WorldWideScience

Sample records for automated parameter optimization

  1. Automated Optimization of Walking Parameters for the Nao Humanoid Robot

    NARCIS (Netherlands)

    Girardi, N.; Kooijman, C.; Wiggers, A.J.; Visser, A.

    2013-01-01

    This paper describes a framework for optimizing walking parameters for a Nao humanoid robot. In this case an omnidirectional walk is learned. The parameters are learned in simulation with an evolutionary approach. The best performance was obtained for a combination of a low mutation rate and a high

  2. Automation of Optimized Gabor Filter Parameter Selection for Road Cracks Detection

    Directory of Open Access Journals (Sweden)

    Haris Ahmad Khan

    2016-03-01

    Full Text Available Automated systems for road crack detection are extremely important in road maintenance for vehicle safety and traveler’s comfort. Emerging cracks in roads need to be detected and accordingly repaired as early as possible to avoid further damage thus reducing rehabilitation cost. In this paper, a robust method for Gabor filter parameters optimization for automatic road crack detection is discussed. Gabor filter has been used in previous literature for similar applications. However, there is a need for automatic selection of optimized Gabor filter parameters due to variation in texture of roads and cracks. The problem of change of background, which in fact is road texture, is addressed through a learning process by using synthetic road crack generation for Gabor filter parameter tuning. Tuned parameters are then tested on real cracks and a thorough quantitative analysis is performed for performance evaluation.

  3. Paramfit: automated optimization of force field parameters for molecular dynamics simulations.

    Science.gov (United States)

    Betz, Robin M; Walker, Ross C

    2015-01-15

    The generation of bond, angle, and torsion parameters for classical molecular dynamics force fields typically requires fitting parameters such that classical properties such as energies and gradients match precalculated quantum data for structures that scan the value of interest. We present a program, Paramfit, distributed as part of the AmberTools software package that automates and extends this fitting process, allowing for simplified parameter generation for applications ranging from single molecules to entire force fields. Paramfit implements a novel combination of a genetic and simplex algorithm to find the optimal set of parameters that replicate either quantum energy or force data. The program allows for the derivation of multiple parameters simultaneously using significantly fewer quantum calculations than previous methods, and can also fit parameters across multiple molecules with applications to force field development. Paramfit has been applied successfully to systems with a sparse number of structures, and has already proven crucial in the development of the Assisted Model Building with Energy Refinement Lipid14 force field.

  4. Achievements and challenges in automated parameter, shape and topology optimization for divertor design

    Science.gov (United States)

    Baelmans, M.; Blommaert, M.; Dekeyser, W.; Van Oevelen, T.

    2017-03-01

    Plasma edge transport codes play a key role in the design of future divertor concepts. Their long simulation times in combination with a large number of control parameters turn the design into a challenging task. In aerodynamics and structural mechanics, adjoint-based optimization techniques have proven successful to tackle similar design challenges. This paper provides an overview of achievements and remaining challenges with these techniques for complex divertor design. It is shown how these developments pave the way for fast sensitivity analysis and improved design from different perspectives.

  5. Parameter Extraction for PSpice Models by means of an Automated Optimization Tool – An IGBT model Study Case

    DEFF Research Database (Denmark)

    Suárez, Carlos Gómez; Reigosa, Paula Diaz; Iannuzzo, Francesco;

    2016-01-01

    An original tool for parameter extraction of PSpice models has been released, enabling a simple parameter identification. A physics-based IGBT model is used to demonstrate that the optimization tool is capable of generating a set of parameters which predicts the steady-state and switching behavio...

  6. Automated reticulocyte parameters for hereditary spherocytosis screening.

    Science.gov (United States)

    Lazarova, Elena; Pradier, Olivier; Cotton, Frédéric; Gulbis, Béatrice

    2014-11-01

    The laboratory diagnosis of hereditary spherocytosis (HS) is based on several screening and confirmatory tests; our algorithm includes clinical features, red blood cell morphology analysis and cryohaemolysis test, and, in case of positive screening, sodium dodecyl sulphate polyacrylamide gel electrophoresis as a diagnostic test. Using the UniCel DxH800 (Beckman Coulter) haematology analyser, we investigated automated reticulocyte parameters as HS screening tool, i.e. mean reticulocyte volume (MRV), immature reticulocyte fraction (IRF) and mean sphered cell volume (MSCV). A total of 410 samples were screened. Gel electrophoresis was applied to 159 samples that were positive for the screening tests. A total of 48 patients were diagnosed as HS, and seven were diagnosed as acquired autoimmune haemolytic anaemia (AIHA). Some other 31 anaemic conditions were also studied. From the receiver operating characteristic (ROC) curve analysis, both delta (mean cell volume (MCV)-MSCV) and MRV presented an area under the curve (AUC) of 0.98. At the diagnostic cut-off of 100 % sensitivity, MRV showed the best specificity of 88 % and a positive likelihood ratio of 8.7. The parameters IRF, MRV and MSCV discriminated HS not only from controls and other tested pathologies but also from AIHA contrary to the cryohaemolysis test. In conclusion, automated reticulocyte parameters might be helpful for haemolytic anaemia diagnostic orientation even for general laboratories. In combination with cryohaemolysis, they ensure an effective and time-saving screening for HS for more specialised laboratories.

  7. Automated parameterization of intermolecular pair potentials using global optimization techniques

    Science.gov (United States)

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

    2014-12-01

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

  8. Parameter optimization in S-system models

    Directory of Open Access Journals (Sweden)

    Vasconcelos Ana

    2008-04-01

    Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.

  9. Automated Cache Performance Analysis And Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Mohror, Kathryn [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-12-23

    While there is no lack of performance counter tools for coarse-grained measurement of cache activity, there is a critical lack of tools for relating data layout to cache behavior to application performance. Generally, any nontrivial optimizations are either not done at all, or are done ”by hand” requiring significant time and expertise. To the best of our knowledge no tool available to users measures the latency of memory reference instructions for partic- ular addresses and makes this information available to users in an easy-to-use and intuitive way. In this project, we worked to enable the Open|SpeedShop performance analysis tool to gather memory reference latency information for specific instructions and memory ad- dresses, and to gather and display this information in an easy-to-use and intuitive way to aid performance analysts in identifying problematic data structures in their codes. This tool was primarily designed for use in the supercomputer domain as well as grid, cluster, cloud-based parallel e-commerce, and engineering systems and middleware. Ultimately, we envision a tool to automate optimization of application cache layout and utilization in the Open|SpeedShop performance analysis tool. To commercialize this soft- ware, we worked to develop core capabilities for gathering enhanced memory usage per- formance data from applications and create and apply novel methods for automatic data structure layout optimizations, tailoring the overall approach to support existing supercom- puter and cluster programming models and constraints. In this Phase I project, we focused on infrastructure necessary to gather performance data and present it in an intuitive way to users. With the advent of enhanced Precise Event-Based Sampling (PEBS) counters on recent Intel processor architectures and equivalent technology on AMD processors, we are now in a position to access memory reference information for particular addresses. Prior to the introduction of PEBS counters

  10. Modeling, Instrumentation, Automation, and Optimization of Water Resource Recovery Facilities.

    Science.gov (United States)

    Sweeney, Michael W; Kabouris, John C

    2016-10-01

    A review of the literature published in 2015 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented.

  11. Automated Cache Performance Analysis And Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Mohror, Kathryn [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-12-23

    While there is no lack of performance counter tools for coarse-grained measurement of cache activity, there is a critical lack of tools for relating data layout to cache behavior to application performance. Generally, any nontrivial optimizations are either not done at all, or are done ”by hand” requiring significant time and expertise. To the best of our knowledge no tool available to users measures the latency of memory reference instructions for partic- ular addresses and makes this information available to users in an easy-to-use and intuitive way. In this project, we worked to enable the Open|SpeedShop performance analysis tool to gather memory reference latency information for specific instructions and memory ad- dresses, and to gather and display this information in an easy-to-use and intuitive way to aid performance analysts in identifying problematic data structures in their codes. This tool was primarily designed for use in the supercomputer domain as well as grid, cluster, cloud-based parallel e-commerce, and engineering systems and middleware. Ultimately, we envision a tool to automate optimization of application cache layout and utilization in the Open|SpeedShop performance analysis tool. To commercialize this soft- ware, we worked to develop core capabilities for gathering enhanced memory usage per- formance data from applications and create and apply novel methods for automatic data structure layout optimizations, tailoring the overall approach to support existing supercom- puter and cluster programming models and constraints. In this Phase I project, we focused on infrastructure necessary to gather performance data and present it in an intuitive way to users. With the advent of enhanced Precise Event-Based Sampling (PEBS) counters on recent Intel processor architectures and equivalent technology on AMD processors, we are now in a position to access memory reference information for particular addresses. Prior to the introduction of PEBS counters

  12. Basic MR sequence parameters systematically bias automated brain volume estimation

    Energy Technology Data Exchange (ETDEWEB)

    Haller, Sven [University of Geneva, Faculty of Medicine, Geneva (Switzerland); Affidea Centre de Diagnostique Radiologique de Carouge CDRC, Geneva (Switzerland); Falkovskiy, Pavel; Roche, Alexis; Marechal, Benedicte [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Meuli, Reto [University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Thiran, Jean-Philippe [LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland); Krueger, Gunnar [Siemens Medical Solutions USA, Inc., Boston, MA (United States); Lovblad, Karl-Olof [University of Geneva, Faculty of Medicine, Geneva (Switzerland); University Hospitals of Geneva, Geneva (Switzerland); Kober, Tobias [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland)

    2016-11-15

    Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. (orig.)

  13. Optimization based automated curation of metabolic reconstructions

    Directory of Open Access Journals (Sweden)

    Maranas Costas D

    2007-06-01

    Full Text Available Abstract Background Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them. Results In this work, an optimization based procedure is proposed to identify and eliminate network gaps in these reconstructions. First we identify the metabolites in the metabolic network reconstruction which cannot be produced under any uptake conditions and subsequently we identify the reactions from a customized multi-organism database that restores the connectivity of these metabolites to the parent network using four mechanisms. This connectivity restoration is hypothesized to take place through four mechanisms: a reversing the directionality of one or more reactions in the existing model, b adding reaction from another organism to provide functionality absent in the existing model, c adding external transport mechanisms to allow for importation of metabolites in the existing model and d restore flow by adding intracellular transport reactions in multi-compartment models. We demonstrate this procedure for the genome- scale reconstruction of Escherichia coli and also Saccharomyces cerevisiae wherein compartmentalization of intra-cellular reactions results in a more complex topology of the metabolic network. We determine that about 10% of metabolites in E. coli and 30% of metabolites in S. cerevisiae cannot carry any flux. Interestingly, the dominant flow restoration mechanism is directionality reversals of existing reactions in the respective models. Conclusion We have proposed systematic methods to identify and

  14. Novel system for modulated lidar parameter optimization

    Institute of Scientific and Technical Information of China (English)

    Bo Zhou; Yong Ma; Kun Liang; Zhiqiang Tu; Hongyuan Wang

    2011-01-01

    We present a novel system for parameter design and optimization of modulated lidar. The system is realized by combining software simulation with hardware circuit. This method is more reliable and flexible for lidar parameter optimization compared with theoretical computation or fiber-simulated system. Experiments confirm that the system is capable of optimizing parameters for modulated lidar. Key parameters are analyzed as well. The optimal filter bandwidth is 200 MHz and the optimal modulation depth is 0.5 under typical application environment.%@@ We present a novel system for parameter design and optimization of modulated lidar.The system is realized by combining software simulation with hardware circuit.This method is more reliable and flexible for lidar parameter optimization compared with theoretical computation or fiber-simulated system.Experiments confirm that the system is capable of optimizing parameters for modulated lidar.Key parameters are analyzed as well.The optimal filter bandwidth is 200 MHz and the optimal modulation depth is 0.5 under typical application environment.

  15. Parameters Optimization of Synergetic Recognition Approach

    Institute of Scientific and Technical Information of China (English)

    GAOJun; DONGHuoming; SHAOJing; ZHAOJing

    2005-01-01

    Synergetic pattern recognition is a novel and effective pattern recognition method, and has some advantages in image recognition. Researches have shown that attention parameters λ and parameters B, C directly influence on the recognition results, but there is no general research theory to control these parameters in the recognition process. We abstractly analyze these parameters in this paper, and purpose a novel parameters optimization method based on simulated annealing algorithm. SA algorithm has good optimization performance and is used to search the global optimized solution of these parameters. Theoretic analysis and experimental results both show that the proposed parameters optimization method is effective, which can fully improve the performance of synergetic recognition approach, and the algorithm realization is simple and fast.

  16. Evaluation of GCC optimization parameters

    Directory of Open Access Journals (Sweden)

    Rodrigo D. Escobar

    2012-12-01

    Full Text Available Compile-time optimization of code can result in significant performance gains. The amount of these gains varies widely depending upon the code being optimized, the hardware being compiled for, the specific performance increase attempted (e.g. speed, throughput, memory utilization, etc. and the used compiler. We used the latest version of the SPEC CPU 2006 benchmark suite to help gain an understanding of possible performance improvements using GCC (GNU Compiler Collection options focusing mainly on speed gains made possible by tuning the compiler with the standard compiler optimization levels as well as a specific compiler option for the hardware processor. We compared the best standardized tuning options obtained for a core i7 processor, to the same relative options used on a Pentium4 to determine whether the GNU project has improved its performance tuning capabilities for specific hardware over time.

  17. QUADRATIC OPTIMIZATION METHOD AND ITS APPLICATION ON OPTIMIZING MECHANISM PARAMETER

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yun; CHEN Jianneng; YU Yaxin; YU Gaohong; ZHU Jianping

    2006-01-01

    In order that the mechanism designed meets the requirements of kinematics with optimal dynamics behaviors, a quadratic optimization method is proposed based on the different characteristics of kinematic and dynamic optimization. This method includes two steps of optimization, that is, kinematic and dynamic optimization. Meanwhile, it uses the results of the kinematic optimization as the constraint equations of dynamic optimization. This method is used in the parameters optimization of transplanting mechanism with elliptic planetary gears of high-speed rice seedling transplanter with remarkable significance. The parameters spectrum, which meets to the kinematic requirements, is obtained through visualized human-computer interactions in the kinematics optimization, and the optimal parameters are obtained based on improved genetic algorithm in dynamic optimization. In the dynamic optimization, the objective function is chosen as the optimal dynamic behavior and the constraint equations are from the results of the kinematic optimization. This method is suitable for multi-objective optimization when both the kinematic and dynamic performances act as objective functions.

  18. Programmable physical parameter optimization for particle plasma simulations

    Science.gov (United States)

    Ragan-Kelley, Benjamin; Verboncoeur, John; Lin, Ming-Chieh

    2012-10-01

    We have developed a scheme for interactive and programmable optimization of physical parameters for plasma simulations. The simulation code Object-Oriented Plasma Device 1-D (OOPD1) has been adapted to a Python interface, allowing sophisticated user or program interaction with simulations, and detailed numerical analysis via numpy. Because the analysis/diagnostic interface is the same as the input mechanism (the Python programming language), it is straightforward to optimize simulation parameters based on analysis of previous runs and automate the optimization process using a user-determined scheme and criteria. An example use case of the Child-Langmuir space charge limit in bipolar flow is demonstrated, where the beam current is iterated upon by measuring the relationship of the measured current and the injected current.

  19. Automated firewall analytics design, configuration and optimization

    CERN Document Server

    Al-Shaer, Ehab

    2014-01-01

    This book provides a comprehensive and in-depth study of automated firewall policy analysis for designing, configuring and managing distributed firewalls in large-scale enterpriser networks. It presents methodologies, techniques and tools for researchers as well as professionals to understand the challenges and improve the state-of-the-art of managing firewalls systematically in both research and application domains. Chapters explore set-theory, managing firewall configuration globally and consistently, access control list with encryption, and authentication such as IPSec policies. The author

  20. Optimized and Automated design of Plasma Diagnostics for Additive Manufacture

    Science.gov (United States)

    Stuber, James; Quinley, Morgan; Melnik, Paul; Sieck, Paul; Smith, Trevor; Chun, Katherine; Woodruff, Simon

    2016-10-01

    Despite having mature designs, diagnostics are usually custom designed for each experiment. Most of the design can be now be automated to reduce costs (engineering labor, and capital cost). We present results from scripted physics modeling and parametric engineering design for common optical and mechanical components found in many plasma diagnostics and outline the process for automated design optimization that employs scripts to communicate data from online forms through proprietary and open-source CAD and FE codes to provide a design that can be sent directly to a printer. As a demonstration of design automation, an optical beam dump, baffle and optical components are designed via an automated process and printed. Supported by DOE SBIR Grant DE-SC0011858.

  1. Optimization of submerged vane parameters

    Indian Academy of Sciences (India)

    H SHARMA; B JAIN; Z AHMAD

    2016-03-01

    Submerged vanes are airfoils which are in general placed at certain angle with respect to the flow direction in a channel to induce artificial circulations downstream. By virtue of these artificially generated circulations, submerged vanes were utilized to protect banks of rivers against erosion, to control shifting of rivers, to avoid blocking of lateral intake with sediment deposition, etc. Odgaard and his associates have experimentally obtained the optimum vane sizes and recommended that it can be used for vane design. Thispaper is an attempt to review and validate the findings of Odgaard and his associates by utilizing computational fluid dynamics and experiments as a tool in which the vane generated vorticity in the downstream was maximized in order to obtain optimum vane parameters for single and multiple vane arrays.

  2. Weigh in Motion Based on Parameters Optimization

    Institute of Scientific and Technical Information of China (English)

    ZHOU Zhi-feng; CAI Ping; CHEN Ri-xing

    2009-01-01

    Dynamic tire forces are the main factor affecting the measurement accuracy of the axle weight of moving vehicle. This paper presents a novel method to reduce the influence of the dynamic tire forces on the weighing accuracy. On the basis of analyzing the characteristic of the dynamic tire forces, the objective optimization equation is constructed. The optimization algorithm is presented to get the optimal estimations of the objective parameters. According to the estimations of the parameters, the dynamic tire forces are separated from the axle weigh signal. The results of simulation and field experiments prove the effectiveness of the proposed method.

  3. An efficient automated parameter tuning framework for spiking neural networks.

    Science.gov (United States)

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  4. Optimal choice of parameters for particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    ZHANG Li-ping; YU Huan-jun; HU Shang-xu

    2005-01-01

    The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the performance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and improper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.

  5. Optimal design criteria - prediction vs. parameter estimation

    Science.gov (United States)

    Waldl, Helmut

    2014-05-01

    G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

  6. Cosmological parameter estimation using Particle Swarm Optimization

    Science.gov (United States)

    Prasad, J.; Souradeep, T.

    2014-03-01

    Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

  7. Towards Automated Design, Analysis and Optimization of Declarative Curation Workflows

    Directory of Open Access Journals (Sweden)

    Tianhong Song

    2014-10-01

    Full Text Available Data curation is increasingly important. Our previous work on a Kepler curation package has demonstrated advantages that come from automating data curation pipelines by using workflow systems. However, manually designed curation workflows can be error-prone and inefficient due to a lack of user understanding of the workflow system, misuse of actors, or human error. Correcting problematic workflows is often very time-consuming. A more proactive workflow system can help users avoid such pitfalls. For example, static analysis before execution can be used to detect the potential problems in a workflow and help the user to improve workflow design. In this paper, we propose a declarative workflow approach that supports semi-automated workflow design, analysis and optimization. We show how the workflow design engine helps users to construct data curation workflows, how the workflow analysis engine detects different design problems of workflows and how workflows can be optimized by exploiting parallelism.

  8. Mixed integer evolution strategies for parameter optimization.

    Science.gov (United States)

    Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C

    2013-01-01

    Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.

  9. An Optimization Model of Tunnel Support Parameters

    Directory of Open Access Journals (Sweden)

    Su Lijuan

    2015-05-01

    Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.

  10. Automated Multivariate Optimization Tool for Energy Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Ellis, P. G.; Griffith, B. T.; Long, N.; Torcellini, P. A.; Crawley, D.

    2006-07-01

    Building energy simulations are often used for trial-and-error evaluation of ''what-if'' options in building design--a limited search for an optimal solution, or ''optimization''. Computerized searching has the potential to automate the input and output, evaluate many options, and perform enough simulations to account for the complex interactions among combinations of options. This paper describes ongoing efforts to develop such a tool. The optimization tool employs multiple modules, including a graphical user interface, a database, a preprocessor, the EnergyPlus simulation engine, an optimization engine, and a simulation run manager. Each module is described and the overall application architecture is summarized.

  11. A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

    Science.gov (United States)

    Zheng, Yuanjie; Wang, Yan; Keller, Brad M.; Conant, Emily; Gee, James C.; Kontos, Despina

    2013-02-01

    Estimating a woman's risk of breast cancer is becoming increasingly important in clinical practice. Mammographic density, estimated as the percent of dense (PD) tissue area within the breast, has been shown to be a strong risk factor. Studies also support a relationship between mammographic texture and breast cancer risk. We have developed a fullyautomated software pipeline for computerized analysis of digital mammography parenchymal patterns by quantitatively measuring both breast density and texture properties. Our pipeline combines advanced computer algorithms of pattern recognition, computer vision, and machine learning and offers a standardized tool for breast cancer risk assessment studies. Different from many existing methods performing parenchymal texture analysis within specific breast subregions, our pipeline extracts texture descriptors for points on a spatial regular lattice and from a surrounding window of each lattice point, to characterize the local mammographic appearance throughout the whole breast. To demonstrate the utility of our pipeline, and optimize its parameters, we perform a case-control study by retrospectively analyzing a total of 472 digital mammography studies. Specifically, we investigate the window size, which is a lattice related parameter, and compare the performance of texture features to that of breast PD in classifying case-control status. Our results suggest that different window sizes may be optimal for raw (12.7mm2) versus vendor post-processed images (6.3mm2). We also show that the combination of PD and texture features outperforms PD alone. The improvement is significant (p=0.03) when raw images and window size of 12.7mm2 are used, having an ROC AUC of 0.66. The combination of PD and our texture features computed from post-processed images with a window size of 6.3 mm2 achieves an ROC AUC of 0.75.

  12. Automated process parameters tuning for an injection moulding machine with soft computing§

    Institute of Scientific and Technical Information of China (English)

    Peng ZHAO; Jian-zhong FU; Hua-min ZHOU; Shu-biao CUI

    2011-01-01

    In injection moulding production, the tuning of the process parameters is a challenging job, which relies heavily on the experience of skilled operators. In this paper, taking into consideration operator assessment during moulding trials, a novel intelligent model for automated tuning of process parameters is proposed. This consists of case based reasoning (CBR), empirical model (EM), and fuzzy logic (FL) methods. CBR and EM are used to imitate recall and intuitive thoughts of skilled operators,respectively, while FL is adopted to simulate the skilled operator optimization thoughts. First, CBR is used to set up the initial process parameters. If CBR fails, EM is employed to calculate the initial parameters. Next, a moulding trial is performed using the initial parameters. Then FL is adopted to optimize these parameters and correct defects repeatedly until the moulded part is found to be satisfactory. Based on the above methodologies, intelligent software was developed and embedded in the controller of an injection moulding machine. Experimental results show that the intelligent software can be effectively used in practical production, and it greatly reduces the dependence on the experience of the operators.

  13. Weighted Constraint Satisfaction for Smart Home Automation and Optimization

    Directory of Open Access Journals (Sweden)

    Noel Nuo Wi Tay

    2016-01-01

    Full Text Available Automation of the smart home binds together services of hardware and software to provide support for its human inhabitants. The rise of web technologies offers applicable concepts and technologies for service composition that can be exploited for automated planning of the smart home, which can be further enhanced by implementation based on service oriented architecture (SOA. SOA supports loose coupling and late binding of devices, enabling a more declarative approach in defining services and simplifying home configurations. One such declarative approach is to represent and solve automated planning through constraint satisfaction problem (CSP, which has the advantage of handling larger domains of home states. But CSP uses hard constraints and thus cannot perform optimization and handle contradictory goals and partial goal fulfillment, which are practical issues smart environments will face if humans are involved. This paper extends this approach to Weighted Constraint Satisfaction Problem (WCSP. Branch and bound depth first search is used, where its lower bound is estimated by bacterial memetic algorithm (BMA on a relaxed version of the original optimization problem. Experiments up to 16-step planning of home services demonstrate the applicability and practicality of the approach, with the inclusion of local search for trivial service combinations in BMA that produces performance enhancements. Besides, this work aims to set the groundwork for further research in the field.

  14. An automated system for measuring parameters of nematode sinusoidal movement

    Directory of Open Access Journals (Sweden)

    Stirbl Robert C

    2005-02-01

    Full Text Available Abstract Background Nematode sinusoidal movement has been used as a phenotype in many studies of C. elegans development, behavior and physiology. A thorough understanding of the ways in which genes control these aspects of biology depends, in part, on the accuracy of phenotypic analysis. While worms that move poorly are relatively easy to describe, description of hyperactive movement and movement modulation presents more of a challenge. An enhanced capability to analyze all the complexities of nematode movement will thus help our understanding of how genes control behavior. Results We have developed a user-friendly system to analyze nematode movement in an automated and quantitative manner. In this system nematodes are automatically recognized and a computer-controlled microscope stage ensures that the nematode is kept within the camera field of view while video images from the camera are stored on videotape. In a second step, the images from the videotapes are processed to recognize the worm and to extract its changing position and posture over time. From this information, a variety of movement parameters are calculated. These parameters include the velocity of the worm's centroid, the velocity of the worm along its track, the extent and frequency of body bending, the amplitude and wavelength of the sinusoidal movement, and the propagation of the contraction wave along the body. The length of the worm is also determined and used to normalize the amplitude and wavelength measurements. To demonstrate the utility of this system, we report here a comparison of movement parameters for a small set of mutants affecting the Go/Gq mediated signaling network that controls acetylcholine release at the neuromuscular junction. The system allows comparison of distinct genotypes that affect movement similarly (activation of Gq-alpha versus loss of Go-alpha function, as well as of different mutant alleles at a single locus (null and dominant negative alleles

  15. Automated assay optimization with integrated statistics and smart robotics.

    Science.gov (United States)

    Taylor, P B; Stewart, F P; Dunnington, D J; Quinn, S T; Schulz, C K; Vaidya, K S; Kurali, E; Lane, T R; Xiong, W C; Sherrill, T P; Snider, J S; Terpstra, N D; Hertzberg, R P

    2000-08-01

    The transition from manual to robotic high throughput screening (HTS) in the last few years has made it feasible to screen hundreds of thousands of chemical entities against a biological target in less than a month. This rate of HTS has increased the visibility of bottlenecks, one of which is assay optimization. In many organizations, experimental methods are generated by therapeutic teams associated with specific targets and passed on to the HTS group. The resulting assays frequently need to be further optimized to withstand the rigors and time frames inherent in robotic handling. Issues such as protein aggregation, ligand instability, and cellular viability are common variables in the optimization process. The availability of robotics capable of performing rapid random access tasks has made it possible to design optimization experiments that would be either very difficult or impossible for a person to carry out. Our approach to reducing the assay optimization bottleneck has been to unify the highly specific fields of statistics, biochemistry, and robotics. The product of these endeavors is a process we have named automated assay optimization (AAO). This has enabled us to determine final optimized assay conditions, which are often a composite of variables that we would not have arrived at by examining each variable independently. We have applied this approach to both radioligand binding and enzymatic assays and have realized benefits in both time and performance that we would not have predicted a priori. The fully developed AAO process encompasses the ability to download information to a robot and have liquid handling methods automatically created. This evolution in smart robotics has proven to be an invaluable tool for maintaining high-quality data in the context of increasing HTS demands.

  16. Optimization of audio - ultrasonic plasma system parameters

    Science.gov (United States)

    Haleem, N. A.; Abdelrahman, M. M.; Ragheb, M. S.

    2016-10-01

    The present plasma is a special glow plasma type generated by an audio ultrasonic discharge voltage. A definite discharge frequency using a gas at a narrow band pressure creates and stabilizes this plasma type. The plasma cell is a self-extracted ion beam; it is featured with its high output intensity and its small size. The influence of the plasma column length on the output beam due to the variation of both the audio discharge frequency and the power applied to the plasma electrodes is investigated. In consequence, the aim of the present work is to put in evidence the parameters that influence the self-extracted collected ion beam and to optimize the conditions that enhance the collected ion beam. The experimental parameters studied are the nitrogen gas, the applied frequency from 10 to 100 kHz, the plasma length that varies from 8 to 14 cm, at a gas pressure of ≈ 0.25 Torr and finally the discharge power from 50 to 500 Watt. A sheet of polyethylene of 5 micrometer covers the collector electrode in order to confirm how much ions from the beam can go through the polymer and reach the collector. To diagnose the occurring events of the beam on the collector, the polymer used is analyzed by means of the FTIR and the XRF techniques. Optimization of the plasma cell parameters succeeded to enhance and to identify the parameters that influence the output ion beam and proved that its particles attaining the collector are multi-energetic.

  17. An optimized method for automated analysis of algal pigments by HPLC

    NARCIS (Netherlands)

    van Leeuwe, M. A.; Villerius, L. A.; Roggeveld, J.; Visser, R. J. W.; Stefels, J.

    2006-01-01

    A recent development in algal pigment analysis by high-performance liquid chromatography (HPLC) is the application of automation. An optimization of a complete sampling and analysis protocol applied specifically in automation has not yet been performed. In this paper we show that automation can only

  18. The optimization of total laboratory automation by simulation of a pull-strategy.

    Science.gov (United States)

    Yang, Taho; Wang, Teng-Kuan; Li, Vincent C; Su, Chia-Lo

    2015-01-01

    Laboratory results are essential for physicians to diagnose medical conditions. Because of the critical role of medical laboratories, an increasing number of hospitals use total laboratory automation (TLA) to improve laboratory performance. Although the benefits of TLA are well documented, systems occasionally become congested, particularly when hospitals face peak demand. This study optimizes TLA operations. Firstly, value stream mapping (VSM) is used to identify the non-value-added time. Subsequently, batch processing control and parallel scheduling rules are devised and a pull mechanism that comprises a constant work-in-process (CONWIP) is proposed. Simulation optimization is then used to optimize the design parameters and to ensure a small inventory and a shorter average cycle time (CT). For empirical illustration, this approach is applied to a real case. The proposed methodology significantly improves the efficiency of laboratory work and leads to a reduction in patient waiting times and increased service level.

  19. Automating Initial Guess Generation for High Fidelity Trajectory Optimization Tools

    Science.gov (United States)

    Villa, Benjamin; Lantoine, Gregory; Sims, Jon; Whiffen, Gregory

    2013-01-01

    Many academic studies in spaceflight dynamics rely on simplified dynamical models, such as restricted three-body models or averaged forms of the equations of motion of an orbiter. In practice, the end result of these preliminary orbit studies needs to be transformed into more realistic models, in particular to generate good initial guesses for high-fidelity trajectory optimization tools like Mystic. This paper reviews and extends some of the approaches used in the literature to perform such a task, and explores the inherent trade-offs of such a transformation with a view toward automating it for the case of ballistic arcs. Sample test cases in the libration point regimes and small body orbiter transfers are presented.

  20. Parameter optimization model in electrical discharge machining process

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.

  1. Automated Modal Parameter Estimation of Civil Engineering Structures

    DEFF Research Database (Denmark)

    Andersen, Palle; Brincker, Rune; Goursat, Maurice

    In this paper the problems of doing automatic modal parameter extraction of ambient excited civil engineering structures is considered. Two different approaches for obtaining the modal parameters automatically are presented: The Frequency Domain Decomposition (FDD) technique and a correlation...

  2. Robust PID Steering Control in Parameter Space for Highly Automated Driving

    Directory of Open Access Journals (Sweden)

    Mümin Tolga Emirler

    2014-01-01

    Full Text Available This paper is on the design of a parameter space based robust PID steering controller. This controller is used for automated steering in automated path following of a midsized sedan. Linear and nonlinear models of this midsized sedan are presented in the paper. Experimental results are used to validate the longitudinal and lateral dynamic models of this vehicle. This paper is on automated steering control and concentrates on the lateral direction of motion. The linear model is used to design a PID steering controller in parameter space that satisfies D-stability. The PID steering controller that is designed is used in a simulation study to illustrate the effectiveness of the proposed method. Simulation results for a circular trajectory and for a curved trajectory are presented and discussed in detail. This study is part of a larger research effort aimed at implementing highly automated driving in a midsized sedan.

  3. PARAMETER COORDINATION AND ROBUST OPTIMIZATION FOR MULTIDISCIPLINARY DESIGN

    Institute of Scientific and Technical Information of China (English)

    HU Jie; PENG Yinghong; XIONG Guangleng

    2006-01-01

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

  4. Development of An Optimization Method for Determining Automation Rate in Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung Min; Seong, Poong Hyun [KAIST, Daejeon (Korea, Republic of); Kim, Jong Hyun [KEPCO, Ulsan (Korea, Republic of)

    2014-08-15

    Since automation was introduced in various industrial fields, it has been known that automation provides positive effects like greater efficiency and fewer human errors, and negative effect defined as out-of-the-loop (OOTL). Thus, before introducing automation in nuclear field, the estimation of the positive and negative effects of automation on human operators should be conducted. In this paper, by focusing on CPS, the optimization method to find an appropriate proportion of automation is suggested by integrating the suggested cognitive automation rate and the concepts of the level of ostracism. The cognitive automation rate estimation method was suggested to express the reduced amount of human cognitive loads, and the level of ostracism was suggested to express the difficulty in obtaining information from the automation system and increased uncertainty of human operators' diagnosis. The maximized proportion of automation that maintains the high level of attention for monitoring the situation is derived by an experiment, and the automation rate is estimated by the suggested automation rate estimation method. It is expected to derive an appropriate inclusion proportion of the automation system avoiding the OOTL problem and having maximum efficacy at the same time.

  5. Optimizing a Drone Network to Deliver Automated External Defibrillators.

    Science.gov (United States)

    Boutilier, Justin J; Brooks, Steven C; Janmohamed, Alyf; Byers, Adam; Buick, Jason E; Zhan, Cathy; Schoellig, Angela P; Cheskes, Sheldon; Morrison, Laurie J; Chan, Timothy C Y

    2017-03-02

    Background -Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest (OHCA), but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an OHCA for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. Methods -We applied our model to 53,702 OHCAs that occurred in the eight regions of the Toronto Regional RescuNET between January 1st 2006 and December 31st 2014. Our primary analysis quantified the drone network size required to deliver an AED one, two, or three minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as one large coordinated region. Results -The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by three minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. Conclusions -An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an OHCA event.

  6. A Discrete Particle Swarm Optimization to Estimate Parameters in Vision Tasks

    Directory of Open Access Journals (Sweden)

    Benchikhi Loubna

    2016-01-01

    Full Text Available The majority of manufacturers demand increasingly powerful vision systems for quality control. To have good outcomes, the installation requires an effort in the vision system tuning, for both hardware and software. As time and accuracy are important, actors are oriented to automate parameter’s adjustment optimization at least in image processing. This paper suggests an approach based on discrete particle swarm optimization (DPSO that automates software setting and provides optimal parameters for industrial vision applications. A novel update functions for our DPSO definition are suggested. The proposed method is applied on some real examples of quality control to validate its feasibility and efficiency, which shows that the new DPSO model furnishes promising results.

  7. Fully automated, gas sensing, and electronic parameter measurement setup for miniaturized nanoparticle gas sensors

    Science.gov (United States)

    Kennedy, M. K.; Kruis, F. E.; Fissan, H.; Mehta, B. R.

    2003-11-01

    In this study, a measurement setup has been designed and fabricated for the measurement of gas sensor characteristics and electronic parameters of nanostructured thin layers in the temperature range from room temperature to 450 °C in controlled gas environments. The setup consists of: (i) a gas environment chamber, (ii) a specially designed substrate and substrate holder, and (iii) control, supply, and measurement electronics. The buried geometry of the contacts is specially designed for the deposition of nanoparticles from the gas phase to guarantee uniform thin layers, and the setup can be used to make measurement on high resistivity (1010 Ω cm) nanoparticle samples. The gas inlet, operating temperature, and electronic control of the measurement system are automated by means of a personal computer. Coupling the measurements of interdependent gas sensing and electronic parameters at identical conditions, in a single setup encompassing a wide range of sensing gas levels and substrate temperatures, makes this system ideally suited for carrying out multiple measurements required for optimizing sensor configuration and understanding the size-dependent properties of nanoparticle sensors.

