x86 binary and generate the corresponding code in the high-level domain-specific language Halide. Using Halide's state-of-the-art optimizations targeting current hardware, we show that new optimized versions of these kernels can replace the originals to rejuvenate the application for newer hardware. The original optimized code for kernels in stripped binaries is nearly impossible to analyze statically. Instead, we rely on dynamic traces to regeneratemore » the kernels. We perform buffer structure reconstruction to identify input, intermediate and output buffer shapes. Here, we abstract from a forest of concrete dependency trees which contain absolute memory addresses to symbolic trees suitable for high-level code generation. This is done by canonicalizing trees, clustering them based on structure, inferring higher-dimensional buffer accesses and finally by solving a set of linear equations based on buffer accesses to lift them up to simple, high-level expressions. Helium can handle highly optimized, complex stencil kernels with input-dependent conditionals. We lift seven kernels from Adobe Photoshop giving a 75 % performance improvement, four kernels from Irfan View, leading to 4.97 x performance, and one stencil from the mini GMG multigrid benchmark netting a 4.25 x improvement in performance. We manually rejuvenated Photoshop by replacing eleven of Photoshop's filters with our lifted implementations, giving 1.12 x speedup without affecting the user experience.« less

Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights

NASA Astrophysics Data System (ADS)

Aydogdu, Ibrahim

2017-03-01

In this article, a new version of a biogeography-based optimization algorithm with Levy flight distribution (LFBBO) is introduced and used for the optimum design of reinforced concrete cantilever retaining walls under seismic loading. The cost of the wall is taken as an objective function, which is minimized under the constraints implemented by the American Concrete Institute (ACI 318-05) design code and geometric limitations. The influence of peak ground acceleration (PGA) on optimal cost is also investigated. The solution of the problem is attained by the LFBBO algorithm, which is developed by adding Levy flight distribution to the mutation part of the biogeography-based optimization (BBO) algorithm. Five design examples, of which two are used in literature studies, are optimized in the study. The results are compared to test the performance of the LFBBO and BBO algorithms, to determine the influence of the seismic load and PGA on the optimal cost of the wall.

Concurrently adjusting interrelated control parameters to achieve optimal engine performance

DOEpatents

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

2015-12-01

Methods and systems for real-time engine control optimization are provided. A value of an engine performance variable is determined, a value of a first operating condition and a value of a second operating condition of a vehicle engine are detected, and initial values for a first engine control parameter and a second engine control parameter are determined based on the detected first operating condition and the detected second operating condition. The initial values for the first engine control parameter and the second engine control parameter are adjusted based on the determined value of the engine performance variable to cause the engine performance variable to approach a target engine performance variable. In order to cause the engine performance variable to approach the target engine performance variable, adjusting the initial value for the first engine control parameter necessitates a corresponding adjustment of the initial value for the second engine control parameter.

Meeting the challenges of developing LED-based projection displays

NASA Astrophysics Data System (ADS)

Geißler, Enrico

2006-04-01

The main challenge in developing a LED-based projection system is to meet the brightness requirements of the market. Therefore a balanced combination of optical, electrical and thermal parameters must be reached to achieve these performance and cost targets. This paper describes the system design methodology for a digital micromirror display (DMD) based optical engine using LEDs as the light source, starting at the basic physical and geometrical parameters of the DMD and other optical elements through characterization of the LEDs to optimizing the system performance by determining optimal driving conditions. LEDs have a luminous flux density which is just at the threshold of acceptance in projection systems and thus only a fully optimized optical system with a matched set of LEDs can be used. This work resulted in two projection engines, one for a compact pocket projector and the other for a rear projection television, both of which are currently in commercialization.

Patterning optimization for 55nm design rule DRAM/flash memory using production-ready customized illuminations

NASA Astrophysics Data System (ADS)

Chen, Ting; Van Den Broeke, Doug; Hsu, Stephen; Hsu, Michael; Park, Sangbong; Berger, Gabriel; Coskun, Tamer; de Vocht, Joep; Chen, Fung; Socha, Robert; Park, JungChul; Gronlund, Keith

2005-11-01

Illumination optimization, often combined with optical proximity corrections (OPC) to the mask, is becoming one of the critical components for a production-worthy lithography process for 55nm-node DRAM/Flash memory devices and beyond. At low-k1, e.g. k1<0.31, both resolution and imaging contrast can be severely limited by the current imaging tools while using the standard illumination sources. Illumination optimization is a process where the source shape is varied, in both profile and intensity distribution, to achieve enhancement in the final image contrast as compared to using the non-optimized sources. The optimization can be done efficiently for repetitive patterns such as DRAM/Flash memory cores. However, illumination optimization often produces source shapes that are "free-form" like and they can be too complex to be directly applicable for production and lack the necessary radial and annular symmetries desirable for the diffractive optical element (DOE) based illumination systems in today's leading lithography tools. As a result, post-optimization rendering and verification of the optimized source shape are often necessary to meet the production-ready or manufacturability requirements and ensure optimal performance gains. In this work, we describe our approach to the illumination optimization for k1<0.31 DRAM/Flash memory patterns, using an ASML XT:1400i at NA 0.93, where the all necessary manufacturability requirements are fully accounted for during the optimization. The imaging contrast in the resist is optimized in a reduced solution space constrained by the manufacturability requirements, which include minimum distance between poles, minimum opening pole angles, minimum ring width and minimum source filling factor in the sigma space. For additional performance gains, the intensity within the optimized source can vary in a gray-tone fashion (eight shades used in this work). Although this new optimization approach can sometimes produce closely spaced solutions as gauged by the NILS based metrics, we show that the optimal and production-ready source shape solution can be easily determined by comparing the best solutions to the "free-form" solution and more importantly, by their respective imaging fidelity and process latitude ranking. Imaging fidelity and process latitude simulations are performed to analyze the impact and sensitivity of the manufacturability requirements on pattern specific illumination optimizations using ASML XT:1400i and other latest imaging systems. Mask model based OPC (MOPC) is applied and optimized sequentially to ensure that the CD uniformity requirements are met.

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm

NASA Astrophysics Data System (ADS)

Burger, Christoph

A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility effects are taken into account with the implementation of Prandtl-Glauert domain stretching. Viscous effects are considered with a simple Reynolds number based model to account for the effects of viscosity in the spanwise direction. An empirical flow separation model developed from experimental lift and drag coefficient data of a NACA 0012 airfoil is included. The propeller geometry is generated using a recently introduced Class/Shape function methodology to allow for efficient use of a wide design space. Optimizing the angle of attack, the chord, the sweep and the local airfoil sections, produced blades with favorable tradeoffs between single and multiple point optimizations of propeller performance and acoustic noise signatures. Optimizations using a binary encoded IMPROVE(c) Genetic Algorithm (GA) and a real encoded GA were obtained after optimization runs with some premature convergence. The newly developed real encoded GA was used to obtain the majority of the results which produced generally better convergence characteristics when compared to the binary encoded GA. The optimization trade-offs show that single point optimized propellers have favorable performance, but circulation distributions were less smooth when compared to dual point or multiobjective optimizations. Some of the single point optimizations generated propellers with proplets which show a loading shift to the blade tip region. When noise is included into the objective functions some propellers indicate a circulation shift to the inboard sections of the propeller as well as a reduction in propeller diameter. In addition the propeller number was increased in some optimizations to reduce the acoustic blade signature.

A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

PubMed

Yang, Shaofu; Liu, Qingshan; Wang, Jun

2018-04-01

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

Analysis and optimization of solid oxide fuel cell-based auxiliary power units using a generic zero-dimensional fuel cell model

NASA Astrophysics Data System (ADS)

Göll, S.; Samsun, R. C.; Peters, R.

Fuel-cell-based auxiliary power units can help to reduce fuel consumption and emissions in transportation. For this application, the combination of solid oxide fuel cells (SOFCs) with upstream fuel processing by autothermal reforming (ATR) is seen as a highly favorable configuration. Notwithstanding the necessity to improve each single component, an optimized architecture of the fuel cell system as a whole must be achieved. To enable model-based analyses, a system-level approach is proposed in which the fuel cell system is modeled as a multi-stage thermo-chemical process using the "flowsheeting" environment PRO/II™. Therein, the SOFC stack and the ATR are characterized entirely by corresponding thermodynamic processes together with global performance parameters. The developed model is then used to achieve an optimal system layout by comparing different system architectures. A system with anode and cathode off-gas recycling was identified to have the highest electric system efficiency. Taking this system as a basis, the potential for further performance enhancement was evaluated by varying four parameters characterizing different system components. Using methods from the design and analysis of experiments, the effects of these parameters and of their interactions were quantified, leading to an overall optimized system with encouraging performance data.

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

NASA Technical Reports Server (NTRS)

Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)

2000-01-01

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for future research are identified.

Soft-Decision Decoding of Binary Linear Block Codes Based on an Iterative Search Algorithm

NASA Technical Reports Server (NTRS)

Lin, Shu; Kasami, Tadao; Moorthy, H. T.

1997-01-01

This correspondence presents a suboptimum soft-decision decoding scheme for binary linear block codes based on an iterative search algorithm. The scheme uses an algebraic decoder to iteratively generate a sequence of candidate codewords one at a time using a set of test error patterns that are constructed based on the reliability information of the received symbols. When a candidate codeword is generated, it is tested based on an optimality condition. If it satisfies the optimality condition, then it is the most likely (ML) codeword and the decoding stops. If it fails the optimality test, a search for the ML codeword is conducted in a region which contains the ML codeword. The search region is determined by the current candidate codeword and the reliability of the received symbols. The search is conducted through a purged trellis diagram for the given code using the Viterbi algorithm. If the search fails to find the ML codeword, a new candidate is generated using a new test error pattern, and the optimality test and search are renewed. The process of testing and search continues until either the MEL codeword is found or all the test error patterns are exhausted and the decoding process is terminated. Numerical results show that the proposed decoding scheme achieves either practically optimal performance or a performance only a fraction of a decibel away from the optimal maximum-likelihood decoding with a significant reduction in decoding complexity compared with the Viterbi decoding based on the full trellis diagram of the codes.

Optimal digital dynamical decoupling for general decoherence via Walsh modulation

NASA Astrophysics Data System (ADS)

Qi, Haoyu; Dowling, Jonathan P.; Viola, Lorenza

2017-11-01

We provide a general framework for constructing digital dynamical decoupling sequences based on Walsh modulation—applicable to arbitrary qubit decoherence scenarios. By establishing equivalence between decoupling design based on Walsh functions and on concatenated projections, we identify a family of optimal Walsh sequences, which can be exponentially more efficient, in terms of the required total pulse number, for fixed cancellation order, than known digital sequences based on concatenated design. Optimal sequences for a given cancellation order are highly non-unique—their performance depending sensitively on the control path. We provide an analytic upper bound to the achievable decoupling error and show how sequences within the optimal Walsh family can substantially outperform concatenated decoupling in principle, while respecting realistic timing constraints.

Experimental test of an online ion-optics optimizer

NASA Astrophysics Data System (ADS)

Amthor, A. M.; Schillaci, Z. M.; Morrissey, D. J.; Portillo, M.; Schwarz, S.; Steiner, M.; Sumithrarachchi, Ch.

2018-07-01

A technique has been developed and tested to automatically adjust multiple electrostatic or magnetic multipoles on an ion optical beam line - according to a defined optimization algorithm - until an optimal tune is found. This approach simplifies the process of determining high-performance optical tunes, satisfying a given set of optical properties, for an ion optical system. The optimization approach is based on the particle swarm method and is entirely model independent, thus the success of the optimization does not depend on the accuracy of an extant ion optical model of the system to be optimized. Initial test runs of a first order optimization of a low-energy (<60 keV) all-electrostatic beamline at the NSCL show reliable convergence of nine quadrupole degrees of freedom to well-performing tunes within a reasonable number of trial solutions, roughly 500, with full beam optimization run times of roughly two hours. Improved tunes were found both for quasi-local optimizations and for quasi-global optimizations, indicating a good ability of the optimizer to find a solution with or without a well defined set of initial multipole settings.

Model-Based Thermal System Design Optimization for the James Webb Space Telescope

NASA Technical Reports Server (NTRS)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-01-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Model-based thermal system design optimization for the James Webb Space Telescope

NASA Astrophysics Data System (ADS)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-10-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

A Visual Basic simulation software tool for performance analysis of a membrane-based advanced water treatment plant.

PubMed

Pal, P; Kumar, R; Srivastava, N; Chaudhuri, J

2014-02-01

A Visual Basic simulation software (WATTPPA) has been developed to analyse the performance of an advanced wastewater treatment plant. This user-friendly and menu-driven software is based on the dynamic mathematical model for an industrial wastewater treatment scheme that integrates chemical, biological and membrane-based unit operations. The software-predicted results corroborate very well with the experimental findings as indicated in the overall correlation coefficient of the order of 0.99. The software permits pre-analysis and manipulation of input data, helps in optimization and exhibits performance of an integrated plant visually on a graphical platform. It allows quick performance analysis of the whole system as well as the individual units. The software first of its kind in its domain and in the well-known Microsoft Excel environment is likely to be very useful in successful design, optimization and operation of an advanced hybrid treatment plant for hazardous wastewater.

Current Perspectives on Profiling and Enhancing Wheelchair Court Sport Performance.

PubMed

Paulson, Thomas; Goosey-Tolfrey, Victoria

2017-03-01

Despite the growing interest in Paralympic sport, the evidence base for supporting elite wheelchair sport performance remains in its infancy when compared with able-bodied (AB) sport. Subsequently, current practice is often based on theory adapted from AB guidelines, with a heavy reliance on anecdotal evidence and practitioner experience. Many principles in training prescription and performance monitoring with wheelchair athletes are directly transferable from AB practice, including the periodization and tapering of athlete loads around competition, yet considerations for the physiological consequences of an athlete's impairment and the interface between athlete and equipment are vital when targeting interventions to optimize in-competition performance. Researchers and practitioners are faced with the challenge of identifying and implementing reliable protocols that detect small but meaningful changes in impairment-specific physical capacities and on-court performance. Technologies to profile both linear and rotational on-court performance are an essential component of sport-science support to understand sport-specific movement profiles and prescribe training intensities. In addition, an individualized approach to the prescription of athlete training and optimization of the "wheelchair-user interface" is required, accounting for an athlete's anthropometrics, sports classification, and positional role on court. In addition to enhancing physical capacities, interventions must focus on the integration of the athlete and his or her equipment, as well as techniques for limiting environmental influence on performance. Taken together, the optimization of wheelchair sport performance requires a multidisciplinary approach based on the individual requirements of each athlete.

Comparison of two optimized readout chains for low light CIS

NASA Astrophysics Data System (ADS)

Boukhayma, A.; Peizerat, A.; Dupret, A.; Enz, C.

2014-03-01

We compare the noise performance of two optimized readout chains that are based on 4T pixels and featuring the same bandwidth of 265kHz (enough to read 1Megapixel with 50frame/s). Both chains contain a 4T pixel, a column amplifier and a single slope analog-to-digital converter operating a CDS. In one case, the pixel operates in source follower configuration, and in common source configuration in the other case. Based on analytical noise calculation of both readout chains, an optimization methodology is presented. Analytical results are confirmed by transient simulations using 130nm process. A total input referred noise bellow 0.4 electrons RMS is reached for a simulated conversion gain of 160μV/e-. Both optimized readout chains show the same input referred 1/f noise. The common source based readout chain shows better performance for thermal noise and requires smaller silicon area. We discuss the possible drawbacks of the common source configuration and provide the reader with a comparative table between the two readout chains. The table contains several variants (column amplifier gain, in-pixel transistor sizes and type).

Optimization of the performance of the polymerase chain reaction in silicon-based microstructures.

PubMed Central

Taylor, T B; Winn-Deen, E S; Picozza, E; Woudenberg, T M; Albin, M

1997-01-01

We have demonstrated the ability to perform real-time homogeneous, sequence specific detection of PCR products in silicon microstructures. Optimal design/ processing result in equivalent performance (yield and specificity) for high surface-to-volume silicon structures as compared to larger volume reactions in polypropylene tubes. Amplifications in volumes as small as 0.5 microl and thermal cycling times reduced as much as 5-fold from that of conventional systems have been demonstrated for the microstructures. PMID:9224619

NASA Human Health and Performance Information Architecture Panel

NASA Technical Reports Server (NTRS)

Johnson-Throop, Kathy; Kadwa, Binafer; VanBaalen, Mary

2014-01-01

The Human Health and Performance (HH&P) Directorate at NASA's Johnson Space Center has a mission to enable optimization of human health and performance throughout all phases of spaceflight. All HH&P functions are ultimately aimed at achieving this mission. Our activities enable mission success, optimizing human health and productivity in space before, during, and after the actual spaceflight experience of our crews, and include support for ground-based functions. Many of our spaceflight innovations also provide solutions for terrestrial challenges, thereby enhancing life on Earth.

Incentive-compatible demand-side management for smart grids based on review strategies

NASA Astrophysics Data System (ADS)

Xu, Jie; van der Schaar, Mihaela

2015-12-01

Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system's as well as the users' performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

NASA Astrophysics Data System (ADS)

Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

2017-12-01

Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

Anti-buckling design of variable stiffness composite cylinder under combined loading based on the multi-objective optimization method

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2018-06-01

Variable stiffness composite structures take full advantages of composite’s design ability. An enlarged design space will make the structure’s performance more excellent. Through an optimal design of a variable stiffness cylinder, the buckling capacity of the cylinder will be increased as compared with its constant stiffness counterpart. In this paper, variable stiffness composite cylinders sustaining combined loadings are considered, and the optimization is conducted based on the multi-objective optimization method. The results indicate that variable stiffness cylinder’s loading capacity is increased significantly as compared with the constant stiffness, especially when an inhomogeneous loading is considered.

Receding horizon online optimization for torque control of gasoline engines.

PubMed

Kang, Mingxin; Shen, Tielong

2016-11-01

This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Design of an optimized biomixture for the degradation of carbofuran based on pesticide removal and toxicity reduction of the matrix.

PubMed

Chin-Pampillo, Juan Salvador; Ruiz-Hidalgo, Karla; Masís-Mora, Mario; Carazo-Rojas, Elizabeth; Rodríguez-Rodríguez, Carlos E

2015-12-01

Pesticide biopurification systems contain a biologically active matrix (biomixture) responsible for the accelerated elimination of pesticides in wastewaters derived from pest control in crop fields. Biomixtures have been typically prepared using the volumetric composition 50:25:25 (lignocellulosic substrate/humic component/soil); nonetheless, formal composition optimization has not been performed so far. Carbofuran is an insecticide/nematicide of high toxicity widely employed in developing countries. Therefore, the composition of a highly efficient biomixture (composed of coconut fiber, compost, and soil, FCS) for the removal of carbofuran was optimized by means of a central composite design and response surface methodology. The volumetric content of soil and the ratio coconut fiber/compost were used as the design variables. The performance of the biomixture was assayed by considering the elimination of carbofuran, the mineralization of (14)C-carbofuran, and the residual toxicity of the matrix, as response variables. Based on the models, the optimal volumetric composition of the FCS biomixture consists of 45:13:42 (coconut fiber/compost/soil), which resulted in minimal residual toxicity and ∼99% carbofuran elimination after 3 days. This optimized biomixture considerably differs from the standard 50:25:25 composition, which remarks the importance of assessing the performance of newly developed biomixtures during the design of biopurification systems.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

An opinion formation based binary optimization approach for feature selection

NASA Astrophysics Data System (ADS)

Hamedmoghadam, Homayoun; Jalili, Mahdi; Yu, Xinghuo

2018-02-01

This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.

Parallel Performance Optimizations on Unstructured Mesh-based Simulations

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

Sarje, Abhinav; Song, Sukhyun; Jacobsen, Douglas

2015-01-01

© The Authors. Published by Elsevier B.V. This paper addresses two key parallelization challenges the unstructured mesh-based ocean modeling code, MPAS-Ocean, which uses a mesh based on Voronoi tessellations: (1) load imbalance across processes, and (2) unstructured data access patterns, that inhibit intra- and inter-node performance. Our work analyzes the load imbalance due to naive partitioning of the mesh, and develops methods to generate mesh partitioning with better load balance and reduced communication. Furthermore, we present methods that minimize both inter- and intranode data movement and maximize data reuse. Our techniques include predictive ordering of data elements for higher cachemore » efficiency, as well as communication reduction approaches. We present detailed performance data when running on thousands of cores using the Cray XC30 supercomputer and show that our optimization strategies can exceed the original performance by over 2×. Additionally, many of these solutions can be broadly applied to a wide variety of unstructured grid-based computations.« less

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.

Structural design of high-performance capacitive accelerometers using parametric optimization with uncertainties

NASA Astrophysics Data System (ADS)

Teves, André da Costa; Lima, Cícero Ribeiro de; Passaro, Angelo; Silva, Emílio Carlos Nelli

2017-03-01

Electrostatic or capacitive accelerometers are among the highest volume microelectromechanical systems (MEMS) products nowadays. The design of such devices is a complex task, since they depend on many performance requirements, which are often conflicting. Therefore, optimization techniques are often used in the design stage of these MEMS devices. Because of problems with reliability, the technology of MEMS is not yet well established. Thus, in this work, size optimization is combined with the reliability-based design optimization (RBDO) method to improve the performance of accelerometers. To account for uncertainties in the dimensions and material properties of these devices, the first order reliability method is applied to calculate the probabilities involved in the RBDO formulation. Practical examples of bulk-type capacitive accelerometer designs are presented and discussed to evaluate the potential of the implemented RBDO solver.

Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster

NASA Technical Reports Server (NTRS)

Story, George

2015-01-01

Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.

Thermodynamics of Gas Turbine Cycles with Analytic Derivatives in OpenMDAO

NASA Technical Reports Server (NTRS)

Gray, Justin; Chin, Jeffrey; Hearn, Tristan; Hendricks, Eric; Lavelle, Thomas; Martins, Joaquim R. R. A.

2016-01-01

A new equilibrium thermodynamics analysis tool was built based on the CEA method using the OpenMDAO framework. The new tool provides forward and adjoint analytic derivatives for use with gradient based optimization algorithms. The new tool was validated against the original CEA code to ensure an accurate analysis and the analytic derivatives were validated against finite-difference approximations. Performance comparisons between analytic and finite difference methods showed a significant speed advantage for the analytic methods. To further test the new analysis tool, a sample optimization was performed to find the optimal air-fuel equivalence ratio, , maximizing combustion temperature for a range of different pressures. Collectively, the results demonstrate the viability of the new tool to serve as the thermodynamic backbone for future work on a full propulsion modeling tool.

Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster

NASA Technical Reports Server (NTRS)

Story, George

2014-01-01

Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and later on solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. A remaining issue is the cost of hybrids vs the existing launch propulsion systems. This paper will review the known state of the art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.

Energy aware swarm optimization with intercluster search for wireless sensor network.

PubMed

Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan

2015-01-01

Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

A study of the optimal transition temperature of PCM (phase change material) wallboard for solar energy storage

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

Drake, J.B.

1987-09-01

In this report, we consider the performance of wallboard impregnated with phase change material. An ideal setting is assumed and several measures of performance discussed. With a definition of optimal performance given, the performance with respect to variation of transition temperature is studied. Results are based on computer simulations of PCM wallboard with a standard stud wall construction. We find the diurnal heat capacity to be overly sensitive to numerical errors for use in PCM applications. The other measures of performance, diurnal effectiveness, net collected to storage ratio, and absolute discharge flux, all indicate similar trends. It is shown thatmore » the optimal transition temperature of the PCM is strongly influenced by amount of solar flux absorbed by the PCM. 6 refs., 5 figs., 5 tabs.« less

Validation of morphing wing methodologies on an unmanned aerial system and a wind tunnel technology demonstrator

NASA Astrophysics Data System (ADS)

Gabor, Oliviu Sugar

To increase the aerodynamic efficiency of aircraft, in order to reduce the fuel consumption, a novel morphing wing concept has been developed. It consists in replacing a part of the wing upper and lower surfaces with a flexible skin whose shape can be modified using an actuation system placed inside the wing structure. Numerical studies in two and three dimensions were performed in order to determine the gains the morphing system achieves for the case of an Unmanned Aerial System and for a morphing technology demonstrator based on the wing tip of a transport aircraft. To obtain the optimal wing skin shapes in function of the flight condition, different global optimization algorithms were implemented, such as the Genetic Algorithm and the Artificial Bee Colony Algorithm. To reduce calculation times, a hybrid method was created by coupling the population-based algorithm with a fast, gradient-based local search method. Validations were performed with commercial state-of-the-art optimization tools and demonstrated the efficiency of the proposed methods. For accurately determining the aerodynamic characteristics of the morphing wing, two new methods were developed, a nonlinear lifting line method and a nonlinear vortex lattice method. Both use strip analysis of the span-wise wing section to account for the airfoil shape modifications induced by the flexible skin, and can provide accurate results for the wing drag coefficient. The methods do not require the generation of a complex mesh around the wing and are suitable for coupling with optimization algorithms due to the computational time several orders of magnitude smaller than traditional three-dimensional Computational Fluid Dynamics methods. Two-dimensional and three-dimensional optimizations of the Unmanned Aerial System wing equipped with the morphing skin were performed, with the objective of improving its performances for an extended range of flight conditions. The chordwise positions of the internal actuators, the spanwise number of actuation stations as well as the displacement limits were established. The performance improvements obtained and the limitations of the morphing wing concept were studied. To verify the optimization results, high-fidelity Computational Fluid Dynamics simulations were also performed, giving very accurate indications of the obtained gains. For the morphing model based on an aircraft wing tip, the skin shapes were optimized in order to control laminar flow on the upper surface. An automated structured mesh generation procedure was developed and implemented. To accurately capture the shape of the skin, a precision scanning procedure was done and its results were included in the numerical model. High-fidelity simulations were performed to determine the upper surface transition region and the numerical results were validated using experimental wind tunnel data.

Statistical estimation via convex optimization for trending and performance monitoring

NASA Astrophysics Data System (ADS)

Samar, Sikandar

This thesis presents an optimization-based statistical estimation approach to find unknown trends in noisy data. A Bayesian framework is used to explicitly take into account prior information about the trends via trend models and constraints. The main focus is on convex formulation of the Bayesian estimation problem, which allows efficient computation of (globally) optimal estimates. There are two main parts of this thesis. The first part formulates trend estimation in systems described by known detailed models as a convex optimization problem. Statistically optimal estimates are then obtained by maximizing a concave log-likelihood function subject to convex constraints. We consider the problem of increasing problem dimension as more measurements become available, and introduce a moving horizon framework to enable recursive estimation of the unknown trend by solving a fixed size convex optimization problem at each horizon. We also present a distributed estimation framework, based on the dual decomposition method, for a system formed by a network of complex sensors with local (convex) estimation. Two specific applications of the convex optimization-based Bayesian estimation approach are described in the second part of the thesis. Batch estimation for parametric diagnostics in a flight control simulation of a space launch vehicle is shown to detect incipient fault trends despite the natural masking properties of feedback in the guidance and control loops. Moving horizon approach is used to estimate time varying fault parameters in a detailed nonlinear simulation model of an unmanned aerial vehicle. An excellent performance is demonstrated in the presence of winds and turbulence.

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.

Aerodynamic Design Using Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan; Madavan, Nateri K.

2003-01-01

The design of aerodynamic components of aircraft, such as wings or engines, involves a process of obtaining the most optimal component shape that can deliver the desired level of component performance, subject to various constraints, e.g., total weight or cost, that the component must satisfy. Aerodynamic design can thus be formulated as an optimization problem that involves the minimization of an objective function subject to constraints. A new aerodynamic design optimization procedure based on neural networks and response surface methodology (RSM) incorporates the advantages of both traditional RSM and neural networks. The procedure uses a strategy, denoted parameter-based partitioning of the design space, to construct a sequence of response surfaces based on both neural networks and polynomial fits to traverse the design space in search of the optimal solution. Some desirable characteristics of the new design optimization procedure include the ability to handle a variety of design objectives, easily impose constraints, and incorporate design guidelines and rules of thumb. It provides an infrastructure for variable fidelity analysis and reduces the cost of computation by using less-expensive, lower fidelity simulations in the early stages of the design evolution. The initial or starting design can be far from optimal. The procedure is easy and economical to use in large-dimensional design space and can be used to perform design tradeoff studies rapidly. Designs involving multiple disciplines can also be optimized. Some practical applications of the design procedure that have demonstrated some of its capabilities include the inverse design of an optimal turbine airfoil starting from a generic shape and the redesign of transonic turbines to improve their unsteady aerodynamic characteristics.

Cat Swarm Optimization algorithm for optimal linear phase FIR filter design.

PubMed

Saha, Suman Kumar; Ghoshal, Sakti Prasad; Kar, Rajib; Mandal, Durbadal

2013-11-01

In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Filling the glass: Effects of a positive psychology intervention on executive task performance in chronic pain patients.

PubMed

Boselie, J J L M; Vancleef, L M G; Peters, M L

2018-03-24

Chronic pain is associated with emotional problems as well as difficulties in cognitive functioning. Prior experimental studies have shown that optimism, the tendency to expect that good things happen in the future, and positive emotions can counteract pain-induced task performance deficits in healthy participants. More specifically, induced optimism was found to buffer against the negative effects of experimental pain on executive functioning. This clinical experiment examined whether this beneficial effect can be extended to a chronic pain population. Patients (N = 122) were randomized to a positive psychology Internet-based intervention (PPI; n = 74) or a waiting list control condition (WLC; n = 48). The PPI consisted of positive psychology exercises that particularly target optimism, positive emotions and self-compassion. Results demonstrated that patients in the PPI condition scored higher on happiness, optimism, positive future expectancies, positive affect, self-compassion and ability to live a desired life despite pain, and scored lower on pain catastrophizing, depression and anxiety compared to patients in the WLC condition. However, executive task performance did not improve following completion of the PPI, compared to the WLC condition. Despite the lack of evidence that positive emotions and optimism can improve executive task performance in chronic pain patients, this study did convincingly demonstrate that it is possible to increase positive emotions and optimism in chronic pain patients with an online positive psychology intervention. It is imperative to further explore amendable psychological factors that may reduce the negative impact of pain on executive functioning. We demonstrated that an Internet-based positive psychology intervention strengthens optimism and positive emotions in chronic pain patients. These emotional improvements are not associated with improved executive task performance. As pain itself often cannot be relieved, it is imperative to have techniques to reduce the burden of living with chronic pain. © 2018 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation -EFIC®.

Topology-optimization-based design method of flexures for mounting the primary mirror of a large-aperture space telescope.

PubMed

Hu, Rui; Liu, Shutian; Li, Quhao

2017-05-20

For the development of a large-aperture space telescope, one of the key techniques is the method for designing the flexures for mounting the primary mirror, as the flexures are the key components. In this paper, a topology-optimization-based method for designing flexures is presented. The structural performances of the mirror system under multiple load conditions, including static gravity and thermal loads, as well as the dynamic vibration, are considered. The mirror surface shape error caused by gravity and the thermal effect is treated as the objective function, and the first-order natural frequency of the mirror structural system is taken as the constraint. The pattern repetition constraint is added, which can ensure symmetrical material distribution. The topology optimization model for flexure design is established. The substructuring method is also used to condense the degrees of freedom (DOF) of all the nodes of the mirror system, except for the nodes that are linked to the mounting flexures, to reduce the computation effort during the optimization iteration process. A potential optimized configuration is achieved by solving the optimization model and post-processing. A detailed shape optimization is subsequently conducted to optimize its dimension parameters. Our optimization method deduces new mounting structures that significantly enhance the optical performance of the mirror system compared to the traditional methods, which only focus on the parameters of existing structures. Design results demonstrate the effectiveness of the proposed optimization method.

Transitioning to Performance-Based State Funding: Concerns, Commitment, and Cautious Optimism

ERIC Educational Resources Information Center

Wayt, Lindsay K.; LaCost, Barbara Y.