  8. Architecture of Automated Database Tuning Using SGA Parameters

    Directory of Open Access Journals (Sweden)

    Hitesh KUMAR SHARMA

    2012-05-01

    Full Text Available Business Data always growth from kilo byte, mega byte, giga byte, tera byte, peta byte, and so far. There is no way to avoid this increasing rate of data till business still running. Because of this issue, database tuning be critical part of a information system. Tuning a database in a cost-effective manner is a growing challenge. The total cost of ownership (TCO of information technology needs to be significantly reduced by minimizing people costs. In fact, mistakes in operations and administration of information systems are the single most reasons for system outage and unacceptable performance [3]. One way of addressing the challenge of total cost of ownership is by making information systems more self-managing. A particularly difficult piece of the ambitious vision of making database systems self-managing is the automation of database performance tuning. In this paper, we will explain the progress made thus far on this important problem. Specifically, we will propose the architecture and Algorithm for this problem.

  9. Optimalization of selected RFID systems Parameters

    Directory of Open Access Journals (Sweden)

    Peter Vestenicky

    2004-01-01

    Full Text Available This paper describes procedure for maximization of RFID transponder read range. This is done by optimalization of magnetics field intensity at transponder place and by optimalization of antenna and transponder coils coupling factor. Results of this paper can be used for RFID with inductive loop, i.e. system working in near electromagnetic field.

  10. A New Approach for Parameter Optimization in Land Surface Model

    Institute of Scientific and Technical Information of China (English)

    LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin

    2011-01-01

    In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

  11. Optimal parameters uncoupling vibration modes of oscillators

    CERN Document Server

    Le, Khanh Chau

    2016-01-01

    A novel optimization concept for an oscillator with two degrees of freedom is proposed. By using specially defined motion ratios, we control the action of springs and dampers to each degree of freedom of the oscillator. If the potential action of the springs in one period of vibration, used as the payoff function for the conservative oscillator, is maximized, then the optimal motion ratios uncouple vibration modes. The same result holds true for the dissipative oscillator. The application to optimal design of vehicle suspension is discussed.

  12. Parameter Optimization Based on GA and HFSS

    Institute of Scientific and Technical Information of China (English)

    SUN Shu-hui; WANG Bing-zhong

    2005-01-01

    A new project based on genetic algorithm (GA) and high frequency simulation software (HFSS) is proposed to optimize microwave passive devices effectively. This project is realized with a general program named as optimization program. The program is compiled by Matlab and the macro language of HFSS which is a fast and effective way to accomplish tasks. In the paper, two examples are used to show the project's feasibility.

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

    Science.gov (United States)

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

    2016-01-01

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

  14. ADVANTG An Automated Variance Reduction Parameter Generator, Rev. 1

    Energy Technology Data Exchange (ETDEWEB)

    Mosher, Scott W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Johnson, Seth R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Bevill, Aaron M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ibrahim, Ahmad M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Daily, Charles R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Evans, Thomas M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wagner, John C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Johnson, Jeffrey O. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Grove, Robert E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2015-08-01

    The primary objective of ADVANTG is to reduce both the user effort and the computational time required to obtain accurate and precise tally estimates across a broad range of challenging transport applications. ADVANTG has been applied to simulations of real-world radiation shielding, detection, and neutron activation problems. Examples of shielding applications include material damage and dose rate analyses of the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source and High Flux Isotope Reactor (Risner and Blakeman 2013) and the ITER Tokamak (Ibrahim et al. 2011). ADVANTG has been applied to a suite of radiation detection, safeguards, and special nuclear material movement detection test problems (Shaver et al. 2011). ADVANTG has also been used in the prediction of activation rates within light water reactor facilities (Pantelias and Mosher 2013). In these projects, ADVANTG was demonstrated to significantly increase the tally figure of merit (FOM) relative to an analog MCNP simulation. The ADVANTG-generated parameters were also shown to be more effective than manually generated geometry splitting parameters.

  15. Optimal control of nonsmooth distributed parameter systems

    CERN Document Server

    Tiba, Dan

    1990-01-01

    The book is devoted to the study of distributed control problems governed by various nonsmooth state systems. The main questions investigated include: existence of optimal pairs, first order optimality conditions, state-constrained systems, approximation and discretization, bang-bang and regularity properties for optimal control. In order to give the reader a better overview of the domain, several sections deal with topics that do not enter directly into the announced subject: boundary control, delay differential equations. In a subject still actively developing, the methods can be more important than the results and these include: adapted penalization techniques, the singular control systems approach, the variational inequality method, the Ekeland variational principle. Some prerequisites relating to convex analysis, nonlinear operators and partial differential equations are collected in the first chapter or are supplied appropriately in the text. The monograph is intended for graduate students and for resea...

  16. Optimizing ELISAs for precision and robustness using laboratory automation and statistical design of experiments.

    Science.gov (United States)

    Joelsson, Daniel; Moravec, Phil; Troutman, Matthew; Pigeon, Joseph; DePhillips, Pete

    2008-08-20

    Transferring manual ELISAs to automated platforms requires optimizing the assays for each particular robotic platform. These optimization experiments are often time consuming and difficult to perform using a traditional one-factor-at-a-time strategy. In this manuscript we describe the development of an automated process using statistical design of experiments (DOE) to quickly optimize immunoassays for precision and robustness on the Tecan EVO liquid handler. By using fractional factorials and a split-plot design, five incubation time variables and four reagent concentration variables can be optimized in a short period of time.

  17. Simultaneous optimal experimental design for in vitro binding parameter estimation.

    Science.gov (United States)

    Ernest, C Steven; Karlsson, Mats O; Hooker, Andrew C

    2013-10-01

    Simultaneous optimization of in vitro ligand binding studies using an optimal design software package that can incorporate multiple design variables through non-linear mixed effect models and provide a general optimized design regardless of the binding site capacity and relative binding rates for a two binding system. Experimental design optimization was employed with D- and ED-optimality using PopED 2.8 including commonly encountered factors during experimentation (residual error, between experiment variability and non-specific binding) for in vitro ligand binding experiments: association, dissociation, equilibrium and non-specific binding experiments. Moreover, a method for optimizing several design parameters (ligand concentrations, measurement times and total number of samples) was examined. With changes in relative binding site density and relative binding rates, different measurement times and ligand concentrations were needed to provide precise estimation of binding parameters. However, using optimized design variables, significant reductions in number of samples provided as good or better precision of the parameter estimates compared to the original extensive sampling design. Employing ED-optimality led to a general experimental design regardless of the relative binding site density and relative binding rates. Precision of the parameter estimates were as good as the extensive sampling design for most parameters and better for the poorly estimated parameters. Optimized designs for in vitro ligand binding studies provided robust parameter estimation while allowing more efficient and cost effective experimentation by reducing the measurement times and separate ligand concentrations required and in some cases, the total number of samples.

  18. Parameters optimization on DHSVM model based on a genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Changqing YAO; Zhifeng YANG

    2009-01-01

    Due to the multiplicity of factors including weather, the underlying surface and human activities, the complexity of parameter optimization for a distributed hydrological model of a watershed land surface goes far beyond the capability of traditional optimization methods. The genetic algorithm is a new attempt to find a solution to this problem. A genetic algorithm design on the Distributed-Hydrology-Soil-Vegetation model (DHSVM) parameter optimization is illustrated in this paper by defining the encoding method, designing the fitness value function, devising the genetic operators, selecting the arithmetic parameters and identifying the arithmetic termination conditions. Finally, a case study of the optimization method is implemented on the Lushi Watershed of the Yellow River Basin and achieves satisfactory results of parameter estimation. The result shows that the genetic algorithm is feasible in optimizing parameters of the DHSVM model.

  19. Parameters Investigation of Mathematical Model of Productivity for Automated Line with Availability by DMAIC Methodology

    Directory of Open Access Journals (Sweden)

    Tan Chan Sin

    2014-01-01

    Full Text Available Automated line is widely applied in industry especially for mass production with less variety product. Productivity is one of the important criteria in automated line as well as industry which directly present the outputs and profits. Forecast of productivity in industry accurately in order to achieve the customer demand and the forecast result is calculated by using mathematical model. Mathematical model of productivity with availability for automated line has been introduced to express the productivity in terms of single level of reliability for stations and mechanisms. Since this mathematical model of productivity with availability cannot achieve close enough productivity compared to actual one due to lack of parameters consideration, the enhancement of mathematical model is required to consider and add the loss parameters that is not considered in current model. This paper presents the investigation parameters of productivity losses investigated by using DMAIC (Define, Measure, Analyze, Improve, and Control concept and PACE Prioritization Matrix (Priority, Action, Consider, and Eliminate. The investigated parameters are important for further improvement of mathematical model of productivity with availability to develop robust mathematical model of productivity in automated line.

  20. An Automated DAKOTA and VULCAN-CFD Framework with Application to Supersonic Facility Nozzle Flowpath Optimization

    Science.gov (United States)

    Axdahl, Erik L.

    2015-01-01

    Removing human interaction from design processes by using automation may lead to gains in both productivity and design precision. This memorandum describes efforts to incorporate high fidelity numerical analysis tools into an automated framework and applying that framework to applications of practical interest. The purpose of this effort was to integrate VULCAN-CFD into an automated, DAKOTA-enabled framework with a proof-of-concept application being the optimization of supersonic test facility nozzles. It was shown that the optimization framework could be deployed on a high performance computing cluster with the flow of information handled effectively to guide the optimization process. Furthermore, the application of the framework to supersonic test facility nozzle flowpath design and optimization was demonstrated using multiple optimization algorithms.

  1. Review of Automated Design and Optimization of MEMS

    DEFF Research Database (Denmark)

    Achiche, Sofiane; Fan, Zhun; Bolognini, Francesca

    2007-01-01

    In recent years MEMS saw a very rapid development. Although many advances have been reached, due to the multiphysics nature of MEMS, their design is still a difficult task carried on mainly by hand calculation. In order to help to overtake such difficulties, attempts to automate MEMS design were...... carried out. This paper presents a review of these techniques. The design task of MEMS is usually divided into four main stages: System Level, Device Level, Physical Level and the Process Level. The state of the art o automated MEMS design in each of these levels is investigated....

  2. Machine Self-Teaching Methods for Parameter Optimization.

    Science.gov (United States)

    1986-12-01

    A199 285 MACHINE SELF- TEACHING METHODS FOR PARAMETER / OPTIMIZATION(U) NAVAL OCEAN SYSTEMS CENTER SAN DIEGO CA R A DILLARD DEC 86 NOSC/TR-1S39...Technical Document 1039 C) ,December 1986 Machine Self- Teaching Methods for Parameter Optimization Robin A. Dillard DTICS ELECTE MAY i01 𔄁 STAra Approved...ELEMEWt NO PROECi’ No TASK NO ARC Locally FundedI I1 I TE (ewd* Seawmy Cft*Wi., Machine Self- Teaching Methods for Parameter Optimization it PERSONAL

  3. Temporal Parameter Optimization in Four-Dimensional Flash Trajectory Imaging

    Institute of Scientific and Technical Information of China (English)

    WANG Xin-Wei; ZHOU Yan; FAN Song-Tao; LIU Yu-Liang

    2011-01-01

    In four-dimensional fiash trajectory imaging, temporal parameters include time delay, laser pulse width, gate time, pulse pair repetition frequency and the frame rate of CCD, which directly impact on the acquisition of target trajectories over time. We propose a method of optimizing the temporal parameters of flash trajectory imaging. All the temporal parameters can be estimated by the spatial parameters of the volumes of interest, target scale and velocity, and target sample number. The formulae for optimizing temporal parameters are derived, and the method is demonstrated in an experiment with a ball oscillating as a pendulum.%In four-dimensional flash trajectory imaging,temporal parameters include time delay,laser pulse width,gate time,pulse pair repetition frequency and the frame rate of CCD,which directly impact on the acquisition of target trajectories over time.We propose a method of optimizing the temporal parameters of flash trajectory imaging.All the temporal parameters can be estimated by the spatial parameters of the volumes of interest,target scale and velocity,and target sample number.The formulae for optimizing temporal parameters are derived,and the method is demonstrated in an experiment with a ball oscillating as a pendulum.Four-dimensional flash trajectory imaging (FTI)based on time-delay-modulated range-gated viewing can directly image the trajectories of moving objects with backgrounds filtered and deduce target 3D positions over time,[1] which has potentials in astronomy,remote sensing and biomedical applications.[2 4] Temporal parameters are crucial for FTI.An unreasonable setting of temporal parameters will lead to failure in obtaining target trajectories.However,in the previous work,[1] the optimization of temporal parameters has not been discussed in detail.Therefore,in this Letter we give a method of estimating the temporal parameters of FTI.

  4. Toward an Integrated Framework for Automated Development and Optimization of Online Advertising Campaigns

    OpenAIRE

    Thomaidou, Stamatina; Vazirgiannis, Michalis; Liakopoulos, Kyriakos

    2012-01-01

    Creating and monitoring competitive and cost-effective pay-per-click advertisement campaigns through the web-search channel is a resource demanding task in terms of expertise and effort. Assisting or even automating the work of an advertising specialist will have an unrivaled commercial value. In this paper we propose a methodology, an architecture, and a fully functional framework for semi- and fully- automated creation, monitoring, and optimization of cost-efficient pay-per-click campaigns ...

  5. Structural Parameter Optimization of Multilayer Conductors in HTS Cable

    Institute of Scientific and Technical Information of China (English)

    Yan Mao; Jie Qiu; Xin-Ying Liu; Zhi-Xuan Wang; Shu-Hong Wang; Jian-Guo Zhu; You-Guang Guo; Zhi-Wei Lin; Jian-Xun Jin

    2008-01-01

    In this paper, the design optimization of the structural parameters of multilayer conductors in high temperature superconducting (HTS) cable is reviewed. Various optimization methods, such as the particle swarm optimization (PSO), the genetic algorithm (GA), and a robust optimization method based on design for six sigma (DFSS), have been applied to realize uniform current distribution among the multi- layer HTS conductors. The continuous and discrete variables, such as the winding angle, radius, and winding direction of each layer, are chosen as the design parameters. Under the constraints of the mechanical properties and critical current, PSO is proven to be a more powerful tool than GA for structural parameter optimization, and DFSS can not only achieve a uniform current distribution, but also improve significantly the reliability and robustness of the HTS cable quality.

  6. OPTIMIZATION OF PARAMETERS OF ELEMENTS COMPUTER SYSTEM

    Directory of Open Access Journals (Sweden)

    Nesterov G. D.

    2016-03-01

    Full Text Available The work is devoted to the topical issue of increasing the productivity of computers. It has an experimental character. Therefore, the description of a number of the carried-out tests and the analysis of their results is offered. Previously basic characteristics of modules of the computer for the regular mode of functioning are provided in the article. Further the technique of regulating their parameters in the course of experiment is described. Thus the special attention is paid to observing the necessary thermal mode in order to avoid an undesirable overheat of the central processor. Also, operability of system in the conditions of the increased energy consumption is checked. The most responsible moment thus is regulating the central processor. As a result of the test its optimum tension, frequency and delays of data reading from memory are found. The analysis of stability of characteristics of the RAM, in particular, a condition of its tires in the course of experiment is made. As the executed tests took place within the standard range of characteristics of modules, and, therefore, the margin of safety put in the computer and capacity of system wasn't used, further experiments were made at extreme dispersal in the conditions of air cooling. The received results are also given in the offered article

  7. Optimizing parameters for magnetorheological finishing supersmooth surface

    Science.gov (United States)

    Cheng, Haobo; Feng, Zhijing; Wang, Yingwei

    2005-02-01

    This paper presents a reasonable approach to this issue, i.e., computer controlled magnetorheological finishing (MRF). In MRF, magnetically stiffened magnetorheological (MR) abrasive fluid flows through a preset converging gap that is formed by a workpiece surface and a moving rigid wall, to create precise material removal and polishing. Tsinghua University recently completed a project with MRF technology, in which a 66 mm diameter, f/5 parabolic mirror was polished to the shape accuracy of λ/17 RMS (λ=632.8nm) and the surface roughness of 1.22 nm Ra. This was done on a home made novel aspheric computer controlled manufacturing system. It is a three-axis, self-rotating wheel machine, the polishing tool is driven with one motor through a belt. This paper presents the manufacturing and testing processes, including establish the mathematics model of MRF optics on the basis of Preston equation, profiler test and relative coefficients, i.e., pressure between workpiece and tool, velocity of MR fluid in polishing spot, tolerance control of geometrical parameters such as radius of curvature and conic constant also been analyzed in the paper. Experiments were carried out on the features of MRF. The results indicated that the required convergent speed, surface roughness could be achieved with high efficiency.

  8. CADLIVE optimizer: web-based parameter estimation for dynamic models

    Directory of Open Access Journals (Sweden)

    Inoue Kentaro

    2012-08-01

    Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.

  9. Optimization of Nanostructuring Burnishing Technological Parameters by Taguchi Method

    Science.gov (United States)

    Kuznetsov, V. P.; Dmitriev, A. I.; Anisimova, G. S.; Semenova, Yu V.

    2016-04-01

    On the basis of application of Taguchi optimization method, an approach for researching influence of nanostructuring burnishing technological parameters, considering the surface layer microhardness criterion, is developed. Optimal values of burnishing force, feed and number of tool passes for hardened steel AISI 420 hardening treatment are defined.

  10. Integral Optimization of Systematic Parameters of Flip-Flow Screens

    Institute of Scientific and Technical Information of China (English)

    翟宏新

    2004-01-01

    The synthetic index Ks for evaluating flip-flow screens is proposed and systematically optimized in view of the whole system. A series of optimized values of relevant parameters are found and then compared with those of the current industrial specifications. The results show that the optimized value Ks approaches the one of those famous flip-flow screens in the world. Some new findings on geometric and kinematics parameters are useful for improving the flip-flow screens with a low Ks value, which is helpful in developing clean coal technology.

  11. Expected optimal feedback with Time-Varying Parameters

    NARCIS (Netherlands)

    Tucci, M.P.; Kendrick, D.A.; Amman, H.M.

    2011-01-01

    In this paper we derive the closed loop form of the Expected Optimal Feedback rule, sometimes called passive learning stochastic control, with time varying parameters. As such this paper extends the work of Kendrick (1981,2002, Chapter 6) where parameters are assumed to vary randomly around a known

  12. Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

    Directory of Open Access Journals (Sweden)

    Ning Zhao

    2015-01-01

    Full Text Available Storage allocation of outbound containers is a key factor of the performance of container handling system in automated container terminals. Improper storage plans of outbound containers make QC waiting inevitable; hence, the vessel handling time will be lengthened. A simulation-based optimization method is proposed in this paper for the storage allocation problem of outbound containers in automated container terminals (SAPOBA. A simulation model is built up by Timed-Colored-Petri-Net (TCPN, used to evaluate the QC waiting time of storage plans. Two optimization approaches, based on Particle Swarm Optimization (PSO and Genetic Algorithm (GA, are proposed to form the complete simulation-based optimization method. Effectiveness of this method is verified by experiment, as the comparison of the two optimization approaches.

  13. Genetic Algorithm Optimizes Q-LAW Control Parameters

    Science.gov (United States)

    Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

    2008-01-01

    A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

  14. Application of Improved Genetic Algorithm in PID Controller Parameters Optimization

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2013-01-01

    Full Text Available Ying Chen, Yong-jie Ma, Wen-xia Yun College of Physics and Electronic Engineering, Northwest Normal University, Anning Road no.967 ,Lanzhou,China,0931-7971503 e-mail:chenying1386685@126.com Abstract The setting and optimization of Proportion Integration Differentiation(PID parameters have been always the important study topics in the automatic control field. The current optimization design methods are often difficult to consider the system requirements for quickness ,reliability and robustness .So a method of PID controller parameters optimization based on Improved Genetic Algorithm(IGA is presented .Simulations with Matlab have proved that the control performance index based on IGA is better than that of the GA method and Z-N method, and is a method which has good practical value of the PID parameter setting and optimization .

  15. A FUZZY CLOPE ALGORITHM AND ITS OPTIMAL PARAMETER CHOICE

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.

  16. The Horizontal Well Drilling Parameter Optimization Design Research

    Directory of Open Access Journals (Sweden)

    Wang Yan

    2013-07-01

    Full Text Available Extended-reach drilling technology has rapidly developed during the past three decades. It requires improved new models and technology. This study has created a rapid optimum method to design the horizontal well drilling parameters. Genetic Algorithm is applied in this method to optimize the parameters such WOB, RPM. According to practical situation, the suitable fitness function and value of operators of GA are given and reasonable convergence delay-independent conditions are set. Based on the intelligence and global quick search of GA and the convergence of GA, the design parameters can be globally optimized quickly and accurately. An example is taken to prove that the application of GA in the field of drilling parameters is successful. This optimization method based on GA can provide guide for field design.

  17. Bacterial growth on surfaces: Automated image analysis for quantification of growth rate-related parameters

    DEFF Research Database (Denmark)

    Møller, S.; Sternberg, Claus; Poulsen, L. K.

    1995-01-01

    species-specific hybridizations with fluorescence-labelled ribosomal probes to estimate the single-cell concentration of RNA. By automated analysis of digitized images of stained cells, we determined four independent growth rate-related parameters: cellular RNA and DNA contents, cell volume......, and the frequency of dividing cells in a cell population. These parameters were used to compare physiological states of liquid-suspended and surfacegrowing Pseudomonas putida KT2442 in chemostat cultures. The major finding is that the correlation between substrate availability and cellular growth rate found...

  18. APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    N. Belavendram

    2010-12-01

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

  19. Optimization of Gas Metal Arc Welding Process Parameters

    Science.gov (United States)

    Kumar, Amit; Khurana, M. K.; Yadav, Pradeep K.

    2016-09-01

    This study presents the application of Taguchi method combined with grey relational analysis to optimize the process parameters of gas metal arc welding (GMAW) of AISI 1020 carbon steels for multiple quality characteristics (bead width, bead height, weld penetration and heat affected zone). An orthogonal array of L9 has been implemented to fabrication of joints. The experiments have been conducted according to the combination of voltage (V), current (A) and welding speed (Ws). The results revealed that the welding speed is most significant process parameter. By analyzing the grey relational grades, optimal parameters are obtained and significant factors are known using ANOVA analysis. The welding parameters such as speed, welding current and voltage have been optimized for material AISI 1020 using GMAW process. To fortify the robustness of experimental design, a confirmation test was performed at selected optimal process parameter setting. Observations from this method may be useful for automotive sub-assemblies, shipbuilding and vessel fabricators and operators to obtain optimal welding conditions.

  20. Estimating cellular parameters through optimization procedures: elementary principles and applications

    Directory of Open Access Journals (Sweden)

    Akatsuki eKimura

    2015-03-01

    Full Text Available Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE in a prediction or to maximize likelihood. A (local maximum of likelihood or (local minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

  1. Optimization of parameters for maximization of plateletpheresis and lymphocytapheresis yields on the Haemonetics Model V50.

    Science.gov (United States)

    AuBuchon, J P; Carter, C S; Adde, M A; Meyer, D R; Klein, H G

    1986-01-01

    Automated apheresis techniques afford the opportunity of tailoring collection parameters for each donor's hematologic profile. This study investigated the effect of various settings of the volume offset parameter as utilized in the Haemonetics Model V50 instrumentation during platelet- and lymphocytapheresis to optimize product yield, purity, and collection efficiency. In both types of procedures, increased product yield could be obtained by using an increased volume offset for donors having lower hematocrits. This improvement was related to an increase in collection efficiency. Platelet products also contained fewer contaminating lymphocytes with this approach. Adjustment of the volume offset parameter can be utilized to make the most efficient use of donors and provide higher-quality products.

  2. Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    P. Sabarinath

    2015-01-01

    Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

  3. Optimization routine for identification of model parameters in soil plasticity

    Science.gov (United States)

    Mattsson, Hans; Klisinski, Marek; Axelsson, Kennet

    2001-04-01

    The paper presents an optimization routine especially developed for the identification of model parameters in soil plasticity on the basis of different soil tests. Main focus is put on the mathematical aspects and the experience from application of this optimization routine. Mathematically, for the optimization, an objective function and a search strategy are needed. Some alternative expressions for the objective function are formulated. They capture the overall soil behaviour and can be used in a simultaneous optimization against several laboratory tests. Two different search strategies, Rosenbrock's method and the Simplex method, both belonging to the category of direct search methods, are utilized in the routine. Direct search methods have generally proved to be reliable and their relative simplicity make them quite easy to program into workable codes. The Rosenbrock and simplex methods are modified to make the search strategies as efficient and user-friendly as possible for the type of optimization problem addressed here. Since these search strategies are of a heuristic nature, which makes it difficult (or even impossible) to analyse their performance in a theoretical way, representative optimization examples against both simulated experimental results as well as performed triaxial tests are presented to show the efficiency of the optimization routine. From these examples, it has been concluded that the optimization routine is able to locate a minimum with a good accuracy, fast enough to be a very useful tool for identification of model parameters in soil plasticity.

  4. Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons.

    Science.gov (United States)

    Rumbell, Timothy H; Draguljić, Danel; Yadav, Aniruddha; Hof, Patrick R; Luebke, Jennifer I; Weaver, Christina M

    2016-08-01

    Conductance-based compartment modeling requires tuning of many parameters to fit the neuron model to target electrophysiological data. Automated parameter optimization via evolutionary algorithms (EAs) is a common approach to accomplish this task, using error functions to quantify differences between model and target. We present a three-stage EA optimization protocol for tuning ion channel conductances and kinetics in a generic neuron model with minimal manual intervention. We use the technique of Latin hypercube sampling in a new way, to choose weights for error functions automatically so that each function influences the parameter search to a similar degree. This protocol requires no specialized physiological data collection and is applicable to commonly-collected current clamp data and either single- or multi-objective optimization. We applied the protocol to two representative pyramidal neurons from layer 3 of the prefrontal cortex of rhesus monkeys, in which action potential firing rates are significantly higher in aged compared to young animals. Using an idealized dendritic topology and models with either 4 or 8 ion channels (10 or 23 free parameters respectively), we produced populations of parameter combinations fitting the target datasets in less than 80 hours of optimization each. Passive parameter differences between young and aged models were consistent with our prior results using simpler models and hand tuning. We analyzed parameter values among fits to a single neuron to facilitate refinement of the underlying model, and across fits to multiple neurons to show how our protocol will lead to predictions of parameter differences with aging in these neurons.

  5. FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis.

    Directory of Open Access Journals (Sweden)

    Alex D Herbert

    Full Text Available Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.

  6. Parameter optimization in the regularized kernel minimum noise fraction transformation

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

    2012-01-01

    Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....

  7. Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Many factors influencing range of extended range guided munition (ERGM) are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm (GA) is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.

  8. Research on Optimization of GLCM Parameter in Cell Classification

    Science.gov (United States)

    Zhang, Xi-Kun; Hou, Jie; Hu, Xin-Hua

    2016-05-01

    Real-time classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. Gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images ,which are too complicated to coordinate with the real-time system for a large amount of calculation. An optimization of GLCM algorithm is provided based on correlation analysis of GLCM parameters. The results of GLCM analysis and subsequent classification demonstrate optimized method can lower the time complexity significantly without loss of classification accuracy.

  9. Reproducible, high-throughput synthesis of colloidal nanocrystals for optimization in multidimensional parameter space.

    Science.gov (United States)

    Chan, Emory M; Xu, Chenxu; Mao, Alvin W; Han, Gang; Owen, Jonathan S; Cohen, Bruce E; Milliron, Delia J

    2010-05-12

    While colloidal nanocrystals hold tremendous potential for both enhancing fundamental understanding of materials scaling and enabling advanced technologies, progress in both realms can be inhibited by the limited reproducibility of traditional synthetic methods and by the difficulty of optimizing syntheses over a large number of synthetic parameters. Here, we describe an automated platform for the reproducible synthesis of colloidal nanocrystals and for the high-throughput optimization of physical properties relevant to emerging applications of nanomaterials. This robotic platform enables precise control over reaction conditions while performing workflows analogous to those of traditional flask syntheses. We demonstrate control over the size, size distribution, kinetics, and concentration of reactions by synthesizing CdSe nanocrystals with 0.2% coefficient of variation in the mean diameters across an array of batch reactors and over multiple runs. Leveraging this precise control along with high-throughput optical and diffraction characterization, we effectively map multidimensional parameter space to tune the size and polydispersity of CdSe nanocrystals, to maximize the photoluminescence efficiency of CdTe nanocrystals, and to control the crystal phase and maximize the upconverted luminescence of lanthanide-doped NaYF(4) nanocrystals. On the basis of these demonstrative examples, we conclude that this automated synthesis approach will be of great utility for the development of diverse colloidal nanomaterials for electronic assemblies, luminescent biological labels, electroluminescent devices, and other emerging applications.

  10. Parameter optimization for tandemregenerative system based on critical path

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    For a tandem queue system, the regenerative path is constructed. In an inter-regeneration cycle, the sensitivity value of performance measure with respect to the adjustable parameter θ can be acquired based on a fixed length of observation. Furthermore, a new algorithm of parameter optimization for the tandem queue system is given,which requires less simulation and no analysis for the perturbation transmission and makes a better estimation for the sen sitivity.

  11. ORDER-PICKING OPTIMIZATION FOR AUTOMATED PICKING SYSTEM WITH PARALLEL DISPENSERS

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Based on the characteristics of parallel dispensers in automated picking system, an order-picking optimization problem is presented. Firstly, the working principle of parallel dispensers is introduced, which implies the time cost of picking each order is influenced by the order-picking sequence. So the order-picking optimization problem can be classified as a dynamic traveling salesman problem (TSP). Then a mathematical model of the problem is established and an improved max-min ant system (MMAS) is adopted to solve the model. The improvement includes two aspects. One is that the initial assignment of ants depends on a probabilistic formula instead of a random deployment; the other is that the heuristic factor is expressed by the extra picking time of each order instead of the total. At last, an actual simulation is made on an automated picking system with parallel dispensers. The simulation results proved the optimization value and the validity of improvement on MMAS.

  12. Geometry Optimization of a Segmented Thermoelectric Generator Based on Multi-parameter and Nonlinear Optimization Method

    Science.gov (United States)

    Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie

    2017-03-01

    As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.

  13. Wrapped Progressive Sampling Search for Optimizing Learning Algorithm Parameters

    NARCIS (Netherlands)

    Bosch, Antal van den

    2005-01-01

    We present a heuristic meta-learning search method for finding a set of optimized algorithmic parameters for a range of machine learning algo- rithms. The method, wrapped progressive sampling, is a combination of classifier wrapping and progressive sampling of training data. A series of experiments

  14. Parameters optimization for magnetic resonance coupling wireless power transmission.

    Science.gov (United States)

    Li, Changsheng; Zhang, He; Jiang, Xiaohua

    2014-01-01

    Taking maximum power transmission and power stable transmission as research objectives, optimal design for the wireless power transmission system based on magnetic resonance coupling is carried out in this paper. Firstly, based on the mutual coupling model, mathematical expressions of optimal coupling coefficients for the maximum power transmission target are deduced. Whereafter, methods of enhancing power transmission stability based on parameters optimal design are investigated. It is found that the sensitivity of the load power to the transmission parameters can be reduced and the power transmission stability can be enhanced by improving the system resonance frequency or coupling coefficient between the driving/pick-up coil and the transmission/receiving coil. Experiment results are well conformed to the theoretical analysis conclusions.

  15. Optimization of polyetherimide processing parameters for optical interconnect applications

    Science.gov (United States)

    Zhao, Wei; Johnson, Peter; Wall, Christopher

    2015-10-01

    ULTEM® polyetherimide (PEI) resins have been used in opto-electronic markets since the optical properties of these materials enable the design of critical components under tight tolerances. PEI resins are the material of choice for injection molded integrated lens applications due to good dimensional stability, near infrared (IR) optical transparency, low moisture uptake and high heat performance. In most applications, parts must be produced consistently with minimal deviations to insure compatibility throughout the lifetime of the part. With the large number of lenses needed for this market, injection molding has been optimized to maximize the production rate. These optimized parameters for high throughput may or may not translate to an optimized optical performance. In this paper, we evaluate and optimize PEI injection molding processes with a focus on optical property performance. A commonly used commercial grade was studied to determine factors and conditions which contribute to optical transparency, color, and birefringence. Melt temperature, mold temperature, injection speed and cycle time were varied to develop optimization trials and evaluate optical properties. These parameters could be optimized to reduce in-plane birefringence from 0.0148 to 0.0006 in this study. In addition, we have studied an optically smooth, sub-10nm roughness mold to re-evaluate material properties with minimal influence from mold quality and further refine resin and process effects for the best optical performance.

  16. EVALUATION OF ANAEMIA USING RED CELL AND RETICULOCYTE PARAMETERS USING AUTOMATED HAEMATOLOGY ANALYSER

    Directory of Open Access Journals (Sweden)

    Vidyadhar Rao

    2016-06-01

    Full Text Available Use of current models of Automated Haematology Analysers help in calculating the haemoglobin contents of the mature Red cells, Reticulocytes and percentages of Microcytic and hypochromic Red cells. This has helped the clinician in reaching early diagnosis and management of Different haemopoietic disorders like Iron Deficiency Anaemia, Thalassaemia and anaemia of chronic diseases. AIM This study is conducted using an Automated Haematology Analyser to evaluate anaemia using the Red Cell and Reticulocyte parameters. Three types of anaemia were evaluated; iron deficiency anaemia, anaemia of long duration and anaemia associated with chronic disease and Iron deficiency. MATERIALS AND METHODS The blood samples were collected from 287 adult patients with anaemia differentiated depending upon their iron status, haemoglobinopathies and inflammatory activity. Iron deficiency anaemia (n=132, anaemia of long duration (ACD, (n=97 and anaemia associated with chronic disease with iron deficiency (ACD Combi, (n=58. Microcytic Red cells, hypochromic red cells percentage and levels of haemoglobin in reticulocytes and matured RBCs were calculated. The accuracy of the parameters was analysed using receiver operating characteristic analyser to differentiate between the types of anaemia. OBSERVATIONS AND RESULTS There was no difference in parameters between the iron deficiency group or anaemia associated with chronic disease and iron deficiency. The hypochromic red cells percentage was the best parameter in differentiating anaemia of chronic disease with or without absolute iron deficiency with a sensitivity of 72.7% and a specificity of 70.4%. CONCLUSIONS The parameters of red cells and reticulocytes were of reasonably good indicators in differentiating the absolute iron deficiency anaemia with chronic disease.

  17. Automation for pattern library creation and in-design optimization

    Science.gov (United States)

    Deng, Rock; Zou, Elain; Hong, Sid; Wang, Jinyan; Zhang, Yifan; Sweis, Jason; Lai, Ya-Chieh; Ding, Hua; Huang, Jason

    2015-03-01

    contain remedies built in so that fixing happens either automatically or in a guided manner. Building a comprehensive library of patterns is a very difficult task especially when a new technology node is being developed or the process keeps changing. The main dilemma is not having enough representative layouts to use for model simulation where pattern locations can be marked and extracted. This paper will present an automatic pattern library creation flow by using a few known yield detractor patterns to systematically expand the pattern library and generate optimized patterns. We will also look at the specific fixing hints in terms of edge movements, additive, or subtractive changes needed during optimization. Optimization will be shown for both the digital physical implementation and custom design methods.