2016-01-01

The introduction of performance-based state funding of higher education can be traced to the late 1970s which eliminated bonuses, and replaced regular state funding in part or completely, with funding tied to achievement of state-defined performance goals, which often include student outcomes, like graduation and retention rates. In this article,…

Structural optimization of structured carbon-based energy-storing composite materials used in space vehicles.

PubMed

Yu, Jia; Yu, Zhichao; Tang, Chenlong

2016-07-04

The hot work environment of electronic components in the instrument cabin of spacecraft was researched, and a new thermal protection structure, namely graphite carbon foam, which is an impregnated phase-transition material, was adopted to implement the thermal control on the electronic components. We used the optimized parameters obtained from ANSYS to conduct 2D optimization, 3-D modeling and simulation, as well as the strength check. Finally, the optimization results were verified by experiments. The results showed that after optimization, the structured carbon-based energy-storing composite material could reduce the mass and realize the thermal control over electronic components. This phase-transition composite material still possesses excellent temperature control performance after its repeated melting and solidifying.

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

PubMed

Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali

2018-05-11

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

Modeling and Development of INS-Aided PLLs in a GNSS/INS Deeply-Coupled Hardware Prototype for Dynamic Applications

PubMed Central

Zhang, Tisheng; Niu, Xiaoji; Ban, Yalong; Zhang, Hongping; Shi, Chuang; Liu, Jingnan

2015-01-01

A GNSS/INS deeply-coupled system can improve the satellite signals tracking performance by INS aiding tracking loops under dynamics. However, there was no literature available on the complete modeling of the INS branch in the INS-aided tracking loop, which caused the lack of a theoretical tool to guide the selections of inertial sensors, parameter optimization and quantitative analysis of INS-aided PLLs. This paper makes an effort on the INS branch in modeling and parameter optimization of phase-locked loops (PLLs) based on the scalar-based GNSS/INS deeply-coupled system. It establishes the transfer function between all known error sources and the PLL tracking error, which can be used to quantitatively evaluate the candidate inertial measurement unit (IMU) affecting the carrier phase tracking error. Based on that, a steady-state error model is proposed to design INS-aided PLLs and to analyze their tracking performance. Based on the modeling and error analysis, an integrated deeply-coupled hardware prototype is developed, with the optimization of the aiding information. Finally, the performance of the INS-aided PLLs designed based on the proposed steady-state error model is evaluated through the simulation and road tests of the hardware prototype. PMID:25569751

Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

PubMed

Heydari, Ali; Balakrishnan, Sivasubramanya N

2013-01-01

To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.

Geometric Reasoning for Automated Planning

NASA Technical Reports Server (NTRS)

Clement, Bradley J.; Knight, Russell L.; Broderick, Daniel

2012-01-01

An important aspect of mission planning for NASA s operation of the International Space Station is the allocation and management of space for supplies and equipment. The Stowage, Configuration Analysis, and Operations Planning teams collaborate to perform the bulk of that planning. A Geometric Reasoning Engine is developed in a way that can be shared by the teams to optimize item placement in the context of crew planning. The ISS crew spends (at the time of this writing) a third or more of their time moving supplies and equipment around. Better logistical support and optimized packing could make a significant impact on operational efficiency of the ISS. Currently, computational geometry and motion planning do not focus specifically on the optimized orientation and placement of 3D objects based on multiple distance and containment preferences and constraints. The software performs reasoning about the manipulation of 3D solid models in order to maximize an objective function based on distance. It optimizes for 3D orientation and placement. Spatial placement optimization is a general problem and can be applied to object packing or asset relocation.

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.

Optimal design of disc-type magneto-rheological brake for mid-sized motorcycle: experimental evaluation

NASA Astrophysics Data System (ADS)

Sohn, Jung Woo; Jeon, Juncheol; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-08-01

In this paper, a disc-type magneto-rheological (MR) brake is designed for a mid-sized motorcycle and its performance is experimentally evaluated. The proposed MR brake consists of an outer housing, a rotating disc immersed in MR fluid, and a copper wire coiled around a bobbin to generate a magnetic field. The structural configuration of the MR brake is first presented with consideration of the installation space for the conventional hydraulic brake of a mid-sized motorcycle. The design parameters of the proposed MR brake are optimized to satisfy design requirements such as the braking torque, total mass of the MR brake, and cruising temperature caused by the magnetic-field friction of the MR fluid. In the optimization procedure, the braking torque is calculated based on the Herschel-Bulkley rheological model, which predicts MR fluid behavior well at high shear rate. An optimization tool based on finite element analysis is used to obtain the optimized dimensions of the MR brake. After manufacturing the MR brake, mechanical performances regarding the response time, braking torque and cruising temperature are experimentally evaluated.

Optimal design of leak-proof SRAM cell using MCDM method

NASA Astrophysics Data System (ADS)

Wang, Qi; Kang, Sung-Mo

2003-04-01

As deep-submicron CMOS technology advances, on-chip cache has become a bottleneck on microprocessor's performance. Meanwhile, it also occupies a big percentage of processor area and consumes large power. Speed, power and area of SRAM are mutually contradicting, and not easy to be met simultaneously. Many existent leakage suppression techniques have been proposed, but they limit the circuit's performance. We apply a Multi-Criteria Decision Making strategy to perform a minimum delay-power-area optimization on SRAM circuit under some certain constraints. Based on an integrated device and circuit-level approach, we search for a process that yields a targeted composite performance. In consideration of the huge amount of simulation workload involved in the optimal design-seeking process, most of this process is automated to facilitate our goal-pursuant. With varying emphasis put on delay, power or area, different optimal SRAM designs are derived and a gate-oxide thickness scaling limit is projected. The result seems to indicate that a better composite performance could be achieved under a thinner oxide thickness. Under the derived optimal oxide thickness, the static leakage power consumption contributes less than 1% in the total power dissipation.

Thermodynamic Analysis of TEG-TEC Device Including Influence of Thomson Effect

NASA Astrophysics Data System (ADS)

Feng, Yuanli; Chen, Lingen; Meng, Fankai; Sun, Fengrui

2018-01-01

A thermodynamic model of a thermoelectric cooler driven by thermoelectric generator (TEG-TEC) device is established considering Thomson effect. The performance is analyzed and optimized using numerical calculation based on non-equilibrium thermodynamic theory. The influence characteristics of Thomson effect on the optimal performance and variable selection are investigated by comparing the condition with and without Thomson effect. The results show that Thomson effect degrades the performance of TEG-TEC device, it decreases the cooling capacity by 27 %, decreases the coefficient of performance (COP) by 19 %, decreases the maximum cooling temperature difference by 11 % when the ratio of thermoelectric elements number is 0.6, the cold junction temperature of thermoelectric cooler (TEC) is 285 K and the hot junction temperature of thermoelectric generator (TEG) is 450 K. Thomson effect degrades the optimal performance of TEG-TEC device, it decreases the maximum cooling capacity by 28 % and decreases the maximum COP by 28 % under the same junction temperatures. Thomson effect narrows the optimal variable range and optimal working range. In the design of the devices, limited-number thermoelectric elements should be more allocated appropriately to TEG when consider Thomson effect. The results may provide some guidelines for the design of TEG-TEC devices.

Optimal Cost Avoidance Investment and Pricing Strategies for Performance-Based Post-Production Service Contracts

DTIC Science & Technology

2011-04-30

a BS degree in Mathematics and an MS degree in Statistics and Financial and Actuarial Mathematics from Kiev National Taras Shevchenko University...degrees from Rutgers University in Industrial Engineering (PhD and MS) and Statistics (MS) and from Universidad Nacional Autonoma de Mexico in Actuarial ...Science. His research efforts focus on developing mathematical models for the analysis, computation, and optimization of system performance with

Evaluation of solar thermal power plants using economic and performance simulations

NASA Technical Reports Server (NTRS)

El-Gabawali, N.

1980-01-01

An energy cost analysis is presented for central receiver power plants with thermal storage and point focusing power plants with electrical storage. The present approach is based on optimizing the size of the plant to give the minimum energy cost (in mills/kWe hr) of an annual plant energy production. The optimization is done by considering the trade-off between the collector field size and the storage capacity for a given engine size. The energy cost is determined by the plant cost and performance. The performance is estimated by simulating the behavior of the plant under typical weather conditions. Plant capital and operational costs are estimated based on the size and performance of different components. This methodology is translated into computer programs for automatic and consistent evaluation.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

A novel model of motor learning capable of developing an optimal movement control law online from scratch.

PubMed

Shimansky, Yury P; Kang, Tao; He, Jiping

2004-02-01

A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

A PERFECT MATCH CONDITION FOR POINT-SET MATCHING PROBLEMS USING THE OPTIMAL MASS TRANSPORT APPROACH

PubMed Central

CHEN, PENGWEN; LIN, CHING-LONG; CHERN, I-LIANG

2013-01-01

We study the performance of optimal mass transport-based methods applied to point-set matching problems. The present study, which is based on the L2 mass transport cost, states that perfect matches always occur when the product of the point-set cardinality and the norm of the curl of the non-rigid deformation field does not exceed some constant. This analytic result is justified by a numerical study of matching two sets of pulmonary vascular tree branch points whose displacement is caused by the lung volume changes in the same human subject. The nearly perfect match performance verifies the effectiveness of this mass transport-based approach. PMID:23687536

Optimization and large scale computation of an entropy-based moment closure

NASA Astrophysics Data System (ADS)

Kristopher Garrett, C.; Hauck, Cory; Hill, Judith

2015-12-01

We present computational advances and results in the implementation of an entropy-based moment closure, MN, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as PN, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. These results show, in particular, load balancing issues in scaling the MN algorithm that do not appear for the PN algorithm. We also observe that in weak scaling tests, the ratio in time to solution of MN to PN decreases.

Optimization and large scale computation of an entropy-based moment closure

DOE PAGES

Hauck, Cory D.; Hill, Judith C.; Garrett, C. Kristopher

2015-09-10

We present computational advances and results in the implementation of an entropy-based moment closure, M N, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as P N, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which aremore » used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. Lastly, these results show, in particular, load balancing issues in scaling the M N algorithm that do not appear for the P N algorithm. We also observe that in weak scaling tests, the ratio in time to solution of M N to P N decreases.« less

Subjective audio quality evaluation of embedded-optimization-based distortion precompensation algorithms.

PubMed

Defraene, Bruno; van Waterschoot, Toon; Diehl, Moritz; Moonen, Marc

2016-07-01

Subjective audio quality evaluation experiments have been conducted to assess the performance of embedded-optimization-based precompensation algorithms for mitigating perceptible linear and nonlinear distortion in audio signals. It is concluded with statistical significance that the perceived audio quality is improved by applying an embedded-optimization-based precompensation algorithm, both in case (i) nonlinear distortion and (ii) a combination of linear and nonlinear distortion is present. Moreover, a significant positive correlation is reported between the collected subjective and objective PEAQ audio quality scores, supporting the validity of using PEAQ to predict the impact of linear and nonlinear distortion on the perceived audio quality.

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

NASA Astrophysics Data System (ADS)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Jig-Shape Optimization of a Low-Boom Supersonic Aircraft

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi

2018-01-01

A simple approach for optimizing the jig-shape is proposed in this study. This simple approach is based on an unconstrained optimization problem and applied to a low-boom supersonic aircraft. In this study, the jig-shape optimization is performed using the two-step approach. First, starting design variables are computed using the least-squares surface fitting technique. Next, the jig-shape is further tuned using a numerical optimization procedure based on an in-house object-oriented optimization tool. During the numerical optimization procedure, a design jig-shape is determined by the baseline jig-shape and basis functions. A total of 12 symmetric mode shapes of the cruise-weight configuration, rigid pitch shape, rigid left and right stabilator rotation shapes, and a residual shape are selected as sixteen basis functions. After three optimization runs, the trim shape error distribution is improved, and the maximum trim shape error of 0.9844 inches of the starting configuration becomes 0.00367 inch by the end of the third optimization run.

Parameter sensitivity analysis and optimization for a satellite-based evapotranspiration model across multiple sites using Moderate Resolution Imaging Spectroradiometer and flux data

NASA Astrophysics Data System (ADS)

Zhang, Kun; Ma, Jinzhu; Zhu, Gaofeng; Ma, Ting; Han, Tuo; Feng, Li Li

2017-01-01

Global and regional estimates of daily evapotranspiration are essential to our understanding of the hydrologic cycle and climate change. In this study, we selected the radiation-based Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model and assessed it at a daily time scale by using 44 flux towers. These towers distributed in a wide range of ecological systems: croplands, deciduous broadleaf forest, evergreen broadleaf forest, evergreen needleleaf forest, grasslands, mixed forests, savannas, and shrublands. A regional land surface evapotranspiration model with a relatively simple structure, the PT-JPL model largely uses ecophysiologically-based formulation and parameters to relate potential evapotranspiration to actual evapotranspiration. The results using the original model indicate that the model always overestimates evapotranspiration in arid regions. This likely results from the misrepresentation of water limitation and energy partition in the model. By analyzing physiological processes and determining the sensitive parameters, we identified a series of parameter sets that can increase model performance. The model with optimized parameters showed better performance (R2 = 0.2-0.87; Nash-Sutcliffe efficiency (NSE) = 0.1-0.87) at each site than the original model (R2 = 0.19-0.87; NSE = -12.14-0.85). The results of the optimization indicated that the parameter β (water control of soil evaporation) was much lower in arid regions than in relatively humid regions. Furthermore, the optimized value of parameter m1 (plant control of canopy transpiration) was mostly between 1 to 1.3, slightly lower than the original value. Also, the optimized parameter Topt correlated well to the actual environmental temperature at each site. We suggest that using optimized parameters with the PT-JPL model could provide an efficient way to improve the model performance.

Particle swarm optimization based space debris surveillance network scheduling

NASA Astrophysics Data System (ADS)

Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao

2017-02-01

The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.

Performance optimization and validation of ADM1 simulations under anaerobic thermophilic conditions.

PubMed

Atallah, Nabil M; El-Fadel, Mutasem; Ghanimeh, Sophia; Saikaly, Pascal; Abou-Najm, Majdi

2014-12-01

In this study, two experimental sets of data each involving two thermophilic anaerobic digesters treating food waste, were simulated using the Anaerobic Digestion Model No. 1 (ADM1). A sensitivity analysis was conducted, using both data sets of one digester, for parameter optimization based on five measured performance indicators: methane generation, pH, acetate, total COD, ammonia, and an equally weighted combination of the five indicators. The simulation results revealed that while optimization with respect to methane alone, a commonly adopted approach, succeeded in simulating methane experimental results, it predicted other intermediary outputs less accurately. On the other hand, the multi-objective optimization has the advantage of providing better results than methane optimization despite not capturing the intermediary output. The results from the parameter optimization were validated upon their independent application on the data sets of the second digester. Copyright © 2014 Elsevier Ltd. All rights reserved.

An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China.

PubMed

Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2014-06-01

In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.

Long-Run Savings and Investment Strategy Optimization

PubMed Central

Gerrard, Russell; Guillén, Montserrat; Pérez-Marín, Ana M.

2014-01-01

We focus on automatic strategies to optimize life cycle savings and investment. Classical optimal savings theory establishes that, given the level of risk aversion, a saver would keep the same relative amount invested in risky assets at any given time. We show that, when optimizing lifecycle investment, performance and risk assessment have to take into account the investor's risk aversion and the maximum amount the investor could lose, simultaneously. When risk aversion and maximum possible loss are considered jointly, an optimal savings strategy is obtained, which follows from constant rather than relative absolute risk aversion. This result is fundamental to prove that if risk aversion and the maximum possible loss are both high, then holding a constant amount invested in the risky asset is optimal for a standard lifetime saving/pension process and outperforms some other simple strategies. Performance comparisons are based on downside risk-adjusted equivalence that is used in our illustration. PMID:24711728

Long-run savings and investment strategy optimization.

PubMed

Gerrard, Russell; Guillén, Montserrat; Nielsen, Jens Perch; Pérez-Marín, Ana M

2014-01-01

We focus on automatic strategies to optimize life cycle savings and investment. Classical optimal savings theory establishes that, given the level of risk aversion, a saver would keep the same relative amount invested in risky assets at any given time. We show that, when optimizing lifecycle investment, performance and risk assessment have to take into account the investor's risk aversion and the maximum amount the investor could lose, simultaneously. When risk aversion and maximum possible loss are considered jointly, an optimal savings strategy is obtained, which follows from constant rather than relative absolute risk aversion. This result is fundamental to prove that if risk aversion and the maximum possible loss are both high, then holding a constant amount invested in the risky asset is optimal for a standard lifetime saving/pension process and outperforms some other simple strategies. Performance comparisons are based on downside risk-adjusted equivalence that is used in our illustration.

Dose-mass inverse optimization for minimally moving thoracic lesions

NASA Astrophysics Data System (ADS)

Mihaylov, I. B.; Moros, E. G.

2015-05-01

In the past decade, several different radiotherapy treatment plan evaluation and optimization schemes have been proposed as viable approaches, aiming for dose escalation or an increase of healthy tissue sparing. In particular, it has been argued that dose-mass plan evaluation and treatment plan optimization might be viable alternatives to the standard of care, which is realized through dose-volume evaluation and optimization. The purpose of this investigation is to apply dose-mass optimization to a cohort of lung cancer patients and compare the achievable healthy tissue sparing to that one achievable through dose-volume optimization. Fourteen non-small cell lung cancer (NSCLC) patient plans were studied retrospectively. The range of tumor motion was less than 0.5 cm and motion management in the treatment planning process was not considered. For each case, dose-volume (DV)-based and dose-mass (DM)-based optimization was performed. Nine-field step-and-shoot IMRT was used, with all of the optimization parameters kept the same between DV and DM optimizations. Commonly used dosimetric indices (DIs) such as dose to 1% the spinal cord volume, dose to 50% of the esophageal volume, and doses to 20 and 30% of healthy lung volumes were used for cross-comparison. Similarly, mass-based indices (MIs), such as doses to 20 and 30% of healthy lung masses, 1% of spinal cord mass, and 33% of heart mass, were also tallied. Statistical equivalence tests were performed to quantify the findings for the entire patient cohort. Both DV and DM plans for each case were normalized such that 95% of the planning target volume received the prescribed dose. DM optimization resulted in more organs at risk (OAR) sparing than DV optimization. The average sparing of cord, heart, and esophagus was 23, 4, and 6%, respectively. For the majority of the DIs, DM optimization resulted in lower lung doses. On average, the doses to 20 and 30% of healthy lung were lower by approximately 3 and 4%, whereas lung volumes receiving 2000 and 3000 cGy were lower by 3 and 2%, respectively. The behavior of MIs was very similar. The statistical analyses of the results again indicated better healthy anatomical structure sparing with DM optimization. The presented findings indicate that dose-mass-based optimization results in statistically significant OAR sparing as compared to dose-volume-based optimization for NSCLC. However, the sparing is case-dependent and it is not observed for all tallied dosimetric endpoints.

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.

Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons.

PubMed

Yaeli, Steve; Meir, Ron

2010-01-01

Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.

Economic optimization of operations for hybrid energy systems under variable markets

DOE PAGES

Chen, Jen; Garcia, Humberto E.

2016-05-21

We prosed a hybrid energy systems (HES) which is an important element to enable increasing penetration of clean energy. Our paper investigates the operations flexibility of HES, and develops a methodology for operations optimization for maximizing economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. In order to compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulationmore » results of two specific HES configurations are included to illustrate the proposed methodology and computational capability. These results demonstrate the economic viability of HES under proposed operations optimizer, suggesting the diversion of energy for alternative energy output while participating in the ancillary service market. Economic advantages of such operations optimizer and associated flexible operations are illustrated by comparing the economic performance of flexible operations against that of constant operations. Sensitivity analysis with respect to market variability and prediction error, are also performed.« less

Economic optimization of operations for hybrid energy systems under variable markets

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

Chen, Jen; Garcia, Humberto E.

We prosed a hybrid energy systems (HES) which is an important element to enable increasing penetration of clean energy. Our paper investigates the operations flexibility of HES, and develops a methodology for operations optimization for maximizing economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. In order to compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulationmore » results of two specific HES configurations are included to illustrate the proposed methodology and computational capability. These results demonstrate the economic viability of HES under proposed operations optimizer, suggesting the diversion of energy for alternative energy output while participating in the ancillary service market. Economic advantages of such operations optimizer and associated flexible operations are illustrated by comparing the economic performance of flexible operations against that of constant operations. Sensitivity analysis with respect to market variability and prediction error, are also performed.« less

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

NASA Astrophysics Data System (ADS)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

A proteomics performance standard to support measurement quality in proteomics.

PubMed

Beasley-Green, Ashley; Bunk, David; Rudnick, Paul; Kilpatrick, Lisa; Phinney, Karen

2012-04-01

The emergence of MS-based proteomic platforms as a prominent technology utilized in biochemical and biomedical research has increased the need for high-quality MS measurements. To address this need, National Institute of Standards and Technology (NIST) reference material (RM) 8323 yeast protein extract is introduced as a proteomics quality control material for benchmarking the preanalytical and analytical performance of proteomics-based experimental workflows. RM 8323 yeast protein extract is based upon the well-characterized eukaryote Saccharomyces cerevisiae and can be utilized in the design and optimization of proteomics-based methodologies from sample preparation to data analysis. To demonstrate its utility as a proteomics quality control material, we coupled LC-MS/MS measurements of RM 8323 with the NIST MS Quality Control (MSQC) performance metrics to quantitatively assess the LC-MS/MS instrumentation parameters that influence measurement accuracy, repeatability, and reproducibility. Due to the complexity of the yeast proteome, we also demonstrate how NIST RM 8323, along with the NIST MSQC performance metrics, can be used in the evaluation and optimization of proteomics-based sample preparation methods. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

High Performance Photodiode Based on p-Si/Copper Phthalocyanine Heterojunction.

PubMed

Zhong, Junkang; Peng, Yingquan; Zheng, Tingcai; Lv, Wenli; Ren, Qiang; Fobao, Huang; Ying, Wang; Chen, Zhen; Tang, Ying

2016-06-01

Hybrid organic-inorganic (HOI) photodiodes have both advantages of organic and inorganic materials, including compatibility of traditional Si-based semiconductor technology, low cost, high photosensitivity and high reliability, showing tremendous value in application. Red light sensitive HOI photodiodes based on the p-Si/copper phthalocyanine (CuPc) hetrojunction were fabricated and characterized. The effects of CuPc layer thickness on the performance were investigated, and an optimal layer thickness of around 30 nm was determined. An analytical expression is derived to describe the measured thickness dependence of the saturation photocurrent. For the device with optimal CuPc layer thickness, a photoresponsivity of 0.35 A/W and external quantum efficiency of 70% were obtained at 9 V reverse voltage bias and 655 nm light illumination of 0.451 mW. Furthermore, optical power dependent performances were investigated.

Optimal inverse functions created via population-based optimization.

PubMed

Jennings, Alan L; Ordóñez, Raúl

2014-06-01

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

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem

PubMed Central

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them. PMID:26819585

Comparison and optimization of radar-based hail detection algorithms in Slovenia

NASA Astrophysics Data System (ADS)

Stržinar, Gregor; Skok, Gregor

2018-05-01

Four commonly used radar-based hail detection algorithms are evaluated and optimized in Slovenia. The algorithms are verified against ground observations of hail at manned stations in the period between May and August, from 2002 to 2010. The algorithms are optimized by determining the optimal values of all possible algorithm parameters. A number of different contingency-table-based scores are evaluated with a combination of Critical Success Index and frequency bias proving to be the best choice for optimization. The best performance indexes are given by Waldvogel and the severe hail index, followed by vertically integrated liquid and maximum radar reflectivity. Using the optimal parameter values, a hail frequency climatology map for the whole of Slovenia is produced. The analysis shows that there is a considerable variability of hail occurrence within the Republic of Slovenia. The hail frequency ranges from almost 0 to 1.7 hail days per year with an average value of about 0.7 hail days per year.

A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem.

PubMed

Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah

2016-01-01

The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.

Displacement Based Multilevel Structural Optimization

NASA Technical Reports Server (NTRS)

Sobieszezanski-Sobieski, J.; Striz, A. G.

1996-01-01

In the complex environment of true multidisciplinary design optimization (MDO), efficiency is one of the most desirable attributes of any approach. In the present research, a new and highly efficient methodology for the MDO subset of structural optimization is proposed and detailed, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures is performed. In the system level optimization, the design variables are the coefficients of assumed polynomially based global displacement functions, and the load unbalance resulting from the solution of the global stiffness equations is minimized. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. The approach is expected to prove very efficient since the design task is broken down into a large number of small and efficient subtasks, each with a small number of variables, which are amenable to parallel computing.

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

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

Oyama, Akira; Liou, Meng-Sing

2001-01-01

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

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

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

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication

ERIC Educational Resources Information Center

Wolf, Michael Maclean

2009-01-01

Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrix-vector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific…

A Novel Hybrid Firefly Algorithm for Global Optimization.

PubMed

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

A Novel Hybrid Firefly Algorithm for Global Optimization

PubMed Central

Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao

2016-01-01

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869

Under-Track CFD-Based Shape Optimization for a Low-Boom Demonstrator Concept

NASA Technical Reports Server (NTRS)

Wintzer, Mathias; Ordaz, Irian; Fenbert, James W.

2015-01-01

The detailed outer mold line shaping of a Mach 1.6, demonstrator-sized low-boom concept is presented. Cruise trim is incorporated a priori as part of the shaping objective, using an equivalent-area-based approach. Design work is performed using a gradient-driven optimization framework that incorporates a three-dimensional, nonlinear flow solver, a parametric geometry modeler, and sensitivities derived using the adjoint method. The shaping effort is focused on reducing the under-track sonic boom level using an inverse design approach, while simultaneously satisfying the trim requirement. Conceptual-level geometric constraints are incorporated in the optimization process, including the internal layout of fuel tanks, landing gear, engine, and crew station. Details of the model parameterization and design process are documented for both flow-through and powered states, and the performance of these optimized vehicles presented in terms of inviscid L/D, trim state, pressures in the near-field and at the ground, and predicted sonic boom loudness.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

PubMed Central

Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

2015-01-01

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

Impacts of Intelligent Automated Quality Control on a Small Animal APD-Based Digital PET Scanner

NASA Astrophysics Data System (ADS)

Charest, Jonathan; Beaudoin, Jean-François; Bergeron, Mélanie; Cadorette, Jules; Arpin, Louis; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

2016-10-01

Stable system performance is mandatory to warrant the accuracy and reliability of biological results relying on small animal positron emission tomography (PET) imaging studies. This simple requirement sets the ground for imposing routine quality control (QC) procedures to keep PET scanners at a reliable optimal performance level. However, such procedures can become burdensome to implement for scanner operators, especially taking into account the increasing number of data acquisition channels in newer generation PET scanners. In systems using pixel detectors to achieve enhanced spatial resolution and contrast-to-noise ratio (CNR), the QC workload rapidly increases to unmanageable levels due to the number of independent channels involved. An artificial intelligence based QC system, referred to as Scanner Intelligent Diagnosis for Optimal Performance (SIDOP), was proposed to help reducing the QC workload by performing automatic channel fault detection and diagnosis. SIDOP consists of four high-level modules that employ machine learning methods to perform their tasks: Parameter Extraction, Channel Fault Detection, Fault Prioritization, and Fault Diagnosis. Ultimately, SIDOP submits a prioritized faulty channel list to the operator and proposes actions to correct them. To validate that SIDOP can perform QC procedures adequately, it was deployed on a LabPET™ scanner and multiple performance metrics were extracted. After multiple corrections on sub-optimal scanner settings, a 8.5% (with a 95% confidence interval (CI) of [7.6, 9.3]) improvement in the CNR, a 17.0% (CI: [15.3, 18.7]) decrease of the uniformity percentage standard deviation, and a 6.8% gain in global sensitivity were observed. These results confirm that SIDOP can indeed be of assistance in performing QC procedures and restore performance to optimal figures.

A study of data representation in Hadoop to optimize data storage and search performance for the ATLAS EventIndex

NASA Astrophysics Data System (ADS)

Baranowski, Z.; Canali, L.; Toebbicke, R.; Hrivnac, J.; Barberis, D.

2017-10-01

This paper reports on the activities aimed at improving the architecture and performance of the ATLAS EventIndex implementation in Hadoop. The EventIndex contains tens of billions of event records, each of which consists of ∼100 bytes, all having the same probability to be searched or counted. Data formats represent one important area for optimizing the performance and storage footprint of applications based on Hadoop. This work reports on the production usage and on tests using several data formats including Map Files, Apache Parquet, Avro, and various compression algorithms. The query engine plays also a critical role in the architecture. We report also on the use of HBase for the EventIndex, focussing on the optimizations performed in production and on the scalability tests. Additional engines that have been tested include Cloudera Impala, in particular for its SQL interface, and the optimizations for data warehouse workloads and reports.

Performance Optimization Control of ECH using Fuzzy Inference Application

NASA Astrophysics Data System (ADS)

Dubey, Abhay Kumar

Electro-chemical honing (ECH) is a hybrid electrolytic precision micro-finishing technology that, by combining physico-chemical actions of electro-chemical machining and conventional honing processes, provides the controlled functional surfaces-generation and fast material removal capabilities in a single operation. Process multi-performance optimization has become vital for utilizing full potential of manufacturing processes to meet the challenging requirements being placed on the surface quality, size, tolerances and production rate of engineering components in this globally competitive scenario. This paper presents an strategy that integrates the Taguchi matrix experimental design, analysis of variances and fuzzy inference system (FIS) to formulate a robust practical multi-performance optimization methodology for complex manufacturing processes like ECH, which involve several control variables. Two methodologies one using a genetic algorithm tuning of FIS (GA-tuned FIS) and another using an adaptive network based fuzzy inference system (ANFIS) have been evaluated for a multi-performance optimization case study of ECH. The actual experimental results confirm their potential for a wide range of machining conditions employed in ECH.

Topology-Aware Performance Optimization and Modeling of Adaptive Mesh Refinement Codes for Exascale

DOE PAGES

Chan, Cy P.; Bachan, John D.; Kenny, Joseph P.; ...

2017-01-26

Here, we introduce a topology-aware performance optimization and modeling workflow for AMR simulation that includes two new modeling tools, ProgrAMR and Mota Mapper, which interface with the BoxLib AMR framework and the SSTmacro network simulator. ProgrAMR allows us to generate and model the execution of task dependency graphs from high-level specifications of AMR-based applications, which we demonstrate by analyzing two example AMR-based multigrid solvers with varying degrees of asynchrony. Mota Mapper generates multiobjective, network topology-aware box mappings, which we apply to optimize the data layout for the example multigrid solvers. While the sensitivity of these solvers to layout and executionmore » strategy appears to be modest for balanced scenarios, the impact of better mapping algorithms can be significant when performance is highly constrained by network hop latency. Furthermore, we show that network latency in the multigrid bottom solve is the main contributing factor preventing good scaling on exascale-class machines.« less

Topology-Aware Performance Optimization and Modeling of Adaptive Mesh Refinement Codes for Exascale

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

Chan, Cy P.; Bachan, John D.; Kenny, Joseph P.