  18. Determining optimal parameters in magnetic spacecraft stabilization via attitude feedback

    Science.gov (United States)

    Bruni, Renato; Celani, Fabio

    2016-10-01

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

  19. Multiexposure imaging and parameter optimization for intensified star trackers.

    Science.gov (United States)

    Yu, Wenbo; Jiang, Jie; Zhang, Guangjun

    2016-12-20

    Due to the introduction of the intensified image detector, the dynamic performance of the intensified star tracker is effectively improved. However, its attitude update rate is still seriously restricted by the transmission and processing of pixel data. In order to break through the above limitation, a multiexposure imaging approach for intensified star trackers is proposed in this paper. One star image formed by this approach actually records N different groups of star positions, and then N corresponding groups of attitude information can be acquired. Compared with the existing exposure imaging approach, the proposed approach improves the attitude update rate by N times. Furthermore, for a dim star, the proposed approach can also accumulate the energy of its N positions and then effectively improve its signal-to-noise ratio. Subsequently, in order to obtain the optimal performance of the proposed approach, parameter optimization is carried out. First, the motion model of the star spot in the image plane is established, and then based on it, all the key parameters are optimized. Simulations and experiments demonstrate the feasibility and effectiveness of the proposed approach and parameter optimization.

  20. Parameter variations in prediction skill optimization at ECMWF

    Science.gov (United States)

    Ollinaho, P.; Bechtold, P.; Leutbecher, M.; Laine, M.; Solonen, A.; Haario, H.; Järvinen, H.

    2013-11-01

    Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our target criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the target criterion. Therefore, we conclude that estimation and cost function-based tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the target criterion. The one used here is, in our view, very selective. Considering the multi-faceted question of improving forecast model performance, a more general target criterion should be developed

  1. Parameters Optimization of Low Carbon Low Alloy Steel Annealing Process

    Institute of Scientific and Technical Information of China (English)

    Maoyu ZHAO; Qianwang CHEN

    2013-01-01

    A suitable match of annealing process parameters is critical for obtaining the fine microstructure of material.Low carbon low alloy steel (20CrMnTi) was heated for various durations near Ac temperature to obtain fine pearlite and ferrite grains.Annealing temperature and time were used as independent variables,and material property data were acquired by orthogonal experiment design under intercritical process followed by subcritical annealing process (IPSAP).The weights of plasticity (hardness,yield strength,section shrinkage and elongation) of annealed material were calculated by analytic hierarchy process,and then the process parameters were optimized by the grey theory system.The results observed by SEM images show that microstructure of optimization annealing material are consisted of smaller lamellar pearlites (ferrite-cementite)and refining ferrites which distribute uniformly.Morphologies on tension fracture surface of optimized annealing material indicate that the numbers of dimple fracture show more finer toughness obviously comparing with other annealing materials.Moreover,the yield strength value of optimization annealing material decreases apparently by tensile test.Thus,the new optimized strategy is accurate and feasible.

  2. Dynamic Experiments for Bioprocess Parameter Optimization with Extreme Halophilic Archaea

    Directory of Open Access Journals (Sweden)

    Bettina Lorantfy

    2013-11-01

    Full Text Available The to-date studies on extreme halophiles were focused on shake flask cultivations. Bioreactor technology with quantitative approaches can offer a wide variety of biotechnological applications to exploit the special biochemical features of halophiles. Enabling industrial use of Haloferax mediterranei, finding the optima of cultivation parameters is of high interest. In general, process parameter optimizations were mainly carried out with laborious and time-consuming chemostat cultures. This work offers a faster alternative for process parameter optimization by applying temperature ramps and pH shifts on a halophilic continuous bioreactor culture. Although the hydraulic equilibrium in continuous culture is not reached along the ramps, the main effects on the activity from the dynamic studies can still be concluded. The results revealed that the optimal temperature range may be limited at the lower end by the activity of the primary metabolism pathways. At the higher end, the mass transfer of oxygen between the gaseous and the liquid phase can be limiting for microbial growth. pH was also shown to be a key parameter for avoiding overflow metabolism. The obtained experimental data were evaluated by clustering with multivariate data analyses. Showing the feasibility on a halophilic example, the presented dynamic methodology offers a tool for accelerating bioprocess development.

  3. Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen

    2006-01-01

    This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.

  4. Cosmological parameter estimation using Particle Swarm Optimization (PSO)

    CERN Document Server

    Prasad, Jayanti

    2011-01-01

    Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search space. MCMC methods sample the full joint probability distribution (posterior) from which one and two dimensional probability distributions, best fit (average) values of parameters and then error bars can be computed. In the present work we demonstrate the application of another stochastic method, named Particle Swarm Optimization (PSO), that is widely used in the field of engineering and artificial intelligence, for cosmo...

  5. Identification of optimal parameter combinations for the emergence of bistability

    Science.gov (United States)

    Májer, Imre; Hajihosseini, Amirhossein; Becskei, Attila

    2015-12-01

    Bistability underlies cellular memory and maintains alternative differentiation states. Bistability can emerge only if its parameter range is either physically realizable or can be enlarged to become realizable. We derived a general rule and showed that the bistable range of a reaction parameter is maximized by a pair of other parameters in any gene regulatory network provided they satisfy a general condition. The resulting analytical expressions revealed whether or not such reaction pairs are present in prototypical positive feedback loops. They are absent from the feedback loop enclosed by protein dimers but present in both the toggle-switch and the feedback circuit inhibited by sequestration. Sequestration can generate bistability even at narrow feedback expression range at which cooperative binding fails to do so, provided inhibition is set to an optimal value. These results help to design bistable circuits and cellular reprogramming and reveal whether bistability is possible in gene networks in the range of realistic parameter values.

  6. Identification of optimal parameter combinations for the emergence of bistability.

    Science.gov (United States)

    Májer, Imre; Hajihosseini, Amirhossein; Becskei, Attila

    2015-11-24

    Bistability underlies cellular memory and maintains alternative differentiation states. Bistability can emerge only if its parameter range is either physically realizable or can be enlarged to become realizable. We derived a general rule and showed that the bistable range of a reaction parameter is maximized by a pair of other parameters in any gene regulatory network provided they satisfy a general condition. The resulting analytical expressions revealed whether or not such reaction pairs are present in prototypical positive feedback loops. They are absent from the feedback loop enclosed by protein dimers but present in both the toggle-switch and the feedback circuit inhibited by sequestration. Sequestration can generate bistability even at narrow feedback expression range at which cooperative binding fails to do so, provided inhibition is set to an optimal value. These results help to design bistable circuits and cellular reprogramming and reveal whether bistability is possible in gene networks in the range of realistic parameter values.

  7. Optimizing object-based image analysis for semi-automated geomorphological mapping

    NARCIS (Netherlands)

    Anders, N.; Smith, M.; Seijmonsbergen, H.; Bouten, W.; Hengl, T.; Evans, I.S.; Wilson, J.P.; Gould, M.

    2011-01-01

    Object-Based Image Analysis (OBIA) is considered a useful tool for analyzing high-resolution digital terrain data. In the past, both segmentation and classification parameters were optimized manually by trial and error. We propose a method to automatically optimize classification parameters for incr

  8. NWP model forecast skill optimization via closure parameter variations

    Science.gov (United States)

    Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

    2012-04-01

    We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

  9. Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations

    Science.gov (United States)

    Hanson, Andrea; Reed, Erik; Cavanagh, Peter

    2011-01-01

    Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.

  10. Optimization of reserve lithium thionyl chloride battery electrochemical design parameters

    Science.gov (United States)

    Doddapaneni, N.; Godshall, N. A.

    The performance of Reserve Lithium Thionyl Chloride (RLTC) batteries was optimized by conducting a parametric study of seven electrochemical parameters: electrode compression, carbon thickness, presence of catalyst, temperature, electrode limitation, discharge rate, and electrolyte acidity. Increasing electrode compression (from 0 to 15 percent) improved battery performance significantly (10 percent greater carbon capacity density). Although thinner carbon cathodes yielded less absolute capacity than did thicker cathodes, they did so with considerably higher volume efficiencies. The effect of these parameters, and their synergistic interactions, on electrochemical cell performance is illustrated.

  11. Using string invariants for prediction searching for optimal parameters

    Science.gov (United States)

    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.

  12. Optimizing bowtie structure parameters for specific incident light

    Institute of Scientific and Technical Information of China (English)

    Wang Qiao; WU Shi-Fa; Li Xu-Feng; Wang Xiao-Gang

    2010-01-01

    We investigate optical properties of a bowtie-shaped aperture using the finite difference time domain method to optimize its geometric parameters for specific incident lights. The influence of the parameters on local field enhancement and resonant wavelength in the visible frequency range is numerically analysed. It is found that the major resonance of the spectrum is exponentially depended on the bowtie angle but independent of the whole aperture size. The simulation also demonstrates that increasing the aperture size raises the local field intensity on the exit plane due to an enlarged interaction area between the light and the metal medium. And the near-field spot size is closely related to the gap.Based on these results, the design rules of the bowtie structure can be optimized for specific wavelengths excited.

  13. The optimization of operating parameters on microalgae upscaling process planning.

    Science.gov (United States)

    Ma, Yu-An; Huang, Hsin-Fu; Yu, Chung-Chyi

    2016-03-01

    The upscaling process planning developed in this study primarily involved optimizing operating parameters, i.e., dilution ratios, during process designs. Minimal variable cost was used as an indicator for selecting the optimal combination of dilution ratios. The upper and lower mean confidence intervals obtained from the actual cultured cell density data were used as the final cell density stability indicator after the operating parameters or dilution ratios were selected. The process planning method and results were demonstrated through three case studies of batch culture simulation. They are (1) final objective cell densities were adjusted, (2) high and low light intensities were used for intermediate-scale cultures, and (3) the number of culture days was expressed as integers for the intermediate-scale culture.

  14. Parameter Optimization for Laser Polishing of Niobium for SRF Applications

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Liang [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States) and William and Mary College, Williamsburg, VA (United States); Klopf, John Michael [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Reece, Charles E. [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States); Kelley, Michael J. [Thomas Jefferson National Accelerator Facility, Newport News, VA (United States) and William and Mary College, Williamsburg, VA (United States)

    2013-06-01

    Surface smoothness is critical to the performance of SRF cavities. As laser technology has been widely applied to metal machining and surface treatment, we are encouraged to use it on niobium as an alternative to the traditional wet polishing process where aggressive chemicals are involved. In this study, we describe progress toward smoothing by optimizing laser parameters on BCP treated niobium surfaces. Results shows that microsmoothing of the surface without ablation is achievable.

  15. Optimal designs for dose response curves with common parameters

    OpenAIRE

    Feller, Chrystel; Schorning, Kirsten; Dette, Holger; Bermann, Georgina; Bornkamp, Björn

    2016-01-01

    A common problem in Phase II clinical trials is the comparison of dose response curves corresponding to different treatment groups. If the effect of the dose level is described by parametric regression models and the treatments differ in the administration frequency (but not in the sort of drug) a reasonable assumption is that the regression models for the different treatments share common parameters. This paper develops optimal design theory for the comparison of different regression models ...

  16. Limiting Behaviour in Parameter Optimal Iterative Learning Control

    Institute of Scientific and Technical Information of China (English)

    David H. Owens; Maria Tomas-Rodriguez; Jari J. Hat(o)nen

    2006-01-01

    This paper analyses the concept of a Limit Set in Parameter Optimal Iterative Learning Control (ILC). We investigate the existence of stable and unstable parts of Limit Set and demonstrates that they will often exist in practice.This is illustrated via a 2-dimensional example where the convergence of the learning algorithm is analyzed from the error's dynamic behaviour. These ideas are extended to the N-dimensional cases by analogy and example.

  17. An automatic and effective parameter optimization method for model tuning

    Directory of Open Access Journals (Sweden)

    T. Zhang

    2015-05-01

    Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

  18. Optimal parameters for clinical implementation of breast cancer patient setup using Varian DTS software.

    Science.gov (United States)

    Ng, Sook Kien; Zygmanski, Piotr; Jeung, Andrew; Mostafavi, Hassan; Hesser, Juergen; Bellon, Jennifer R; Wong, Julia S; Lyatskaya, Yulia

    2012-05-10

    Digital tomosynthesis (DTS) was evaluated as an alternative to cone-beam computed tomography (CBCT) for patient setup. DTS is preferable when there are constraints with setup time, gantry-couch clearance, and imaging dose using CBCT. This study characterizes DTS data acquisition and registration parameters for the setup of breast cancer patients using nonclinical Varian DTS software. DTS images were reconstructed from CBCT projections acquired on phantoms and patients with surgical clips in the target volume. A shift-and-add algorithm was used for DTS volume reconstructions, while automated cross-correlation matches were performed within Varian DTS software. Triangulation on two short DTS arcs separated by various angular spread was done to improve 3D registration accuracy. Software performance was evaluated on two phantoms and ten breast cancer patients using the registration result as an accuracy measure; investigated parameters included arc lengths, arc orientations, angular separation between two arcs, reconstruction slice spacing, and number of arcs. The shifts determined from DTS-to-CT registration were compared to the shifts based on CBCT-to-CT registration. The difference between these shifts was used to evaluate the software accuracy. After findings were quantified, optimal parameters for the clinical use of DTS technique were determined. It was determined that at least two arcs were necessary for accurate 3D registration for patient setup. Registration accuracy of 2 mm was achieved when the reconstruction arc length was > 5° for clips with HU ≥ 1000; larger arc length (≥ 8°) was required for very low HU clips. An optimal arc separation was found to be ≥ 20° and optimal arc length was 10°. Registration accuracy did not depend on DTS slice spacing. DTS image reconstruction took 10-30 seconds and registration took less than 20 seconds. The performance of Varian DTS software was found suitable for the accurate setup of breast cancer patients

  19. PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    S. Kalaivani

    2012-07-01

    Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.

  20. Optimization of laser butt welding parameters with multiple performance characteristics

    Science.gov (United States)

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

    2011-04-01

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

  1. Optimal construction parameters of electrosprayed trilayer organic photovoltaic devices

    Science.gov (United States)

    Shah, S. K.; Abbas, M.; Ali, M.; Hirsch, L.; Gunnella, R.

    2014-01-01

    A detailed investigation of the optimal set of parameters employed in multilayer device fabrication obtained through successive electrospray deposited layers is reported. In this scheme, the donor/acceptor (D/A) bulk heterojunction layer is sandwiched between two thin stacked layers of individual donor and acceptor materials. The stacked layers geometry with optimal thicknesses plays a decisive role in improving operation characteristics. Among the parameters of the multilayer organic photovoltaics device, the D/A concentration ratio, blend thickness and stacking layers thicknesses are optimized. Other parameters, such as thermal annealing and the role of top metal contacts, are also discussed. Internal photon to current efficiency is found to attain a strong response in the 500 nm optical region for the most efficient device architectures. Such an observation indicates a clear interplay between photon harvesting of active layers and transport by ancillary stacking layers, opening up the possibility to engineer both the material fine structure and the device architecture to obtain the best photovoltaic response from a complex organic heterostructure.

  2. Optimizing spectral CT parameters for material classification tasks

    Science.gov (United States)

    Rigie, D. S.; La Rivière, P. J.

    2016-06-01

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

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

    Science.gov (United States)

    Chen, Yi; Huang, Weina; Peng, Bei

    2014-01-01

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

  4. Automation of data processing and calculation of retention parameters and thermodynamic data for gas chromatography

    Science.gov (United States)

    Makarycheva, A. I.; Faerman, V. A.

    2017-02-01

    The analyses of automation patterns is performed and the programming solution for the automation of data processing of the chromatographic data and their further information storage with a help of a software package, Mathcad and MS Excel spreadsheets, is developed. The offered approach concedes the ability of data processing algorithm modification and does not require any programming experts participation. The approach provides making a measurement of the given time and retention volumes, specific retention volumes, a measurement of differential molar free adsorption energy, and a measurement of partial molar solution enthalpies and isosteric heats of adsorption. The developed solution is focused on the appliance in a small research group and is tested on the series of some new gas chromatography sorbents. More than 20 analytes were submitted to calculation of retention parameters and thermodynamic sorption quantities. The received data are provided in the form accessible to comparative analysis, and they are able to find sorbing agents with the most profitable properties to solve some concrete analytic issues.

  5. Optimization of a filter-lysis protocol to purify rat testicular homogenates for automated spermatid counting.

    Science.gov (United States)

    Pacheco, Sara E; Anderson, Linnea M; Boekelheide, Kim

    2012-01-01

    Quantifying testicular homogenization-resistant spermatid heads (HRSH) is a powerful indicator of spermatogenesis. These counts have traditionally been performed manually using a hemocytometer, but this method can be time consuming and biased. We aimed to develop a protocol to reduce debris for the application of automated counting, which would allow for efficient and unbiased quantification of rat HRSH. We developed a filter-lysis protocol that effectively removes debris from rat testicular homogenates. After filtering and lysing the homogenates, we found no statistical differences between manual (classic and filter-lysis) and automated (filter-lysis) counts using 1-way analysis of variance with Bonferroni's multiple comparison test. In addition, Pearson's correlation coefficients were calculated to compare the counting methods, and there was a strong correlation between the classic manual counts and the filter-lysis manual (r = 0.85, P = .002) and the filter-lysis automated (r = 0.89, P = .0005) counts. We also tested the utility of the automated method in a low-dose exposure model known to decrease HRSH. Adult Fischer 344 rats exposed to 0.33% 2,5-hexanedione in the drinking water for 12 weeks demonstrated decreased body (P = .02) and testes (P = .002) weights. In addition, there was a significant reduction in the number of HRSH per testis (P = .002) when compared to controls. A filterlysis protocol was optimized to purify rat testicular homogenates for automated HRSH counts. Automated counting systems yield unbiased data and can be applied to detect changes in the testis after low-dose toxicant exposure.

  6. A Novel Optimization Tool for Automated Design of Integrated Circuits based on MOSGA

    Directory of Open Access Journals (Sweden)

    Maryam Dehbashian

    2011-11-01

    Full Text Available In this paper a novel optimization method based on Multi-Objective Gravitational Search Algorithm (MOGSA is presented for automated design of analog integrated circuits. The recommended method firstly simulates a selected circuit using a simulator and then simulated results are optimized by MOGSA algorithm. Finally this process continues to meet its optimum result. The main programs of the proposed method have been implemented in MATLAB while analog circuits are simulated by HSPICE software. To show the capability of this method, its proficiency will be examined in the optimization of analog integrated circuits design. In this paper, an analog circuit sizing scheme -Optimum Automated Design of a Temperature independent Differential Op-amp using Widlar Current Source- is illustrated as a case study. The computer results obtained from implementing this method indicate that the design specifications are closely met. Moreover, according to various design criteria, this tool by proposing a varied set of answers can give more options to designers to choose a desirable scheme among other suggested results. MOGSA, the proposal algorithm, introduces a novel method in multi objective optimization on the basis of Gravitational Search Algorithm in which the concept of “Pareto-optimality” is used to determine “non-dominated” positions as well as an external repository to keep these positions. To ensure the accuracy of MOGSA performance, this algorithm is validated using several standard test functions from some specialized literatures. Final results indicate that our method is highly competitive with current multi objective optimization algorithms.

  7. A Combined Method in Parameters Optimization of Hydrocyclone

    Directory of Open Access Journals (Sweden)

    Jing-an Feng

    2016-01-01

    Full Text Available To achieve efficient separation of calcium hydroxide and impurities in carbide slag by using hydrocyclone, the physical granularity property of carbide slag, hydrocyclone operation parameters for slurry concentration, and the slurry velocity inlet are designed to be optimized. The optimization methods are combined with the Design of Experiment (DOE method and the Computational Fluid Dynamics (CFD method. Based on Design Expert software, the central composite design (CCD with three factors and five levels amounting to five groups of 20 test responses was constructed, and the experiments were performed by numerical simulation software FLUENT. Through the analysis of variance deduced from numerical simulation experiment results, the regression equations of pressure drop, overflow concentration, purity, and separation efficiencies of two solid phases were, respectively, obtained. The influences of factors were analyzed by the responses, respectively. Finally, optimized results were obtained by the multiobjective optimization method through the Design Expert software. Based on the optimized conditions, the validation test by numerical simulation and separation experiment were separately proceeded. The results proved that the combined method could be efficiently used in studying the hydrocyclone and it has a good performance in application engineering.

  8. Efficient global optimization of a limited parameter antenna design

    Science.gov (United States)

    O'Donnell, Teresa H.; Southall, Hugh L.; Kaanta, Bryan

    2008-04-01

    Efficient Global Optimization (EGO) is a competent evolutionary algorithm suited for problems with limited design parameters and expensive cost functions. Many electromagnetics problems, including some antenna designs, fall into this class, as complex electromagnetics simulations can take substantial computational effort. This makes simple evolutionary algorithms such as genetic algorithms or particle swarms very time-consuming for design optimization, as many iterations of large populations are usually required. When physical experiments are necessary to perform tradeoffs or determine effects which may not be simulated, use of these algorithms is simply not practical at all due to the large numbers of measurements required. In this paper we first present a brief introduction to the EGO algorithm. We then present the parasitic superdirective two-element array design problem and results obtained by applying EGO to obtain the optimal element separation and operating frequency to maximize the array directivity. We compare these results to both the optimal solution and results obtained by performing a similar optimization using the Nelder-Mead downhill simplex method. Our results indicate that, unlike the Nelder-Mead algorithm, the EGO algorithm did not become stuck in local minima but rather found the area of the correct global minimum. However, our implementation did not always drill down into the precise minimum and the addition of a local search technique seems to be indicated.

  9. Determination of the Optimized Automation Rate considering Effects of Automation on Human Operators in Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung Min; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Daejon (Korea, Republic of); Kim, Jong Hyun [KEPCO International Nuclear Graduate School, Seosaeng (Korea, Republic of); Kim, Man Cheol [Chung-Ang University, Seoul (Korea, Republic of)

    2015-05-15

    Automation refers to the use of a device or a system to perform a function previously performed by a human operator. It is introduced to reduce the human errors and to enhance the performance in various industrial fields, including the nuclear industry. However, these positive effects are not always achieved in complex systems such as nuclear power plants (NPPs). An excessive introduction of automation can generate new roles for human operators and change activities in unexpected ways. As more automation systems are accepted, the ability of human operators to detect automation failures and resume manual control is diminished. This disadvantage of automation is called the Out-of-the- Loop (OOTL) problem. We should consider the positive and negative effects of automation at the same time to determine the appropriate level of the introduction of automation. Thus, in this paper, we suggest an estimation method to consider the positive and negative effects of automation at the same time to determine the appropriate introduction of automation. This concept is limited in that it does not consider the effects of automation on human operators. Thus, a new estimation method for automation rate was suggested to overcome this problem.

  10. Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes

    CERN Document Server

    Yang, Xin-She; Luan, Tao; Koziel, Slawomir

    2014-01-01

    Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.

  11. Density-based penalty parameter optimization on C-SVM.

    Science.gov (United States)

    Liu, Yun; Lian, Jie; Bartolacci, Michael R; Zeng, Qing-An

    2014-01-01

    The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

  12. Optimization of Neutrino Oscillation Parameters Using Differential Evolution

    Institute of Scientific and Technical Information of China (English)

    Ghulam Mustafa; Faisal Akram; Bilal Masud

    2013-01-01

    We show how the traditional grid based method for finding neutrino oscillation parameters △m2 and tan2θ can be combined with an optimization technique,Differential Evolution (DE),to get a significant decrease in computer processing time required to obtain minimal chi-square (x2) in four different regions of the parameter space.We demonstrate efficiency for the two-neutrinos case.For this,the x2 function for neutrino oscillations is evaluated for grids with different density of points in standard allowed regions of the parameter space of △m2 and tan2 θ using experimental and theoretical total event rates of chlorine (Homestake),Gallex+GNO,SAGE,Superkamiokande,and SNO detectors.We find that using DE in combination with the grid based method with small density of points can produce the results comparable with the one obtained using high density grid,in much lesser computation time.

  13. Considerations for parameter optimization and sensitivity in climate models.

    Science.gov (United States)

    Neelin, J David; Bracco, Annalisa; Luo, Hao; McWilliams, James C; Meyerson, Joyce E

    2010-12-14

    Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.

  14. Difference Tracker: ImageJ plugins for fully automated analysis of multiple axonal transport parameters.

    Science.gov (United States)

    Andrews, Simon; Gilley, Jonathan; Coleman, Michael P

    2010-11-30

    Studies of axonal transport are critical, not only to understand its normal regulation, but also to determine the roles of transport impairment in disease. Exciting new resources have recently become available allowing live imaging of axonal transport in physiologically relevant settings, such as mammalian nerves. Thus the effects of disease, ageing and therapies can now be assessed directly in nervous system tissue. However, these imaging studies present new challenges. Manual or semi-automated analysis of the range of transport parameters required for a suitably complete evaluation is very time-consuming and can be subjective due to the complexity of the particle movements in axons in ex vivo explants or in vivo. We have developed Difference Tracker, a program combining two new plugins for the ImageJ image-analysis freeware, to provide fast, fully automated and objective analysis of a number of relevant measures of trafficking of fluorescently labeled particles so that axonal transport in different situations can be easily compared. We confirm that Difference Tracker can accurately track moving particles in highly simplified, artificial simulations. It can also identify and track multiple motile fluorescently labeled mitochondria simultaneously in time-lapse image stacks from live imaging of tibial nerve axons, reporting values for a number of parameters that are comparable to those obtained through manual analysis of the same axons. Difference Tracker therefore represents a useful free resource for the comparative analysis of axonal transport under different conditions, and could potentially be used and developed further in many other studies requiring quantification of particle movements.

  15. Process Parameters Optimization in Single Point Incremental Forming

    Science.gov (United States)

    Gulati, Vishal; Aryal, Ashmin; Katyal, Puneet; Goswami, Amitesh

    2016-04-01

    This work aims to optimize the formability and surface roughness of parts formed by the single-point incremental forming process for an Aluminium-6063 alloy. The tests are based on Taguchi's L18 orthogonal array selected on the basis of DOF. The tests have been carried out on vertical machining center (DMC70V); using CAD/CAM software (SolidWorks V5/MasterCAM). Two levels of tool radius, three levels of sheet thickness, step size, tool rotational speed, feed rate and lubrication have been considered as the input process parameters. Wall angle and surface roughness have been considered process responses. The influential process parameters for the formability and surface roughness have been identified with the help of statistical tool (response table, main effect plot and ANOVA). The parameter that has the utmost influence on formability and surface roughness is lubrication. In the case of formability, lubrication followed by the tool rotational speed, feed rate, sheet thickness, step size and tool radius have the influence in descending order. Whereas in surface roughness, lubrication followed by feed rate, step size, tool radius, sheet thickness and tool rotational speed have the influence in descending order. The predicted optimal values for the wall angle and surface roughness are found to be 88.29° and 1.03225 µm. The confirmation experiments were conducted thrice and the value of wall angle and surface roughness were found to be 85.76° and 1.15 µm respectively.

  16. DOE Based Robust Optimization Considering Tolerance Bands of Design Parameters

    Science.gov (United States)

    Lee, Jongsoo; Ahn, Byongchul

    The paper describes a robust optimization method to account for the tolerance of design variable and the variation in problem parameter. The proposed post-optimization effort is initiated from the deterministic optimum as a baseline. The successive process to find search directions and step sizes toward the robust optimum is conducted by determining the worst design that has the highest level in constraint violation. During the selection of the worst design, an orthogonal array table in the context of design of experiemtns (DOE) is used to reduce the constraint function evaluations especially for higher dimensionality problem. The analysis of means (ANOM) is adopted in a case where the variation in problem parameter is considered. The measurement criterion to select the worst design is based on the degree of cumulative constraint violation. A mathematical function problem is first conducted to examine the tolerance of design variable. A cantilever beam problem described by four design variables and a bracket problem with seven design variables are subsequently explored by considering both tolerance of design variable and variation in problem parameter.

  17. Adaptive Estimation of Intravascular Shear Rate Based on Parameter Optimization

    Science.gov (United States)

    Nitta, Naotaka; Takeda, Naoto

    2008-05-01

    The relationships between the intravascular wall shear stress, controlled by flow dynamics, and the progress of arteriosclerosis plaque have been clarified by various studies. Since the shear stress is determined by the viscosity coefficient and shear rate, both factors must be estimated accurately. In this paper, an adaptive method for improving the accuracy of quantitative shear rate estimation was investigated. First, the parameter dependence of the estimated shear rate was investigated in terms of the differential window width and the number of averaged velocity profiles based on simulation and experimental data, and then the shear rate calculation was optimized. The optimized result revealed that the proposed adaptive method of shear rate estimation was effective for improving the accuracy of shear rate calculation.

  18. Input Parameters Optimization in Swarm DS-CDMA Multiuser Detectors

    CERN Document Server

    Abrão, Taufik; Angelico, Bruno A; Jeszensky, Paul Jean E

    2010-01-01

    In this paper, the uplink direct sequence code division multiple access (DS-CDMA) multiuser detection problem (MuD) is studied into heuristic perspective, named particle swarm optimization (PSO). Regarding different system improvements for future technologies, such as high-order modulation and diversity exploitation, a complete parameter optimization procedure for the PSO applied to MuD problem is provided, which represents the major contribution of this paper. Furthermore, the performance of the PSO-MuD is briefly analyzed via Monte-Carlo simulations. Simulation results show that, after convergence, the performance reached by the PSO-MuD is much better than the conventional detector, and somewhat close to the single user bound (SuB). Rayleigh flat channel is initially considered, but the results are further extend to diversity (time and spatial) channels.

  19. Optimal segmentation of pupillometric images for estimating pupil shape parameters.

    Science.gov (United States)

    De Santis, A; Iacoviello, D

    2006-12-01

    The problem of determining the pupil morphological parameters from pupillometric data is considered. These characteristics are of great interest for non-invasive early diagnosis of the central nervous system response to environmental stimuli of different nature, in subjects suffering some typical diseases such as diabetes, Alzheimer disease, schizophrenia, drug and alcohol addiction. Pupil geometrical features such as diameter, area, centroid coordinates, are estimated by a procedure based on an image segmentation algorithm. It exploits the level set formulation of the variational problem related to the segmentation. A discrete set up of this problem that admits a unique optimal solution is proposed: an arbitrary initial curve is evolved towards the optimal segmentation boundary by a difference equation; therefore no numerical approximation schemes are needed, as required in the equivalent continuum formulation usually adopted in the relevant literature.

  20. Total energy control system autopilot design with constrained parameter optimization

    Science.gov (United States)

    Ly, Uy-Loi; Voth, Christopher

    1990-01-01

    A description is given of the application of a multivariable control design method (SANDY) based on constrained parameter optimization to the design of a multiloop aircraft flight control system. Specifically, the design method is applied to the direct synthesis of a multiloop AFCS inner-loop feedback control system based on total energy control system (TECS) principles. The design procedure offers a structured approach for the determination of a set of stabilizing controller design gains that meet design specifications in closed-loop stability, command tracking performance, disturbance rejection, and limits on control activities. The approach can be extended to a broader class of multiloop flight control systems. Direct tradeoffs between many real design goals are rendered systematic by proper formulation of the design objectives and constraints. Satisfactory designs are usually obtained in few iterations. Performance characteristics of the optimized TECS design have been improved, particularly in the areas of closed-loop damping and control activity in the presence of turbulence.

  1. Power Saving Optimization for Linear Collider Interaction Region Parameters

    Energy Technology Data Exchange (ETDEWEB)

    Seryi, Andrei; /SLAC

    2009-10-30

    Optimization of Interaction Region parameters of a TeV energy scale linear collider has to take into account constraints defined by phenomena such as beam-beam focusing forces, beamstrahlung radiation, and hour-glass effect. With those constraints, achieving a desired luminosity of about 2E34 would require use of e{sup +}e{sup -} beams with about 10 MW average power. Application of the 'travelling focus' regime may allow the required beam power to be reduced by at least a factor of two, helping reduce the cost of the collider, while keeping the beamstrahlung energy loss reasonably low. The technique is illustrated for the 500 GeV CM parameters of the International Linear Collider. This technique may also in principle allow recycling the e{sup +}e{sup -} beams and/or recuperation of their energy.

  2. PARAMETER OPTIMIZATION OF CARBIDIC AUSTEMPERED DUCTILE IRON USING TAGUCHI METHOD

    Directory of Open Access Journals (Sweden)

    P.DHANAPAL

    2010-08-01

    Full Text Available Carbidic austempered ductile iron [CADI] is the family of ductile iron containing wear resistance alloy carbides in the ausferrite matrix. This CADI is manufactured by selecting proper material composition through the melting route.In an effort to obtain the optimal production parameters, Taguchi method is applied. To analyse the effect of production parameters on the machanical properties, signal-to-noise (S/N ratio is calculated based on the design ofexperiments and the linear graph. The analysis of varience is calculated to find the amount of contribution of factors on individual mechanical properties and its significancy. The analytical results of taguchi method are compared with the experimental values, and it shows both are identical.

  3. Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

    CERN Document Server

    Srivastava, Praveen Ranjan

    2011-01-01

    Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing

  4. GAUFRE: A tool for an automated determination of atmospheric parameters from spectroscopy

    Directory of Open Access Journals (Sweden)

    Fossati L.

    2013-03-01

    Full Text Available We present an automated tool for measuring atmospheric parameters (Teff, log g, [Fe/H] for F-G-K dwarf and giant stars. The tool, called GAUFRE, is composed of several routines written in C++: GAUFRE-RV measures radial velocity from spectra via cross-correlation against a synthetic template, GAUFRE-EW measures atmospheric parameters through the classic line-by-line technique and GAUFRE-CHI2 performs a ��2 fitting to a library of synthetic spectra. A set of F-G-K stars extensively studied in the literature were used as a benchmark for the program: their high signal-to-noise and high resolution spectra were analyzed by using GAUFRE and results were compared with those present in literature. The tool is also implemented in order to perform the spectral analysis after fixing the surface gravity (log g to the accurate value provided by asteroseismology. A set of CoRoT stars, belonging to LRc01 and LRa01 fields was used for first testing the performances and the behavior of the program when using the seismic log g.

  5. GAUFRE: a tool for an automated determination of atmospheric parameters from spectroscopy

    CERN Document Server

    Valentini, Marica; Miglio, Andrea; Fossati, Luca; Munari, Ulisse

    2013-01-01

    We present an automated tool for measuring atmospheric parameters (T_eff, log(g), [Fe/H]) for F-G-K dwarf and giant stars. The tool, called GAUFRE, is written in C++ and composed of several routines: GAUFRE-RV measures radial velocity from spectra via cross-correlation against a synthetic template, GAUFRE-EW measures atmospheric parameters through the classic line-by-line technique and GAUFRE-CHI2 performs a chi^2 fitting to a library of synthetic spectra. A set of F-G-K stars extensively studied in the literature were used as a benchmark for the program: their high signal-to-noise and high resolution spectra were analysed by using GAUFRE and results were compared with those present in literature. The tool is also implemented in order to perform the spectral analysis after fixing the surface gravity (log(g)) to the accurate value provided by asteroseismology. A set of CoRoT stars, belonging to LRc01 and LRa01 fields was used for first testing the performances and the behaviour of the program when using the se...

  6. Process parameter optimization for fly ash brick by Taguchi method

    Energy Technology Data Exchange (ETDEWEB)

    Prabir Kumar Chaulia; Reeta Das [Central Mechanical Engineering Research Institute, Durgapur (India)

    2008-04-15

    This paper presents the results of an experimental investigation carried out to optimize the mix proportions of the fly ash brick by Taguchi method of parameter design. The experiments have been designed using an L9 orthogonal array with four factors and three levels each. Small quantity of cement has been mixed as binding materials. Both cement and the fly ash used are indicated as binding material and water binder ratio has been considered as one of the control factors. So the effects of water/binder ratio, fly ash, coarse sand, and stone dust on the performance characteristic are analyzed using signal-to-noise ratios and mean response data. According to the results, water/binder ratio and stone dust play the significant role on the compressive strength of the brick. Furthermore, the estimated optimum values of the process parameters are corresponding to water/binder ratio of 0.4, fly ash of 39%, coarse sand of 24% and stone dust of 30%. The mean value of optimal strength is predicted as 166.22 kg.cm{sup -2} with a tolerance of 10.97 kg.cm{sup -2}. The confirmatory experimental result obtained for the optimum conditions is 160.17 kg.cm{sup -2}. 13 refs., 3 figs., 7 tabs.

  7. Optimizing the Pulsed Current Gas Tungsten Arc Welding Parameters

    Institute of Scientific and Technical Information of China (English)

    M. Balasubramanian; V. Jayabalan; V. Balasubramanian

    2006-01-01

    The selection of process parameter in the gas tungsten arc (GTA) welding of titanium alloy was presented for obtaining optimum grain size and hardness. Titanium alloy (Ti-6Al-4V) is one of the most important non-ferrous metals which offers great potential application in aerospace, biomedical and chemical industries,because of its low density (4.5 g/cm3), excellent corrosion resistance, high strength, attractive fracture behaviour and high melting point (1678℃). The preferred welding process for titanium alloy is frequent GTA welding due to its comparatively easier applicability and better economy. In the case of single pass (GTA)welding of thinner section of this alloy, the pulsed current has been found beneficial due to its advantages over the conventional continuous current process. Many considerations come into the picture and one needs to carefully balance various pulse current parameters to reach an optimum combination. Four factors, five level, central composite, rotatable design matrix were used to optimize the required number of experimental conditions. Mathematical models were developed to predict the fusion zone grain size using analysis of variance (ANOVA) and regression analysis. The developed models were optimized using the traditional Hooke and Jeeve's algorithm. Experimental results were provided to illustrate the proposed approach.

  8. Biohydrogen Production from Simple Carbohydrates with Optimization of Operating Parameters.

    Science.gov (United States)

    Muri, Petra; Osojnik-Črnivec, Ilja Gasan; Djinovič, Petar; Pintar, Albin

    2016-01-01

    Hydrogen could be alternative energy carrier in the future as well as source for chemical and fuel synthesis due to its high energy content, environmentally friendly technology and zero carbon emissions. In particular, conversion of organic substrates to hydrogen via dark fermentation process is of great interest. The aim of this study was fermentative hydrogen production using anaerobic mixed culture using different carbon sources (mono and disaccharides) and further optimization by varying a number of operating parameters (pH value, temperature, organic loading, mixing intensity). Among all tested mono- and disaccharides, glucose was shown as the preferred carbon source exhibiting hydrogen yield of 1.44 mol H(2)/mol glucose. Further evaluation of selected operating parameters showed that the highest hydrogen yield (1.55 mol H(2)/mol glucose) was obtained at the initial pH value of 6.4, T=37 °C and organic loading of 5 g/L. The obtained results demonstrate that lower hydrogen yield at all other conditions was associated with redirection of metabolic pathways from butyric and acetic (accompanied by H(2) production) to lactic (simultaneous H(2) production is not mandatory) acid production. These results therefore represent an important foundation for the optimization and industrial-scale production of hydrogen from organic substrates.

  9. Automated in-situ optimization of bimorph mirrors at Diamond Light Source

    Science.gov (United States)

    Sutter, John P.; Alcock, Simon G.; Sawhney, Kawal J. S.

    2011-09-01

    Bimorph mirrors are used on many synchrotron beamlines to focus or collimate light. They are highly adaptable because not only their overall figure but also their local slope errors can be corrected. However, the optimization procedure is complex. At Diamond Light Source, highly repeatable and accurate pencil beam measurements are used to determine a mirror's slope errors. These data are then used by automated scripts to calculate the necessary corrections. This procedure may be applied to any type of active mirror, but for hard X-ray mirrors, diffraction from the slits must be considered.

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

    Science.gov (United States)

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

    2015-11-01

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

  11. Parameter Estimation of Induction Motors Using Water Cycle Optimization

    Directory of Open Access Journals (Sweden)

    M. Yazdani-Asrami

    2013-12-01

    Full Text Available This paper presents the application of recently introduced water cycle algorithm (WCA to optimize the parameters of exact and approximate induction motor from the nameplate data. Considering that induction motors are widely used in industrial applications, these parameters have a significant effect on the accuracy and efficiency of the motors and, ultimately, the overall system performance. Therefore, it is essential to develop algorithms for the parameter estimation of the induction motor. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. The objective function is defined as the minimization of the real values of the relative error between the measured and estimated torques of the machine in different slip points. The proposed WCA approach has been applied on two different sample motors. Results of the proposed method have been compared with other previously applied Meta heuristic methods on the problem, which show the feasibility and the fast convergence of the proposed approach.

  12. Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters.

    Science.gov (United States)

    Raess, Philipp W; van de Geijn, Gert-Jan M; Njo, Tjin L; Klop, Boudewijn; Sukhachev, Dmitry; Wertheim, Gerald; McAleer, Tom; Master, Stephen R; Bagg, Adam

    2014-04-01

    The diagnosis of myelodysplastic syndromes (MDS) requires a high clinical index of suspicion to prompt bone marrow studies as well as subjective assessment of dysplastic morphology. We sought to determine if data collected by automated hematology analyzers during complete blood count (CBC) analysis might help to identify MDS in a routine clinical setting. We collected CBC parameters (including those for research use only and cell population data) and demographic information in a large (>5,000), unselected sequential cohort of outpatients. The cohort was divided into independent training and test groups to develop and validate a random forest classifier that identifies MDS. The classifier effectively identified MDS and had a receiver operating characteristic area under the curve (AUC) of 0.942. Platelet distribution width and the standard deviation of red blood cell distribution width were the most discriminating variables within the classifier. Additionally, a similar classifier was validated with an additional, independent set of >200 patients from a second institution with an AUC of 0.93. This retrospective study demonstrates the feasibility of identifying MDS in an unselected outpatient population using data routinely collected during CBC analysis with a classifier that has been validated using two independent data sets from different institutions.

  13. Optimal Control and Coordination of Connected and Automated Vehicles at Urban Traffic Intersections

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yue J. [Boston University; Malikopoulos, Andreas [ORNL; Cassandras, Christos G. [Boston University

    2016-01-01

    We address the problem of coordinating online a continuous flow of connected and automated vehicles (CAVs) crossing two adjacent intersections in an urban area. We present a decentralized optimal control framework whose solution yields for each vehicle the optimal acceleration/deceleration at any time in the sense of minimizing fuel consumption. The solu- tion, when it exists, allows the vehicles to cross the intersections without the use of traffic lights, without creating congestion on the connecting road, and under the hard safety constraint of collision avoidance. The effectiveness of the proposed solution is validated through simulation considering two intersections located in downtown Boston, and it is shown that coordination of CAVs can reduce significantly both fuel consumption and travel time.

  14. Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods.

    Science.gov (United States)

    Suleimanov, Yury V; Green, William H

    2015-09-08

    We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

  15. Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods

    CERN Document Server

    Suleimanov, Yury V

    2015-01-01

    We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation single- and double-ended transition-state optimization algorithms - the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the possibility of discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.

  16. Optimization-based particle filter for state and parameter estimation

    Institute of Scientific and Technical Information of China (English)

    Li Fu; Qi Fei; Shi Guangming; Zhang Li

    2009-01-01

    In recent years, the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance density is a critical issue in particle filter design. In order to improve the approximation of posterior distribution, this paper provides an optimization-based algorithm (the steepest descent method) to generate the proposal distribution and then sample particles from the distribution. This algorithm is applied in 1-D case, and the simulation results show that the proposed particle filter performs better than the extended Kalman filter (EKF), the standard particle filter (PF), the extended Kalman particle filter (PF-EKF) and the unscented particle filter (UPF) both in efficiency and in estimation precision.

  17. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  18. Automated ARGET ATRP Accelerates Catalyst Optimization for the Synthesis of Thiol-Functionalized Polymers.

    Science.gov (United States)

    Siegwart, Daniel J; Leiendecker, Matthias; Langer, Robert; Anderson, Daniel G

    2012-02-14

    Conventional synthesis of polymers by ATRP is relatively low throughput, involving iterative optimization of conditions in an inert atmosphere. Automated, high-throughput controlled radical polymerization was developed to accelerate catalyst optimization and production of disulfide-functionalized polymers without the need of an inert gas. Using ARGET ATRP, polymerization conditions were rapidly identified for eight different monomers, including the first ARGET ATRP of 2-(diethylamino)ethyl methacrylate and di(ethylene glycol) methyl ether methacrylate. In addition, butyl acrylate, oligo(ethylene glycol) methacrylate 300 and 475, 2-(dimethylamino)ethyl methacrylate, styrene, and methyl methacrylate were polymerized using bis(2-hydroxyethyl) disulfide bis(2-bromo-2-methylpropionate) as the initiator, tris(2-pyridylmethyl)amine as the ligand, and tin(II) 2-ethylhexanoate as the reducing agent. The catalyst and reducing agent concentration was optimized specifically for each monomer, and then a library of polymers was synthesized systematically using the optimized conditions. The disulfide-functionalized chains could be cleaved to two thiol-terminated chains upon exposure to dithiothreitol, which may have utility for the synthesis of polymer bioconjugates. Finally, we demonstrated that these new conditions translated perfectly to conventional batch polymerization. We believe the methods developed here may prove generally useful to accelerate the systematic optimization of a variety of chemical reactions and polymerizations.

  19. Automated Gravimetric Calibration to Optimize the Accuracy and Precision of TECAN Freedom EVO Liquid Handler.

    Science.gov (United States)

    Bessemans, Laurent; Jully, Vanessa; de Raikem, Caroline; Albanese, Mathieu; Moniotte, Nicolas; Silversmet, Pascal; Lemoine, Dominique

    2016-10-01

    High-throughput screening technologies are increasingly integrated into the formulation development process of biopharmaceuticals. The performance of liquid handling systems is dependent on the ability to deliver accurate and precise volumes of specific reagents to ensure process quality. We have developed an automated gravimetric calibration procedure to adjust the accuracy and evaluate the precision of the TECAN Freedom EVO liquid handling system. Volumes from 3 to 900 µL using calibrated syringes and fixed tips were evaluated with various solutions, including aluminum hydroxide and phosphate adjuvants, β-casein, sucrose, sodium chloride, and phosphate-buffered saline. The methodology to set up liquid class pipetting parameters for each solution was to split the process in three steps: (1) screening of predefined liquid class, including different pipetting parameters; (2) adjustment of accuracy parameters based on a calibration curve; and (3) confirmation of the adjustment. The run of appropriate pipetting scripts, data acquisition, and reports until the creation of a new liquid class in EVOware was fully automated. The calibration and confirmation of the robotic system was simple, efficient, and precise and could accelerate data acquisition for a wide range of biopharmaceutical applications.

  20. High Temperature Epoxy Foam: Optimization of Process Parameters

    Directory of Open Access Journals (Sweden)

    Samira El Gazzani

    2016-06-01

    Full Text Available For many years, reduction of fuel consumption has been a major aim in terms of both costs and environmental concerns. One option is to reduce the weight of fuel consumers. For this purpose, the use of a lightweight material based on rigid foams is a relevant choice. This paper deals with a new high temperature epoxy expanded material as substitution of phenolic resin, classified as potentially mutagenic by European directive Reach. The optimization of thermoset foam depends on two major parameters, the reticulation process and the expansion of the foaming agent. Controlling these two phenomena can lead to a fully expanded and cured material. The rheological behavior of epoxy resin is studied and gel time is determined at various temperatures. The expansion of foaming agent is investigated by thermomechanical analysis. Results are correlated and compared with samples foamed in the same temperature conditions. The ideal foaming/gelation temperature is then determined. The second part of this research concerns the optimization of curing cycle of a high temperature trifunctional epoxy resin. A two-step curing cycle was defined by considering the influence of different curing schedules on the glass transition temperature of the material. The final foamed material has a glass transition temperature of 270 °C.

  1. Parameter optimization in differential geometry based solvation models.

    Science.gov (United States)

    Wang, Bao; Wei, G W

    2015-10-01

    Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules.

  2. Robust integrated autopilot/autothrottle design using constrained parameter optimization

    Science.gov (United States)

    Ly, Uy-Loi; Voth, Christopher; Sanjay, Swamy

    1990-01-01

    A multivariable control design method based on constrained parameter optimization was applied to the design of a multiloop aircraft flight control system. Specifically, the design method is applied to the following: (1) direct synthesis of a multivariable 'inner-loop' feedback control system based on total energy control principles; (2) synthesis of speed/altitude-hold designs as 'outer-loop' feedback/feedforward control systems around the above inner loop; and (3) direct synthesis of a combined 'inner-loop' and 'outer-loop' multivariable control system. The design procedure offers a direct and structured approach for the determination of a set of controller gains that meet design specifications in closed-loop stability, command tracking performance, disturbance rejection, and limits on control activities. The presented approach may be applied to a broader class of multiloop flight control systems. Direct tradeoffs between many real design goals are rendered systematic by this method following careful problem formulation of the design objectives and constraints. Performance characteristics of the optimization design were improved over the current autopilot design on the B737-100 Transport Research Vehicle (TSRV) at the landing approach and cruise flight conditions; particularly in the areas of closed-loop damping, command responses, and control activity in the presence of turbulence.

  3. The mass movement routing tool r.randomwalk and its functionalities for parameter sensitivity analysis and optimization

    Science.gov (United States)

    Krenn, Julia; Mergili, Martin

    2016-04-01

    r.randomwalk is a GIS-based, multi-functional conceptual tool for mass movement routing. Starting from one to many release points or release areas, mass points are routed down through the digital elevation model until a defined break criterion is reached. Break criteria are defined by the user and may consist in an angle of reach or a related parameter (empirical-statistical relationships), in the drop of the flow velocity to zero (two-parameter friction model), or in the exceedance of a maximum runup height. Multiple break criteria may be combined. A constrained random walk approach is applied for the routing procedure, where the slope and the perpetuation of the flow direction determine the probability of the flow to move in a certain direction. r.randomwalk is implemented as a raster module of the GRASS GIS software and, as such, is open source. It can be obtained from http://www.mergili.at/randomwalk.html. Besides other innovative functionalities, r.randomwalk serves with built-in functionalities for the derivation of an impact indicator index (III) map with values in the range 0-1. III is derived from multiple model runs with different combinations of input parameters varied in a random or controlled way. It represents the fraction of model runs predicting an impact at a given pixel and is evaluated against the observed impact area through an ROC Plot. The related tool r.ranger facilitates the automated generation and evaluation of many III maps from a variety of sets of parameter combinations. We employ r.randomwalk and r.ranger for parameter optimization and sensitivity analysis. Thereby we do not focus on parameter values, but - accounting for the uncertainty inherent in all parameters - on parameter ranges. In this sense, we demonstrate two strategies for parameter sensitivity analysis and optimization. We avoid to (i) use one-at-a-time parameter testing which would fail to account for interdependencies of the parameters, and (ii) to explore all possible

  4. Optimization of struvite precipitation in synthetic biologically treated swine wastewater - Determination of the optimal process parameters

    OpenAIRE

    Capdevielle, Aurélie; Sýkorová, Eva; Biscans, Béatrice; Béline, Fabrice; Daumer, Marie-Line

    2013-01-01

    International audience; A sustainable way to recover phosphorus (P) in swine wastewater involves a preliminary step of P dissolution followed by the separation of particulate organic matter. The next two steps are firstly the precipitation of struvite crystals done by adding a crystallization reagent (magnesia) and secondly the filtration of the crystals. A design of experiments with five process parameters was set up to optimize the size of the struvite crystals in a synthetic swine wastewat...

  5. Parallel axes gear set optimization in two-parameter space

    Science.gov (United States)

    Theberge, Y.; Cardou, A.; Cloutier, L.

    1991-05-01

    This paper presents a method for optimal spur and helical gear transmission design that may be used in a computer aided design (CAD) approach. The design objective is generally taken as obtaining the most compact set for a given power input and gear ratio. A mixed design procedure is employed which relies both on heuristic considerations and computer capabilities. Strength and kinematic constraints are considered in order to define the domain of feasible designs. Constraints allowed include: pinion tooth bending strength, gear tooth bending strength, surface stress (resistance to pitting), scoring resistance, pinion involute interference, gear involute interference, minimum pinion tooth thickness, minimum gear tooth thickness, and profile or transverse contact ratio. A computer program was developed which allows the user to input the problem parameters, to select the calculation procedure, to see constraint curves in graphic display, to have an objective function level curve drawn through the design space, to point at a feasible design point and to have constraint values calculated at that point. The user can also modify some of the parameters during the design process.

  6. Optimized drying parameters of water hyacinths (Eichhornia crassipes. L

    Directory of Open Access Journals (Sweden)

    Edgardo V. Casas

    2012-12-01

    Full Text Available The study investigated the optimum drying conditions of water hyacinth to contribute in the improvement of present drying processes. The effects of independent parameters (drying temperature, airflow rate, and number of passes on the responses were determined using the Response Surface Methodology. The response parameters were composed of (1 final moisture content, (2 moisture ratio, (3 drying rate,(4 tensile strength, and (5 browning index. Box and Behnken experimental design represented the design of experiments that resulted in 15 drying runs. Statistical analysis evaluated the treatment effects. Drying temperature significantly affected the drying rate, moisture ratio, and browning index. Airflow rate had a significant effect only on the drying rate, while the number of passes significantly affected both the drying rate and browning index. The optimized conditions for drying the water hyacinth were at drying temperature of 90C, airflow rate of 0.044m3/s, and number of passes equivalent to five. The best modelthat characterizes the drying of water hyacinth is a rational function expressed as:

  7. Optimal vibration control of curved beams using distributed parameter models

    Science.gov (United States)

    Liu, Fushou; Jin, Dongping; Wen, Hao

    2016-12-01

    The design of linear quadratic optimal controller using spectral factorization method is studied for vibration suppression of curved beam structures modeled as distributed parameter models. The equations of motion for active control of the in-plane vibration of a curved beam are developed firstly considering its shear deformation and rotary inertia, and then the state space model of the curved beam is established directly using the partial differential equations of motion. The functional gains for the distributed parameter model of curved beam are calculated by extending the spectral factorization method. Moreover, the response of the closed-loop control system is derived explicitly in frequency domain. Finally, the suppression of the vibration at the free end of a cantilevered curved beam by point control moment is studied through numerical case studies, in which the benefit of the presented method is shown by comparison with a constant gain velocity feedback control law, and the performance of the presented method on avoidance of control spillover is demonstrated.

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

    Science.gov (United States)

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

    2016-06-01

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

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

    Science.gov (United States)

    Gao, Hao

    2016-04-01

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

  10. Multi-objective Genetic Algorithm for System Identification and Controller Optimization of Automated Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Xing Wu

    2011-07-01

    Full Text Available This paper presents a multi-objective genetic algorithm (MOGA with Pareto optimality and elitist tactics for the control system design of automated guided vehicle (AGV. The MOGA is used to identify AGV driving system model and optimize its servo control system sequentially. In system identification, the model identified by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objectives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as decision-making preferences which select Pareto optimal solutions as elitists. According to different objectives and elitist tactics, several sub-populations are constructed and they evolve concurrently by using independent reproduction, neighborhood mutation and heuristic crossover. The lossless finite precision method and the multi-objective normalized increment distance are proposed to keep the population diversity with a low computational complexity. Experiment results show that the cascaded MOGA have the capability to make the system model consistent with AGV driving system both in amplitude and phase, and to make its servo control system satisfy the requirements on dynamic performance and steady-state accuracy in AGV path tracking.

  11. Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    I. Hameem Shanavas

    2014-01-01

    Full Text Available In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

  12. Computer controlled automated assay for comprehensive studies of enzyme kinetic parameters.

    Directory of Open Access Journals (Sweden)

    Felix Bonowski

    Full Text Available Stability and biological activity of proteins is highly dependent on their physicochemical environment. The development of realistic models of biological systems necessitates quantitative information on the response to changes of external conditions like pH, salinity and concentrations of substrates and allosteric modulators. Changes in just a few variable parameters rapidly lead to large numbers of experimental conditions, which go beyond the experimental capacity of most research groups. We implemented a computer-aided experimenting framework ("robot lab assistant" that allows us to parameterize abstract, human-readable descriptions of micro-plate based experiments with variable parameters and execute them on a conventional 8 channel liquid handling robot fitted with a sensitive plate reader. A set of newly developed R-packages translates the instructions into machine commands, executes them, collects the data and processes it without user-interaction. By combining script-driven experimental planning, execution and data-analysis, our system can react to experimental outcomes autonomously, allowing outcome-based iterative experimental strategies. The framework was applied in a response-surface model based iterative optimization of buffer conditions and investigation of substrate, allosteric effector, pH and salt dependent activity profiles of pyruvate kinase (PYK. A diprotic model of enzyme kinetics was used to model the combined effects of changing pH and substrate concentrations. The 8 parameters of the model could be estimated from a single two-hour experiment using nonlinear least-squares regression. The model with the estimated parameters successfully predicted pH and PEP dependence of initial reaction rates, while the PEP concentration dependent shift of optimal pH could only be reproduced with a set of manually tweaked parameters. Differences between model-predictions and experimental observations at low pH suggest additional protonation

  13. Optimal Design of Variable Stiffness Composite Structures using Lamination Parameters

    NARCIS (Netherlands)

    IJsselmuiden, S.T.

    2011-01-01

    Fiber reinforced composite materials have gained widespread acceptance for a multitude of applications in the aerospace, automotive, maritime and wind-energy industries. Automated fiber placement technologies have developed rapidly over the past two decades, driven primarily by a need to reduce m

  14. Plug-and-play monitoring and performance optimization for industrial automation processes

    CERN Document Server

    Luo, Hao

    2017-01-01

    Dr.-Ing. Hao Luo demonstrates the developments of advanced plug-and-play (PnP) process monitoring and control systems for industrial automation processes. With aid of the so-called Youla parameterization, a novel PnP process monitoring and control architecture (PnP-PMCA) with modularized components is proposed. To validate the developments, a case study on an industrial rolling mill benchmark is performed, and the real-time implementation on a laboratory brushless DC motor is presented. Contents PnP Process Monitoring and Control Architecture Real-Time Configuration Techniques for PnP Process Monitoring Real-Time Configuration Techniques for PnP Performance Optimization Benchmark Study and Real-Time Implementation Target Groups Researchers and students of Automation and Control Engineering Practitioners in the area of Industrial and Production Engineering The Author Hao Luo received the Ph.D. degree at the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany, ...

  15. Multivariable norm optimal and parameter optimal iterative learning control: a unified formulation

    Science.gov (United States)

    Owens, D. H.

    2012-08-01

    This article investigates the two paradigms of norm optimal iterative learning control (NOILC) and parameter optimal iterative learning control (POILC) for multivariable (MIMO) ℓ-input, m-output linear discrete-time systems. The main result is a proof that, despite their algebraic and conceptual differences, they can be unified using linear quadratic multi-parameter optimisation techniques. In particular, whilst POILC has been naturally regarded as an approximation to NOILC, it is shown that the NOILC control law can be generated from a suitable choice of control law parameterisation and objective function in a multi-parameter MIMO POILC problem. The form of this equivalence is used to propose a new general approach to the construction of POILC problems for MIMO systems that approximates the solution of a given NOILC problem. An infinite number of such approximations exist. This great diversity is illustrated by the derivation of new convergent algorithms based on time interval and gradient partition that extend previously published work.

  16. Network synthesis and parameter optimization for vehicle suspension with inerter

    Directory of Open Access Journals (Sweden)

    Long Chen

    2016-12-01

    Full Text Available In order to design a comfortable-oriented vehicle suspension structure, the network synthesis method was utilized to transfer the problem into solving a timing robust control problem and determine the structure of “inerter–spring–damper” suspension. Bilinear Matrix Inequality was utilized to obtain the timing transfer function. Then, the transfer function of suspension system can be physically implemented by passive elements such as spring, damper, and inerter. By analyzing the sensitivity and quantum genetic algorithm, the optimized parameters of inerter–spring–damper suspension were determined. A quarter-car model was established. The performance of the inerter–spring–damper suspension was verified under random input. The simulation results manifested that the dynamic performance of the proposed suspension was enhanced in contrast with traditional suspension. The root mean square of vehicle body acceleration decreases by 18.9%. The inerter–spring–damper suspension can inhibit the vertical vibration within the frequency of 1–3 Hz effectively and enhance the performance of ride comfort significantly.

  17. Application of RBF Neural Network in OptimizingMachining Parameters

    Institute of Scientific and Technical Information of China (English)

    朱喜林; 吴博达; 武星星

    2004-01-01

    In machining processes, errors of rough in dimension, shape and location lead to changes in processing quantity, and the material of a workpiece may not be uniform. For these reasons, cutting force changes in machining, making the machining system deformable. Consequently errors in workpieces may occur. This is called the error reflection phenomenon. Generally, such errors can be reduced through repeated processing while using appropriate processing quantity in each processing based on operator's experience.According to the theory of error reflection, the error reflection coefficient indicates the extent to which errors of rough influence errors of workpieces. It is related to several factors such as machining condition, hardness of the workpiece, etc. This non-linear relation cannot be worked out using any formula. RBF neural network can approximate a non-linear function within any precision and be trained fast. In this paper, non-linear mapping ability of a fuzzy-neural network is utilized to approximate the non-linear relation. After training of the network with swatch collection obtained in experiments, an appropriate output can be obtained when an input is given. In this way, one can get the required number of processing and the processing quantity each time from the machining condition. Angular rigidity of a machining system,hardness of workpiece, etc., can be input in a form of fuzzy values. Feasibility in solving error reflection and optimizing machining parameters with a RBF neural network is verified by a simulation test with MATLAB.

  18. Analytically optimal parameters of dynamic vibration absorber with negative stiffness

    Science.gov (United States)

    Shen, Yongjun; Peng, Haibo; Li, Xianghong; Yang, Shaopu

    2017-02-01

    In this paper the optimal parameters of a dynamic vibration absorber (DVA) with negative stiffness is analytically studied. The analytical solution is obtained by Laplace transform method when the primary system is subjected to harmonic excitation. The research shows there are still two fixed points independent of the absorber damping in the amplitude-frequency curve of the primary system when the system contains negative stiffness. Then the optimum frequency ratio and optimum damping ratio are respectively obtained based on the fixed-point theory. A new strategy is proposed to obtain the optimum negative stiffness ratio and make the system remain stable at the same time. At last the control performance of the presented DVA is compared with those of three existing typical DVAs, which were presented by Den Hartog, Ren and Sims respectively. The comparison results in harmonic and random excitation show that the presented DVA in this paper could not only reduce the peak value of the amplitude-frequency curve of the primary system significantly, but also broaden the efficient frequency range of vibration mitigation.

  19. Inversion of generalized relaxation time distributions with optimized damping parameter

    Science.gov (United States)

    Florsch, Nicolas; Revil, André; Camerlynck, Christian

    2014-10-01

    Retrieving the Relaxation Time Distribution (RDT), the Grains Size Distribution (GSD) or the Pore Size Distribution (PSD) from low-frequency impedance spectra is a major goal in geophysics. The “Generalized RTD” generalizes parametric models like Cole-Cole and many others, but remains tricky to invert since this inverse problem is ill-posed. We propose to use generalized relaxation basis function (for instance by decomposing the spectra on basis of generalized Cole-Cole relaxation elements instead of the classical Debye basis) and to use the L-curve approach to optimize the damping parameter required to get smooth and realistic inverse solutions. We apply our algorithm to three examples, one synthetic and two real data sets, and the program includes the possibility of converting the RTD into GSD or PSD by choosing the value of the constant connecting the relaxation time to the characteristic polarization size of interest. A high frequencies (typically above 1 kHz), a dielectric term in taken into account in the model. The code is provided as an open Matlab source as a supplementary file associated with this paper.

  20. Evaluation of a Multi-Parameter Sensor for Automated, Continuous Cell Culture Monitoring in Bioreactors

    Science.gov (United States)

    Pappas, D.; Jeevarajan, A.; Anderson, M. M.

    2004-01-01

    Compact and automated sensors are desired for assessing the health of cell cultures in biotechnology experiments in microgravity. Measurement of cell culture medium allows for the optirn.jzation of culture conditions on orbit to maximize cell growth and minimize unnecessary exchange of medium. While several discrete sensors exist to measure culture health, a multi-parameter sensor would simplify the experimental apparatus. One such sensor, the Paratrend 7, consists of three optical fibers for measuring pH, dissolved oxygen (p02), dissolved carbon dioxide (pC02) , and a thermocouple to measure temperature. The sensor bundle was designed for intra-arterial placement in clinical patients, and potentially can be used in NASA's Space Shuttle and International Space Station biotechnology program bioreactors. Methods: A Paratrend 7 sensor was placed at the outlet of a rotating-wall perfused vessel bioreactor system inoculated with BHK-21 (baby hamster kidney) cells. Cell culture medium (GTSF-2, composed of 40% minimum essential medium, 60% L-15 Leibovitz medium) was manually measured using a bench top blood gas analyzer (BGA, Ciba-Corning). Results: A Paratrend 7 sensor was used over a long-term (>120 day) cell culture experiment. The sensor was able to track changes in cell medium pH, p02, and pC02 due to the consumption of nutrients by the BHK-21. When compared to manually obtained BGA measurements, the sensor had good agreement for pH, p02, and pC02 with bias [and precision] of 0.02 [0.15], 1 mm Hg [18 mm Hg], and -4.0 mm Hg [8.0 mm Hg] respectively. The Paratrend oxygen sensor was recalibrated (offset) periodically due to drift. The bias for the raw (no offset or recalibration) oxygen measurements was 42 mm Hg [38 mm Hg]. The measured response (rise) time of the sensor was 20 +/- 4s for pH, 81 +/- 53s for pC02, 51 +/- 20s for p02. For long-term cell culture measurements, these response times are more than adequate. Based on these findings , the Paratrend sensor could

  1. An automated approach for tone mapping operator parameter adjustment in security applications

    Science.gov (United States)

    Krasula, LukáÅ.¡; Narwaria, Manish; Le Callet, Patrick

    2014-05-01

    High Dynamic Range (HDR) imaging has been gaining popularity in recent years. Different from the traditional low dynamic range (LDR), HDR content tends to be visually more appealing and realistic as it can represent the dynamic range of the visual stimuli present in the real world. As a result, more scene details can be faithfully reproduced. As a direct consequence, the visual quality tends to improve. HDR can be also directly exploited for new applications such as video surveillance and other security tasks. Since more scene details are available in HDR, it can help in identifying/tracking visual information which otherwise might be difficult with typical LDR content due to factors such as lack/excess of illumination, extreme contrast in the scene, etc. On the other hand, with HDR, there might be issues related to increased privacy intrusion. To display the HDR content on the regular screen, tone-mapping operators (TMO) are used. In this paper, we present the universal method for TMO parameters tuning, in order to maintain as many details as possible, which is desirable in security applications. The method's performance is verified on several TMOs by comparing the outcomes from tone-mapping with default and optimized parameters. The results suggest that the proposed approach preserves more information which could be of advantage for security surveillance but, on the other hand, makes us consider possible increase in privacy intrusion.

  2. Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy.

    Science.gov (United States)

    Mohammadi, Pejman; Beerenwinkel, Niko; Benenson, Yaakov

    2017-02-22

    Cell classifiers are genetic logic circuits that transduce endogenous molecular inputs into cell-type-specific responses. Designing classifiers that achieve optimal differential response between specific cell types is a hard computational problem because it involves selection of endogenous inputs and optimization of both biochemical parameters and a logic function. To address this problem, we first derive an optimal set of biochemical parameters with the largest expected differential response over a diverse set of logic circuits, and second, we use these parameters in an evolutionary algorithm to select circuit inputs and optimize the logic function. Using this approach, we design experimentally feasible microRNA-based circuits capable of perfect discrimination for several real-world cell-classification tasks. We also find that under realistic cell-to-cell variation, circuit performance is comparable to standard cross-validation performance estimates. Our approach facilitates the generation of candidate circuits for experimental testing in therapeutic settings that require precise cell targeting, such as cancer therapy.

  3. Aggregation Pheromone System: A Real-parameter Optimization Algorithm using Aggregation Pheromones as the Base Metaphor

    Science.gov (United States)

    Tsutsui, Shigeyosi

    This paper proposes an aggregation pheromone system (APS) for solving real-parameter optimization problems using the collective behavior of individuals which communicate using aggregation pheromones. APS was tested on several test functions used in evolutionary computation. The results showed APS could solve real-parameter optimization problems fairly well. The sensitivity analysis of control parameters of APS is also studied.

  4. Design And Modeling An Automated Digsilent Power System For Optimal New Load Locations

    Directory of Open Access Journals (Sweden)

    Mohamed Saad

    2015-08-01

    Full Text Available Abstract The electric power utilities seek to take advantage of novel approaches to meet growing energy demand. Utilities are under pressure to evolve their classical topologies to increase the usage of distributed generation. Currently the electrical power engineers in many regions of the world are implementing manual methods to measure power consumption for farther assessment of voltage violation. Such process proved to be time consuming costly and inaccurate. Also demand response is a grid management technique where retail or wholesale customers are requested either electronically or manually to reduce their load. Therefore this paper aims to design and model an automated power system for optimal new load locations using DPL DIgSILENT Programming Language. This study is a diagnostic approach that assists system operator about any voltage violation cases that would happen during adding new load to the grid. The process of identifying the optimal bus bar location involves a complicated calculation of the power consumptions at each load bus As a result the DPL program would consider all the IEEE 30 bus internal networks data then a load flow simulation will be executed. To add the new load to the first bus in the network. Therefore the developed model will simulate the new load at each available bus bar in the network and generate three analytical reports for each case that captures the overunder voltage and the loading elements among the grid.

  5. Optimization of Laser Beam Transformation Hardening by One Single Parameter

    NARCIS (Netherlands)

    Meijer, J.; Sprang, van I.

    1991-01-01

    The process of laser beam transformation hardening is principally controlled by two independent parameters, the absorbed laser power on a given area and the interaction time. These parameters can be transformed into two functional parameters: the maximum surface temperature and the hardening depth.

  6. Characterization and optimized control by means of multi-parameter controllers

    Energy Technology Data Exchange (ETDEWEB)

    Nielsen, Carsten; Hoeg, S.; Thoegersen, A. (Dan-Ejendomme, Hellerup (Denmark)) (and others)

    2009-07-01

    Poorly functioning HVAC systems (Heating, Ventilation and Air Conditioning), but also separate heating, ventilation and air conditioning systems are costing the Danish society billions of kroner every year: partly because of increased energy consumption and high operational and maintenance costs, but mainly due to reduced productivity and absence due to illness because of a poor indoor climate. Typically, the operation of buildings and installations takes place today with traditional build-ing automation, which is characterised by 1) being based on static considerations 2) the individual sensor being coupled with one actuator/valve, i.e. the sensor's signal is only used in one place in the system 3) subsystems often being controlled independently of each other 4) the dynamics in building constructions and systems which is very important to the system and comfort regulation is not being considered. This, coupled with the widespread tendency to use large glass areas in the facades without sufficient sun shading, means that it is difficult to optimise comfort and energy consumption. Therefore, the last 10-20 years have seen a steady increase in the complaints of the indoor climate in Danish buildings and, at the same time, new buildings often turn out to be considerably higher energy consuming than expected. The purpose of the present project is to investigate the type of multi parameter sensors which may be generated for buildings and further to carry out a preliminary evaluation on how such multi parameter controllers may be utilized for optimal control of buildings. The aim of the project isn't to develop multi parameter controllers - this requires much more effort than possible in the present project. The aim is to show the potential of using multi parameter sensors when controlling buildings. For this purpose a larger office building has been chosen - an office building with at high energy demand and complaints regarding the indoor climate. In order to

  7. Optimization of operational aircraft parameters Reducing Noise Emission

    CERN Document Server

    Abdallah, Lina; Khardi, Salah

    2008-01-01

    The objective of this paper is to develop a model and a minimization method to provide flight path optimums reducing aircraft noise in the vicinity of airports. Optimization algorithm has solved a complex optimal control problem, and generates flight paths minimizing aircraft noise levels. Operational and safety constraints have been considered and their limits satisfied. Results are here presented and discussed.