Here, we introduce a topology-aware performance optimization and modeling workflow for AMR simulation that includes two new modeling tools, ProgrAMR and Mota Mapper, which interface with the BoxLib AMR framework and the SSTmacro network simulator. ProgrAMR allows us to generate and model the execution of task dependency graphs from high-level specifications of AMR-based applications, which we demonstrate by analyzing two example AMR-based multigrid solvers with varying degrees of asynchrony. Mota Mapper generates multiobjective, network topology-aware box mappings, which we apply to optimize the data layout for the example multigrid solvers. While the sensitivity of these solvers to layout and executionmore » strategy appears to be modest for balanced scenarios, the impact of better mapping algorithms can be significant when performance is highly constrained by network hop latency. Furthermore, we show that network latency in the multigrid bottom solve is the main contributing factor preventing good scaling on exascale-class machines.« less

An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems

NASA Astrophysics Data System (ADS)

Qian, Xiaohong; Wang, Xumei; Su, Yonghong; He, Liu

2018-04-01

There are many different algorithms for solving complex optimization problems. Each algorithm has been applied successfully in solving some optimization problems, but not efficiently in other problems. In this paper the Cauchy mutation and the multi-parent hybrid operator are combined to propose a hybrid evolutionary algorithm based on the communication (Mixed Evolutionary Algorithm based on Communication), hereinafter referred to as CMEA. The basic idea of the CMEA algorithm is that the initial population is divided into two subpopulations. Cauchy mutation operators and multiple paternal crossover operators are used to perform two subpopulations parallelly to evolve recursively until the downtime conditions are met. While subpopulation is reorganized, the individual is exchanged together with information. The algorithm flow is given and the performance of the algorithm is compared using a number of standard test functions. Simulation results have shown that this algorithm converges significantly faster than FEP (Fast Evolutionary Programming) algorithm, has good performance in global convergence and stability and is superior to other compared algorithms.

Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models

NASA Astrophysics Data System (ADS)

Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P.

2018-01-01

We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

Trust regions in Kriging-based optimization with expected improvement

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2016-06-01

The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive black-box optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Kriging-based optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application in appendices supplied as an online supplement available at http://dx.doi.org/10.1080/0305215X.2015.1082350. The results show that both algorithms yield substantial improvements over EGO and they are competitive with a radial basis function method.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

NASA Astrophysics Data System (ADS)

Vallières, Martin; Laberge, Sébastien; Diamant, André; El Naqa, Issam

2017-11-01

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of 0.84 +/- 0.01 in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (p = 0.04 ), with an average AUC of 0.89 +/- 0.01 . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

On the balancing of structural and acoustic performance of a sandwich panel based on topology, property, and size optimization

NASA Astrophysics Data System (ADS)

Cameron, Christopher J.; Lind Nordgren, Eleonora; Wennhage, Per; Göransson, Peter

2014-06-01

Balancing structural and acoustic performance of a multi-layered sandwich panel is a formidable undertaking. Frequently the gains achieved in terms of reduced weight, still meeting the structural design requirements, are lost by the changes necessary to regain acceptable acoustic performance. To alleviate this, a design method for a multifunctional load bearing vehicle body panel is proposed which attempts to achieve a balance between structural and acoustic performance. The approach is based on numerical modelling of the structural and acoustic behaviour in a combined topology, size, and property optimization in order to achieve a three dimensional optimal distribution of structural and acoustic foam materials within the bounding surfaces of a sandwich panel. In particular the effects of the coupling between one of the bounding surface face sheets and acoustic foam are examined for its impact on both the structural and acoustic overall performance of the panel. The results suggest a potential in introducing an air gap between the acoustic foam parts and one of the face sheets, provided that the structural design constraints are met without prejudicing the layout of the different foam types.

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

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

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE PAGES

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

2016-07-26

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose calculations.

PubMed

Neylon, J; Min, Y; Kupelian, P; Low, D A; Santhanam, A

2017-04-01

In this paper, a multi-GPU cloud-based server (MGCS) framework is presented for dose calculations, exploring the feasibility of remote computing power for parallelization and acceleration of computationally and time intensive radiotherapy tasks in moving toward online adaptive therapies. An analytical model was developed to estimate theoretical MGCS performance acceleration and intelligently determine workload distribution. Numerical studies were performed with a computing setup of 14 GPUs distributed over 4 servers interconnected by a 1 Gigabits per second (Gbps) network. Inter-process communication methods were optimized to facilitate resource distribution and minimize data transfers over the server interconnect. The analytically predicted computation time predicted matched experimentally observations within 1-5 %. MGCS performance approached a theoretical limit of acceleration proportional to the number of GPUs utilized when computational tasks far outweighed memory operations. The MGCS implementation reproduced ground-truth dose computations with negligible differences, by distributing the work among several processes and implemented optimization strategies. The results showed that a cloud-based computation engine was a feasible solution for enabling clinics to make use of fast dose calculations for advanced treatment planning and adaptive radiotherapy. The cloud-based system was able to exceed the performance of a local machine even for optimized calculations, and provided significant acceleration for computationally intensive tasks. Such a framework can provide access to advanced technology and computational methods to many clinics, providing an avenue for standardization across institutions without the requirements of purchasing, maintaining, and continually updating hardware.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions

PubMed Central

Box, Simon

2014-01-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable. PMID:26064570

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

PubMed

Box, Simon

2014-12-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

Physiologically Based Absorption Modeling to Design Extended-Release Clinical Products for an Ester Prodrug.

PubMed

Ding, Xuan; Day, Jeffrey S; Sperry, David C

2016-11-01

Absorption modeling has demonstrated its great value in modern drug product development due to its utility in understanding and predicting in vivo performance. In this case, we integrated physiologically based modeling in the development processes to effectively design extended-release (ER) clinical products for an ester prodrug LY545694. By simulating the trial results of immediate-release products, we delineated complex pharmacokinetics due to prodrug conversion and established an absorption model to describe the clinical observations. This model suggested the prodrug has optimal biopharmaceutical properties to warrant developing an ER product. Subsequently, we incorporated release profiles of prototype ER tablets into the absorption model to simulate the in vivo performance of these products observed in an exploratory trial. The models suggested that the absorption of these ER tablets was lower than the IR products because the extended release from the formulations prevented the drug from taking advantage of the optimal absorption window. Using these models, we formed a strategy to optimize the ER product to minimize the impact of the absorption window limitation. Accurate prediction of the performance of these optimized products by modeling was confirmed in a third clinical trial.

Design and Optimization of Composite Automotive Hatchback Using Integrated Material-Structure-Process-Performance Method

NASA Astrophysics Data System (ADS)

Yang, Xudong; Sun, Lingyu; Zhang, Cheng; Li, Lijun; Dai, Zongmiao; Xiong, Zhenkai

2018-03-01

The application of polymer composites as a substitution of metal is an effective approach to reduce vehicle weight. However, the final performance of composite structures is determined not only by the material types, structural designs and manufacturing process, but also by their mutual restrict. Hence, an integrated "material-structure-process-performance" method is proposed for the conceptual and detail design of composite components. The material selection is based on the principle of composite mechanics such as rule of mixture for laminate. The design of component geometry, dimension and stacking sequence is determined by parametric modeling and size optimization. The selection of process parameters are based on multi-physical field simulation. The stiffness and modal constraint conditions were obtained from the numerical analysis of metal benchmark under typical load conditions. The optimal design was found by multi-discipline optimization. Finally, the proposed method was validated by an application case of automotive hatchback using carbon fiber reinforced polymer. Compared with the metal benchmark, the weight of composite one reduces 38.8%, simultaneously, its torsion and bending stiffness increases 3.75% and 33.23%, respectively, and the first frequency also increases 44.78%.

Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking

PubMed Central

Ge, Quanbo; Wei, Zhongliang; Cheng, Tianfa; Chen, Shaodong; Wang, Xiangfeng

2017-01-01

Compared with the fixed fusion structure, the flexible fusion structure with mixed fusion methods has better adjustment performance for the complex air task network systems, and it can effectively help the system to achieve the goal under the given constraints. Because of the time-varying situation of the task network system induced by moving nodes and non-cooperative target, and limitations such as communication bandwidth and measurement distance, it is necessary to dynamically adjust the system fusion structure including sensors and fusion methods in a given adjustment period. Aiming at this, this paper studies the design of a flexible fusion algorithm by using an optimization learning technology. The purpose is to dynamically determine the sensors’ numbers and the associated sensors to take part in the centralized and distributed fusion processes, respectively, herein termed sensor subsets selection. Firstly, two system performance indexes are introduced. Especially, the survivability index is presented and defined. Secondly, based on the two indexes and considering other conditions such as communication bandwidth and measurement distance, optimization models for both single target tracking and multi-target tracking are established. Correspondingly, solution steps are given for the two optimization models in detail. Simulation examples are demonstrated to validate the proposed algorithms. PMID:28481243

Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

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

Yoginath, Srikanth B; Perumalla, Kalyan S

2013-01-01

With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES) over multiple virtual machines, in contrast to executing in native mode directly over hardware as is traditionally done over the past decades. While mature VM-based parallel systems now offer new, compelling benefits such as serviceability, dynamic reconfigurability and overall cost effectiveness, the runtime performance of parallel applications can be significantly affected. In particular, most VM-based platforms are optimized for general workloads, but PDES execution exhibits unique dynamics significantly different from other workloads. Here we first present results frommore » experiments that highlight the gross deterioration of the runtime performance of VM-based PDES simulations when executed using traditional VM schedulers, quantitatively showing the bad scaling properties of the scheduler as the number of VMs is increased. The mismatch is fundamental in nature in the sense that any fairness-based VM scheduler implementation would exhibit this mismatch with PDES runs. We also present a new scheduler optimized specifically for PDES applications, and describe its design and implementation. Experimental results obtained from running PDES benchmarks (PHOLD and vehicular traffic simulations) over VMs show over an order of magnitude improvement in the run time of the PDES-optimized scheduler relative to the regular VM scheduler, with over 20 reduction in run time of simulations using up to 64 VMs. The observations and results are timely in the context of emerging systems such as cloud platforms and VM-based high performance computing installations, highlighting to the community the need for PDES-specific support, and the feasibility of significantly reducing the runtime overhead for scalable PDES on VM platforms.« less

Performance Optimization of Irreversible Air Heat Pumps Considering Size Effect

NASA Astrophysics Data System (ADS)

Bi, Yuehong; Chen, Lingen; Ding, Zemin; Sun, Fengrui

2018-06-01

Considering the size of an irreversible air heat pump (AHP), heating load density (HLD) is taken as thermodynamic optimization objective by using finite-time thermodynamics. Based on an irreversible AHP with infinite reservoir thermal-capacitance rate model, the expression of HLD of AHP is put forward. The HLD optimization processes are studied analytically and numerically, which consist of two aspects: (1) to choose pressure ratio; (2) to distribute heat-exchanger inventory. Heat reservoir temperatures, heat transfer performance of heat exchangers as well as irreversibility during compression and expansion processes are important factors influencing on the performance of an irreversible AHP, which are characterized with temperature ratio, heat exchanger inventory as well as isentropic efficiencies, respectively. Those impacts of parameters on the maximum HLD are thoroughly studied. The research results show that HLD optimization can make the size of the AHP system smaller and improve the compactness of system.

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

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

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

2013-01-07

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive controlmore » (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.« less

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

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

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

2013-04-03

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive controlmore » (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.« less

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

NASA Astrophysics Data System (ADS)

Ju, Yaping; Zhang, Chuhua

2016-03-01

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression (SVR) metamodel is combined with the Monte Carlo simulation (MCS) method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.

Shared Memory Parallelization of an Implicit ADI-type CFD Code

NASA Technical Reports Server (NTRS)

Hauser, Th.; Huang, P. G.

1999-01-01

A parallelization study designed for ADI-type algorithms is presented using the OpenMP specification for shared-memory multiprocessor programming. Details of optimizations specifically addressed to cache-based computer architectures are described and performance measurements for the single and multiprocessor implementation are summarized. The paper demonstrates that optimization of memory access on a cache-based computer architecture controls the performance of the computational algorithm. A hybrid MPI/OpenMP approach is proposed for clusters of shared memory machines to further enhance the parallel performance. The method is applied to develop a new LES/DNS code, named LESTool. A preliminary DNS calculation of a fully developed channel flow at a Reynolds number of 180, Re(sub tau) = 180, has shown good agreement with existing data.

Exponential H ∞ Synchronization of Chaotic Cryptosystems Using an Improved Genetic Algorithm

PubMed Central

Hsiao, Feng-Hsiag

2015-01-01

This paper presents a systematic design methodology for neural-network- (NN-) based secure communications in multiple time-delay chaotic (MTDC) systems with optimal H ∞ performance and cryptography. On the basis of the Improved Genetic Algorithm (IGA), which is demonstrated to have better performance than that of a traditional GA, a model-based fuzzy controller is then synthesized to stabilize the MTDC systems. A fuzzy controller is synthesized to not only realize the exponential synchronization, but also achieve optimal H ∞ performance by minimizing the disturbance attenuation level. Furthermore, the error of the recovered message is stated by using the n-shift cipher and key. Finally, a numerical example with simulations is given to demonstrate the effectiveness of our approach. PMID:26366432

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

PubMed

Gomez-Cardona, Daniel; Hayes, John W; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong

2018-05-01

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs. The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object. © 2018 American Association of Physicists in Medicine.

Automated Scenario Generation: Toward Tailored and Optimized Military Training in Virtual Environments

DTIC Science & Technology

2012-01-01

us.army.mil ABSTRACT Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper , we enumerate...through a narrative, mission, quest, or scenario. In this paper we argue for a combinatorial optimization search approach to selecting and ordering...the role of an expert for the purposes of practicing skills and knowledge in realistic situations in a learning-by-doing approach to performance

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Diagnostic performance and optimal cut-off scores of the Massachusetts youth screening instrument-second version in a sample of Swiss youths in welfare and juvenile justice institutions.

PubMed

Dölitzsch, Claudia; Leenarts, Laura E W; Schmeck, Klaus; Fegert, Jorg M; Grisso, Thomas; Schmid, Marc

2017-02-08

There is a growing consensus about the importance of mental health screening of youths in welfare and juvenile justice institutions. The Massachusetts Youth Screening Instrument-second version (MAYSI-2) was specifically designed, normed and validated to assist juvenile justice facilities in the United States of America (USA), in identifying youths with potential emotional or behavioral problems. However, it is not known if the USA norm-based cut-off scores can be used in Switzerland. Therefore, the primary purpose of the current study was to estimate the diagnostic performance and optimal cut-off scores of the MAYSI-2 in a sample of Swiss youths in welfare and juvenile justice institutions. As the sample was drawn from the French-, German- and Italian-speaking parts of Switzerland, the three languages were represented in the total sample of the current study and consequently we could estimate the diagnostic performance and the optimal cut-off scores of the MAYSI-2 for the language regions separately. The other main purpose of the current study was to identify potential gender differences in the diagnostic performance and optimal cut-off scores. Participants were 297 boys and 149 girls (mean age = 16.2, SD = 2.5) recruited from 64 youth welfare and juvenile justice institutions (drawn from the French-, German- and Italian-speaking parts of Switzerland). The MAYSI-2 was used to screen for mental health or behavioral problems that could require further evaluation. Psychiatric classification was based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children, Present and Lifetime version (K-SADS-PL). The MAYSI-2 scores were submitted into Receiver-Operating Characteristic (ROC) analyses to estimate the diagnostic performance and optimal 'caution' cut-off scores of the MAYSI-2. The ROC analyses revealed that nearly all homotypic mappings of MAYSI-2 scales onto (cluster of) psychiatric disorders revealed above chance level accuracy. The optimal 'caution' cut-off scores derived from the ROC curve for predicting (cluster of) psychiatric disorders were, for several MAYSI-2 scales, comparable to the USA norm-based 'caution' cut-off scores. For some MAYSI-2 scales, however, higher optimal 'caution' cut-off scores were found. With adjusted optimal 'caution' cut-off scores, the MAYSI-2 screens potential emotional or behavioral problems well in a sample of Swiss youths in welfare and juvenile justice institutions. However, as for choosing the optimal 'caution' cut off score for the MAYSI-2, both language as well as gender seems to be of importance. The results of this study point to a compelling need to test the diagnostic performance and optimal 'caution' cut-off scores of the MAYSI-2 more elaborately in larger differentiated language samples in Europe.

Optimal short-range trajectories for helicopters

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

Slater, G.L.; Erzberger, H.

1982-12-01

An optimal flight path algorithm using a simplified altitude state model and a priori climb cruise descent flight profile was developed and applied to determine minimum fuel and minimum cost trajectories for a helicopter flying a fixed range trajectory. In addition, a method was developed for obtaining a performance model in simplified form which is based on standard flight manual data and which is applicable to the computation of optimal trajectories. The entire performance optimization algorithm is simple enough that on line trajectory optimization is feasible with a relatively small computer. The helicopter model used is the Silorsky S-61N. Themore » results show that for this vehicle the optimal flight path and optimal cruise altitude can represent a 10% fuel saving on a minimum fuel trajectory. The optimal trajectories show considerable variability because of helicopter weight, ambient winds, and the relative cost trade off between time and fuel. In general, reasonable variations from the optimal velocities and cruise altitudes do not significantly degrade the optimal cost. For fuel optimal trajectories, the optimum cruise altitude varies from the maximum (12,000 ft) to the minimum (0 ft) depending on helicopter weight.« less

A Joint Optimization Criterion for Blind DS-CDMA Detection

NASA Astrophysics Data System (ADS)

Durán-Díaz, Iván; Cruces-Alvarez, Sergio A.

2006-12-01

This paper addresses the problem of the blind detection of a desired user in an asynchronous DS-CDMA communications system with multipath propagation channels. Starting from the inverse filter criterion introduced by Tugnait and Li in 2001, we propose to tackle the problem in the context of the blind signal extraction methods for ICA. In order to improve the performance of the detector, we present a criterion based on the joint optimization of several higher-order statistics of the outputs. An algorithm that optimizes the proposed criterion is described, and its improved performance and robustness with respect to the near-far problem are corroborated through simulations. Additionally, a simulation using measurements on a real software-radio platform at 5 GHz has also been performed.

Expected value based fuzzy programming approach to solve integrated supplier selection and inventory control problem with fuzzy demand

NASA Astrophysics Data System (ADS)

Sutrisno; Widowati; Sunarsih; Kartono

2018-01-01

In this paper, a mathematical model in quadratic programming with fuzzy parameter is proposed to determine the optimal strategy for integrated inventory control and supplier selection problem with fuzzy demand. To solve the corresponding optimization problem, we use the expected value based fuzzy programming. Numerical examples are performed to evaluate the model. From the results, the optimal amount of each product that have to be purchased from each supplier for each time period and the optimal amount of each product that have to be stored in the inventory for each time period were determined with minimum total cost and the inventory level was sufficiently closed to the reference level.

Structural Performance’s Optimally Analysing and Implementing Based on ANSYS Technology

NASA Astrophysics Data System (ADS)

Han, Na; Wang, Xuquan; Yue, Haifang; Sun, Jiandong; Wu, Yongchun

2017-06-01

Computer-aided Engineering (CAE) is a hotspot both in academic field and in modern engineering practice. Analysis System(ANSYS) simulation software for its excellent performance become outstanding one in CAE family, it is committed to the innovation of engineering simulation to help users to shorten the design process, improve product innovation and performance. Aimed to explore a structural performance’s optimally analyzing model for engineering enterprises, this paper introduced CAE and its development, analyzed the necessity for structural optimal analysis as well as the framework of structural optimal analysis on ANSYS Technology, used ANSYS to implement a reinforced concrete slab structural performance’s optimal analysis, which was display the chart of displacement vector and the chart of stress intensity. Finally, this paper compared ANSYS software simulation results with the measured results,expounded that ANSYS is indispensable engineering calculation tools.

It looks easy! Heuristics for combinatorial optimization problems.

PubMed

Chronicle, Edward P; MacGregor, James N; Ormerod, Thomas C; Burr, Alistair

2006-04-01

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks.

In-flight performance optimization for rotorcraft with redundant controls

NASA Astrophysics Data System (ADS)

Ozdemir, Gurbuz Taha

A conventional helicopter has limits on performance at high speeds because of the limitations of main rotor, such as compressibility issues on advancing side or stall issues on retreating side. Auxiliary lift and thrust components have been suggested to improve performance of the helicopter substantially by reducing the loading on the main rotor. Such a configuration is called the compound rotorcraft. Rotor speed can also be varied to improve helicopter performance. In addition to improved performance, compound rotorcraft and variable RPM can provide a much larger degree of control redundancy. This additional redundancy gives the opportunity to further enhance performance and handling qualities. A flight control system is designed to perform in-flight optimization of redundant control effectors on a compound rotorcraft in order to minimize power required and extend range. This "Fly to Optimal" (FTO) control law is tested in simulation using the GENHEL model. A model of the UH-60, a compound version of the UH-60A with lifting wing and vectored thrust ducted propeller (VTDP), and a generic compound version of the UH-60A with lifting wing and propeller were developed and tested in simulation. A model following dynamic inversion controller is implemented for inner loop control of roll, pitch, yaw, heave, and rotor RPM. An outer loop controller regulates airspeed and flight path during optimization. A Golden Section search method was used to find optimal rotor RPM on a conventional helicopter, where the single redundant control effector is rotor RPM. The FTO builds off of the Adaptive Performance Optimization (APO) method of Gilyard by performing low frequency sweeps on a redundant control for a fixed wing aircraft. A method based on the APO method was used to optimize trim on a compound rotorcraft with several redundant control effectors. The controller can be used to optimize rotor RPM and compound control effectors through flight test or simulations in order to establish a schedule. The method has been expanded to search a two-dimensional control space. Simulation results demonstrate the ability to maximize range by optimizing stabilator deflection and an airspeed set point. Another set of results minimize power required in high speed flight by optimizing collective pitch and stabilator deflection. Results show that the control laws effectively hold the flight condition while the FTO method is effective at improving performance. Optimizations show there can be issues when the control laws regulating altitude push the collective control towards it limits. So a modification was made to the control law to regulate airspeed and altitude using propeller pitch and angle of attack while the collective is held fixed or used as an optimization variable. A dynamic trim limit avoidance algorithm is applied to avoid control saturation in other axes during optimization maneuvers. Range and power optimization FTO simulations are compared with comprehensive sweeps of trim solutions and FTO optimization shown to be effective and reliable in reaching an optimal when optimizing up to two redundant controls. Use of redundant controls is shown to be beneficial for improving performance. The search method takes almost 25 minutes of simulated flight for optimization to be complete. The optimization maneuver itself can sometimes drive the power required to high values, so a power limit is imposed to restrict the search to avoid conditions where power is more than5% higher than that of the initial trim state. With this modification, the time the optimization maneuver takes to complete is reduced down to 21 minutes without any significant change in the optimal power value.

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.

Mitigation of Engine Inlet Distortion Through Adjoint-Based Design

NASA Technical Reports Server (NTRS)

Ordaz, Irian; Rallabhandi, Sriram; Nielsen, Eric J.; Diskin, Boris

2017-01-01

The adjoint-based design capability in FUN3D is extended to allow efficient gradient- based optimization and design of concepts with highly integrated aero-propulsive systems. A circumferential distortion calculation, along with the derivatives needed to perform adjoint-based design, have been implemented in FUN3D. This newly implemented distortion calculation can be used not only for design but also to drive the existing mesh adaptation process and reduce the error associated with the fan distortion calculation. The design capability is demonstrated by the shape optimization of an in-house aircraft concept equipped with an aft fuselage propulsor. The optimization objective is the minimization of flow distortion at the aerodynamic interface plane of this aft fuselage propulsor.

Optimization of batteries for plug-in hybrid electric vehicles

NASA Astrophysics Data System (ADS)

English, Jeffrey Robb

This thesis presents a method to quickly determine the optimal battery for an electric vehicle given a set of vehicle characteristics and desired performance metrics. The model is based on four independent design variables: cell count, cell capacity, state-of-charge window, and battery chemistry. Performance is measured in seven categories: cost, all-electric range, maximum speed, acceleration, battery lifetime, lifetime greenhouse gas emissions, and charging time. The performance of each battery is weighted according to a user-defined objective function to determine its overall fitness. The model is informed by a series of battery tests performed on scaled-down battery samples. Seven battery chemistries were tested for capacity at different discharge rates, maximum output power at different charge levels, and performance in a real-world automotive duty cycle. The results of these tests enable a prediction of the performance of the battery in an automobile. Testing was performed at both room temperature and low temperature to investigate the effects of battery temperature on operation. The testing highlighted differences in behavior between lithium, nickel, and lead based batteries. Battery performance decreased with temperature across all samples with the largest effect on nickel-based chemistries. Output power also decreased with lead acid batteries being the least affected by temperature. Lithium-ion batteries were found to be highly efficient (>95%) under a vehicular duty cycle; nickel and lead batteries have greater losses. Low temperatures hindered battery performance and resulted in accelerated failure in several samples. Lead acid, lead tin, and lithium nickel alloy batteries were unable to complete the low temperature testing regime without losing significant capacity and power capability. This is a concern for their applicability in electric vehicles intended for cold climates which have to maintain battery temperature during long periods of inactivity. Three sample optimizations were performed: a compact car, a, truck, and a sports car. The compact car benefits from increased battery capacity despite the associated higher cost. The truck returned the smallest possible battery of each chemistry, indicating that electrification is not advisable. The sports car optimization resulted in the largest possible battery, indicating large performance from increased electrification. These results mirror the current state of the electric vehicle market.

Design synthesis and optimization of permanent magnet synchronous machines based on computationally-efficient finite element analysis

NASA Astrophysics Data System (ADS)

Sizov, Gennadi Y.

In this dissertation, a model-based multi-objective optimal design of permanent magnet ac machines, supplied by sine-wave current regulated drives, is developed and implemented. The design procedure uses an efficient electromagnetic finite element-based solver to accurately model nonlinear material properties and complex geometric shapes associated with magnetic circuit design. Application of an electromagnetic finite element-based solver allows for accurate computation of intricate performance parameters and characteristics. The first contribution of this dissertation is the development of a rapid computational method that allows accurate and efficient exploration of large multi-dimensional design spaces in search of optimum design(s). The computationally efficient finite element-based approach developed in this work provides a framework of tools that allow rapid analysis of synchronous electric machines operating under steady-state conditions. In the developed modeling approach, major steady-state performance parameters such as, winding flux linkages and voltages, average, cogging and ripple torques, stator core flux densities, core losses, efficiencies and saturated machine winding inductances, are calculated with minimum computational effort. In addition, the method includes means for rapid estimation of distributed stator forces and three-dimensional effects of stator and/or rotor skew on the performance of the machine. The second contribution of this dissertation is the development of the design synthesis and optimization method based on a differential evolution algorithm. The approach relies on the developed finite element-based modeling method for electromagnetic analysis and is able to tackle large-scale multi-objective design problems using modest computational resources. Overall, computational time savings of up to two orders of magnitude are achievable, when compared to current and prevalent state-of-the-art methods. These computational savings allow one to expand the optimization problem to achieve more complex and comprehensive design objectives. The method is used in the design process of several interior permanent magnet industrial motors. The presented case studies demonstrate that the developed finite element-based approach practically eliminates the need for using less accurate analytical and lumped parameter equivalent circuit models for electric machine design optimization. The design process and experimental validation of the case-study machines are detailed in the dissertation.

Optimal Bayesian Adaptive Design for Test-Item Calibration.

PubMed

van der Linden, Wim J; Ren, Hao

2015-06-01

An optimal adaptive design for test-item calibration based on Bayesian optimality criteria is presented. The design adapts the choice of field-test items to the examinees taking an operational adaptive test using both the information in the posterior distributions of their ability parameters and the current posterior distributions of the field-test parameters. Different criteria of optimality based on the two types of posterior distributions are possible. The design can be implemented using an MCMC scheme with alternating stages of sampling from the posterior distributions of the test takers' ability parameters and the parameters of the field-test items while reusing samples from earlier posterior distributions of the other parameters. Results from a simulation study demonstrated the feasibility of the proposed MCMC implementation for operational item calibration. A comparison of performances for different optimality criteria showed faster calibration of substantial numbers of items for the criterion of D-optimality relative to A-optimality, a special case of c-optimality, and random assignment of items to the test takers.

Focusing light through dynamical samples using fast continuous wavefront optimization.

PubMed

Blochet, B; Bourdieu, L; Gigan, S

2017-12-01

We describe a fast continuous optimization wavefront shaping system able to focus light through dynamic scattering media. A micro-electro-mechanical system-based spatial light modulator, a fast photodetector, and field programmable gate array electronics are combined to implement a continuous optimization of a wavefront with a single-mode optimization rate of 4.1 kHz. The system performances are demonstrated by focusing light through colloidal solutions of TiO 2 particles in glycerol with tunable temporal stability.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimizing lighting, thermal performance, and energy production of building facades by using automated blinds and PV cells

NASA Astrophysics Data System (ADS)

Alzoubi, Hussain Hendi

Energy consumption in buildings has recently become a major concern for environmental designers. Within this field, daylighting and solar energy design are attractive strategies for saving energy. This study seeks the integrity and the optimality of building envelopes' performance. It focuses on the transparent parts of building facades, specifically, the windows and their shading devices. It suggests a new automated method of utilizing solar energy while keeping optimal solutions for indoor daylighting. The method utilizes a statistical approach to produce mathematical equations based on physical experimentation. A full-scale mock-up representing an actual office was built. Heat gain and lighting levels were measured empirically and correlated with blind angles. Computational methods were used to estimate the power production from photovoltaic cells. Mathematical formulas were derived from the results of the experiments; these formulas were utilized to construct curves as well as mathematical equations for the purpose of optimization. The mathematical equations resulting from the optimization process were coded using Java programming language to enable future users to deal with generic locations of buildings with a broader context of various climatic conditions. For the purpose of optimization by automation under different climatic conditions, a blind control system was developed based on the findings of this study. This system calibrates the blind angles instantaneously based upon the sun position, the indoor daylight, and the power production from the photovoltaic cells. The functions of this system guarantee full control of the projected solar energy on buildings' facades for indoor lighting and heat gain. In winter, the system automatically blows heat into the space, whereas it expels heat from the space during the summer season. The study showed that the optimality of building facades' performance is achievable for integrated thermal, energy, and lighting models in buildings. There are blind angles that produce maximum energy from the photovoltaic cells while keeping indoor light within the acceptable limits that prevent undesired heat gain in summer.

An auxiliary optimization method for complex public transit route network based on link prediction

NASA Astrophysics Data System (ADS)

Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

2018-02-01

Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

Optimal structural design of the midship of a VLCC based on the strategy integrating SVM and GA

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Deyu

2012-03-01

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine (SVM) and genetic algorithm (GA), was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR), which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

Estimation of power lithium-ion battery SOC based on fuzzy optimal decision

NASA Astrophysics Data System (ADS)

He, Dongmei; Hou, Enguang; Qiao, Xin; Liu, Guangmin

2018-06-01

In order to improve vehicle performance and safety, need to accurately estimate the power lithium battery state of charge (SOC), analyzing the common SOC estimation methods, according to the characteristics open circuit voltage and Kalman filter algorithm, using T - S fuzzy model, established a lithium battery SOC estimation method based on the fuzzy optimal decision. Simulation results show that the battery model accuracy can be improved.

Implementation and Optimization of miniGMG - a Compact Geometric Multigrid Benchmark

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

Williams, Samuel; Kalamkar, Dhiraj; Singh, Amik

2012-12-01

Multigrid methods are widely used to accelerate the convergence of iterative solvers for linear systems used in a number of different application areas. In this report, we describe miniGMG, our compact geometric multigrid benchmark designed to proxy the multigrid solves found in AMR applications. We explore optimization techniques for geometric multigrid on existing and emerging multicore systems including the Opteron-based Cray XE6, Intel Sandy Bridge and Nehalem-based Infiniband clusters, as well as manycore-based architectures including NVIDIA's Fermi and Kepler GPUs and Intel's Knights Corner (KNC) co-processor. This report examines a variety of novel techniques including communication-aggregation, threaded wavefront-based DRAM communication-avoiding,more » dynamic threading decisions, SIMDization, and fusion of operators. We quantify performance through each phase of the V-cycle for both single-node and distributed-memory experiments and provide detailed analysis for each class of optimization. Results show our optimizations yield significant speedups across a variety of subdomain sizes while simultaneously demonstrating the potential of multi- and manycore processors to dramatically accelerate single-node performance. However, our analysis also indicates that improvements in networks and communication will be essential to reap the potential of manycore processors in large-scale multigrid calculations.« less

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

NASA Astrophysics Data System (ADS)

Hwang, Donghyeok; Tahk, Min-Jea

2018-04-01

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

Periodic Application of Stochastic Cost Optimization Methodology to Achieve Remediation Objectives with Minimized Life Cycle Cost

NASA Astrophysics Data System (ADS)

Kim, U.; Parker, J.