  8. Optimization of structural parameters for spatial flexible redundant manipulators with maximum ratio of load to mass

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xu-ping; YU Yue-qing

    2005-01-01

    Optimization of structural parameters aimed at improving the load carrying capacity of spatial flexible redundant manipulators is presented in this paper. In order to increase the ratio of load to mass of robots, the cross-sectional parameters and constructional parameters are optimized respectively. The cross-sectional and configurational parameters are optimized simultaneously. The numerical simulation of a 4R spatial manipulator is performed. The results show that the load capacity of robots has been greatly improved through the optimization strategies proposed in this paper.

  9. Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning.

    Science.gov (United States)

    Nguyen, A; Yosinski, J; Clune, J

    2016-01-01

    The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search mitigates this problem by encouraging exploration in all interesting directions by replacing the performance objective with a reward for novel behaviors. This reward for novel behaviors has traditionally required a human-crafted, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a DNN-based novelty search in the image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g., churches, mosques, obelisks, etc.). Here, we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: for example, producing intelligent software, robot controllers, optimized physical components, and art.

  10. A Filled Function with Adjustable Parameters for Unconstrained Global Optimization

    Institute of Scientific and Technical Information of China (English)

    SHANGYou-lin; LIXiao-yan

    2004-01-01

    A filled function with adjustable parameters is suggested in this paper for finding a global minimum point of a general class of nonlinear programming problems with a bounded and closed domain. This function has two adjustable parameters. We will discuss the properties of the proposed filled function. Conditions on this function and on the values of parameters are given so that the constructed function has the desired properties of traditional filled function.

  11. Application of Factorial Design for Gas Parameter Optimization in CO2 Laser Welding

    DEFF Research Database (Denmark)

    Gong, Hui; Dragsted, Birgitte; Olsen, Flemming Ove

    1997-01-01

    The effect of different gas process parameters involved in CO2 laser welding has been studied by applying two-set of three-level complete factorial designs. In this work 5 gas parameters, gas type, gas flow rate, gas blowing angle, gas nozzle diameter, gas blowing point-offset, are optimized...... to be a very useful tool for parameter optimi-zation in laser welding process. Keywords: CO2 laser welding, gas parameters, factorial design, Analysis of Variance....

  12. Adaptive neuro-fuzzy estimation of optimal lens system parameters

    Science.gov (United States)

    Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani

    2014-04-01

    Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

  13. Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming

    Science.gov (United States)

    Jones, Lee W.; Al-Sakran, Sameer H.; Koza, John R.

    2005-08-01

    This paper describes how genetic programming was used as an automated invention machine to synthesize both the topology and numerical parameters for seven previously patented optical lens systems, including one aspherical system and one issued in the 21st-century. Two of the evolved optical lens systems infringe the claims of the patents and the others are novel solutions that satisfy the design goals stated in the patent. The automatic synthesis was done "from scratch"--that is, without starting from a pre-existing good design and without pre-specifying the number of lenses, the topological layout of the lenses, or the numerical parameters of the lenses. Genetic programming is a form of evolutionary computation used to automatically solve problems. It starts from a high-level statement of what needs to be done and progressively breeds a population of candidate individuals over many generations using the principle of Darwinian natural selection and genetic recombination. The paper describes how genetic programming created eyepieces that duplicated the functionality of seven previously patented lens systems. The seven designs were created in a substantially similar and routine way, suggesting that the use of genetic programming in the automated design of both the topology and numerical parameters for optical lens systems may have widespread utility.

  14. An automatic and effective parameter optimization method for model tuning

    Directory of Open Access Journals (Sweden)

    T. Zhang

    2015-11-01

    simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

  15. An automated optimization tool for high-dose-rate (HDR) prostate brachytherapy with divergent needle pattern

    Science.gov (United States)

    Borot de Battisti, M.; Maenhout, M.; de Senneville, B. Denis; Hautvast, G.; Binnekamp, D.; Lagendijk, J. J. W.; van Vulpen, M.; Moerland, M. A.

    2015-10-01

    Focal high-dose-rate (HDR) for prostate cancer has gained increasing interest as an alternative to whole gland therapy as it may contribute to the reduction of treatment related toxicity. For focal treatment, optimal needle guidance and placement is warranted. This can be achieved under MR guidance. However, MR-guided needle placement is currently not possible due to space restrictions in the closed MR bore. To overcome this problem, a MR-compatible, single-divergent needle-implant robotic device is under development at the University Medical Centre, Utrecht: placed between the legs of the patient inside the MR bore, this robot will tap the needle in a divergent pattern from a single rotation point into the tissue. This rotation point is just beneath the perineal skin to have access to the focal prostate tumor lesion. Currently, there is no treatment planning system commercially available which allows optimization of the dose distribution with such needle arrangement. The aim of this work is to develop an automatic inverse dose planning optimization tool for focal HDR prostate brachytherapy with needle insertions in a divergent configuration. A complete optimizer workflow is proposed which includes the determination of (1) the position of the center of rotation, (2) the needle angulations and (3) the dwell times. Unlike most currently used optimizers, no prior selection or adjustment of input parameters such as minimum or maximum dose or weight coefficients for treatment region and organs at risk is required. To test this optimizer, a planning study was performed on ten patients (treatment volumes ranged from 8.5 cm3to 23.3 cm3) by using 2-14 needle insertions. The total computation time of the optimizer workflow was below 20 min and a clinically acceptable plan was reached on average using only four needle insertions.

  16. Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

    Directory of Open Access Journals (Sweden)

    Jingxian Hao

    2016-11-01

    Full Text Available The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

  17. Optimal parameters for the FFA-Beddoes dynamic stall model

    Energy Technology Data Exchange (ETDEWEB)

    Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)

    1999-03-01

    Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)

  18. SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Li, Dengwang; Wang, Jie [College of Physics and Electronics, Shandong Normal University, Jinan, Shandong (China); Kapp, Daniel S.; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States)

    2015-06-15

    Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

  19. A Particle Swarm Optimization Algorithm for Optimal Operating Parameters of VMI Systems in a Two-Echelon Supply Chain

    Science.gov (United States)

    Sue-Ann, Goh; Ponnambalam, S. G.

    This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.

  20. Optimization of multilayer neural network parameters for speaker recognition

    Science.gov (United States)

    Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

    2016-05-01

    This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

  1. Computer-automated multi-disciplinary analysis and design optimization of internally cooled turbine blades

    Science.gov (United States)

    Martin, Thomas Joseph

    This dissertation presents the theoretical methodology, organizational strategy, conceptual demonstration and validation of a fully automated computer program for the multi-disciplinary analysis, inverse design and optimization of convectively cooled axial gas turbine blades and vanes. Parametric computer models of the three-dimensional cooled turbine blades and vanes were developed, including the automatic generation of discretized computational grids. Several new analysis programs were written and incorporated with existing computational tools to provide computer models of the engine cycle, aero-thermodynamics, heat conduction and thermofluid physics of the internally cooled turbine blades and vanes. A generalized information transfer protocol was developed to provide the automatic mapping of geometric and boundary condition data between the parametric design tool and the numerical analysis programs. A constrained hybrid optimization algorithm controlled the overall operation of the system and guided the multi-disciplinary internal turbine cooling design process towards the objectives and constraints of engine cycle performance, aerodynamic efficiency, cooling effectiveness and turbine blade and vane durability. Several boundary element computer programs were written to solve the steady-state non-linear heat conduction equation inside the internally cooled and thermal barrier-coated turbine blades and vanes. The boundary element method (BEM) did not require grid generation inside the internally cooled turbine blades and vanes, so the parametric model was very robust. Implicit differentiations of the BEM thermal and thereto-elastic analyses were done to compute design sensitivity derivatives faster and more accurately than via explicit finite differencing. A factor of three savings of computer processing time was realized for two-dimensional thermal optimization problems, and a factor of twenty was obtained for three-dimensional thermal optimization problems

  2. Stability and optimal parameters for continuous feedback chaos control.

    Science.gov (United States)

    Kouomou, Y Chembo; Woafo, P

    2002-09-01

    We investigate the conditions under which an optimal continuous feedback control can be achieved. Chaotic oscillations in the single-well Duffing model, with either a positive or a negative nonlinear stiffness term, are tuned to their related Ritz approximation. The Floquet theory enables the stability analysis of the control. Critical values of the feedback control coefficient fulfilling the optimization criteria are derived. The influence of the chosen target orbit, of the feedback coefficient, and of the onset time of control on its duration is discussed. The analytic approach is confirmed by numerical simulations.

  3. Key safety parameters in the optimization of fuel management

    Energy Technology Data Exchange (ETDEWEB)

    Kollmar, W.; Boehm, R.; Dernedde, I.; Haase, H.; Kiehlmann, H.D.; Neufert, A.

    1988-08-01

    Nuclear design related key safety parameters and admissible parameter ranges are defined for reload cycles which are so similar in safety terms as to allow these to be covered by generic reload safety analyses in advance. The conceptual frame of such safety analyses together with the resulting economic benefits are illustrated by four concrete applications demonstrating reduction of excessive safety margins, increase in discharge burnup, streamlining of steam break analysis, and increase in operational flexibility of first cores.

  4. Optimization parameter design of a circular e+e-Higgs factory

    Institute of Scientific and Technical Information of China (English)

    WANG Dou; GAO Jie; XIAO Ming; GENG Hui-Ping; GUO Yuan-Yuan; XU Shou-Yan; WANG Na

    2013-01-01

    In this paper we will show a general method of how to make an optimized parameter design of a circular e+e-Higgs factory by using analytical expression of maximum beam-beam parameter and beamstrahlung beam lifetime starting from a given design goal and technical limitations.A parameter space has been explored.Based on beam parameters scan and RF parameters scan,a set of optimized parameter designs for 50 km Circular Higgs Factory (CHF) with different RF frequency was proposed.

  5. Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Jimin; SHANG Chaoxuan; ZOU Minghu

    2007-01-01

    The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.

  6. Certain optimal parameters of high-velocity Venturi ejection tubes

    Science.gov (United States)

    Stark, S. B.; Reznichenko, I. G.; Pavlenko, Y. P.

    1984-11-01

    The influence of the geometrical characteristics of centrifugal nozzles in high velocity Venturi ejection tubes for atomizing liquid in gas cleaning plant is analyzed. An optimal value of the nozzle geometrical characteristic, which is a function of the degree of filling of the nozzle outlet opening by the liquid, is given, at which the throat length is independent of water pressure before the nozzle.

  7. Air Compressor Driving with Synchronous Motors at Optimal Parameters

    Directory of Open Access Journals (Sweden)

    Iuliu Petrica

    2010-10-01

    Full Text Available In this paper a method of optimal compensation of the reactive load by the synchronous motors, driving the air compressors, used in mining enterprises is presented, taking into account that in this case, the great majority of the equipment (compressors, pumps are generally working a constant load.

  8. Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning

    1993-01-01

    Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...

  9. AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n—DIMENSIONAL SPACE

    Institute of Scientific and Technical Information of China (English)

    TangBin; HuGuangrui

    2002-01-01

    An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented,which encodes movement direction and distance and searches from coarse to precise.The algorithm can realize global optimization and improve the search efficiency,and can be applied effectively in industrial optimization ,data mining and pattern recognition.

  10. AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n-DIMENSIONAL SPACE

    Institute of Scientific and Technical Information of China (English)

    Tang Bin; Hu Guangrui

    2002-01-01

    An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.

  11. Parameter selection of support vector machine for function approximation based on chaos optimization

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The support vector machine (SVM) is a novel machine learning method,which has the ability to approximate nonlinear functions with arbitrary accuracy.Setting parameters well is very crucial for SVM learning results and generalization ability,and now there is no systematic,general method for parameter selection.In this article,the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values.The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy.Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.

  12. Parameter estimation for chaotic systems based on improved boundary chicken swarm optimization

    Science.gov (United States)

    Chen, Shaolong; Yan, Renhuan

    2016-10-01

    Estimating unknown parameters for chaotic system is a key problem in the field of chaos control and synchronization. Through constructing an appropriate fitness function, parameter estimation of chaotic system could be converted to a multidimensional parameter optimization problem. In this paper, a new method base on improved boundary chicken swarm optimization (IBCSO) algorithm is proposed for solving the problem of parameter estimation in chaotic system. However, to the best of our knowledge, there is no published research work on chicken swarm optimization for parameters estimation of chaotic system. Computer simulation based on Lorenz system and comparisons with chicken swarm optimization, particle swarm optimization, and genetic algorithm shows the effectiveness and feasibility of the proposed method.

  13. Optimization design of resistance spot welding parameters of magnesium alloy

    Institute of Scientific and Technical Information of China (English)

    Lang Bo; Sun Daqian; Wu Qiong; Xuan Zhaozhi

    2008-01-01

    By means of the quadratic regression combination design process, the regression equations of nugget diameter and tensile shear load of spot welded joint were established. Effects of welding parameters on the nugget diameter and the tensile shear load were investigated. The results show that effect of welding current on nugget diameter is the most evident. And higher welding current will result in bigger nugget diameter. Besides, interaction effect of electrode force and welding current on tensile shear load is the most evident compared with others. The optimum welding parameters corresponding to the maximum of tensile shear load have been obtained by programming using Matlab software, which is 4.7kN electrode force, 28kA welding current and 4 cycle welding time. Under the condition of the optimum welding parameters, the joint having no visible defects can be obtained, nugget diameter and tensile shear load being 6.8mm and 3 256N, respectively.

  14. Design optimization on the front wheel orientation parameters of a vehicle

    Institute of Scientific and Technical Information of China (English)

    CHU Zhigang; DENG Zhaoxiang; HU Yumei; ZHU Ming

    2003-01-01

    A uniform optimization object function for front wheel orientation parameters of a vehicle is reported, which includes the tolerances of practical values and set values of front wheel orientation parameters under full load, and the changing value of each parameter with front wheel fluctuation to build a front suspension model for optimization analysis based on the multi-body dynamic (MD) theory. The original suspension is optimized with this model, and the variation law of each parameter with front wheel fluctuation is obtained. The results of a case study demonstrate that the front wheel orientation parameters of the optimized vehicle are reasonable under typical conditions and the variation of each parameter is in an ideal range with the wheel fluctuating within ±40 mm. In addition, the driving performance is improved greatly in the road test and practical use.

  15. Parameter optimization of temperature field in RF-capacitive hyperthermia

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    To realize a certain target temperature distribution in tumor tissues and avoid over-heating in normal tissues in radio frequency (RF)-capacitive hyperthermia, an objective function and some weight coefficients are introduced. Then using the 2-D finite element method, the electromagnetic and bio-heat transfer equations are solved, and using the genetic algorithm the heating configurations are recursively modified to minimize the objective function. Finally an optimum solution of the expected heating field distribution in hyperthermia is achieved. And with a human heterogeneous tissue model extracted from X-ray CT images, satisfactory optimization results are obtained in the simulations on a biplate RF-capacitive hyperthermia device. This optimization technique for controlling the body temperature field has shown scientific importance and practical values in the research of hyperthermia.

  16. Optimization of electrical stimulation parameters for cardiac tissue engineering.

    Science.gov (United States)

    Tandon, Nina; Marsano, Anna; Maidhof, Robert; Wan, Leo; Park, Hyoungshin; Vunjak-Novakovic, Gordana

    2011-06-01

    In vitro application of pulsatile electrical stimulation to neonatal rat cardiomyocytes cultured on polymer scaffolds has been shown to improve the functional assembly of cells into contractile engineered cardiac tissues. However, to date, the conditions of electrical stimulation have not been optimized. We have systematically varied the electrode material, amplitude and frequency of stimulation to determine the conditions that are optimal for cardiac tissue engineering. Carbon electrodes, exhibiting the highest charge-injection capacity and producing cardiac tissues with the best structural and contractile properties, were thus used in tissue engineering studies. Engineered cardiac tissues stimulated at 3 V/cm amplitude and 3 Hz frequency had the highest tissue density, the highest concentrations of cardiac troponin-I and connexin-43 and the best-developed contractile behaviour. These findings contribute to defining bioreactor design specifications and electrical stimulation regime for cardiac tissue engineering.

  17. Optimization of control parameters for petroleum waste composting

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Composting is being widely employed in the treatment of petroleum waste. The purpose of this study was to find the optimum control parameters for petroleum waste in-vessel composting. Various physical and chemical parameters were monitored to evaluate their influence on the microbial communities present in composting. The CO2 evolution and the number of microorganisms were measured as theactivity of composting. The results demonstrated that the optimum temperature, pH and moisture content were 56.5-59.5, 7.0-8.5 and 55%-60%, respectively. Under the optimum conditions, the removal efficiency of petroleum hydrocarbon reached 83.29% after 30 days composting.

  18. Design of Digital Imaging System for Optimization of Control Parameters

    Institute of Scientific and Technical Information of China (English)

    SONG Yong; HAO Qun; YANG Guang; SUN Hong-wei

    2007-01-01

    The design of experimental system of digital imaging system for control parameter is discussed in detail. Signal processing of digital CCD imaging system is first analyzed. Then the real time control of CCD driver and digital processing circuit and man-machine interaction are achieved by the design of digital CCD imaging module and control module. Experimental results indicate that the image quality of CCD experimental system makes a good response to the change of control parameters. The system gives an important base for improving image quality and the applicability of micro imaging system in complex environment.

  19. A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization

    Directory of Open Access Journals (Sweden)

    Sukanta Nama

    2016-04-01

    Full Text Available Differential evolution (DE is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA is a new evolutionary algorithm (EA for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy.

  20. Optimization of technological parameters for preparation of lycopene microcapsules

    OpenAIRE

    Guo, Hui; Huang, Ying; Qian, Jun-qing; Gong, Qiu-yi; Tang, Ying

    2012-01-01

    Lycopene belongs to the carotenoid family with high degree of unsaturation and all-trans form. Lycopene is easy to isomerize and auto oxide by heat, light, oxygen and different food matrices. With an increasing understanding of the health benefit of lycopene, to enhance stability and bioavailability of lycopene, ultrasonic emulsification was used to prepare lycopene microcapsules in this article. The results optimized by response surface methodology (RSM) for microcapsules consisted of four m...

  1. On the implementation of an automated acoustic output optimization algorithm for subharmonic aided pressure estimation.

    Science.gov (United States)

    Dave, J K; Halldorsdottir, V G; Eisenbrey, J R; Merton, D A; Liu, J B; Machado, P; Zhao, H; Park, S; Dianis, S; Chalek, C L; Thomenius, K E; Brown, D B; Forsberg, F

    2013-04-01

    Incident acoustic output (IAO) dependent subharmonic signal amplitudes from ultrasound contrast agents can be categorized into occurrence, growth or saturation stages. Subharmonic aided pressure estimation (SHAPE) is a technique that utilizes growth stage subharmonic signal amplitudes for hydrostatic pressure estimation. In this study, we developed an automated IAO optimization algorithm to identify the IAO level eliciting growth stage subharmonic signals and also studied the effect of pulse length on SHAPE. This approach may help eliminate the problems of acquiring and analyzing the data offline at all IAO levels as was done in previous studies and thus, pave the way for real-time clinical pressure monitoring applications. The IAO optimization algorithm was implemented on a Logiq 9 (GE Healthcare, Milwaukee, WI) scanner interfaced with a computer. The optimization algorithm stepped the ultrasound scanner from 0% to 100% IAO. A logistic equation fitting function was applied with the criterion of minimum least squared error between the fitted subharmonic amplitudes and the measured subharmonic amplitudes as a function of the IAO levels and the optimum IAO level was chosen corresponding to the inflection point calculated from the fitted data. The efficacy of the optimum IAO level was investigated for in vivo SHAPE to monitor portal vein (PV) pressures in 5 canines and was compared with the performance of IAO levels, below and above the optimum IAO level, for 4, 8 and 16 transmit cycles. The canines received a continuous infusion of Sonazoid microbubbles (1.5 μl/kg/min; GE Healthcare, Oslo, Norway). PV pressures were obtained using a surgically introduced pressure catheter (Millar Instruments, Inc., Houston, TX) and were recorded before and after increasing PV pressures. The experiments showed that optimum IAO levels for SHAPE in the canines ranged from 6% to 40%. The best correlation between changes in PV pressures and in subharmonic amplitudes (r=-0.76; p=0

  2. Data Mining and Optimization Tools for Developing Engine Parameters Tools

    Science.gov (United States)

    Dhawan, Atam P.

    1998-01-01

    This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. Tricia Erhardt and I studied the problem domain for developing an Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy, datasets. From the study and discussion with NASA LeRC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of tile data for GA based multi-resolution optimal search.

  3. Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

    Institute of Scientific and Technical Information of China (English)

    Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN

    2005-01-01

    Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.

  4. THE APPLICATION OF AUTOMATED CORRELATION OPTIMIZED WARPING TO THE QUALITY EVALUATION OF Radix Puerariae thomsonii: CORRECTING RETENTION TIME SHIFT IN THE CHROMATOGRAPHIC FINGERPRINTS

    Directory of Open Access Journals (Sweden)

    Long Jiao

    2015-01-01

    Full Text Available The application of automated correlation optimized warping (ACOW to the correction of retention time shift in the chromatographic fingerprints of Radix Puerariae thomsonii (RPT was investigated. Twenty-seven samples were extracted from 9 batches of RPT products. The fingerprints of the 27 samples were established by the HPLC method. Because there is a retention time shift in the established fingerprints, the quality of these samples cannot be correctly evaluated by using similarity estimation and principal component analysis (PCA. Thus, the ACOW method was used to align these fingerprints. In the ACOW procedure, the warping parameters, which have a significant influence on the alignment result, were optimized by an automated algorithm. After correcting the retention time shift, the quality of these RPT samples was correctly evaluated by similarity estimation and PCA. It is demonstrated that ACOW is a practical method for aligning the chromatographic fingerprints of RPT. The combination of ACOW, similarity estimation, and PCA is shown to be a promising method for evaluating the quality of Traditional Chinese Medicine.

  5. Optimization of mean-shift scale parameters on the EGEE grid.

    Science.gov (United States)

    Li, Ting; Camarasu-Pop, Sorina; Glatard, Tristan; Grenier, Thomas; Benoit-Cattin, Hugues

    2010-01-01

    This paper studies the optimization of Mean-Shift (MS) image filtering scale parameters. A parameter sweep experiment representing 164 days of CPU is performed on the EGEE grid. The mathematical foundations of Mean-Shift and the grid environment used for the deployment are described in details. The experiments and results are then discussed highlighting the efficiency of gradient ascent algorithm for MS parameters optimization and a number of grid observations related to data transfers, reliability, task scheduling, CPU time and usability.

  6. OPTIMIZATION OF OPERATING PARAMETERS FOR EDM PROCESS BASED ON THE TAGUCHI METHOD AND ARTIFICIAL NEURAL NETWORK

    OpenAIRE

    A.Thillaivanan,; P. Asokan,; K.N.Srinivasan,; Saravanan, R.

    2010-01-01

    In this paper the complexity of electrical discharge machining process which is very difficult to determine optimal cutting parameters for improving cutting performance has been reported. Optimization of operating parameters is an important step in machining, particularly for operating unconventional machiningprocedure like EDM. A suitable selection of machining parameters for the electrical discharge machining process relies heavily on the operators’ technologies and experience because of th...

  7. Optimal Two Parameter Bounds for the Seiffert Mean

    Directory of Open Access Journals (Sweden)

    Hui Sun

    2013-01-01

    Full Text Available We obtain sharp bounds for the Seiffert mean in terms of a two parameter family of means. Our results generalize and extend the recent bounds presented in the Journal of Inequalities and Applications (2012 and Abstract and Applied Analysis (2012.

  8. PLAN’S PARAMETERS OF ELEMENTS OPTIMIZATION IN CLASSIFICATION TRACKS

    Directory of Open Access Journals (Sweden)

    V. I. Bobrovskyi

    2010-12-01

    Full Text Available The method of determination of optimum parameters of connecting curves on marshalling tracks, which insure the minimum distance from the first bunch switch to the seating of park car retarders is developed. This method can be applied in designing the plan of marshalling tracks.

  9. Automatic Optimization of Alignment Parameters for Tomography Datasets

    NARCIS (Netherlands)

    Bleichrodt, F.; Batenburg, K.J.; Kämäräinen, J.-K.; Koskela, M.

    2013-01-01

    As tomographic imaging is being performed at increasingly smaller scales, the stability of the scanning hardware is of great importance to the quality of the reconstructed image. Instabilities lead to perturbations in the geometrical parameters used in the acquisition of the projections. In partic

  10. Adjoint Parameter Sensitivity Analysis for the Hydrodynamic Lattice Boltzmann Method with Applications to Design Optimization

    DEFF Research Database (Denmark)

    Pingen, Georg; Evgrafov, Anton; Maute, Kurt

    2009-01-01

    We present an adjoint parameter sensitivity analysis formulation and solution strategy for the lattice Boltzmann method (LBM). The focus is on design optimization applications, in particular topology optimization. The lattice Boltzmann method is briefly described with an in-depth discussion...... a generalized geometry optimization formulation and derive the corresponding sensitivity analysis for the single relaxation LBM for both topology and shape optimization applications. Using numerical examples, we verify the accuracy of the analytical sensitivity analysis through a comparison with finite...

  11. Optimal Parameter Tuning in a Predictive Nonlinear Control Method for a Mobile Robot

    Directory of Open Access Journals (Sweden)

    D. Hazry

    2006-01-01

    Full Text Available This study contributes to a new optimal parameter tuning in a predictive nonlinear control method for stable trajectory straight line tracking with a non-holonomic mobile robot. In this method, the focus lies in finding the optimal parameter estimation and to predict the path that the mobile robot will follow for stable trajectory straight line tracking system. The stability control contains three parameters: 1 deflection parameter for the traveling direction of the mobile robot 2 deflection parameter for the distance across traveling direction of the mobile robot and 3 deflection parameter for the steering angle of the mobile robot . Two hundred and seventy three experimental were performed and the results have been analyzed and described herewith. It is found that by using a new optimal parameter tuning in a predictive nonlinear control method derived from the extension of kinematics model, the movement of the mobile robot is stabilized and adhered to the reference posture

  12. The ESO-LV project - Automated parameter extraction for 16000 ESO/Uppsala galaxies

    NARCIS (Netherlands)

    Lauberts, Andris; Valentijn, Edwin A.

    1987-01-01

    A program to extract photometric and morphological parameters of the galaxies in the ESO/Uppsala survey (Lauberts and Valentijn, 1982) is discussed. The completeness and accuracy of the survey are evaluated and compared with other surveys. The parameters obtained in the program are listed.

  13. Optimizing the Yb:YAG thin disc laser design parameters

    Science.gov (United States)

    Javadi-Dashcasan, M.; Hajiesmaeilbaigi, F.; Razzaghi, H.; Mahdizadeh, M.; Moghadam, M.

    2008-09-01

    Based on quasi-three-level system, a numerical model of continuous wave thin disc laser is proposed. The fluorescence concentration quenching (FCQ), refractive index depending concentration effects and temperature distribution in the gain medium have been taken into account in the model. The first and second phenomena are not included in previously models. The model is used to determine optimum design parameters and to calculate the influence of various parameters like temperature, number of pump beam passes, active ions concentration and the crystal thickness on the operational efficiency of the laser. This model shows that for higher doping concentrations (>15%) the optical efficiency is decreased due to fluorescence concentration quenching. Our results are excellently in agreement with experimental results.

  14. Optimization of Process Parameters of Tool Wear in Turning Operation

    Directory of Open Access Journals (Sweden)

    Manik Barman

    2015-04-01

    Full Text Available Tool Wear is of great apprehension in machining industries since itaffects the surface quality, dimensional accuracy and production cost of the materials / components. In the present study twenty seven experiments were conducted as per 3 parameter 3 level full factorial design for turning operation of a mild steel specimen with high speed steel (HSS cutting tool. An experimental investigation on cutting tool wear and a mathematical model for tool wear estimation is reported in this paper where the model was simulated by computer programming and it has been found that this model is capable of estimating the wear rate of cutting tool and it provides an optimum set of process parameters for minimum tool wear.

  15. Method for Predicting and Optimizing System Parameters for Electrospinning System

    Science.gov (United States)

    Wincheski, Russell A. (Inventor)

    2011-01-01

    An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

  16. Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

    Directory of Open Access Journals (Sweden)

    Norlina Mohd Sabri

    2016-06-01

    Full Text Available This research is focusing on the radio frequency (RF magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO, Genetic Algorithm (GA, Artificial Immune System (AIS and Ant Colony Optimization (ACO. Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

  17. Relationships among various parameters for decision tree optimization

    KAUST Repository

    Hussain, Shahid

    2014-01-14

    In this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.

  18. Optimization of technological parameters for preparation of lycopene microcapsules.

    Science.gov (United States)

    Guo, Hui; Huang, Ying; Qian, Jun-Qing; Gong, Qiu-Yi; Tang, Ying

    2014-07-01

    Lycopene belongs to the carotenoid family with high degree of unsaturation and all-trans form. Lycopene is easy to isomerize and auto oxide by heat, light, oxygen and different food matrices. With an increasing understanding of the health benefit of lycopene, to enhance stability and bioavailability of lycopene, ultrasonic emulsification was used to prepare lycopene microcapsules in this article. The results optimized by response surface methodology (RSM) for microcapsules consisted of four major steps: (1) 0.54 g glycerin monostearate was fully dissolved in 5 mL ethyl acetate and then added 0.02 g lycopene to form an organic phase, 100.7 mL distilled water which dissolved 0.61 g synperonic pe(R)/F68 as the aqueous phase; (2) the organic phase was pulled into the aqueous phase under stirring at 60 °C water bath for 5 min; (3) the mixture was then ultrasonic homogenized at 380 W for 20 min to form a homogenous emulsion; (4) the resulting emulsion was rotary evaporated at 50 °C water bath for 10 min under a pressure of 20 MPa. Encapsulation efficiency (EE) of lycopene microcapsules under the optimized conditions approached to 64.4%.

  19. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)

    2014-04-15

    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  20. Moving Toward an Optimal and Automated Geospatial Network for CCUS Infrastructure

    Energy Technology Data Exchange (ETDEWEB)

    Hoover, Brendan Arthur [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-08-05

    Modifications in the global climate are being driven by the anthropogenic release of greenhouse gases (GHG) including carbon dioxide (CO2) (Middleton et al. 2014). CO2 emissions have, for example, been directly linked to an increase in total global temperature (Seneviratne et al. 2016). Strategies that limit CO2 emissions—like CO2 capture, utilization, and storage (CCUS) technology—can greatly reduce emissions by capturing CO2 before it is released to the atmosphere. However, to date CCUS technology has not been developed at a large commercial scale despite several promising high profile demonstration projects (Middleton et al. 2015). Current CCUS research has often focused on capturing CO2 emissions from coal-fired power plants, but recent research at Los Alamos National Laboratory (LANL) suggests focusing CCUS CO2 capture research upon industrial sources might better encourage CCUS deployment. To further promote industrial CCUS deployment, this project builds off current LANL research by continuing the development of a software tool called SimCCS, which estimates a regional system of transport to inject CO2 into sedimentary basins. The goal of SimCCS, which was first developed by Middleton and Bielicki (2009), is to output an automated and optimal geospatial industrial CCUS pipeline that accounts for industrial source and sink locations by estimating a Delaunay triangle network which also minimizes topographic and social costs (Middleton and Bielicki 2009). Current development of SimCCS is focused on creating a new version that accounts for spatial arrangements that were not available in the previous version. This project specifically addresses the issue of non-unique Delaunay triangles by adding additional triangles to the network, which can affect how the CCUS network is calculated.

  1. Automation of sample preparation for mass cytometry barcoding in support of clinical research: protocol optimization.

    Science.gov (United States)

    Nassar, Ala F; Wisnewski, Adam V; Raddassi, Khadir

    2017-03-01

    Analysis of multiplexed assays is highly important for clinical diagnostics and other analytical applications. Mass cytometry enables multi-dimensional, single-cell analysis of cell type and state. In mass cytometry, the rare earth metals used as reporters on antibodies allow determination of marker expression in individual cells. Barcode-based bioassays for CyTOF are able to encode and decode for different experimental conditions or samples within the same experiment, facilitating progress in producing straightforward and consistent results. Herein, an integrated protocol for automated sample preparation for barcoding used in conjunction with mass cytometry for clinical bioanalysis samples is described; we offer results of our work with barcoding protocol optimization. In addition, we present some points to be considered in order to minimize the variability of quantitative mass cytometry measurements. For example, we discuss the importance of having multiple populations during titration of the antibodies and effect of storage and shipping of labelled samples on the stability of staining for purposes of CyTOF analysis. Data quality is not affected when labelled samples are stored either frozen or at 4 °C and used within 10 days; we observed that cell loss is greater if cells are washed with deionized water prior to shipment or are shipped in lower concentration. Once the labelled samples for CyTOF are suspended in deionized water, the analysis should be performed expeditiously, preferably within the first hour. Damage can be minimized if the cells are resuspended in phosphate-buffered saline (PBS) rather than deionized water while waiting for data acquisition.

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

    Science.gov (United States)

    Singh, R.; Verma, H. K.

    2013-12-01

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

  3. The Study of the Optimal Parameter Settings in a Hospital Supply Chain System in Taiwan

    Directory of Open Access Journals (Sweden)

    Hung-Chang Liao

    2014-01-01

    Full Text Available This study proposed the optimal parameter settings for the hospital supply chain system (HSCS when either the total system cost (TSC or patient safety level (PSL (or both simultaneously was considered as the measure of the HSCS’s performance. Four parameters were considered in the HSCS: safety stock, maximum inventory level, transportation capacity, and the reliability of the HSCS. A full-factor experimental design was used to simulate an HSCS for the purpose of collecting data. The response surface method (RSM was used to construct the regression model, and a genetic algorithm (GA was applied to obtain the optimal parameter settings for the HSCS. The results show that the best method of obtaining the optimal parameter settings for the HSCS is the simultaneous consideration of both the TSC and the PSL to measure performance. Also, the results of sensitivity analysis based on the optimal parameter settings were used to derive adjustable strategies for the decision-makers.

  4. Hybridization of Response Surface Methodology and Genetic Algorithm optimization for CO2 laser cutting parameter on AA6061 material

    Directory of Open Access Journals (Sweden)

    A.Parthiban

    2014-03-01

    Full Text Available Investigation of laser cutting parameters on aluminium alloy (AA6061 is important due to its high reflectivity and thermal conductivity. Generally Aluminium alloy is a widely used material in aeronautical and automation industries for its inherent properties. Although the main problem during laser cutting is occurrence of recasting layer and laser beam incidence that affecting the cutting quality is known as kerf dimensions. In a sense the relationship between the laser cutting parameters such as laser power, cutting speed, gas pressure and focal position with kerf dimensions are having important role in laser cutting operation. So this work considers the response surface methodology (RSM, for making empirical relationship between dependent and independent variables. Simultaneously, this work reveals that laser power, cutting speed, gas pressure and focal position have significant effects on kerf dimension. Thus the development of empirical model and the selection of best parameters are important for manufacturing industries. Hence this work develops the statistical model with RSM and optimizes the cutting parameters with genetic algorithm (GA.

  5. Automated property optimization via ab initio O(N) elongation method: Application to (hyper-)polarizability in DNA

    Science.gov (United States)

    Orimoto, Yuuichi; Aoki, Yuriko

    2016-07-01

    An automated property optimization method was developed based on the ab initio O(N) elongation (ELG) method and applied to the optimization of nonlinear optical (NLO) properties in DNA as a first test. The ELG method mimics a polymerization reaction on a computer, and the reaction terminal of a starting cluster is attacked by monomers sequentially to elongate the electronic structure of the system by solving in each step a limited space including the terminal (localized molecular orbitals at the terminal) and monomer. The ELG-finite field (ELG-FF) method for calculating (hyper-)polarizabilities was used as the engine program of the optimization method, and it was found to show linear scaling efficiency while maintaining high computational accuracy for a random sequenced DNA model. Furthermore, the self-consistent field convergence was significantly improved by using the ELG-FF method compared with a conventional method, and it can lead to more feasible NLO property values in the FF treatment. The automated optimization method successfully chose an appropriate base pair from four base pairs (A, T, G, and C) for each elongation step according to an evaluation function. From test optimizations for the first order hyper-polarizability (β) in DNA, a substantial difference was observed depending on optimization conditions between "choose-maximum" (choose a base pair giving the maximum β for each step) and "choose-minimum" (choose a base pair giving the minimum β). In contrast, there was an ambiguous difference between these conditions for optimizing the second order hyper-polarizability (γ) because of the small absolute value of γ and the limitation of numerical differential calculations in the FF method. It can be concluded that the ab initio level property optimization method introduced here can be an effective step towards an advanced computer aided material design method as long as the numerical limitation of the FF method is taken into account.

  6. Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Dorin Sendrescu

    2013-01-01

    Full Text Available This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.

  7. Automated Modal Parameter Estimation for Operational Modal Analysis of Large Systems

    DEFF Research Database (Denmark)

    Andersen, Palle; Brincker, Rune; Goursat, Maurice;

    2007-01-01

    In this paper the problems of doing automatic modal parameter extraction and how to account for large number of data to process are considered. Two different approaches for obtaining the modal parameters automatically using OMA are presented: The Frequency Domain Decomposition (FDD) technique and...... and a correlation-driven Stochastic Subspace Identification (SSI) technique. Special attention is given to the problem of data reduction, where many sensors are available. Finally, the techniques are demonstrated on real data....

  8. Software integration for automated stability analysis and design optimization of a bearingless rotor blade

    Science.gov (United States)

    Gunduz, Mustafa Emre

    Many government agencies and corporations around the world have found the unique capabilities of rotorcraft indispensable. Incorporating such capabilities into rotorcraft design poses extra challenges because it is a complicated multidisciplinary process. The concept of applying several disciplines to the design and optimization processes may not be new, but it does not currently seem to be widely accepted in industry. The reason for this might be the lack of well-known tools for realizing a complete multidisciplinary design and analysis of a product. This study aims to propose a method that enables engineers in some design disciplines to perform a fairly detailed analysis and optimization of a design using commercially available software as well as codes developed at Georgia Tech. The ultimate goal is when the system is set up properly, the CAD model of the design, including all subsystems, will be automatically updated as soon as a new part or assembly is added to the design; or it will be updated when an analysis and/or an optimization is performed and the geometry needs to be modified. Designers and engineers will be involved in only checking the latest design for errors or adding/removing features. Such a design process will take dramatically less time to complete; therefore, it should reduce development time and costs. The optimization method is demonstrated on an existing helicopter rotor originally designed in the 1960's. The rotor is already an effective design with novel features. However, application of the optimization principles together with high-speed computing resulted in an even better design. The objective function to be minimized is related to the vibrations of the rotor system under gusty wind conditions. The design parameters are all continuous variables. Optimization is performed in a number of steps. First, the most crucial design variables of the objective function are identified. With these variables, Latin Hypercube Sampling method is used

  9. Optimization of Experimental Model Parameter Identification for Energy Storage Systems

    Directory of Open Access Journals (Sweden)

    Rosario Morello

    2013-09-01

    Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.

  10. Optimization of process parameters in drilling of fibre hybrid composite using Taguchi and grey relational analysis

    Science.gov (United States)

    Vijaya Ramnath, B.; Sharavanan, S.; Jeykrishnan, J.

    2017-03-01

    Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi’s L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.

  11. Measuring Digital PCR Quality: Performance Parameters and Their Optimization.

    Science.gov (United States)

    Lievens, A; Jacchia, S; Kagkli, D; Savini, C; Querci, M

    2016-01-01

    Digital PCR is rapidly being adopted in the field of DNA-based food analysis. The direct, absolute quantification it offers makes it an attractive technology for routine analysis of food and feed samples for their composition, possible GMO content, and compliance with labelling requirements. However, assessing the performance of dPCR assays is not yet well established. This article introduces three straightforward parameters based on statistical principles that allow users to evaluate if their assays are robust. In addition, we present post-run evaluation criteria to check if quantification was accurate. Finally, we evaluate the usefulness of Poisson confidence intervals and present an alternative strategy to better capture the variability in the analytical chain.

  12. Measuring Digital PCR Quality: Performance Parameters and Their Optimization.

    Directory of Open Access Journals (Sweden)

    A Lievens

    Full Text Available Digital PCR is rapidly being adopted in the field of DNA-based food analysis. The direct, absolute quantification it offers makes it an attractive technology for routine analysis of food and feed samples for their composition, possible GMO content, and compliance with labelling requirements. However, assessing the performance of dPCR assays is not yet well established. This article introduces three straightforward parameters based on statistical principles that allow users to evaluate if their assays are robust. In addition, we present post-run evaluation criteria to check if quantification was accurate. Finally, we evaluate the usefulness of Poisson confidence intervals and present an alternative strategy to better capture the variability in the analytical chain.

  13. Optimal FES parameters based on mechanomyographic efficiency index.

    Science.gov (United States)

    Krueger-Beck, Eddy; Scheeren, Eduardo M; Nogueira-Neto, Guilherme N; Button, Vera Lucia S N; Nohama, Percy

    2010-01-01

    Functional electrical stimulation (FES) can artificially elicit movements in spinal cord injured (SCI) subjects. FES control strategies involve monitoring muscle features and setting FES profiles so as to postpone the installation of muscle fatigue or nerve cell adaptation. Mechanomyography (MMG) sensors register the lateral oscillations of contracting muscles. This paper presents an MMG efficiency index (EI) that may indicate most efficient FES electrical parameters to control functional movements. Ten healthy and three SCI volunteers participated in the study. Four FES profiles with two FES sessions were applied with in-between 15min rest interval. MMG RMS and median frequency were inserted into the EI equation. EI increased along the test. FES profile set to 1kHz pulse frequency, 200εs active pulse duration and burst frequency of 50Hz was the most efficient.

  14. 1000 MW ultra-supercritical turbine steam parameter optimization

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The 2 ×1000 MW ultra-supercritical steam turbine of Shanghai Waigaoqiao Phase Ⅲ project,which uses grid frequency regulation and overload control through an overload valve,is manufactured by Shanghai Turbine Company using Siemens technology.Through optimization,the steam pressure is regarded as the criterion between constant pressure and sliding pressure operation.At high circulating water temperature,the turbine overload valve is kept closed when the unit load is lower than 1000 MW while at other circulating water temperatures the turbine can run in sliding pressure operation when the unit load is higher than 1000 MW and the pressure is lower than 27 MPa This increases the unit operation efficiency.The 3D bending technology in the critical piping helps to reduce the project investment and minimize the reheat system pressure drop which improves the unit operation efficiency and safety.By choosing lower circulating water design temperature and by setting the individual Boiler Feedwater Turbine condenser to reduce the exhaust steam flow and the heat load to the main condenser,the unit average back pressure and the terminal temperature difference are minimized.Therefore,the unit heat efficiency is increased.

  15. Critical parameters for optimal biomass refineries: the case of biohydrogen

    Energy Technology Data Exchange (ETDEWEB)

    Koukios, Emmanuel; Koullas, Dimitrios; Koukios, Irene Daouti; Avgerinos, Emmanouil [National Technical University of Athens, Bioresource Technology Unit, School of Chemical Engineering, Athens (Greece)

    2010-04-15

    The object of this paper is to identify and assess the elements taken from agro-industries and fossil hydrocarbon refineries, especially with respect to biomass logistics, fractionation kinetics, and process energetics. Such critical information will be of immediate use by policy and decision makers, especially in the early phase of planning and designing the first generation of biorefineries. Concerning feedstock logistics, biorefineries have a lot to learn from food and wood supply chains. This learning could lead to the deployment of complex, decentralised, stage-wise biorefining systems, consisting of local agro-refineries, regional biorefineries, where the primary plant fractions are processed and upgraded to useful intermediates, and central bioconversion units for the generation of market-grade biofuels, such as biohydrogen and other high value-added vectors. The kinetic aspects of biorefineries are related to the physico-chemical nature of the macromolecules. Finally, to solve the problem of the non-optimal energy transformations a tailored-up bioenergy plan is proposed for each biorefinery. The example of a wheat bran-based biorefinery, aiming at the production of biohydrogen will be used to illustrate the way ahead. (orig.)

  16. Optimal Control of Distributed Parameter Systems with Application to Transient Thermoelectric Cooling

    Directory of Open Access Journals (Sweden)

    KOTSUR, M.

    2015-05-01

    Full Text Available We give a solution of optimal control problem for distributed parameter systems described by nonlinear partial differential equations with nonstandard boundary conditions. The variational method is used to obtain the general form of the necessary conditions of optimality. A suitable algorithm based on the numerical method of successive approximations has been constructed for computing the optimal control functions. The results are applied for optimization of transient thermoelectric cooling process. Optimal dependences of current on time have been calculated for thermoelectric cooler power supply with the purpose of minimizing the cooling temperature within a preset time interval.

  17. Parameter optimization of controllable local degree of freedom for reducing vibration of flexible manipulator

    Institute of Scientific and Technical Information of China (English)

    Bian Yushu; Gao Zhihui

    2013-01-01

    Parameter optimization of the controllable local degree of freedom is studied for reducing vibration of the flexible manipulator at the lowest possible cost.The controllable local degrees of freedom are suggested and introduced to the topological structure of the flexible manipulator,and used as an effective way to alleviate vibration through dynamic coupling.Parameters introduced by the controllable local degrees of freedom are analyzed and their influences on vibration reduction are investigated.A strategy to optimize these parameters is put forward and the corresponding optimization method is suggested based on Particle Swarm Optimization (PSO).Simulations are conducted and results of case studies confirm that the proposed optimization method is effective in reducing vibration of the flexible manipulator at the lowest possible cost.

  18. MULTI-OBJECTIVE OPTIMIZATION OF EDM PARAMETERS USING GREY RELATION ANALYSIS

    Directory of Open Access Journals (Sweden)

    N. RADHIKA

    2015-01-01

    Full Text Available This paper involves the multi-objective optimization of process parameters of AlSi10Mg/9 wt% alumina/3 wt% graphite in Electrical Discharge Machining for obtaining minimum surface roughness, minimum tool wear rate and maximum material removal rate. The important machining parameters were selected as peak current, flushing pressure and pulse-on time. Experiments were conducted by selecting different operating levels for the three parameters according to Taguchi’s Design of Experiments. The multi-objective optimization was performed using Grey Relation Analysis to determine the optimal solution. The Grey Relation Grade values were then analysed using Analysis of Variance to determine the most contributing input parameter. On analysis it was found that peak current, flushing pressure and pulse-on time had an influence of 61.36%, 17.81% and 8.09% respectively on the optimal solution.

  19. THE PARAMETER OPTIMIZATION MODEL OF INVESTMENT AND CONSTRUCTION PROJECTS AND MANAGERIAL FEASIBILITY OF THEIR BEHAVIOR

    Directory of Open Access Journals (Sweden)

    P. Ye. Uvarov

    2009-09-01

    Full Text Available In the article the basic problem of substantiation of parameters of optimization model of organizationaltechnological solutions for investment-building projects in the system of project management is considered.

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

    Energy Technology Data Exchange (ETDEWEB)

    Zarepisheh, M; Li, R; Xing, L [Stanford UniversitySchool of Medicine, Stanford, CA (United States); Ye, Y [Stanford Univ, Management Science and Engineering, Stanford, Ca (United States); Boyd, S [Stanford University, Electrical Engineering, Stanford, CA (United States)

    2014-06-01

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

  1. Extended Application of the Conditional Nonlinear Optimal Parameter Perturbation Method in the Common Land Model

    Institute of Scientific and Technical Information of China (English)

    WANG Bo; HUO Zhenhua

    2013-01-01

    An extension of the conditional nonlinear optimal parameter perturbation (CNOP-P) method is applied to the parameter optimization of the Common Land Model (CoLM) for the North China Plain with the differential evolution (DE) method.Using National Meteorological Center (NMC) Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy (NCEP/DOE)Atmospheric Model Intercomparison Project II (AMIP-II) 6-hourly Reanalysis Gaussian Grid data,two experiments (I and II) were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture.A third experiment (III) was designed to study the shallow soil moisture and latent heat flux simultaneously.In all the three experiments,after the optimization stage,the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month.The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture,with the simulation results of CoLM after the double-parameter optimal experiment being better than the single-parameter optimal experiment in the optimization slot.Furthermore,the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage.In addition,whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization,and the more accurate the data are,the more significant the results of optimization may be.

  2. Automated estimation of stellar fundamental parameters from low resolution spectra: the PLS method

    Institute of Scientific and Technical Information of China (English)

    Jian-Nan Zhang; A-Li Luo; Yong-Heng Zhao

    2009-01-01

    PLS (Partial Least Squares regression) is introduced into an automatic esti-mation of fundamental stellar spectral parameters. It extracts the most correlative spec-tral component to the parameters (Teff, log g and [Fe/H]), and sets up a linear regres-sion function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at dif-ferent signal-to-noise ratios (SNRs) and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolu-tion 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ~ G stars: for flux calibrated spectra, the systematic offsets are 3.8%, 0.14dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do.

  3. Automation of reverse engineering process in aircraft modeling and related optimization problems

    Science.gov (United States)

    Li, W.; Swetits, J.

    1994-01-01

    During the year of 1994, the engineering problems in aircraft modeling were studied. The initial concern was to obtain a surface model with desirable geometric characteristics. Much of the effort during the first half of the year was to find an efficient way of solving a computationally difficult optimization model. Since the smoothing technique in the proposal 'Surface Modeling and Optimization Studies of Aerodynamic Configurations' requires solutions of a sequence of large-scale quadratic programming problems, it is important to design algorithms that can solve each quadratic program in a few interactions. This research led to three papers by Dr. W. Li, which were submitted to SIAM Journal on Optimization and Mathematical Programming. Two of these papers have been accepted for publication. Even though significant progress has been made during this phase of research and computation times was reduced from 30 min. to 2 min. for a sample problem, it was not good enough for on-line processing of digitized data points. After discussion with Dr. Robert E. Smith Jr., it was decided not to enforce shape constraints in order in order to simplify the model. As a consequence, P. Dierckx's nonparametric spline fitting approach was adopted, where one has only one control parameter for the fitting process - the error tolerance. At the same time the surface modeling software developed by Imageware was tested. Research indicated a substantially improved fitting of digitalized data points can be achieved if a proper parameterization of the spline surface is chosen. A winning strategy is to incorporate Dierckx's surface fitting with a natural parameterization for aircraft parts. The report consists of 4 chapters. Chapter 1 provides an overview of reverse engineering related to aircraft modeling and some preliminary findings of the effort in the second half of the year. Chapters 2-4 are the research results by Dr. W. Li on penalty functions and conjugate gradient methods for

  4. An adaptive image denoising method based on local parameters optimization

    Indian Academy of Sciences (India)

    Hari Om; Mantosh Biswas

    2014-08-01

    In image denoising algorithms, the noise is handled by either modifying term-by-term, i.e., individual pixels or block-by-block, i.e., group of pixels, using suitable shrinkage factor and threshold function. The shrinkage factor is generally a function of threshold and some other characteristics of the neighbouring pixels of the pixel to be thresholded (denoised). The threshold is determined in terms of the noise variance present in the image and its size. The VisuShrink, SureShrink, and NeighShrink methods are important denoising methods that provide good results. The first two, i.e., VisuShrink and SureShrink methods follow term-by-term approach, i.e., modify the individual pixel and the third one, i.e., NeighShrink and its variants: ModiNeighShrink, IIDMWD, and IAWDMBMC, follow block-by-block approach, i.e., modify the pixels in groups, in order to remove the noise. The VisuShrink, SureShrink, and NeighShrink methods however do not give very good visual quality because they remove too many coefficients due to their high threshold values. In this paper, we propose an image denoising method that uses the local parameters of the neighbouring coefficients of the pixel to be denoised in the noisy image. In our method, we propose two new shrinkage factors and the threshold at each decomposition level, which lead to better visual quality. We also establish the relationship between both the shrinkage factors. We compare the performance of our method with that of the VisuShrink and NeighShrink including various variants. Simulation results show that our proposed method has high peak signal-to-noise ratio and good visual quality of the image as compared to the traditional methods:Weiner filter, VisuShrink, SureShrink, NeighBlock, NeighShrink, ModiNeighShrink, LAWML, IIDMWT, and IAWDMBNC methods.

  5. The same number of optimized parameters scheme for determining intermolecular interaction energies

    DEFF Research Database (Denmark)

    Kristensen, Kasper; Ettenhuber, Patrick; Eriksen, Janus Juul;

    2015-01-01

    We propose the Same Number Of Optimized Parameters (SNOOP) scheme as an alternative to the counterpoise method for treating basis set superposition errors in calculations of intermolecular interaction energies. The key point of the SNOOP scheme is to enforce that the number of optimized wave...

  6. Multi-parameter optimization of a neutron backscattering landmine detection system.

    Science.gov (United States)

    Metwally, Walid A

    2015-11-01

    A study was performed to enhance the neutron moderation and investigate the response of a landmine detection system based on measuring backscattered neutrons with a (3)He detector. Parameters affecting the system's response were simultaneously optimized to improve the sensitivity of the detection system. The optimized sensitivity of the detection system was analyzed for different horizontal and lateral positions of a landmine.

  7. A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model

    Science.gov (United States)

    Sumata, H.; Kauker, F.; Gerdes, R.; Köberle, C.; Karcher, M.

    2013-07-01

    Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean-sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (μGA) as an example of a stochastic approach. The optimizations were performed by minimizing a cost function composed of model-data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations were conducted that differ in the model formulation ("smoothed code" versus standard code) with respect to the FD method and in the population size and number of possibilities with respect to the μGA method. The FD method fails to estimate optimal parameters due to the ill-shaped nature of the cost function caused by the strong non-linearity of the system, whereas the genetic algorithms can effectively estimate near optimal parameters. The results of the study indicate that the sophisticated stochastic approach (μGA) is of practical use for parameter optimization of a coupled ocean-sea ice model with a medium-sized horizontal resolution of 50 km × 50 km as used in this study.

  8. Determination of the optimal relaxation parameter in a numerical procedure for solitons propagation

    CERN Document Server

    Cirilo, Eliandro Rodrigues; Romeiro, Neyva Maria Lopes; Natti, Erica Regina Takano

    2010-01-01

    In this work, considering a numerical procedure developed to solve a system of coupled nonlinear complex differential equations, which describes the solitons propagation in dielectric optical fibers, we optimize the numerical processing time, in relation to the relaxation parameter of the procedure, for relevant groups of values of the dielectric variables of the optic fiber. Key-words: optical soliton, processing time, optimization.

  9. A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Shaolong Chen

    2016-01-01

    Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

  10. Optimization of RNA Purification and Analysis for Automated, Pre-Symptomatic Disease Diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Vaidya, A; Nasarabadi, S; Milanovich, F

    2005-06-28

    When diagnosing disease, time is often a more formidable enemy than the pathogen itself. Current detection methods rely primarily on post-symptomatic protein production (i.e. antibodies), which does not occur in noticeable levels until several weeks after infection. As such, a major goal among researchers today is to expedite pre-symptomatic disease recognition and treatment. Since most pathogens are known to leave a unique signature on the genetic expression of the host, one potential diagnostic tool is host mRNA. In my experiments, I examined several methods of isolating RNA and reading its genetic sequence. I first used two types of reverse transcriptase polymerase chain reactions (using commercial RNA) and examined the resultant complementary DNA through gel electrophoresis. I then proceeded to isolate and purify whole RNA from actual human monocytes and THP-1 cells using several published methods, and examined gene expression on the RNA itself. I compared the two RT-PCR methods and concluded that a double step RT-PCR is superior to the single step method. I also compared the various techniques of RNA isolation by examining the yield and purity of the resultant RNA. Finally, I studied the level of cellular IL-8 and IL-1 gene expression, two genes involved in the human immune response, which can serve as a baseline for future genetic comparison with LPS-exposed cells. Based on the results, I have determined which conditions and procedures are optimal for RNA isolation, RT-PCR, and RNA yield assessment. The overall goal of my research is to develop a flow-through system of RNA analysis, whereby blood samples can be collected and analyzed for disease prior to the onset of symptoms. The Pathomics group hopes to automate this process by removing the human labor factor, thereby decreasing the procedure's cost and increasing its availability to the general population. Eventually, our aim is to have an autonomous diagnostic system based on RNA analysis that would

  11. Automated Method for Estimating Nutation Time Constant Model Parameters for Spacecraft Spinning on Axis

    Science.gov (United States)

    2008-01-01

    Calculating an accurate nutation time constant (NTC), or nutation rate of growth, for a spinning upper stage is important for ensuring mission success. Spacecraft nutation, or wobble, is caused by energy dissipation anywhere in the system. Propellant slosh in the spacecraft fuel tanks is the primary source for this dissipation and, if it is in a state of resonance, the NTC can become short enough to violate mission constraints. The Spinning Slosh Test Rig (SSTR) is a forced-motion spin table where fluid dynamic effects in full-scale fuel tanks can be tested in order to obtain key parameters used to calculate the NTC. We accomplish this by independently varying nutation frequency versus the spin rate and measuring force and torque responses on the tank. This method was used to predict parameters for the Genesis, Contour, and Stereo missions, whose tanks were mounted outboard from the spin axis. These parameters are incorporated into a mathematical model that uses mechanical analogs, such as pendulums and rotors, to simulate the force and torque resonances associated with fluid slosh.

  12. Optimizing lighting, thermal performance, and energy production of building facades by using automated blinds and PV cells

    Science.gov (United States)

    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

  13. Brake squeal reduction of vehicle disc brake system with interval parameters by uncertain optimization

    Science.gov (United States)

    Lü, Hui; Yu, Dejie

    2014-12-01

    An uncertain optimization method for brake squeal reduction of vehicle disc brake system with interval parameters is presented in this paper. In the proposed method, the parameters of frictional coefficient, material properties and the thicknesses of wearing components are treated as uncertain parameters, which are described as interval variables. Attention is focused on the stability analysis of a brake system in squeal, and the stability of brake system is investigated via the complex eigenvalue analysis (CEA) method. The dominant unstable mode is extracted by performing CEA based on a linear finite element (FE) model, and the negative damping ratio corresponding to the dominant unstable mode is selected as the indicator of instability. The response surface method (RSM) is applied to approximate the implicit relationship between the unstable mode and the system parameters. A reliability-based optimization model for improving the stability of the vehicle disc brake system with interval parameters is constructed based on RSM, interval analysis and reliability analysis. The Genetic Algorithm is used to get the optimal values of design parameters from the optimization model. The stability analysis and optimization of a disc brake system are carried out, and the results show that brake squeal propensity can be reduced by using stiffer back plates. The proposed approach can be used to improve the stability of the vehicle disc brake system with uncertain parameters effectively.

  14. Network optimization including gas lift and network parameters under subsurface uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)

    2013-08-01

    Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A

  15. Multi-Objective Optimization of Vehicle Suspension Parameters Considering Various Road Classes

    Directory of Open Access Journals (Sweden)

    Havelka Ferdinand

    2014-12-01

    Full Text Available Vehicle suspension optimization for various road classes travelled at different velocities is performed. Road excitation is modeled using a first order shaping filter. A half-car model is adopted to simulate the vehicle’s vertical dynamics. The excitation time delay between the rear and the front tire is modeled using Pade approximation. Suspension parameters are optimized using a random search method with respect to “comfort” and “sporty driving” considering the design constraints of the suspension and road holding and maximum suspension travel constraints. Optimal suspension parameters suitable for various road classes and vehicle velocities have been chosen.

  16. Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune

    The design of a measured program devoted to parameter identification of structural dynamic systems is considered, the design problem is formulated as an optimization problem due to minimize the total expected cost of the measurement program. All the calculations are based on a priori knowledge an...... in a simply supported plane, vibrating beam model. Results show optimal number of sensors and their locations....... and engineering judgement. One of the contribution of the approach is that the optimal nmber of sensors can be estimated. This is sown in an numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement program for estimating the modal damping parameters...

  17. Experimental Verification of Statistically Optimized Parameters for Low-Pressure Cold Spray Coating of Titanium

    Directory of Open Access Journals (Sweden)

    Damilola Isaac Adebiyi

    2016-06-01

    Full Text Available The cold spray coating process involves many process parameters which make the process very complex, and highly dependent and sensitive to small changes in these parameters. This results in a small operational window of the parameters. Consequently, mathematical optimization of the process parameters is key, not only to achieving deposition but also improving the coating quality. This study focuses on the mathematical identification and experimental justification of the optimum process parameters for cold spray coating of titanium alloy with silicon carbide (SiC. The continuity, momentum and the energy equations governing the flow through the low-pressure cold spray nozzle were solved by introducing a constitutive equation to close the system. This was used to calculate the critical velocity for the deposition of SiC. In order to determine the input temperature that yields the calculated velocity, the distribution of velocity, temperature, and pressure in the cold spray nozzle were analyzed, and the exit values were predicted using the meshing tool of Solidworks. Coatings fabricated using the optimized parameters and some non-optimized parameters are compared. The coating of the CFD-optimized parameters yielded lower porosity and higher hardness.

  18. Sensitivity Analysis of the Optimal Parameter Settings of an LTE Packet Scheduler

    NARCIS (Netherlands)

    Fernandez Diaz, I.; Litjens, R.; Berg, J.L. van den; Dimitrova, D.C.; Spaey, K.

    2010-01-01

    Advanced packet scheduling schemes in 3G/3G+ mobile networks provide one or more parameters to optimise the trade-off between QoS and resource efficiency. In this paper we study the sensitivity of the optimal parameter setting for packet scheduling in LTE radio networks with respect to various traff

  19. Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Li

    2017-01-01

    Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

  20. Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.

    Science.gov (United States)

    Kok, Kai Yit; Rajendran, Parvathy

    2016-01-01

    The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.

  1. Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.

    Directory of Open Access Journals (Sweden)

    Kai Yit Kok

    Full Text Available The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.

  2. Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2014-01-01

    Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.

  3. Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2015-07-01

    Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.

  4. A New Chaotic Parameters Disturbance Annealing Neural Network for Solving Global Optimization Problems

    Institute of Scientific and Technical Information of China (English)

    MA Wei; WANG Zheng-Ou

    2003-01-01

    Since there were few chaotic neural networks applicable to the global optimization, in this paper, we proposea new neural network model - chaotic parameters disturbance annealing (CPDA) network, which is superior to otherexisting neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the presentCPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escapefrom the attraction of a local minimal solution and with the parameter p1 annealing, our model will converge to theglobal optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper.The benchmark examples show the present CPDA neuralnetwork's merits in nonlinear global optimization.

  5. Optimal selection of regularization parameter for l1-based image restoration based on SURE

    Science.gov (United States)

    Xue, Feng; Liu, Xin; Liu, Hongyan; Liu, Jiaqi

    2016-10-01

    To exploit the sparsity in transform domain (e.g. wavelets), the image deconvolution can be typically formulated as a l1-penalized minimization problem, which, however, generally requires proper selection of regularization parameter for desired reconstruction quality. The key contribution of this paper is to develop a novel data-driven scheme to optimize regularization parameter, such that the resultant restored image achieves minimum prediction error (p-error). First, we develop Stein's unbiased risk estimate (SURE), an unbiased estimate of p-error, for image degradation model. Then, we propose a recursive evaluation of SURE for the basic iterative shrinkage/thresholding (IST), which enables us to find the optimal value of regularization parameter by exhaustive search. The numerical experiments show that the proposed SURE-based optimization leads to nearly optimal deconvolution performance in terms of peak signal-to-noise ratio (PSNR).

  6. Automated Large Scale Parameter Extraction of Road-Side Trees Sampled by a Laser Mobile Mapping System

    Science.gov (United States)

    Lindenbergh, R. C.; Berthold, D.; Sirmacek, B.; Herrero-Huerta, M.; Wang, J.; Ebersbach, D.

    2015-08-01

    In urbanized Western Europe trees are considered an important component of the built-up environment. This also means that there is an increasing demand for tree inventories. Laser mobile mapping systems provide an efficient and accurate way to sample the 3D road surrounding including notable roadside trees. Indeed, at, say, 50 km/h such systems collect point clouds consisting of half a million points per 100m. Method exists that extract tree parameters from relatively small patches of such data, but a remaining challenge is to operationally extract roadside tree parameters at regional level. For this purpose a workflow is presented as follows: The input point clouds are consecutively downsampled, retiled, classified, segmented into individual trees and upsampled to enable automated extraction of tree location, tree height, canopy diameter and trunk diameter at breast height (DBH). The workflow is implemented to work on a laser mobile mapping data set sampling 100 km of road in Sachsen, Germany and is tested on a stretch of road of 7km long. Along this road, the method detected 315 trees that were considered well detected and 56 clusters of tree points were no individual trees could be identified. Using voxels, the data volume could be reduced by about 97 % in a default scenario. Processing the results of this scenario took ~2500 seconds, corresponding to about 10 km/h, which is getting close to but is still below the acquisition rate which is estimated at 50 km/h.

  7. Solar collector parameter identification from unsteady data by a discrete-gradient optimization algorithm

    Science.gov (United States)

    Hotchkiss, G. B.; Burmeister, L. C.; Bishop, K. A.

    1980-01-01

    A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.

  8. A procedure for multi-objective optimization of tire design parameters

    Directory of Open Access Journals (Sweden)

    Nikola Korunović

    2015-04-01

    Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

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

    Directory of Open Access Journals (Sweden)

    Huanqing Cui

    2017-03-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  12. Process Parameter Optimization of the Pulsed Current Argon Tungsten Arc Welding of Titanium Alloy

    Institute of Scientific and Technical Information of China (English)

    M.Balasubramanian; V.Jayabalan; V.Balasubramanian

    2008-01-01

    The selection of process parameters for obtaining optimal tensile properties in the pulsed current gas tungsten arc welding is presented. The tensile properties include ultimate tensile strength, yield strength and notch tensile strength. All these characteristics are considered together in the selection of process parameters by modified taguchi method to analyse the effect of each welding process parameter on tensile properties. Experimental results are furnished to illustrate the approach.

  13. Posture parameters optimization of a structured light 3D angle measuring system

    OpenAIRE

    2015-01-01

    To improve the measurement precision of the structured light target angle, this paper studies the relation between the structured light system parameters and measurement accuracy of the angle. Firstly, the main system structure parameters influencing the angle measurement precision are analyzed based on the structured light measurement principle; secondly, simulation research on the laws of how the structured parameters influence the angle measurement precision are conducted, and the optimize...

  14. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  15. A Multi-Criteria Framework with Voxel-Dependent Parameters for Radiotherapy Treatment Plan Optimization

    CERN Document Server

    Zarepisheh, Masoud; Li, Nan; Jia, Xun; Jiang, Steve B

    2012-01-01

    In a treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. To answer questions related to this problem, we establish in this work a new mathematical framework equipped with two theorems. The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the different effects of adjusting weighting factors versus reference doses in the optimization process. The main discoveries are threefold: 1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model sin...

  16. Global optimization of parameters in the reactive force field ReaxFF for SiOH.

    Science.gov (United States)

    Larsson, Henrik R; van Duin, Adri C T; Hartke, Bernd

    2013-09-30

    We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code that is parallelized across reference data items via the message-passing interface (MPI). Details of GA tuning turn-ed out to be far less important for global optimization efficiency than using suitable ranges within which the parameters are varied. To establish these ranges, either prior knowledge can be used or successive stages of GA optimizations, each building upon the best parameter vectors and ranges found in the previous stage. We have finally arrive-ed at optimized force fields with smaller error measures than those published previously. Hence, this optimization approach will contribute to converting force-field fitting from a specialist task to an everyday commodity, even for the more difficult case of reactive force fields.

  17. Global parameter optimization of Mather type plasma focus in the framework of the Gratton-Vargas two-dimensional snowplow model

    CERN Document Server

    Auluck, S K H

    2014-01-01

    Dense Plasma Focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding of short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool which can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited [S K H Auluck, Physics of Plasmas 20, 112501 (2013)] Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather type plasma focus fitted to thousands of automated computations, which enables construction of such design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a 4-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy ...

  18. Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Shuang Zhao

    2015-01-01

    Full Text Available Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.

  19. Optimized and Automated Radiosynthesis of [18F]DHMT for Translational Imaging of Reactive Oxygen Species with Positron Emission Tomography

    Directory of Open Access Journals (Sweden)

    Wenjie Zhang

    2016-12-01

    Full Text Available Reactive oxygen species (ROS play important roles in cell signaling and homeostasis. However, an abnormally high level of ROS is toxic, and is implicated in a number of diseases. Positron emission tomography (PET imaging of ROS can assist in the detection of these diseases. For the purpose of clinical translation of [18F]6-(4-((1-(2-fluoroethyl-1H-1,2,3-triazol-4-ylmethoxyphenyl-5-methyl-5,6-dihydrophenanthridine-3,8-diamine ([18F]DHMT, a promising ROS PET radiotracer, we first manually optimized the large-scale radiosynthesis conditions and then implemented them in an automated synthesis module. Our manual synthesis procedure afforded [18F]DHMT in 120 min with overall radiochemical yield (RCY of 31.6% ± 9.3% (n = 2, decay-uncorrected and specific activity of 426 ± 272 GBq/µmol (n = 2. Fully automated radiosynthesis of [18F]DHMT was achieved within 77 min with overall isolated RCY of 6.9% ± 2.8% (n = 7, decay-uncorrected and specific activity of 155 ± 153 GBq/µmol (n = 7 at the end of synthesis. This study is the first demonstration of producing 2-[18F]fluoroethyl azide by an automated module, which can be used for a variety of PET tracers through click chemistry. It is also the first time that [18F]DHMT was successfully tested for PET imaging in a healthy beagle dog.

  20. Automated optimization of measurement setups for the inspection of specular surfaces

    Science.gov (United States)

    Kammel, Soeren

    2002-02-01

    Specular surfaces are used in a wide variety of industrial and consumer products like varnished or chrome plated parts of car bodies, dies or molds. Defects of these parts reduce the quality regarding their visual appearance and/or their technical performance. Even defects that are only about 1 micrometer deep can lead to a rejection during quality control. Deflectometric techniques are an adequate approach to recognize and measure defects on specular surfaces, because the principle of measurement of these methods mimics the behavior of a human observer inspecting the surface. With these methods, the specular object is considered as a part of the optical system. Not the object itself but the surrounding that is reflected by the specular surface is observed in order to obtain information about the object. This technique has proven sensitive for slope and topography measurement. Inherited from the principle of measurement, especially surface parts with high curvature need a special illumination which surrounds the object under inspection to guarantee that light from any direction is reflected onto the sensor. Thus the design of a specific measurement setup requires a substantial engineering effort. To avoid the time consuming process of building, testing and redesigning the measurement setup, a system to simulate and automatically optimize the setup has been developed. Based on CAD data of the object under inspection and a model of the optical system, favorable realizations of the shape, the position and the pattern of the lighting device are determined. In addition, optimization of other system parameters, such as object position and distance relative to the camera, is performed. Finally, constraints are imposed to ascertain the feasibility of illumination system construction.

  1. Effect of experimental parameters on optimal reflection of light from opaque media

    CERN Document Server

    Anderson, Benjamin R; Eilers, Hergen

    2016-01-01

    Previously we considered the effect of experimental parameters on optimized transmission through opaque media using spatial light modulator (SLM)-based wavefront shaping. In this study we consider the opposite geometry, in which we optimize reflection from an opaque surface such that the backscattered light is focused onto a spot on an imaging detector. By systematically varying different experimental parameters (genetic algorithm iterations, bin size, SLM active area, target area, spot size, and sample angle with respect to the optical axis) and optimizing the reflected light we determine how each parameter affects the intensity enhancement. We find that the effects of the experimental parameters on the enhancement are similar to those measured for a transmissive geometry, but with the exact functional forms changed due to the different geometry and the use of a genetic algorithm instead of an iterative algorithm. Additionally, we find preliminary evidence of greater enhancements than predicted by random mat...