2016-12-01

Many dense non-aqueous phase liquid (DNAPL) contaminated sites in the U.S. are reported as "remediation in progress" (RIP). However, the cost to complete (CTC) remediation at these sites is highly uncertain and in many cases, the current remediation plan may need to be modified or replaced to achieve remediation objectives. This study evaluates the effectiveness of iterative stochastic cost optimization that incorporates new field data for periodic parameter recalibration to incrementally reduce prediction uncertainty and implement remediation design modifications as needed to minimize the life cycle cost (i.e., CTC). This systematic approach, using the Stochastic Cost Optimization Toolkit (SCOToolkit), enables early identification and correction of problems to stay on track for completion while minimizing the expected (i.e., probability-weighted average) CTC. This study considers a hypothetical site involving multiple DNAPL sources in an unconfined aquifer using thermal treatment for source reduction and electron donor injection for dissolved plume control. The initial design is based on stochastic optimization using model parameters and their joint uncertainty based on calibration to site characterization data. The model is periodically recalibrated using new monitoring data and performance data for the operating remediation systems. Projected future performance using the current remediation plan is assessed and reoptimization of operational variables for the current system or consideration of alternative designs are considered depending on the assessment results. We compare remediation duration and cost for the stepwise re-optimization approach with single stage optimization as well as with a non-optimized design based on typical engineering practice.

A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization.

PubMed

Hou, Zeyu; Lu, Wenxi; Xue, Haibo; Lin, Jin

2017-08-01

Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods. In this regard, we present set pair analysis (SPA) as a new method to build ensemble surrogate (ES) model, and conducted a comparative research to select a better ES modeling pattern for the SEAR strategy optimization problems. Surrogate models were developed using radial basis function artificial neural network (RBFANN), support vector regression (SVR), and Kriging. One ES model is assembling RBFANN model, SVR model, and Kriging model using set pair weights according their performance, and the other is assembling several Kriging (the best surrogate modeling method of three) models built with different training sample datasets. Finally, an optimization model, in which the ES model was embedded, was established to obtain the optimal remediation strategy. The results showed the residuals of the outputs between the best ES model and simulation model for 100 testing samples were lower than 1.5%. Using an ES model instead of the simulation model was critical for considerably reducing the computation time of simulation-optimization process and maintaining high computation accuracy simultaneously. Copyright © 2017 Elsevier B.V. All rights reserved.

On the estimation algorithm used in adaptive performance optimization of turbofan engines

NASA Technical Reports Server (NTRS)

Espana, Martin D.; Gilyard, Glenn B.

1993-01-01

The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

Multivariable optimization of liquid rocket engines using particle swarm algorithms

NASA Astrophysics Data System (ADS)

Jones, Daniel Ray

Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.

Is Performance Feedback for Educators an Evidence-Based Practice? A Systematic Review and Evaluation Based on Single-Case Research

ERIC Educational Resources Information Center

Fallon, Lindsay M.; Collier-Meek, Melissa A.; Maggin, Daniel M.; Sanetti, Lisa M. H.; Johnson, Austin H.

2015-01-01

Optimal levels of treatment fidelity, a critical moderator of intervention effectiveness, are often difficult to sustain in applied settings. It is unknown whether performance feedback, a widely researched method for increasing educators' treatment fidelity, is an evidence-based practice. The purpose of this review was to evaluate the current…

High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis.

PubMed

Foong, Shaohui; Sun, Zhenglong

2016-08-12

In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison.

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

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

GPU color space conversion

NASA Astrophysics Data System (ADS)

Chase, Patrick; Vondran, Gary

2011-01-01

Tetrahedral interpolation is commonly used to implement continuous color space conversions from sparse 3D and 4D lookup tables. We investigate the implementation and optimization of tetrahedral interpolation algorithms for GPUs, and compare to the best known CPU implementations as well as to a well known GPU-based trilinear implementation. We show that a 500 NVIDIA GTX-580 GPU is 3x faster than a 1000 Intel Core i7 980X CPU for 3D interpolation, and 9x faster for 4D interpolation. Performance-relevant GPU attributes are explored including thread scheduling, local memory characteristics, global memory hierarchy, and cache behaviors. We consider existing tetrahedral interpolation algorithms and tune based on the structure and branching capabilities of current GPUs. Global memory performance is improved by reordering and expanding the lookup table to ensure optimal access behaviors. Per multiprocessor local memory is exploited to implement optimally coalesced global memory accesses, and local memory addressing is optimized to minimize bank conflicts. We explore the impacts of lookup table density upon computation and memory access costs. Also presented are CPU-based 3D and 4D interpolators, using SSE vector operations that are faster than any previously published solution.

GEANT4 simulations of a novel 3He-free thermalization neutron detector

NASA Astrophysics Data System (ADS)

Mazzone, A.; Finocchiaro, P.; Lo Meo, S.; Colonna, N.

2018-05-01

A novel concept for 3He-free thermalization detector is here investigated by means of GEANT4 simulations. The detector is based on strips of solid-state detectors with 6Li deposit for neutron conversion. Various geometrical configurations have been investigated in order to find the optimal solution, in terms of value and energy dependence of the efficiency for neutron energies up to 10 MeV. The expected performance of the new detector are compared with those of an optimized thermalization detector based on standard 3He tubes. Although an 3He-based detector is superior in terms of performance and simplicity, the proposed solution may become more appealing in terms of costs in case of shortage of 3He supply.

Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

NASA Astrophysics Data System (ADS)

Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh

2017-07-01

In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

Optimization of small long-life PWR based on thorium fuel

NASA Astrophysics Data System (ADS)

Subkhi, Moh Nurul; Suud, Zaki; Waris, Abdul; Permana, Sidik

2015-09-01

A conceptual design of small long-life Pressurized Water Reactor (PWR) using thorium fuel has been investigated in neutronic aspect. The cell-burn up calculations were performed by PIJ SRAC code using nuclear data library based on JENDL 3.2, while the multi-energy-group diffusion calculations were optimized in three-dimension X-Y-Z geometry of core by COREBN. The excess reactivity of thorium nitride with ZIRLO cladding is considered during 5 years of burnup without refueling. Optimization of 350 MWe long life PWR based on 5% 233U & 2.8% 231Pa, 6% 233U & 2.8% 231Pa and 7% 233U & 6% 231Pa give low excess reactivity.

Model-based optimizations of packaged rooftop air conditioners using low global warming potential refrigerants

DOE PAGES

Shen, Bo; Abdelaziz, Omar; Shrestha, Som; ...

2017-10-31

Based on laboratory investigations for R-22 and R-410A alternative low GWP refrigerants in two baseline rooftop air conditioners (RTU), the DOE/ORNL Heat Pump Design Model was used to model the two RTUs and the models were calibrated against the experimental data. We compared the compressor efficiencies and heat exchanger performances. An efficiency-based compressor mapping method was developed. Extensive model-based optimizations were conducted to provide a fair comparison between all the low GWP candidates by selecting optimal configurations. The results illustrate that all the R-22 low GWP refrigerants will lead to slightly lower COPs. ARM-20B appears to be the best R-22more » replacement at normal conditions. At higher ambient temperatures, ARM-20A exhibits better performance. All R-410A low GWP candidates will result in similar or better efficiencies than R-410A. R-32 has the best COP while requiring the smallest compressor. Finally, R-452B uses the closest compressor displacement volume and achieves the same efficiency as R-410A.« less

Model-based optimizations of packaged rooftop air conditioners using low global warming potential refrigerants

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

Shen, Bo; Abdelaziz, Omar; Shrestha, Som

Based on laboratory investigations for R-22 and R-410A alternative low GWP refrigerants in two baseline rooftop air conditioners (RTU), the DOE/ORNL Heat Pump Design Model was used to model the two RTUs and the models were calibrated against the experimental data. We compared the compressor efficiencies and heat exchanger performances. An efficiency-based compressor mapping method was developed. Extensive model-based optimizations were conducted to provide a fair comparison between all the low GWP candidates by selecting optimal configurations. The results illustrate that all the R-22 low GWP refrigerants will lead to slightly lower COPs. ARM-20B appears to be the best R-22more » replacement at normal conditions. At higher ambient temperatures, ARM-20A exhibits better performance. All R-410A low GWP candidates will result in similar or better efficiencies than R-410A. R-32 has the best COP while requiring the smallest compressor. Finally, R-452B uses the closest compressor displacement volume and achieves the same efficiency as R-410A.« less

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.

Tooth shape optimization of brushless permanent magnet motors for reducing torque ripples

NASA Astrophysics Data System (ADS)

Hsu, Liang-Yi; Tsai, Mi-Ching

2004-11-01

This paper presents a tooth shape optimization method based on a generic algorithm to reduce the torque ripple of brushless permanent magnet motors under two different magnetization directions. The analysis of this design method mainly focuses on magnetic saturation and cogging torque and the computation of the optimization process is based on an equivalent magnetic network circuit. The simulation results, obtained from the finite element analysis, are used to confirm the accuracy and performance. Finite element analysis results from different tooth shapes are compared to show the effectiveness of the proposed method.

Effect of heater geometry and cavity volume on the sensitivity of a thermal convection-based tilt sensor

NASA Astrophysics Data System (ADS)

Han, Maeum; Keon Kim, Jae; Kong, Seong Ho; Kang, Shin-Won; Jung, Daewoong

2018-06-01

This paper reports a micro-electro-mechanical-system (MEMS)-based tilt sensor using air medium. Since the working mechanism of the sensor is the thermal convection in a sealed chamber, structural parameters that can affect thermal convection must be considered to optimize the performance of the sensor. This paper presents the experimental results that were conducted by optimizing several parameters such as the heater geometry, input power and cavity volume. We observed that an increase in the heating power and cavity volume can improve the sensitivity, and heater geometry plays important role in performance of the sensor.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Genetics-based control of a mimo boiler-turbine plant

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

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

1994-12-31

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

A novel load balanced energy conservation approach in WSN using biogeography based optimization

NASA Astrophysics Data System (ADS)

Kaushik, Ajay; Indu, S.; Gupta, Daya

2017-09-01

Clustering sensor nodes is an effective technique to reduce energy consumption of the sensor nodes and maximize the lifetime of Wireless sensor networks. Balancing load of the cluster head is an important factor in long run operation of WSNs. In this paper we propose a novel load balancing approach using biogeography based optimization (LB-BBO). LB-BBO uses two separate fitness functions to perform load balancing of equal and unequal load respectively. The proposed method is simulated using matlab and compared with existing methods. The proposed method shows better performance than all the previous works implemented for energy conservation in WSN

Design of a correlated validated CFD and genetic algorithm model for optimized sensors placement for indoor air quality monitoring

NASA Astrophysics Data System (ADS)

Mousavi, Monireh Sadat; Ashrafi, Khosro; Motlagh, Majid Shafie Pour; Niksokhan, Mohhamad Hosein; Vosoughifar, HamidReza

2018-02-01

In this study, coupled method for simulation of flow pattern based on computational methods for fluid dynamics with optimization technique using genetic algorithms is presented to determine the optimal location and number of sensors in an enclosed residential complex parking in Tehran. The main objective of this research is costs reduction and maximum coverage with regard to distribution of existing concentrations in different scenarios. In this study, considering all the different scenarios for simulation of pollution distribution using CFD simulations has been challenging due to extent of parking and number of cars available. To solve this problem, some scenarios have been selected based on random method. Then, maximum concentrations of scenarios are chosen for performing optimization. CFD simulation outputs are inserted as input in the optimization model using genetic algorithm. The obtained results stated optimal number and location of sensors.

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.

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Information fusion based optimal control for large civil aircraft system.

PubMed

Zhen, Ziyang; Jiang, Ju; Wang, Xinhua; Gao, Chen

2015-03-01

Wind disturbance has a great influence on landing security of Large Civil Aircraft. Through simulation research and engineering experience, it can be found that PID control is not good enough to solve the problem of restraining the wind disturbance. This paper focuses on anti-wind attitude control for Large Civil Aircraft in landing phase. In order to improve the riding comfort and the flight security, an information fusion based optimal control strategy is presented to restrain the wind in landing phase for maintaining attitudes and airspeed. Data of Boeing707 is used to establish a nonlinear mode with total variables of Large Civil Aircraft, and then two linear models are obtained which are divided into longitudinal and lateral equations. Based on engineering experience, the longitudinal channel adopts PID control and C inner control to keep longitudinal attitude constant, and applies autothrottle system for keeping airspeed constant, while an information fusion based optimal regulator in the lateral control channel is designed to achieve lateral attitude holding. According to information fusion estimation, by fusing hard constraint information of system dynamic equations and the soft constraint information of performance index function, optimal estimation of the control sequence is derived. Based on this, an information fusion state regulator is deduced for discrete time linear system with disturbance. The simulation results of nonlinear model of aircraft indicate that the information fusion optimal control is better than traditional PID control, LQR control and LQR control with integral action, in anti-wind disturbance performance in the landing phase. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Software for optimization of SNP and PCR-RFLP genotyping to discriminate many genomes with the fewest assays

PubMed Central

Gardner, Shea N; Wagner, Mark C

2005-01-01

Background Microbial forensics is important in tracking the source of a pathogen, whether the disease is a naturally occurring outbreak or part of a criminal investigation. Results A method and SPR Opt (SNP and PCR-RFLP Optimization) software to perform a comprehensive, whole-genome analysis to forensically discriminate multiple sequences is presented. Tools for the optimization of forensic typing using Single Nucleotide Polymorphism (SNP) and PCR-Restriction Fragment Length Polymorphism (PCR-RFLP) analyses across multiple isolate sequences of a species are described. The PCR-RFLP analysis includes prediction and selection of optimal primers and restriction enzymes to enable maximum isolate discrimination based on sequence information. SPR Opt calculates all SNP or PCR-RFLP variations present in the sequences, groups them into haplotypes according to their co-segregation across those sequences, and performs combinatoric analyses to determine which sets of haplotypes provide maximal discrimination among all the input sequences. Those set combinations requiring that membership in the fewest haplotypes be queried (i.e. the fewest assays be performed) are found. These analyses highlight variable regions based on existing sequence data. These markers may be heterogeneous among unsequenced isolates as well, and thus may be useful for characterizing the relationships among unsequenced as well as sequenced isolates. The predictions are multi-locus. Analyses of mumps and SARS viruses are summarized. Phylogenetic trees created based on SNPs, PCR-RFLPs, and full genomes are compared for SARS virus, illustrating that purported phylogenies based only on SNP or PCR-RFLP variations do not match those based on multiple sequence alignment of the full genomes. Conclusion This is the first software to optimize the selection of forensic markers to maximize information gained from the fewest assays, accepting whole or partial genome sequence data as input. As more sequence data becomes available for multiple strains and isolates of a species, automated, computational approaches such as those described here will be essential to make sense of large amounts of information, and to guide and optimize efforts in the laboratory. The software and source code for SPR Opt is publicly available and free for non-profit use at . PMID:15904493

Distributed Space Mission Design for Earth Observation Using Model-Based Performance Evaluation

NASA Technical Reports Server (NTRS)

Nag, Sreeja; LeMoigne-Stewart, Jacqueline; Cervantes, Ben; DeWeck, Oliver

2015-01-01

Distributed Space Missions (DSMs) are gaining momentum in their application to earth observation missions owing to their unique ability to increase observation sampling in multiple dimensions. DSM design is a complex problem with many design variables, multiple objectives determining performance and cost and emergent, often unexpected, behaviors. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal design. This paper presents a software tool that can multiple DSM architectures based on pre-defined design variable ranges and size those architectures in terms of predefined science and cost metrics. The tool will help a user select Pareto optimal DSM designs based on design of experiments techniques. The tool will be applied to some earth observation examples to demonstrate its applicability in making some key decisions between different performance metrics and cost metrics early in the design lifecycle.