  2. Optimizing reliability, maintainability and testability parameters of equipment based on GSPN

    Institute of Scientific and Technical Information of China (English)

    Yongcheng Xu

    2015-01-01

    Reliability, maintainability and testability (RMT) are important properties of equipment, since they have important influ-ence on operational availability and life cycle costs (LCC). There-fore, weighting and optimizing the three properties are of great significance. A new approach for optimization of RMT parameters is proposed. First of al , the model for the equipment operation pro-cess is established based on the generalized stochastic Petri nets (GSPN) theory. Then, by solving the GSPN model, the quantitative relationship between operational availability and RMT parameters is obtained. Afterwards, taking history data of similar equipment and operation process into consideration, a cost model of design, manufacture and maintenance is developed. Based on operational availability, the cost model and parameters ranges, an optimization model of RMT parameters is built. Final y, the effectiveness and practicability of this approach are validated through an example.

  3. Optimization of Process Parameters in Turning of AISI 8620 Steel Using Taguchi and Grey Taguchi Analysis

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Sharma

    2014-03-01

    Full Text Available The aim of this research is to investigate the optimization of cutting parameters (cutting speed, feed rate and depth of cut for surface roughness and metal removal rate in turning of AISI 8620 steel using coated carbide insert. Experiments have been carried out based on Taguchi L9 standard orthogonal array design with three process parameters namely cutting speed, feed rate and depth of cut for surface roughness and metal removal rate. The objective function has been chosen in relation to surface roughness and metal removal rate for quality target. Optimal parameters contribution of the CNC turning operation was obtained via grey relational analysis. The analysis of variance is applied to identify the most significant factor. Experiment with the optimized parameter setting, which has been obtained from the analysis, are giving to validate the results.

  4. Multi-objective Optimization of Continuous Drive Friction Welding Process Parameters Using Response Surface Methodology with Intelligent Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    P M AJITH; T MAFSAL HUSAIN; P SATHIYA; S ARAVINDAN

    2015-01-01

    The optimum friction welding (FW) parameters of duplex stainless steel (DSS) UNS S32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure (F), upset pressure (U), speed (S) and burn-off length (B), and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology (RSM) was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint inlfuenced the upset pressure, friction pressure and speed of rotation.

  5. Automation of the CHARMM General Force Field (CGenFF) II: Assignment of bonded parameters and partial atomic charges

    Science.gov (United States)

    Vanommeslaeghe, K.; Raman, E. Prabhu; MacKerell, A. D.

    2012-01-01

    Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug candidates interacting with biological systems. In these simulations, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters and partial atomic charges is required. In the present article, algorithms for the assignment of parameters and charges for the CHARMM General Force Field (CGenFF) are presented. These algorithms rely on the existing parameters and charges that were determined as part of the parametrization of the force field. Bonded parameters are assigned based on the similarity between the atom types that define said parameters, while charges are determined using an extended bond-charge increment scheme. Charge increments were optimized to reproduce the charges on model compounds that were part of the parametrization of the force field. A “penalty score” is returned for every bonded parameter and charge, allowing the user to quickly and conveniently assess the quality of the force field representation of different parts of the compound of interest. Case studies are presented to clarify the functioning of the algorithms and the significance of their output data. PMID:23145473

  6. Guiding automated NMR structure determination using a global optimization metric, the NMR DP score

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Yuanpeng Janet, E-mail: yphuang@cabm.rutgers.edu; Mao, Binchen; Xu, Fei; Montelione, Gaetano T., E-mail: gtm@rutgers.edu [Rutgers, The State University of New Jersey, Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium (United States)

    2015-08-15

    ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD–NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases {sup 15}N–{sup 1}H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD–NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.

  7. Load Reconstruction Technique Using D-Optimal Design and Markov Parameters

    Directory of Open Access Journals (Sweden)

    Deepak K. Gupta

    2015-01-01

    Full Text Available This paper develops a technique for identifying dynamic loads acting on a structure based on impulse response of the structure, also referred to as the system Markov parameters, and structure response measured at optimally placed sensors on the structure. Inverse Markov parameters are computed from the forward Markov parameters using a linear prediction algorithm and have the roles of input and output reversed. The applied loads are then reconstructed by convolving the inverse Markov parameters with the system response to the loads measured at optimal locations on the structure. The structure essentially acts as its own load transducer. It has been noted that the computation of inverse Markov parameters, like most other inverse problems, is ill-conditioned which causes their convolution with the measured response to become quite sensitive to errors in system modeling and response measurements. The computation of inverse Markov parameters (and thereby the quality of load estimates depends on the locations of sensors on the structure. To ensure that the computation of inverse Markov parameters is well-conditioned, a solution approach, based on the construction of D-optimal designs, is presented to determine the optimal sensor locations such that precise load estimates are obtained.

  8. Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery

    Directory of Open Access Journals (Sweden)

    Matt Aitkenhead

    2016-12-01

    Full Text Available Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale, soil texture (sand, silt, clay, bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils.

  9. Automated criterion-based analysis for Cole parameters assessment from cerebral neonatal electrical bioimpedance spectroscopy measurements.

    Science.gov (United States)

    Seoane, F; Ward, L C; Lindecrantz, Kaj; Lingwood, B E

    2012-08-01

    Hypothermia has been proven as an effective rescue therapy for infants with moderate or severe neonatal hypoxic ischemic encephalopathy. Hypoxia-ischemia alters the electrical impedance characteristics of the brain in neonates; therefore, spectroscopic analysis of the cerebral bioimpedance of the neonate may be useful for the detection of candidate neonates eligible for hypothermia treatment. Currently, in addition to the lack of reference bioimpedance data obtained from healthy neonates, there is no standardized approach established for bioimpedance spectroscopy data analysis. In this work, cerebral bioimpedance measurements (12 h postpartum) in a cross-section of 84 term and near-term healthy neonates were performed at the bedside in the post-natal ward. To characterize the impedance spectra, Cole parameters (R(0), R(∞), f(C) and α) were extracted from the obtained measurements using an analysis process based on a best measurement and highest likelihood selection process. The results obtained in this study complement previously reported work and provide a standardized criterion-based method for data analysis. The availability of electrical bioimpedance spectroscopy reference data and the automatic criterion-based analysis method might support the development of a non-invasive method for prompt selection of neonates eligible for cerebral hypothermic rescue therapy.

  10. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581

  11. Analysis of Process Parameters for Optimization of Plastic Extrusion in Pipe Manufacturing

    Directory of Open Access Journals (Sweden)

    Mr. Sandip S. Gadekar

    2015-05-01

    Full Text Available The objective of this paper is to study the defects in the plastic pipe, to optimize the plastic pipe manufacturing process. It is very essential to learn the process parameter and the defect in the plastic pipe manufacturing process to optimize it. For the optimization Taguchi techniques is used in this paper. For the research work Shivraj HY-Tech Drip Irrigation pipe manufacturing, Company was selected. This paper is specifically design for the optimization in the current process. The experiment was analyzed using commercial Minitab16 software, interpretation has made, and optimized factor settings were chosen. After prediction of result the quality loss is calculated and it is compare with before implementation of DOE. The research works has improves the Production, quality and optimizes the process.

  12. Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS

    Energy Technology Data Exchange (ETDEWEB)

    Manivannan, R.; Kumar, M. Pradeep [CEG, Anna University, Chennai (India)

    2016-01-15

    The Technique for order preference by similarity to ideal solution (TOPSIS) method of optimization is used to analyze the process parameters of the micro-Electrical discharge machining (micro-EDM) of an AISI 304 steel with multi-performance characteristics. The Taguchi method of experimental design L27 is performed to obtain the optimal parameters for inputs, including feed rate, current, pulse on time, and gap voltage. Several output responses, such as the material removal rate, electrode wear rate, overcut, taper angle, and circularity at entry and exit points, are analyzed for the optimal conditions. Among all the investigated parameters, feed rate exerts a greater influence on the hole quality. ANOVA is employed to identify the contribution of each experiment. The optimal level of parameter setting is maintained at a feed rate of 4 μm/s, a current of 10 A, a pulse on time of 10 μs, and a gap voltage of 10 V. Scanning electron microscope analysis is conducted to examine the hole quality. The experimental results indicate that the optimal level of the process parameter setting over the overall performance of the micro-EDM is improved through TOPSIS.

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

    Science.gov (United States)

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

    2016-12-01

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

  14. Selection of Near Optimal Laser Cutting Parameters in CO2 Laser Cutting by the Taguchi Method

    Directory of Open Access Journals (Sweden)

    Miloš MADIĆ

    2013-12-01

    Full Text Available Identification of laser cutting conditions that are insensitive to parameter variations and noise is of great importance. This paper demonstrates the application of Taguchi method for optimization of surface roughness in CO2 laser cutting of stainless steel. The laser cutting experiment was planned and conducted according to the Taguchi’s experimental design using the L27 orthogonal array. Four laser cutting parameters such as laser power, cutting speed, assist gas pressure, and focus position were considered in the experiment. Using the analysis of means and analysis of variance, the significant laser cutting parameters were identified, and subsequently the optimal combination of laser cutting parameter levels was determined. The results showed that the cutting speed is the most significant parameter affecting the surface roughness whereas the influence of the assist gas pressure can be neglected. It was observed, however, that interaction effects have predominant influence over the main effects on the surface roughness.

  15. Optimization of the WEDM Parameters on Machining Incoloy800 Super alloy with Multiple Quality Characteristics

    Directory of Open Access Journals (Sweden)

    Muthu Kumar V

    2010-06-01

    Full Text Available The present work demonstrates optimization of Wire Electrical Discharge Machining process parameters of Incoloy800 super alloy with multiple performance characteristics such as Material Removal Rate (MRR, surface roughness and Kerf based on the Grey–Taguchi Method. The process parameters considered in this research work are Gap Voltage, Pulse On-time, Pulse Off-time and Wire Feed. Taguchi’s L9 Orthogonal Array was used to conduct experiments. Optimal levels of process parameters were identified using Grey Relational Analysis and the relatively significant parameters were determined by Analysis of Variance. The variation of output responses with process parameters were mathematically modelled by using non-linear regression analysismethod and the models were checked for their adequacy. Result of confirmation experiments shows that the established mathematical models can predict the output responses with reasonable accuracy.

  16. Posture parameters optimization of a structured light 3D angle measuring system

    Directory of Open Access Journals (Sweden)

    Xun DING

    2015-10-01

    Full Text Available To improve the measurement precision of the structured light target angle, this paper studies the relation between the structured light system parameters and measurement accuracy of the angle. Firstly, the main system structure parameters influencing the angle measurement precision are analyzed based on the structured light measurement principle; secondly, simulation research on the laws of how the structured parameters influence the angle measurement precision are conducted, and the optimized range of values of the structured parameters is proposed; finally, the experimental studies show that, the optimized parameters can improve the angle measurement precision effectively, which lays the design foundation for later improvement schemes of the structured light 3D four-wheel alignment instrument.

  17. Parametric optimal bounded feedback control for smart parameter-controllable composite structures

    Science.gov (United States)

    Ying, Z. G.; Ni, Y. Q.; Duan, Y. F.

    2015-03-01

    Deterministic and stochastic parametric optimal bounded control problems are presented for smart composite structures such as magneto-rheological visco-elastomer based sandwich beam with controllable bounded parameters subjected to initial disturbances and stochastic excitations. The parametric controls by actively adjusting system parameters differ from the conventional additive controls by systemic external inputs. The dynamical programming equations for the optimal parametric controls are derived based on the deterministic and stochastic dynamical programming principles. The optimal bounded functions of controls are firstly obtained from the equations with the bounded control constraints based on the bang-bang control strategy. Then the optimal bounded parametric control laws are obtained by the inversion of the nonlinear functions. The stability of the optimally controlled systems is proved according to the Lyapunov method. Finally, the proposed optimal bounded parametric feedback control strategy is applied to single-degree-of-freedom and two-degree-of-freedom dynamic systems with nonlinear parametric bounded control terms under initial disturbances and earthquake excitations and then to a magneto-rheological visco-elastomer based sandwich beam system with nonlinear parametric bounded control terms under stochastic excitations. The effective vibration suppression is illustrated with numerical results. The proposed optimal parametric control strategy is applicable to other smart composite structures with nonlinear controllable parameters.

  18. Multi-parameter Optimization of a Thermoelectric Power Generator and Its Working Conditions

    Science.gov (United States)

    Zhang, T.

    2016-09-01

    The global optimal working conditions and optimal couple design for thermoelectric (TE) generators with realistic thermal coupling between the heat reservoirs and the TE couple were studied in the current work. The heat fluxes enforced by the heat reservoirs at the hot and the cold junctions of the TE couple were used in combination with parameter normalization to obtain a single cubic algebraic equation relating the temperature differences between the TE couple junctions and between the heat reservoirs, through the electric load resistance ratio, the reservoir thermal conductance ratio, the reservoir thermal conductance to the TE couple thermal conductance ratio, the Thomson to Seebeck coefficient ratio, and the figure of merit (Z) of the material based on the linear TE transport equations and their solutions. A broad reservoir thermal conductance ranging between 0.01 W/K and 100 W/K and TE element length ranging from 10-7 m to 10-3 m were explored to find the global optimal systems. The global optimal parameters related to the working conditions, i.e., reservoir thermal conductance ratio and electric load resistance ratio, and the optimal design parameter related to the TE couple were determined for a given TE material. These results demonstrated that the internal and external electric resistance, the thermal resistance between the reservoirs, the thermal resistance between the reservoir and the TE couple, and the optimal thermoelement length have to be well coordinated to obtain optimal power production.

  19. Microprocessor-based integration of microfluidic control for the implementation of automated sensor monitoring and multithreaded optimization algorithms.

    Science.gov (United States)

    Ezra, Elishai; Maor, Idan; Bavli, Danny; Shalom, Itai; Levy, Gahl; Prill, Sebastian; Jaeger, Magnus S; Nahmias, Yaakov

    2015-08-01

    Microfluidic applications range from combinatorial synthesis to high throughput screening, with platforms integrating analog perfusion components, digitally controlled micro-valves and a range of sensors that demand a variety of communication protocols. Currently, discrete control units are used to regulate and monitor each component, resulting in scattered control interfaces that limit data integration and synchronization. Here, we present a microprocessor-based control unit, utilizing the MS Gadgeteer open framework that integrates all aspects of microfluidics through a high-current electronic circuit that supports and synchronizes digital and analog signals for perfusion components, pressure elements, and arbitrary sensor communication protocols using a plug-and-play interface. The control unit supports an integrated touch screen and TCP/IP interface that provides local and remote control of flow and data acquisition. To establish the ability of our control unit to integrate and synchronize complex microfluidic circuits we developed an equi-pressure combinatorial mixer. We demonstrate the generation of complex perfusion sequences, allowing the automated sampling, washing, and calibrating of an electrochemical lactate sensor continuously monitoring hepatocyte viability following exposure to the pesticide rotenone. Importantly, integration of an optical sensor allowed us to implement automated optimization protocols that require different computational challenges including: prioritized data structures in a genetic algorithm, distributed computational efforts in multiple-hill climbing searches and real-time realization of probabilistic models in simulated annealing. Our system offers a comprehensive solution for establishing optimization protocols and perfusion sequences in complex microfluidic circuits.

  20. Application of an Evolutionary Algorithm for Parameter Optimization in a Gully Erosion Model

    Energy Technology Data Exchange (ETDEWEB)

    Rengers, Francis; Lunacek, Monte; Tucker, Gregory

    2016-06-01

    Herein we demonstrate how to use model optimization to determine a set of best-fit parameters for a landform model simulating gully incision and headcut retreat. To achieve this result we employed the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an iterative process in which samples are created based on a distribution of parameter values that evolve over time to better fit an objective function. CMA-ES efficiently finds optimal parameters, even with high-dimensional objective functions that are non-convex, multimodal, and non-separable. We ran model instances in parallel on a high-performance cluster, and from hundreds of model runs we obtained the best parameter choices. This method is far superior to brute-force search algorithms, and has great potential for many applications in earth science modeling. We found that parameters representing boundary conditions tended to converge toward an optimal single value, whereas parameters controlling geomorphic processes are defined by a range of optimal values.

  1. Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

    Science.gov (United States)

    Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

    2016-01-01

    Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

  2. Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

    Directory of Open Access Journals (Sweden)

    Hongchun Zhu

    Full Text Available Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

  3. Optimal parameters for the Green-Ampt infiltration model under rainfall conditions

    Directory of Open Access Journals (Sweden)

    Chen Li

    2015-06-01

    Full Text Available The Green-Ampt (GA model is widely used in hydrologic studies as a simple, physically-based method to estimate infiltration processes. The accuracy of the model for applications under rainfall conditions (as opposed to initially ponded situations has not been studied extensively. We compared calculated rainfall infiltration results for various soils obtained using existing GA parameterizations with those obtained by solving the Richards equation for variably saturated flow. Results provided an overview of GA model performance evaluated by means of a root-meansquare- error-based objective function across a large region in GA parameter space as compared to the Richards equation, which showed a need for seeking optimal GA parameters. Subsequent analysis enabled the identification of optimal GA parameters that provided a close fit with the Richards equation. The optimal parameters were found to substantially outperform the standard theoretical parameters, thus improving the utility and accuracy of the GA model for infiltration simulations under rainfall conditions. A sensitivity analyses indicated that the optimal parameters may change for some rainfall scenarios, but are relatively stable for high-intensity rainfall events.

  4. Improving hot region prediction by parameter optimization of density clustering in PPI.

    Science.gov (United States)

    Hu, Jing; Zhang, Xiaolong

    2016-11-01

    This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius.

  5. D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks

    CERN Document Server

    Poczos, Barnabas

    2008-01-01

    We introduce a novel online Bayesian method for the identification of a family of noisy recurrent neural networks (RNNs). We develop Bayesian active learning technique in order to optimize the interrogating stimuli given past experiences. In particular, we consider the unknown parameters as stochastic variables and use the D-optimality principle, also known as `\\emph{infomax method}', to choose optimal stimuli. We apply a greedy technique to maximize the information gain concerning network parameters at each time step. We also derive the D-optimal estimation of the additive noise that perturbs the dynamical system of the RNN. Our analytical results are approximation-free. The analytic derivation gives rise to attractive quadratic update rules.

  6. Parameter estimation of fractional-order chaotic systems by using quantum parallel particle swarm optimization algorithm.

    Science.gov (United States)

    Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

    2015-01-01

    Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.

  7. Parameter estimation of fractional-order chaotic systems by using quantum parallel particle swarm optimization algorithm.

    Directory of Open Access Journals (Sweden)

    Yu Huang

    Full Text Available Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.

  8. Optimization of TRPO Process Parameters for Americium Extraction from High Level Waste

    Institute of Scientific and Technical Information of China (English)

    CHEN Jing; WANG Jianchen; SONG Chongli

    2001-01-01

    The numerical calculations for Am multistage fractional extraction by trialkyl phosphine oxide (TRPO) were verified by a hot test.1750 L/t-U high level waste (HLW) was used as the feed to the TRPO process.The analysis used the simple objective function to minimize the total waste content in the TRPO process streams.Some process parameters were optimized after other parameters were selected.The optimal process parameters for Am extraction by TRPO are:10 stages for extraction and 2 stages for scrubbing;a flow rate ratio of 0.931 for extraction and 4.42 for scrubbing;nitric acid concentration of 1.35 mol/L for the feed and 0.5 mol/L for the scrubbing solution.Finally,the nitric acid and Am concentration profiles in the optimal TRPO extraction process are given.

  9. Towards an Automated Pipeline for the Translation and Optimization of Geospatial Data for Virtual Environments

    Science.gov (United States)

    2008-12-01

    clearly observed in the game industry ( Introversion , 2008). Currently there are many tools available to assist in automating the production of large...Maya, there is the option to embed in it more abstract- level information that can be used by the artificial intelligence (AI) or human user within...Graphics and Interactive Techniques, Melbourne, Australia, February 11 – 14. Introversion Software, 2008: Procedural Content Generation. http

  10. The Parkinsonian Gait Spatiotemporal Parameters Quantified by a Single Inertial Sensor before and after Automated Mechanical Peripheral Stimulation Treatment

    Directory of Open Access Journals (Sweden)

    Ana Kleiner

    2015-01-01

    Full Text Available This study aims to evaluate the change in gait spatiotemporal parameters in subjects with Parkinson’s disease (PD before and after Automated Mechanical Peripheral Stimulation (AMPS treatment. Thirty-five subjects with PD and 35 healthy age-matched subjects took part in this study. A dedicated medical device (Gondola was used to administer the AMPS. All patients with PD were treated in off levodopa phase and their gait performances were evaluated by an inertial measurement system before and after the intervention. The one-way ANOVA for repeated measures was performed to assess the differences between pre- and post-AMPS and the one-way ANOVA to assess the differences between PD patients and the control group. Spearman’s correlations assessed the associations between patients with PD clinical status (H&Y and the percentage of improvement of the gait variables after AMPS (α<0.05 for all tests. The PD group had an improvement of 14.85% in the stride length; 14.77% in the gait velocity; and 29.91% in the gait propulsion. The correlation results showed that the higher the H&Y classification, the higher the stride length percentage of improvement. The treatment based on AMPS intervention seems to induce a better performance in the gait pattern of PD patients, mainly in intermediate and advanced stages of the condition.

  11. A CLINICO- HEMATOLOGICAL STUDY IN CASES OF PANCYTO PENIA: CORRELATION OF AUTOMATED CELL COUNTER PARAMETERS IN VARIOUS ETIOLOGIES

    Directory of Open Access Journals (Sweden)

    Soma

    2013-05-01

    Full Text Available ORIGINAL ARTICLE Journal of Evolution of Medical and Dental Sciences / Volume 2/ Issue 22/ June 3, 2013 Page 4013 A CLINICO- HEMATOLOGICAL STUDY IN CASES OF PANCYTO PENIA: CORRELATION OF AUTOMATED CELL COUNTER PARAMETERS IN VARIOUS ETIOLOGIES Soma Yadav 1 , Rashmi Kushwaha 2 , Kamal Aggrawal 3 , A.K Tripathi 4 , U.S Singh 5 , Ashutosh Kumar 6 . 1. Junior Resident, Department. Of pathology, King George’s Medical Uni versity 2. Assistant Professor, Department. Of pathology, King George’s Medical University 3. Professor, Department. Of pathology, King George’s Medical University 4. Professor and Head, Department. Of Clinical Hematol ogy, King George’s Medical University 5. Professor, Department. Of pathology, King George’s Medical University 6. Professor and officer in charge, Lymphoma- Leukemia Lab, Department. Of pathology, King George’s Medic al University. CORRESPONDING AUTHOR: Dr. Rashmi Kushwaha, King George’s Medical University, Lucknow. E-mail: docrashmi27@yahoo.co.in

  12. Multi-objective optimization of cutting parameters in turning using grey relational analysis

    OpenAIRE

    2013-01-01

    This study presents optimization of performance characteristics in unidirectional glass fiber reinforced plastic composites using Taguchi method and Grey relational analysis. Performance characteristics such as surface roughness and material removal rate are optimized during rough cutting operation. Process parameters including tool nose radius, tool rake angle, feed rate, cutting speed, cutting environment and depth of cut are investigated using mixed L18 orthogonal array. Grey relation anal...

  13. PI Stabilization for Congestion Control of AQM Routers with Tuning Parameter Optimization

    Directory of Open Access Journals (Sweden)

    S. Chebli

    2016-09-01

    Full Text Available In this paper, we consider the problem of stabilizing network using a new proportional- integral (PI based congestion controller in active queue management (AQM router; with appropriate model approximation in the first order delay systems, we seek a stability region of the controller by using the Hermite- Biehler theorem, which isapplicable to quasipolynomials. A Genetic Algorithm technique is employed to derive optimal or near optimal PI controller parameters.

  14. Bio-inspired optimization algorithms for optical parameter extraction of dielectric materials: A comparative study

    Science.gov (United States)

    Ghulam Saber, Md; Arif Shahriar, Kh; Ahmed, Ashik; Hasan Sagor, Rakibul

    2016-10-01

    Particle swarm optimization (PSO) and invasive weed optimization (IWO) algorithms are used for extracting the modeling parameters of materials useful for optics and photonics research community. These two bio-inspired algorithms are used here for the first time in this particular field to the best of our knowledge. The algorithms are used for modeling graphene oxide and the performances of the two are compared. Two objective functions are used for different boundary values. Root mean square (RMS) deviation is determined and compared.

  15. OPTIMIZATION OF OPERATING PARAMETERS FOR EDM PROCESS BASED ON THE TAGUCHI METHOD AND ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    A.Thillaivanan,

    2010-12-01

    Full Text Available In this paper the complexity of electrical discharge machining process which is very difficult to determine optimal cutting parameters for improving cutting performance has been reported. Optimization of operating parameters is an important step in machining, particularly for operating unconventional machiningprocedure like EDM. A suitable selection of machining parameters for the electrical discharge machining process relies heavily on the operators’ technologies and experience because of their numerous and diverse range. Machining parameters tables provided by the machine tool builder can not meet the operators’ requirements, since for anarbitrary desired machining time for a particular job, they do not provide the optimal machining conditions. An approach to determine parameters setting is proposed. Based on the Taguchi parameter design method and the analysis of variance, the significant factors affecting the machining performance such as total machining time, oversize and taper for a hole machined by EDM process, are determined.Artificial neural networks are highly flexible modeling tools with an ability to learn the mapping between input variables and output feature spaces. The superiority of using artificial neural networks inmodeling machining processes make easier to model the EDM process with dimensional input and output spaces. On the basis of the developed neural network model, for a required total machining time, oversize and taper the corresponding process parameters to be set in EDM by using the developed and trained ANN are determined.

  16. Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm

    Directory of Open Access Journals (Sweden)

    Yourim Yoon

    2015-01-01

    Full Text Available This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches.

  17. APPLICATION OF ARCHITECTURE-BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER

    Institute of Scientific and Technical Information of China (English)

    Yang Haiwei; Zhan Yongqi; Qiao Junwei; Shi Guanglin

    2003-01-01

    The dynamic working process of 52SFZ-140-207B type of hydraulic bumper is analyzed. The modeling method using architecture-based neural networks is introduced. Using this modeling method, the dynamic model of the hydraulic bumper is established; Based on this model the structural parameters of the hydraulic bumper are optimized with Genetic algorithm. The result shows that the performance of the dynamic model is close to that of the hydraulic bumper, and the dynamic performance of the hydraulic bumper is improved through parameter optimization.

  18. Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang

    2007-01-01

    For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.

  19. CH4 parameter estimation in CLM4.5bgc using surrogate global optimization

    Directory of Open Access Journals (Sweden)

    J. Müller

    2015-01-01

    Full Text Available Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility, only relatively few simulations can be allowed in order to find a near optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50%. The CLM predictions with the optimized parameters agree for northern and tropical latitudes more with the observed data than when using the default parameters and the emission predictions are higher than with default settings in northern latitudes and lower than default settings in the

  20. Global parameter optimization of a Mather-type plasma focus in the framework of the Gratton-Vargas two-dimensional snowplow model

    Science.gov (United States)

    Auluck, S. K. H.

    2014-12-01

    Dense plasma focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool that can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited (Auluck 2013 Phys. Plasmas 20 112501) Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather-type plasma focus fitted to thousands of automated computations, which enables the construction of such a design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a four-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy of the drive parameter over eight decades in capacitor bank energy. The optimized geometry of plasma focus normalized to the anode radius is shown to be independent of voltage, while the optimized anode radius is shown to be related to capacitor bank inductance.

  1. Design Optimization of RFI Parameters by Manufacturing T-shaped Composite Panel

    Institute of Scientific and Technical Information of China (English)

    ZHANG Guo-li; HUANG Gu

    2005-01-01

    The aim of this project is to develop a novel approach for optimizing design resin film infusion (RFI) processing parameters by manufacturing T-shaped composite panel. The dimensional accuracy was selected as the objective function. By investigating the rheological properties of resin film, the compaction behavior of fiber preform and characteristics of RFI process, an optimal mathematical model was established, it was found that the numerical results obtained from the RFICOMP program package have good consistency with the experimental results, and this optimization procedure can be applied to other composites manufacture processes.

  2. Cellular scanning strategy for selective laser melting: Generating reliable, optimized scanning paths and processing parameters

    DEFF Research Database (Denmark)

    Mohanty, Sankhya; Hattel, Jesper Henri

    2015-01-01

    to generate optimized cellular scanning strategies and processing parameters, with an objective of reducing thermal asymmetries and mechanical deformations. The optimized scanning strategies are used for selective laser melting of the standard samples, and experimental and numerical results are compared....... gradients that occur during the process. While process monitoring and control of selective laser melting is an active area of research, establishing the reliability and robustness of the process still remains a challenge.In this paper, a methodology for generating reliable, optimized scanning paths...

  3. An adjoint data assimilation method for optimizing frictional parameters on the afterslip area

    Science.gov (United States)

    Kano, Masayuki; Miyazaki, Shin'ichi; Ito, Kosuke; Hirahara, Kazuro

    2013-12-01

    Afterslip sometimes triggers subsequent earthquakes within a timescale of days to several years. Thus, it may be possible to predict the occurrence of such a triggered earthquake by simulating the spatio-temporal evolution of afterslip with estimated frictional parameters. To demonstrate the feasibility of this idea, we consider a plate interface model where afterslip propagates between two asperities following a rate-and-state friction law, and we adopt an adjoint data assimilation method to optimize frictional parameters. Synthetic observation data are sampled as the slip velocities on the plate interface during 20 days. It is found that: (1) all frictional parameters are optimized if the data sets consists not only of the early phase of afterslip or acceleration, but also of the decaying phase or deceleration; and (2) the prediction of the timing of the triggered earthquake is improved by using adjusted frictional parameters.

  4. Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system

    Science.gov (United States)

    Duran, Ahmet; Tuncel, Mehmet

    2016-10-01

    It is important to have a scalable parallel numerical parameter optimization algorithm for a dynamical system used in financial applications where time limitation is crucial. We use Message Passing Interface parallel programming and present such a new parallel algorithm for parameter estimation. For example, we apply the algorithm to the asset flow differential equations that have been developed and analyzed since 1989 (see [3-6] and references contained therein). We achieved speed-up for some time series to run up to 512 cores (see [10]). Unlike [10], we consider more extensive financial market situations, for example, in presence of low volatility, high volatility and stock market price at a discount/premium to its net asset value with varying magnitude, in this work. Moreover, we evaluated the convergence of the model parameter vector, the nonlinear least squares error and maximum improvement factor to quantify the success of the optimization process depending on the number of initial parameter vectors.

  5. OPTIMIZATION OF MACHINING PARAMETERS IN TURNING PROCESS USING GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION WITH EXPERIMENTAL VERIFICATION

    Directory of Open Access Journals (Sweden)

    K.RAMESH KUMAR

    2011-02-01

    Full Text Available Optimization of cutting parameters is one of the most important elements in any process planning of metal parts. Economy of machining operation plays a key role in competitiveness in the market. All CNCmachines produce finished components from cylindrical bar. Finished profiles consist of straight turning, facing, taper and circular machining. Finished profile from a cylindrical bar is done in two stages, rough machining and finish machining. Numbers of passes are required for rough machining and single pass is required for the finished pass. The machining parameters in multipass turning are depth of cut, cutting speed and feed. The machining performance is measured by the minimum production time. In this paper the optimal machining parameters for continuous profile machining are determinedwith respect to the minimum production time, subject to a set of practical constraints, cutting force, power and dimensional accuracy and surface finish. Due to complexity of this machining optimizationproblem, a genetic algorithm (GA and Particle Swarm Optimization (PSO are applied to resolve the problem and the results obtained from GA and PSO are compared.

  6. Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models

    CERN Document Server

    Vesselinov, Velimir V

    2011-01-01

    A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physical models. The new strategy is designed to be computationally efficient and robust in identification of the global optimum (e.g. maximum or minimum value of an objective function). It integrates a global Adaptive Particle Swarm Optimization (APSO) strategy with a local Levenberg-Marquardt (LM) optimization strategy using adaptive rules based on runtime performance. The global strategy optimizes the location of a set of solutions (particles) in the parameter space. The LM strategy is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to the APSO strategy. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg-Marquardt (LM), Particl...

  7. Parameter identification of a distributed runoff model by the optimization software Colleo

    Science.gov (United States)

    Matsumoto, Kazuhiro; Miyamoto, Mamoru; Yamakage, Yuzuru; Tsuda, Morimasa; Anai, Hirokazu; Iwami, Yoichi

    2015-04-01

    The introduction of Colleo (Collection of Optimization software) is presented and case studies of parameter identification for a distributed runoff model are illustrated. In order to calculate discharge of rivers accurately, a distributed runoff model becomes widely used to take into account various land usage, soil-type and rainfall distribution. Feasibility study of parameter optimization is desired to be done in two steps. The first step is to survey which optimization algorithms are suitable for the problems of interests. The second step is to investigate the performance of the specific optimization algorithm. Most of the previous studies seem to focus on the second step. This study will focus on the first step and complement the previous studies. Many optimization algorithms have been proposed in the computational science field and a large number of optimization software have been developed and opened to the public with practically applicable performance and quality. It is well known that it is important to use suitable algorithms for the problems to obtain good optimization results efficiently. In order to achieve algorithm comparison readily, optimization software is needed with which performance of many algorithms can be compared and can be connected to various simulation software. Colleo is developed to satisfy such needs. Colleo provides a unified user interface to several optimization software such as pyOpt, NLopt, inspyred and R and helps investigate the suitability of optimization algorithms. 74 different implementations of optimization algorithms, Nelder-Mead, Particle Swarm Optimization and Genetic Algorithm, are available with Colleo. The effectiveness of Colleo was demonstrated with the cases of flood events of the Gokase River basin in Japan (1820km2). From 2002 to 2010, there were 15 flood events, in which the discharge exceeded 1000m3/s. The discharge was calculated with the PWRI distributed hydrological model developed by ICHARM. The target

  8. Optimization of regularization parameter of inversion in particle sizing using light extinction method

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In particle sizing by light extinction method, the regularization parameter plays an important role in applying regularization to find the solution to ill-posed inverse problems. We combine the generalized cross-validation (GCV) and L-curve criteria with the Twomey-NNLS algorithm in parameter optimization. Numerical simulation and experimental validation show that the resistance of the newly developed algorithms to measurement errors can be improved leading to stable inversion results for unimodal particle size distribution.

  9. Diagnosis parameters of mold filling pattern for optimization of a casting system

    OpenAIRE

    2012-01-01

    For optimal design of a gating system, the setting of diagnosis parameters is very important. In this study, the permanent mold casting process was selected because most of the other casting processes have more complicated factors that influence the mold filling pattern compared to the permanent mold casting process, such as the surface roughness of mold, gas generation from the mold wash and binder of sand mold, and the gas permeability through a sand mold, etc. Two diagnosis parameters (fl...

  10. Levenberg – Marquardt’s Algorithm used for PID Controller Parameters Optimization

    OpenAIRE

    Ahmed S. Abd El-Hamid; Ahmed H. Eissa; Aly M. Radwan

    2015-01-01

    The determination of parameters of controllers is an important problem in automatic control systems. In this paper, the Levenberg Marquardt (LM) Algorithm is used to effectively solve this problem with reasonable computational effort. The Levenberg Marquardt (LM) Algorithm for optimization of three term (PID) controller parameters with dynamic model of pH neutralization process is presented. The main goal is to show the merits of Levenberg Marquardt algorithm optimizat...

  11. Automatic parameter optimization in epsilon-filter for acoustical signal processing utilizing correlation coefficient.

    Science.gov (United States)

    Abe, Tomomi; Hashimoto, Shuji; Matsumoto, Mitsuharu

    2010-02-01

    epsilon-filter can reduce most kinds of noise from a single-channel noisy signal while preserving signals that vary drastically such as speech signals. It can reduce not only stationary noise but also nonstationary noise. However, it has some parameters whose values are set empirically. So far, there have been few studies to evaluate the appropriateness of the parameter settings for epsilon-filter. This paper employs the correlation coefficient of the filter output and the difference between the filter input and output as the evaluation function of the parameter setting. This paper also describes the algorithm to set the optimal parameter value of epsilon-filter automatically. To evaluate the adequateness of the obtained parameter, the mean absolute error is calculated. The experimental results show that the adequate parameter in epsilon-filter can be obtained automatically by using the proposed method.