Profiling optimization for big data transfer over dedicated channels

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

Yun, D.; Wu, Qishi; Rao, Nageswara S

The transfer of big data is increasingly supported by dedicated channels in high-performance networks, where transport protocols play an important role in maximizing applicationlevel throughput and link utilization. The performance of transport protocols largely depend on their control parameter settings, but it is prohibitively time consuming to conduct an exhaustive search in a large parameter space to find the best set of parameter values. We propose FastProf, a stochastic approximation-based transport profiler, to quickly determine the optimal operational zone of a given data transfer protocol/method over dedicated channels. We implement and test the proposed method using both emulations based onmore » real-life performance measurements and experiments over physical connections with short (2 ms) and long (380 ms) delays. Both the emulation and experimental results show that FastProf significantly reduces the profiling overhead while achieving a comparable level of end-to-end throughput performance with the exhaustive search-based approach.« less

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints.

PubMed

Tchamna, Rodrigue; Lee, Moonyong

2018-01-01

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Investigation into Improvement for Anti-Rollover Propensity of SUV

NASA Astrophysics Data System (ADS)

Xiong, Fei; Lan, Fengchong; Chen, Jiqing; Yang, Yuedong

2017-05-01

Currently, many research from domestic and foreign on improving anti-rollover performance of vehicle mainly focus on the electronic control of auxiliary equipment, do not make full use of suspension layout to optimize anti-rollover performance of vehicle. This investigation into anti-rollover propensity improvement concentrates on the vehicle parameters greatly influencing on anti-rollover propensity of vehicle. A simulation based on fishhook procedure is used to perform design trials and evaluations aimed at ensuring an optimal balance between vehicle's design parameters and various engineering capacities, the anti-rollover propensity is optimized at the detailed design stage of a new SUV model. Firstly a four-DOF theoretical kinematic model is established, then a complete multi-body dynamics model built in ADAMS/car based on the whole vehicle parameters is correlated to the objective handing and stability test results of a mule car. Secondly, in fishhook test simulations, the Design of Experiments method is used to quantify the effect of the vehicle parameters on the anti-rollover performance. By means of the simulation, the roll center height of front suspension should be more than 30 mm, that of rear suspension less than 150 mm, and the HCG less than 620 mm for the SUV. The ratio of front to rear suspension roll stiffness should be ranged from 1.4 to 1.6 for the SUV. As a result, at the detailed design stage of product, the anti-rollover performance of vehicle can be improved by optimizing chassis and integrated vehicle parameters.

A Model for Determining Optimal Governance Structure in DoD Acquisition Projects in a Performance-Based Environment

DTIC Science & Technology

2011-03-09

task stability, technology application certainty, risk, and transaction-specific investments impact the selection of the optimal mode of governance...technology application certainty, risk, and transaction-specific investments impact the selection of the optimal mode of governance. Our model views...U.S. Defense Industry. The 1990s were a perfect storm of technological change, consolidation , budget downturns, environmental uncertainty, and the

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.

Neutron beam optimization based on a 7Li(p,n)7Be reaction for treatment of deep-seated brain tumors by BNCT

NASA Astrophysics Data System (ADS)

Zahra Ahmadi, Ganjeh; S. Farhad, Masoudi

2014-10-01

Neutron beam optimization for accelerator-based Boron Neutron Capture Therapy (BNCT) is investigated using a 7Li(p,n)7Be reaction. Design and optimization have been carried out for the target, cooling system, moderator, filter, reflector, and collimator to achieve a high flux of epithermal neutron and satisfy the IAEA criteria. Also, the performance of the designed beam in tissue is assessed by using a simulated Snyder head phantom. The results show that the optimization of the collimator and reflector is critical to finding the best neutron beam based on the 7Li(p,n)7Be reaction. Our designed beam has 2.49×109n/cm2s epithermal neutron flux and is suitable for BNCT of deep-seated brain tumors.

Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model.

PubMed

Xu, Gang; Liang, Xifeng; Yao, Shuanbao; Chen, Dawei; Li, Zhiwei

2017-01-01

Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements.

OPTIMIZATION METHODOLOGY FOR LAND USE PATTERNS-EVALUATION BASED ON MULTISCALE HABITAT PATTERN COMPARISON. (R827169)

EPA Science Inventory

In this paper, the methodological concept of landscape optimization presented by Seppelt and Voinov [Ecol. Model. 151 (2/3) (2002) 125] is analyzed. Two aspects are chosen for detailed study. First, we generalize the performance criterion to assess a vector of ecosystem functi...

Optimizing hydraulic retention times in denitrifying woodchip bioreactors treating recirculating aquaculture system wastewater

USDA-ARS?s Scientific Manuscript database

The performance of wood-based denitrifying bioreactors to treat high-nitrate wastewaters from aquaculture systems has not previously been demonstrated. Four pilot-scale woodchip bioreactors (approximately 1:10 scale) were constructed and operated for 268 d to determine the optimal range of design hy...

Optimal wavelets for biomedical signal compression.

PubMed

Nielsen, Mogens; Kamavuako, Ernest Nlandu; Andersen, Michael Midtgaard; Lucas, Marie-Françoise; Farina, Dario

2006-07-01

Signal compression is gaining importance in biomedical engineering due to the potential applications in telemedicine. In this work, we propose a novel scheme of signal compression based on signal-dependent wavelets. To adapt the mother wavelet to the signal for the purpose of compression, it is necessary to define (1) a family of wavelets that depend on a set of parameters and (2) a quality criterion for wavelet selection (i.e., wavelet parameter optimization). We propose the use of an unconstrained parameterization of the wavelet for wavelet optimization. A natural performance criterion for compression is the minimization of the signal distortion rate given the desired compression rate. For coding the wavelet coefficients, we adopted the embedded zerotree wavelet coding algorithm, although any coding scheme may be used with the proposed wavelet optimization. As a representative example of application, the coding/encoding scheme was applied to surface electromyographic signals recorded from ten subjects. The distortion rate strongly depended on the mother wavelet (for example, for 50% compression rate, optimal wavelet, mean+/-SD, 5.46+/-1.01%; worst wavelet 12.76+/-2.73%). Thus, optimization significantly improved performance with respect to previous approaches based on classic wavelets. The algorithm can be applied to any signal type since the optimal wavelet is selected on a signal-by-signal basis. Examples of application to ECG and EEG signals are also reported.

Multidisciplinary design optimization using genetic algorithms

NASA Technical Reports Server (NTRS)

Unal, Resit

1994-01-01

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System

PubMed Central

Wang, Ruiping; Jiang, Yonggen; Guo, Xiaoqin; Wu, Yiling; Zhao, Genming

2017-01-01

Objective The Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance. Methods Chickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivity (Se), false alarm rate (FAR), and time to detection (TTD). Performance of selected proper thresholds was assessed by data external to the study period. Results The optimized early alert threshold for chickenpox during the epidemic season was the percentile P65, which demonstrated an Se of 93.33%, FAR of 0%, and TTD of 0 days. The optimized early alert threshold in the nonepidemic season was P50, demonstrating an Se of 100%, FAR of 18.94%, and TTD was 2.5 days. The performance evaluation demonstrated that the use of an optimized threshold adjusted for seasonality could reduce the FAR and shorten the TTD. Conclusions Selection of optimized early alert thresholds based on local infectious disease seasonality could improve the performance of the CIDARS. PMID:28728470

Optimization of a miniature Maglev ventricular assist device for pediatric circulatory support.

PubMed

Zhang, Juntao; Koert, Andrew; Gellman, Barry; Gempp, Thomas M; Dasse, Kurt A; Gilbert, Richard J; Griffith, Bartley P; Wu, Zhongjun J

2007-01-01

A miniature Maglev blood pump based on magnetically levitated bearingless technology is being developed and optimized for pediatric patients. We performed impeller optimization by characterizing the hemodynamic and hemocompatibility performances using a combined computational and experimental approach. Both three-dimensional flow features and hemolytic characteristics were analyzed using computational fluid dynamics (CFD) modeling. Hydraulic pump performances and hemolysis levels of three different impeller designs were quantified and compared numerically. Two pump prototypes were constructed from the two impeller designs and experimentally tested. Comparison of CFD predictions with experimental results showed good agreement. The optimized impeller remarkably increased overall pump hydraulic output by more than 50% over the initial design. The CFD simulation demonstrated a clean and streamlined flow field in the main flow path. The numerical results by hemolysis model indicated no significant high shear stress regions. Through the use of CFD analysis and bench-top testing, the small pediatric pump was optimized to achieve a low level of blood damage and improved hydraulic performance and efficiency. The Maglev pediatric blood pump is innovative due to its small size, very low priming volume, excellent hemodynamic and hematologic performance, and elimination of seal-related and bearing-related failures due to adoption of magnetically levitated bearingless motor technology, making it ideal for pediatric applications.

Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System.

PubMed

Wang, Ruiping; Jiang, Yonggen; Guo, Xiaoqin; Wu, Yiling; Zhao, Genming

2018-01-01

Objective The Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance. Methods Chickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivity (Se), false alarm rate (FAR), and time to detection (TTD). Performance of selected proper thresholds was assessed by data external to the study period. Results The optimized early alert threshold for chickenpox during the epidemic season was the percentile P65, which demonstrated an Se of 93.33%, FAR of 0%, and TTD of 0 days. The optimized early alert threshold in the nonepidemic season was P50, demonstrating an Se of 100%, FAR of 18.94%, and TTD was 2.5 days. The performance evaluation demonstrated that the use of an optimized threshold adjusted for seasonality could reduce the FAR and shorten the TTD. Conclusions Selection of optimized early alert thresholds based on local infectious disease seasonality could improve the performance of the CIDARS.

Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs

PubMed Central

Javaid, Nadeem; Ahmed, Farwa; Wadud, Zahid; Alrajeh, Nabil; Alabed, Mohamad Souheil; Ilahi, Manzoor

2017-01-01

Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network. PMID:28763014

Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs.

PubMed

Javaid, Nadeem; Ahmed, Farwa; Wadud, Zahid; Alrajeh, Nabil; Alabed, Mohamad Souheil; Ilahi, Manzoor

2017-08-01

Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network.

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

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

Xie, Huiqiao; Yang, Yi; Tang, Xiangyang

2015-06-15

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, whichmore » are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.« less

Battery management systems (BMS) optimization for electric vehicles (EVs) in Malaysia

NASA Astrophysics Data System (ADS)

Salehen, P. M. W.; Su'ait, M. S.; Razali, H.; Sopian, K.

2017-04-01

Following the UN Climate Change Conference 2009 in Copenhagen, Denmark, Malaysia seriously committed on "Go Green" campaign with the aim to reduce 40% GHG emission by the year 2020. Therefore, the National Green Technology Policy has been legalised in 2009 with transportation as one of its focused sectors, which include hybrid (HEVs), electric vehicles (EVs) and fuel cell vehicles with the purpose of to keep up with the worst scenario. While the number of registered cars has been increasing by 1 million yearly, the amount has doubled in the last two decades. Consequently, CO2 emission in Malaysia reaches up to 97.1% and will continue to increase mainly due to the activities in the transportation sector. Nevertheless, Malaysia is now moving towards on green car which battery-based EVs. This type of transportation mainly needs power performance optimization, which is controlled by the Batteries Management System (BMS). BMS is an essential module which leads to reliable power management, optimal power performance and safe vehicle that lead back for power optimization in EVs. Thus, this paper proposes power performance optimization for various setups of lithium-ion cathode with graphene anode using MATLAB/SIMULINK software for better management performance and extended EVs driving range.

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Group search optimiser-based optimal bidding strategies with no Karush-Kuhn-Tucker optimality conditions

NASA Astrophysics Data System (ADS)

Yadav, Naresh Kumar; Kumar, Mukesh; Gupta, S. K.

2017-03-01

General strategic bidding procedure has been formulated in the literature as a bi-level searching problem, in which the offer curve tends to minimise the market clearing function and to maximise the profit. Computationally, this is complex and hence, the researchers have adopted Karush-Kuhn-Tucker (KKT) optimality conditions to transform the model into a single-level maximisation problem. However, the profit maximisation problem with KKT optimality conditions poses great challenge to the classical optimisation algorithms. The problem has become more complex after the inclusion of transmission constraints. This paper simplifies the profit maximisation problem as a minimisation function, in which the transmission constraints, the operating limits and the ISO market clearing functions are considered with no KKT optimality conditions. The derived function is solved using group search optimiser (GSO), a robust population-based optimisation algorithm. Experimental investigation is carried out on IEEE 14 as well as IEEE 30 bus systems and the performance is compared against differential evolution-based strategic bidding, genetic algorithm-based strategic bidding and particle swarm optimisation-based strategic bidding methods. The simulation results demonstrate that the obtained profit maximisation through GSO-based bidding strategies is higher than the other three methods.

Perceived Effects of Emotion Intensity on Athletic Performance: A Contingency-Based Individualized Approach

ERIC Educational Resources Information Center

Robazza, Claudio; Bortoli, Laura; Hanin, Yuri

2006-01-01

This study, based on the Individual Zones of Optimal Functioning model, examined the perceived effect of idiosyncratic emotions and bodily symptoms on athletic performance along the entire emotion-intensity range. The participants were 35 elite Italian athletes, 16 women and 19 men, competing in either figure skating or gymnastics. Idiosyncratic…

Performance comparison of some evolutionary algorithms on job shop scheduling problems

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Rapid Deployment of Optimal Control for Building HVAC Systems using Innovative Software Tools and a Hybrid Heuristic/Model Based Control Approach

DTIC Science & Technology

2017-03-21

Energy and Water Projects March 21, 2017 REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of...included reduced system energy use and cost as well as improved performance driven by autonomous commissioning and optimized system control. In the end...improve system performance and reduce energy use and cost. However, implementing these solutions into the extremely heterogeneous and often

Combining agreement and frequency rating scales to optimize psychometrics in measuring behavioral health functioning.

PubMed

Marfeo, Elizabeth E; Ni, Pengsheng; Chan, Leighton; Rasch, Elizabeth K; Jette, Alan M

2014-07-01

The goal of this article was to investigate optimal functioning of using frequency vs. agreement rating scales in two subdomains of the newly developed Work Disability Functional Assessment Battery: the Mood & Emotions and Behavioral Control scales. A psychometric study comparing rating scale performance embedded in a cross-sectional survey used for developing a new instrument to measure behavioral health functioning among adults applying for disability benefits in the United States was performed. Within the sample of 1,017 respondents, the range of response category endorsement was similar for both frequency and agreement item types for both scales. There were fewer missing values in the frequency items than the agreement items. Both frequency and agreement items showed acceptable reliability. The frequency items demonstrated optimal effectiveness around the mean ± 1-2 standard deviation score range; the agreement items performed better at the extreme score ranges. Findings suggest an optimal response format requires a mix of both agreement-based and frequency-based items. Frequency items perform better in the normal range of responses, capturing specific behaviors, reactions, or situations that may elicit a specific response. Agreement items do better for those whose scores are more extreme and capture subjective content related to general attitudes, behaviors, or feelings of work-related behavioral health functioning. Copyright © 2014 Elsevier Inc. All rights reserved.

Gaussian process regression for geometry optimization

NASA Astrophysics Data System (ADS)

Denzel, Alexander; Kästner, Johannes

2018-03-01

We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.

Improved Propulsion Modeling for Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Knittel, Jeremy M.; Englander, Jacob A.; Ozimek, Martin T.; Atchison, Justin A.; Gould, Julian J.

2017-01-01

Low-thrust trajectory design is tightly coupled with spacecraft systems design. In particular, the propulsion and power characteristics of a low-thrust spacecraft are major drivers in the design of the optimal trajectory. Accurate modeling of the power and propulsion behavior is essential for meaningful low-thrust trajectory optimization. In this work, we discuss new techniques to improve the accuracy of propulsion modeling in low-thrust trajectory optimization while maintaining the smooth derivatives that are necessary for a gradient-based optimizer. The resulting model is significantly more realistic than the industry standard and performs well inside an optimizer. A variety of deep-space trajectory examples are presented.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.