  12. Optimization of the Machining parameter of LM6 Alminium alloy in CNC Turning using Taguchi method

    Science.gov (United States)

    Arunkumar, S.; Muthuraman, V.; Baskaralal, V. P. M.

    2017-03-01

    Due to widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining process. In machining of parts, surface quality is one of the most specified customer requirements. In order for manufactures to maximize their gains from utilizing CNC turning, accurate predictive models for surface roughness must be constructed. The prediction of optimum machining conditions for good surface finish plays an important role in process planning. This work deals with the study and development of a surface roughness prediction model for machining LM6 aluminum alloy. Two important tools used in parameter design are Taguchi orthogonal arrays and signal to noise ratio (S/N). Speed, feed, depth of cut and coolant are taken as process parameter at three levels. Taguchi’s parameters design is employed here to perform the experiments based on the various level of the chosen parameter. The statistical analysis results in optimum parameter combination of speed, feed, depth of cut and coolant as the best for obtaining good roughness for the cylindrical components. The result obtained through Taguchi is confirmed with real time experimental work.

  13. Parameter identification theory of a complex model based on global optimization method

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.

  14. Quantifying dynamic sensitivity of optimization algorithm parameters to improve hydrological model calibration

    Science.gov (United States)

    Qi, Wei; Zhang, Chi; Fu, Guangtao; Zhou, Huicheng

    2016-02-01

    It is widely recognized that optimization algorithm parameters have significant impacts on algorithm performance, but quantifying the influence is very complex and difficult due to high computational demands and dynamic nature of search parameters. The overall aim of this paper is to develop a global sensitivity analysis based framework to dynamically quantify the individual and interactive influence of algorithm parameters on algorithm performance. A variance decomposition sensitivity analysis method, Analysis of Variance (ANOVA), is used for sensitivity quantification, because it is capable of handling small samples and more computationally efficient compared with other approaches. The Shuffled Complex Evolution method developed at the University of Arizona algorithm (SCE-UA) is selected as an optimization algorithm for investigation, and two criteria, i.e., convergence speed and success rate, are used to measure the performance of SCE-UA. Results show the proposed framework can effectively reveal the dynamic sensitivity of algorithm parameters in the search processes, including individual influences of parameters and their interactive impacts. Interactions between algorithm parameters have significant impacts on SCE-UA performance, which has not been reported in previous research. The proposed framework provides a means to understand the dynamics of algorithm parameter influence, and highlights the significance of considering interactive parameter influence to improve algorithm performance in the search processes.

  15. Optimization of the dressing parameters in cylindrical grinding based on a generalized utility function

    Science.gov (United States)

    Aleksandrova, Irina

    2016-01-01

    The existing studies, concerning the dressing process, focus on the major influence of the dressing conditions on the grinding response variables. However, the choice of the dressing conditions is often made, based on the experience of the qualified staff or using data from reference books. The optimal dressing parameters, which are only valid for the particular methods and dressing and grinding conditions, are also used. The paper presents a methodology for optimization of the dressing parameters in cylindrical grinding. The generalized utility function has been chosen as an optimization parameter. It is a complex indicator determining the economic, dynamic and manufacturing characteristics of the grinding process. The developed methodology is implemented for the dressing of aluminium oxide grinding wheels by using experimental diamond roller dressers with different grit sizes made of medium- and high-strength synthetic diamonds type ??32 and ??80. To solve the optimization problem, a model of the generalized utility function is created which reflects the complex impact of dressing parameters. The model is built based on the results from the conducted complex study and modeling of the grinding wheel lifetime, cutting ability, production rate and cutting forces during grinding. They are closely related to the dressing conditions (dressing speed ratio, radial in-feed of the diamond roller dresser and dress-out time), the diamond roller dresser grit size/grinding wheel grit size ratio, the type of synthetic diamonds and the direction of dressing. Some dressing parameters are determined for which the generalized utility function has a maximum and which guarantee an optimum combination of the following: the lifetime and cutting ability of the abrasive wheels, the tangential cutting force magnitude and the production rate of the grinding process. The results obtained prove the possibility of control and optimization of grinding by selecting particular dressing

  16. Optimization of Friction Welding Process Parameters for Joining Carbon Steel and Stainless Steel%Optimization of Friction Welding Process Parameters for Joining Carbon Steel and Stainless Steel

    Institute of Scientific and Technical Information of China (English)

    R Paventhan; P R Lakshminarayanan; V Balasubramanian

    2012-01-01

    Friction weIding is a solid state joining process used extensively currently owing to its advantages such as low heat input, high production efficiency, ease of manufacture, and environment friendliness. Materials difficult to be welded by fusion welding processes can be successfully welded by friction welding. An attempt was made to develop an empirical relationship to predict the tensile strength of friction welded AISI 1040 grade medium carbon steel and AISI 304 austenitic stainless steel, incorporating the process parameters such as friction pressure, forging pressure, friction time and forging time, which have great influence on strength of the joints. Response surface methodology was applied to optimize the friction welding process parameters to attain maximum tensile strength of the joint. The maximum tensile strength of 543 MPa could be obtained for the joints fabricated under the welding conditions of friction pressure of 90 MPa, forging pressure of 90 MPa, friction time of 6 s and forging time of 6 s.

  17. Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Michala Jakubcová

    2015-01-01

    Full Text Available The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

  18. Optimization of injection molding parameters for poly(styrene-isobutylene-styrene) block copolymer

    Science.gov (United States)

    Fittipaldi, Mauro; Garcia, Carla; Rodriguez, Luis A.; Grace, Landon R.

    2016-03-01

    Poly(styrene-isobutylene-styrene) (SIBS) is a widely used thermoplastic elastomer in bioimplantable devices due to its inherent stability in vivo. However, the properties of the material are highly dependent on the fabrication conditions, molecular weight, and styrene content. An optimization method for injection molding is herein proposed which can be applied to varying SIBS formulations in order to maximize ultimate tensile strength, which is critical to certain load-bearing implantable applications. The number of injection molded samples required to ascertain the optimum conditions for maximum ultimate tensile strength is limited in order to minimize experimental time and effort. Injection molding parameters including nozzle temperature (three levels: 218, 246, and 274 °C), mold temperature (three levels: 50, 85, and 120 °C), injection speed (three levels: slow, medium and fast) and holding pressure time (three levels: 2, 6, and 10 seconds) were varied to fabricate dumbbell specimens for tensile testing. A three-level L9 Taguchi method utilizing orthogonal arrays was used in order to rank the importance of the different injection molding parameters and to find an optimal parameter setting to maximize the ultimate tensile strength of the thermoplastic elastomer. Based on the Taguchi design results, a Response Surface Methodology (RSM) was applied in order to build a model to predict the tensile strength of the material at different injection parameters. Finally, the model was optimized to find the injection molding parameters providing maximum ultimate tensile strength. Subsequently, the theoretically-optimum injection molding parameters were used to fabricate additional dumbbell specimens. The experimentally-determined ultimate tensile strength of these samples was found to be in close agreement (1.2%) with the theoretical results, successfully demonstrating the suitability of the Taguchi Method and RSM for optimizing injection molding parameters of SIBS.

  19. Optimization of Polishing Parameters with Taguchi Method for LBO Crystal in CMP

    Institute of Scientific and Technical Information of China (English)

    Jun Li; Yongwei Zhu; Dunwen Zuo; Yong Zhu; Chuangtian Chen

    2009-01-01

    Chemical mechanical polishing (CMP) was used to polish Lithium triborate (UB_3O_5 or LBO) crystal. Taguchi method was applied for optimization of the polishing parameters. Material removal rate (MRR) and surface roughness are considered as criteria for the optimization. The polishing pressure, the abrasive concentration and the table velocity are important parameters which influence MRR and surface roughness in CMP of LBO crystal. Experiment results indicate that for MRR the polishing pressure is the most significant polishing parameter followed by table velocity; while for the surface roughness, the abrasive concentration is the most important one. For high MRR in CMP of LBO crystal the optimal conditions are: pressure 620 g/cm~2, concentration 5.0 wt pct, and velocity 60 r/min, respectively. For the best surface roughness the optimal conditions are: pressure 416 g/cm~2, concentration 5.0 wt pct, and velocity 40 r/min, respectively. The contributions of individual parameters for MRR and surface roughness were obtained.

  20. Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune

    1991-01-01

    The design of a measurement program devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost, i.e. the cost of failure and the cost of the measurement program. All the calculati...

  1. Optimization of EDM Process Parameters on Titanium Super Alloys Based on the Grey Relational Analysis

    Directory of Open Access Journals (Sweden)

    J. Laxman, Dr. K. Guru Raj

    2014-05-01

    Full Text Available Electrical discharge machining (EDM is a unconventional machining process for the machining of complex shapes and hard materials that are difficult of machining by conventional machining process. In this paper deals with the optimization of EDM process parameters using the grey relational analysis (GRA based on an orthogonal array for the multi response process. The experiments are conducted on Titanium super alloys with copper electrode based on the Taguchi design of experiments L27 orthogonal array by choosing various parameters such as peak current, pulse on time, pulse off time and tool lift time for EDM process to obtain multiple process responses namely Metal removal rate (MRR and Tool Wear Rate (TWR. The combination of Taguchi method with GRA enables to determine the optimal parameters for multiple response process. Gray relational analysis is used to obtain a performance index called gray relational grade to optimize the EDM process with higher MRR and lower TWR and it is clearly found that the performance of the EDM has greatly increased by optimizing the responses the influence of individual machining parameters also investigated by using analysis of variance for the grey relational grade.

  2. Optimization of WEDM process parameters using deep cryo-treated Inconel 718 as work material

    Directory of Open Access Journals (Sweden)

    Bijaya Bijeta Nayak

    2016-03-01

    Full Text Available The present work proposes an experimental investigation and optimization of various process parameters during taper cutting of deep cryo-treated Inconel 718 in wire electrical discharge machining process. Taguchi's design of experiment is used to gather information regarding the process with less number of experimental runs considering six input parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension. Since traditional Taguchi method fails to optimize multiple performance characteristics, maximum deviation theory is applied to convert multiple performance characteristics into an equivalent single performance characteristic. Due to the complexity and non-linearity involved in this process, good functional relationship with reasonable accuracy between performance characteristics and process parameters is difficult to obtain. To address this issue, the present study proposes artificial neural network (ANN model to determine the relationship between input parameters and performance characteristics. Finally, the process model is optimized to obtain a best parametric combination by a new meta-heuristic approach known as bat algorithm. The results of the proposed algorithm show that the proposed method is an effective tool for simultaneous optimization of performance characteristics during taper cutting in WEDM process.

  3. On Optimization Control Parameters in an Adaptive Error-Control Scheme in Satellite Networks

    Directory of Open Access Journals (Sweden)

    Ranko Vojinović

    2011-09-01

    Full Text Available This paper presents a method for optimization of control parameters of an adaptive GBN scheme in error-prone satellite channel. Method is based on the channel model with three state, where channel have the variable noise level.

  4. Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

    Science.gov (United States)

    Zhang, Liqiang; Li, Luoxing; Wang, Shiuping; Zhu, Biwu

    2012-04-01

    In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.

  5. Jet Drilling and Optimizing Parameter Drilling Technology in Shengli Oil Fields

    Institute of Scientific and Technical Information of China (English)

    Chen Yue; Peng Junsheng

    1996-01-01

    @@ In Shengli oilfield, remarkable achievements have been obtained in research and tests on the technologies of jet drilling and optimizing parameter drilling, extensive applications of the technologies have greatly improved drilling speed and sharply decreased drilling time and costs, thus achieving excellent social and economic benefits.

  6. Optimization of reaction parameters for the electrochemical oxidation of lidocaine with a Design of Experiments approach

    NARCIS (Netherlands)

    Gul, Turan; Bischoff, Rainer; Permentier, Hjalmar

    2015-01-01

    Identification of potentially toxic oxidative drug metabolites is a crucial step in the development of new drugs. Electrochemical methods are useful to study oxidative drug metabolism, but are not widely used to synthesize metabolites for follow-up studies. Careful optimization of reaction parameter

  7. Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Heide, Janus; Zhang, Qi; Fitzek, Frank

    2013-01-01

    This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...

  8. Multi Objective Optimization of Weld Parameters of Boiler Steel Using Fuzzy Based Desirability Function

    Directory of Open Access Journals (Sweden)

    M. Satheesh

    2014-01-01

    Full Text Available The high pressure differential across the wall of pressure vessels is potentially dangerous and has caused many fatal accidents in the history of their development and operation. For this reason the structural integrity of weldments is critical to the performance of pressure vessels. In recent years much research has been conducted to the study of variations in welding parameters and consumables on the mechanical properties of pressure vessel steel weldments to optimize weld integrity and ensure pressure vessels are safe. The quality of weld is a very important working aspect for the manufacturing and construction industries. Because of high quality and reliability, Submerged Arc Welding (SAW is one of the chief metal joining processes employed in industry. This paper addresses the application of desirability function approach combined with fuzzy logic analysis to optimize the multiple quality characteristics (bead reinforcement, bead width, bead penetration and dilution of submerged arc welding process parameters of SA 516 Grade 70 steels(boiler steel. Experiments were conducted using Taguchi’s L27 orthogonal array with varying the weld parameters of welding current, arc voltage, welding speed and electrode stickout. By analyzing the response table and response graph of the fuzzy reasoning grade, optimal parameters were obtained. Solutions from this method can be useful for pressure vessel manufacturers and operators to search an optimal solution of welding condition.

  9. Research of Optimization Method of Swabbing Parameters of All Rods Pumping Wells in the Entire Oilfield

    Directory of Open Access Journals (Sweden)

    Zhang Xishun

    2013-03-01

    Full Text Available Aiming at the drawbacks of the optimization and design methods and the practical production goal of least energy consumption, a new theory is raised that the gas of the layer released energy in the lifting process including two parts: dissolved-gas expansion energy and free-gas expansion energy. The motor’s input power of rod pumping system is divided into hydraulic horse power, gas expansion power, surface mechanical loss power, subsurface loss power. Using the theory of energy-conservation, the simulation model of free-gas expansion power has been established, the simulating models of the motor’s input power which are based on the energy method have been improved and the simulation precision of system efficiency has been enhanced. The entire optimization design models have been set up in which the single-well output is taken as the optimum design variable, the planed production of all oil wells in an overall oilfield as the restraint condition and the least input power of the overall oilfield as the object. Synthesizing the optimization design results of the single well and the entire oilfield, the optimal output and the optimal swabbing parameters of all wells can be got. The actual optimizing examples show that the total power consumption designed by the entire optimization method is less 12.95% than that by the single optimization method.

  10. Joint state and parameter estimation in particle filtering and stochastic optimization

    Institute of Scientific and Technical Information of China (English)

    Xiaojun YANG; Keyi XING; Kunlin SHI; Quan PAN

    2008-01-01

    In this paper,an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approximation(SPSA)technique.The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework,and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function.The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systerns.Simulation result demonstrates the feasibility and efficiency of the proposed algorithm.

  11. Optimization of Cutting Parameters for Face Milling Titanium Alloy Using MQL

    Institute of Scientific and Technical Information of China (English)

    AHMED Hassan; YAO Zhen-qiang

    2005-01-01

    When using MQL as a cooling technique, many parameters have to be adjusted. The Taguchi method was used in this study to investigate the cutting characteristics of face milling of titanium alloys using PVD-coated inserts. To find the optimal volume removed and surface roughness, an orthogonal array, the signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) were employed. The optimum cutting parameters was obtained. Throughout this study, it was found that the feed rate is the most influencing cutting parameter in the face milling of titanium alloys.

  12. Optimal Estimation of Phenological Crop Model Parameters for Rice (Oryza sativa)

    Science.gov (United States)

    Sharifi, H.; Hijmans, R. J.; Espe, M.; Hill, J. E.; Linquist, B.

    2015-12-01

    Crop phenology models are important components of crop growth models. In the case of phenology models, generally only a few parameters are calibrated and default cardinal temperatures are used which can lead to a temperature-dependent systematic phenology prediction error. Our objective was to evaluate different optimization approaches in the Oryza2000 and CERES-Rice phenology sub-models to assess the importance of optimizing cardinal temperatures on model performance and systematic error. We used two optimization approaches: the typical single-stage (planting to heading) and three-stage model optimization (for planting to panicle initiation (PI), PI to heading (HD), and HD to physiological maturity (MT)) to simultaneously optimize all model parameters. Data for this study was collected over three years and six locations on seven California rice cultivars. A temperature-dependent systematic error was found for all cultivars and stages, however it was generally small (systematic error changes in cardinal temperature relative to the default values and thus optimization of cardinal temperatures did not affect systematic error or model performance. Compared to single stage optimization, three-stage optimization had little effect on determining time to PI or HD but significantly improved the precision in determining the time from HD to MT: the RMSE reduced from an average of 6 to 3.3 in Oryza2000 and from 6.6 to 3.8 in CERES-Rice. With regards to systematic error, we found a trade-off between RMSE and systematic error when optimization objective set to minimize RMSE or systematic error. Therefore, it is important to find the limits within which the trade-offs between RMSE and systematic error are acceptable, especially in climate change studies where this can prevent erroneous conclusions.

  13. Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.

    Directory of Open Access Journals (Sweden)

    Vasanthan Maruthapillai

    Full Text Available In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face and change in marker distance (change in distance between the original and new marker positions, were used to extract three statistical features (mean, variance, and root mean square from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.

  14. On the optimal experimental design for heat and moisture parameter estimation

    CERN Document Server

    Berger, Julien; Mendes, Nathan

    2016-01-01

    In the context of estimating material properties of porous walls based on in-site measurements and identification method, this paper presents the concept of Optimal Experiment Design (OED). It aims at searching the best experimental conditions in terms of quantity and position of sensors and boundary conditions imposed to the material. These optimal conditions ensure to provide the maximum accuracy of the identification method and thus the estimated parameters. The search of the OED is done by using the Fisher information matrix and a priori knowledge of the parameters. The methodology is applied for two case studies. The first one deals with purely conductive heat transfer. The concept of optimal experiment design is detailed and verified with 100 inverse problems for different experiment designs. The second case study combines a strong coupling between heat and moisture transfer through a porous building material. The methodology presented is based on a scientific formalism for efficient planning of experim...

  15. Optimization of process parameters for production of volatile fatty acid, biohydrogen and methane from anaerobic digestion.

    Science.gov (United States)

    Khan, M A; Ngo, H H; Guo, W S; Liu, Y; Nghiem, L D; Hai, F I; Deng, L J; Wang, J; Wu, Y

    2016-11-01

    The anaerobic digestion process has been primarily utilized for methane containing biogas production over the past few years. However, the digestion process could also be optimized for producing volatile fatty acids (VFAs) and biohydrogen. This is the first review article that combines the optimization approaches for all three possible products from the anaerobic digestion. In this review study, the types and configurations of the bioreactor are discussed for each type of product. This is followed by a review on optimization of common process parameters (e.g. temperature, pH, retention time and organic loading rate) separately for the production of VFA, biohydrogen and methane. This review also includes additional parameters, treatment methods or special additives that wield a significant and positive effect on production rate and these products' yield.

  16. Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune

    The design of measurement programs devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost that is the cost of failure and the cost of the measurement program. All the calculat...... for estimating the modal damping parameters in a simply supported plane, vibrating beam model. Results show optimal number of sensors and their locations....... the calculations are based on a priori knowledge and engineering judgement. One of the contribution of the approach is that the optimal number of sensors can be estimated. This is shown in a numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement program...

  17. Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

    CERN Document Server

    Tiwana, Moazzam Islam; Altman, Zwi

    2010-01-01

    This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an iterative manner. The statistical learning processes the data using Logistic Regression (LR) to extract closed form (functional) relations between Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These functional relations are then processed by an optimization engine which proposes new parameter values. The advantage of the proposed formulation is the small number of iterations required by the automated healing method to converge, making it suitable for off-line implementation. The proposed method is applied to heal an Inter-Cell Interference Coordination (ICIC) process in a 3G Long Term Evolution (LTE) network which is based on soft-frequency reuse scheme. Numerical simulat...

  18. Automated measurement of parameters related to the deformities of lower limbs based on x-rays images.

    Science.gov (United States)

    Wojciechowski, Wadim; Molka, Adrian; Tabor, Zbisław

    2016-03-01

    Measurement of the deformation of the lower limbs in the current standard full-limb X-rays images presents significant challenges to radiologists and orthopedists. The precision of these measurements is deteriorated because of inexact positioning of the leg during image acquisition, problems with selecting reliable anatomical landmarks in projective X-ray images, and inevitable errors of manual measurements. The influence of the random errors resulting from the last two factors on the precision of the measurement can be reduced if an automated measurement method is used instead of a manual one. In the paper a framework for an automated measurement of various metric and angular quantities used in the description of the lower extremity deformation in full-limb frontal X-ray images is described. The results of automated measurements are compared with manual measurements. These results demonstrate that an automated method can be a valuable alternative to the manual measurements.

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

    Science.gov (United States)

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

    2016-11-01

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

  20. Land cover classification using random forest with genetic algorithm-based parameter optimization

    Science.gov (United States)

    Ming, Dongping; Zhou, Tianning; Wang, Min; Tan, Tian

    2016-07-01

    Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time.

  1. OPTESIM, a versatile toolbox for numerical simulation of electron spin echo envelope modulation (ESEEM) that features hybrid optimization and statistical assessment of parameters.

    Science.gov (United States)

    Sun, Li; Hernandez-Guzman, Jessica; Warncke, Kurt

    2009-09-01

    Electron spin echo envelope modulation (ESEEM) is a technique of pulsed-electron paramagnetic resonance (EPR) spectroscopy. The analyis of ESEEM data to extract information about the nuclear and electronic structure of a disordered (powder) paramagnetic system requires accurate and efficient numerical simulations. A single coupled nucleus of known nuclear g value (g(N)) and spin I=1 can have up to eight adjustable parameters in the nuclear part of the spin Hamiltonian. We have developed OPTESIM, an ESEEM simulation toolbox, for automated numerical simulation of powder two- and three-pulse one-dimensional ESEEM for arbitrary number (N) and type (I, g(N)) of coupled nuclei, and arbitrary mutual orientations of the hyperfine tensor principal axis systems for N>1. OPTESIM is based in the Matlab environment, and includes the following features: (1) a fast algorithm for translation of the spin Hamiltonian into simulated ESEEM, (2) different optimization methods that can be hybridized to achieve an efficient coarse-to-fine grained search of the parameter space and convergence to a global minimum, (3) statistical analysis of the simulation parameters, which allows the identification of simultaneous confidence regions at specific confidence levels. OPTESIM also includes a geometry-preserving spherical averaging algorithm as default for N>1, and global optimization over multiple experimental conditions, such as the dephasing time (tau) for three-pulse ESEEM, and external magnetic field values. Application examples for simulation of (14)N coupling (N=1, N=2) in biological and chemical model paramagnets are included. Automated, optimized simulations by using OPTESIM lead to a convergence on dramatically shorter time scales, relative to manual simulations.

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

    Science.gov (United States)

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

    2016-09-01

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

  3. THE IMPLEMENTATION OF TAGUCHI METHODOLOGY FOR OPTIMIZATION OF END MILLING PROCESS PARAMETER OF MILD STEEL

    Directory of Open Access Journals (Sweden)

    ANIL CHOUBEY

    2012-07-01

    Full Text Available In this paper Taguchi method is applied to find optimum process parameters for end milling while machining of mild steel. A L9 orthogonal array, taguchi method and analysis of variance (ANOVA are used to formulate the experimental layout, to analyses the effect of each parameter on the machining characteristics and to predict the optimal choice for each end milling parameter such as spindle speed, feed rate, depth of cut and width of cut, and analysed the effect of these parameter on the material removal rate (MRR and surfaceroughness (SR. Results obtained by taguchi method match with ANOVA and cutting speed are highly influencing parameter. The analysis of the taguchi method reveals that, in general the spindle speedsignificantly affects the SR, while, the feed mainly affects the MRR. Experimental results are provided to verify this approach.

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

    CERN Document Server

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

    2016-01-01

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

  5. Automated Identification of the Heart Wall Throughout the Entire Cardiac Cycle Using Optimal Cardiac Phase for Extracted Features

    Science.gov (United States)

    Takahashi, Hiroki; Hasegawa, Hideyuki; Kanai, Hiroshi

    2011-07-01

    In most methods for evaluation of cardiac function based on echocardiography, the heart wall is currently identified manually by an operator. However, this task is very time-consuming and suffers from inter- and intraobserver variability. The present paper proposes a method that uses multiple features of ultrasonic echo signals for automated identification of the heart wall region throughout an entire cardiac cycle. In addition, the optimal cardiac phase to select a frame of interest, i.e., the frame for the initiation of tracking, was determined. The heart wall region at the frame of interest in this cardiac phase was identified by the expectation-maximization (EM) algorithm, and heart wall regions in the following frames were identified by tracking each point classified in the initial frame as the heart wall region using the phased tracking method. The results for two subjects indicate the feasibility of the proposed method in the longitudinal axis view of the heart.

  6. Process parameters optimization for friction stir welding of RDE-40 aluminium alloy using Taguchi technique

    Institute of Scientific and Technical Information of China (English)

    A.K.LAKSHMINARAYANAN; V.BALASUBRAMANIAN

    2008-01-01

    Taguchi approach was applied to determine the most influential control factors which will yield better tensile strength of the joints of friction stir welded RDE-40 aluminium alloy. In order to evaluate the effect of process parameters such as tool rotational speed, traverse speed and axial force on tensile strength of friction stir welded RDE-40 aluminium alloy, Taguchi parametric design and optimization approach was used. Through the Taguchi parametric design approach, the optimum levels of process parameters were determined. The results indicate that the rotational speed, welding speed and axial force are the significant parameters in deciding the tensile strength of the joint. The predicted optimal value of tensile strength of friction stir welded RDE-40 aluminium alloy is 303 MPa. The results were confirmed by further experiments.

  7. An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation.

    Science.gov (United States)

    Wang, Jun; Zhou, Bihua; Zhou, Shudao

    2016-01-01

    This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior.

  8. An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2016-01-01

    Full Text Available This paper proposes an improved cuckoo search (ICS algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior.

  9. Strategic Optimization and Investigation Effect Of Process Parameters On Performance Of Wire Electric Discharge Machine (WEDM

    Directory of Open Access Journals (Sweden)

    ATUL KUMAR

    2012-06-01

    Full Text Available Wire electrical discharge machining (WEDM is widely used in machining of conductive materials when precision is of primary significance. Wire-cut electric discharge machining of Skd 61alloy has been considered in the present work. Experimentation has been completed by using Taguchi’s L18 (21x37 orthogonal array under different conditions of parameters. Optimal combinations of parameters were obtained by this technique. The study shows that with the minimum number of experiments the complete problem can be solvedwhen compared to full factorial design. Experimental results make obvious that the machining model is proper and the Taguchi’s method satisfies the practical conditions. The results obtained are analyzed for the selection of an optimal combination of WEDM parameters for proper machining of Skd 61 alloy to achieve better surface finish. Different analysis was made on the data obtained from the experiments.

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

    Science.gov (United States)

    Whitaker, P. H.

    1977-01-01

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

  11. Optimizing Friction Stir Welding via Statistical Design of Tool Geometry and Process Parameters

    Science.gov (United States)

    Blignault, C.; Hattingh, D. G.; James, M. N.

    2012-06-01

    This article considers optimization procedures for friction stir welding (FSW) in 5083-H321 aluminum alloy, via control of weld process parameters and tool design modifications. It demonstrates the potential utility of the "force footprint" (FF) diagram in providing a real-time graphical user interface (GUI) for process optimization of FSW. Multiple force, torque, and temperature responses were recorded during FS welding using 24 different tool pin geometries, and these data were statistically analyzed to determine the relative influence of a number of combinations of important process and tool geometry parameters on tensile strength. Desirability profile charts are presented, which show the influence of seven key combinations of weld process variables on tensile strength. The model developed in this study allows the weld tensile strength to be predicted for other combinations of tool geometry and process parameters to fall within an average error of 13%. General guidelines for tool profile selection and the likelihood of influencing weld tensile strength are also provided.

  12. Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

    Science.gov (United States)

    Rudrapati, R.; Sahoo, P.; Bandyopadhyay, A.

    2016-09-01

    The main aim of the present work is to analyse the significance of turning parameters on surface roughness in computer numerically controlled (CNC) turning operation while machining of aluminium alloy material. Spindle speed, feed rate and depth of cut have been considered as machining parameters. Experimental runs have been conducted as per Box-Behnken design method. After experimentation, surface roughness is measured by using stylus profile meter. Factor effects have been studied through analysis of variance. Mathematical modelling has been done by response surface methodology, to made relationships between the input parameters and output response. Finally, process optimization has been made by teaching learning based optimization (TLBO) algorithm. Predicted turning condition has been validated through confirmatory experiment.

  13. Optimal feedback scheme and universal time scaling for Hamiltonian parameter estimation.

    Science.gov (United States)

    Yuan, Haidong; Fung, Chi-Hang Fred

    2015-09-11

    Time is a valuable resource and it is expected that a longer time period should lead to better precision in Hamiltonian parameter estimation. However, recent studies in quantum metrology have shown that in certain cases more time may even lead to worse estimations, which puts this intuition into question. In this Letter we show that by including feedback controls this intuition can be restored. By deriving asymptotically optimal feedback controls we quantify the maximal improvement feedback controls can provide in Hamiltonian parameter estimation and show a universal time scaling for the precision limit under the optimal feedback scheme. Our study reveals an intriguing connection between noncommutativity in the dynamics and the gain of feedback controls in Hamiltonian parameter estimation.

  14. Connecting parameters optimization on unsymmetrical twin-tower structure linked by sky-bridge

    Institute of Scientific and Technical Information of China (English)

    孙黄胜; 刘默涵; 朱宏平

    2014-01-01

    Based on a simplified 3-DOF model of twin-tower structure linked by a sky-bridge, the frequency response functions, the displacement power spectral density (PSD) functions, and the time-averaged total vibration energy were derived, by assuming the white noise as the earthquake excitation. The effects of connecting parameters, such as linking stiffness ratio and linking damping ratio, on the structural vibration responses were then studied, and the optimal connecting parameters were obtained to minimize the vibration energy of either the independent monomer tower or the integral structure. The influences of sky-bridge elevation position on the optimal connecting parameters were also discussed. Finally, the distribution characteristics of the top displacement PSD and the structural responses, excited by El Centro, Taft and artificial waves, were compared in both frequency and time domain. It is found that the connecting parameters at either end of connection interactively affect the responses of the towers. The optimal connecting parameters can greatly improve the damping connections on their seismic reduction effectiveness, but are unable to reduce the seismic responses of the towers to the best extent simultaneously. It is also indicated that the optimal connecting parameters derived from the simplified 3-DOF model are applicable for two multi-story structures linked by a sky-bridge with dampers. The seismic reduction effectiveness obtained varies from 0.3 to 1.0 with different sky-bridge mass ratio. The displacement responses of the example structures are reduced by approximately 22% with sky-bridge connections.

  15. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    Science.gov (United States)

    Janardhanan, S.; Datta, B.

    2011-12-01

    Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

  16. GPRS and Bluetooth Based Devices/Mobile Connectivity Shifting From Manual To Automation For Performance Optimization

    Directory of Open Access Journals (Sweden)

    Nazia Bibi

    2011-09-01

    Full Text Available Many companies/organizations are trying to move towards automation and provide their workers with the internet facility on their mobile in order to carry out their routine tasks to save time and resources. The proposed system is based on GPRS technology aims to provide a solution to problem faced in carryout routine tasks considering mobility. The system is designed in a way that facilitates Workers/field staff get updates on their mobile phone regarding tasks at hand. This System is beneficial in a sense that it saves resources in term of time, human resources and cuts down the paper work. The proposed system has been developed in view of research study conducted in the software development and telecom industry and provides a high end solution to the customers/fieldworkers that use GPRS technology for transactions updates of databases.

  17. Optimizing Automated Classification of Periodic Variable Stars in New Synoptic Surveys

    CERN Document Server

    Long, James P; Rice, John A; Richards, Joseph W; Bloom, Joshua S

    2012-01-01

    Efficient and automated classification of periodic variable stars is becoming increasingly important as the scale of astronomical surveys grows. Several recent papers have used methods from machine learning and statistics to construct classifiers on databases of labeled, multi--epoch sources with the intention of using these classifiers to automatically infer the classes of unlabeled sources from new surveys. However, the same source observed with two different synoptic surveys will generally yield different derived metrics (features) from the light curve. Since such features are used in classifiers, this survey-dependent mismatch in feature space will typically lead to degraded classifier performance. In this paper we show how and why feature distributions change using OGLE and \\textit{Hipparcos} light curves. To overcome survey systematics, we apply a method, \\textit{noisification}, which attempts to empirically match distributions of features between the labeled sources used to construct the classifier and...

  18. New strategies for medical data mining, part 3: automated workflow analysis and optimization.

    Science.gov (United States)

    Reiner, Bruce

    2011-02-01

    The practice of evidence-based medicine calls for the creation of "best practice" guidelines, leading to improved clinical outcomes. One of the primary factors limiting evidence-based medicine in radiology today is the relative paucity of standardized databases. The creation of standardized medical imaging databases offer the potential to enhance radiologist workflow and diagnostic accuracy through objective data-driven analytics, which can be categorized in accordance with specific variables relating to the individual examination, patient, provider, and technology being used. In addition to this "global" database analysis, "individual" radiologist workflow can be analyzed through the integration of electronic auditing tools into the PACS. The combination of these individual and global analyses can ultimately identify best practice patterns, which can be adapted to the individual attributes of end users and ultimately used in the creation of automated evidence-based medicine workflow templates.

  19. Process Parameter Optimization for Wobbling Laser Spot Welding of Ti6Al4V Alloy

    Science.gov (United States)

    Vakili-Farahani, F.; Lungershausen, J.; Wasmer, K.

    Laser beam welding (LBW) coupled with "wobble effect" (fast oscillation of the laser beam) is very promising for high precision micro-joining industry. For this process, similarly to the conventional LBW, the laser welding process parameters play a very significant role in determining the quality of a weld joint. Consequently, four process parameters (laser power, wobble frequency, number of rotations within a single laser pulse and focused position) and 5 responses (penetration, width, heat affected zone (HAZ), area of the fusion zone, area of HAZ and hardness) were investigated for spot welding of Ti6Al4V alloy (grade 5) using a design of experiments (DoE) approach. This paper presents experimental results showing the effects of variating the considered most important process parameters on the spot weld quality of Ti6Al4V alloy. Semi-empirical mathematical models were developed to correlate laser welding parameters to each of the measured weld responses. Adequacies of the models were then examined by various methods such as ANOVA. These models not only allows a better understanding of the wobble laser welding process and predict the process performance but also determines optimal process parameters. Therefore, optimal combination of process parameters was determined considering certain quality criteria set.

  20. Comparison of direct machine parameter optimization versus fluence optimization with sequential sequencing in IMRT of hypopharyngeal carcinoma

    Directory of Open Access Journals (Sweden)

    Bogner Ludwig

    2007-09-01

    Full Text Available Abstract Background To evaluate the effects of direct machine parameter optimization in the treatment planning of intensity-modulated radiation therapy (IMRT for hypopharyngeal cancer as compared to subsequent leaf sequencing in Oncentra Masterplan v1.5. Methods For 10 hypopharyngeal cancer patients IMRT plans were generated in Oncentra Masterplan v1.5 (Nucletron BV, Veenendal, the Netherlands for a Siemens Primus linear accelerator. For optimization the dose volume objectives (DVO for the planning target volume (PTV were set to 53 Gy minimum dose and 59 Gy maximum dose, in order to reach a dose of 56 Gy to the average of the PTV. For the parotids a median dose of 22 Gy was allowed and for the spinal cord a maximum dose of 35 Gy. The maximum DVO to the external contour of the patient was set to 59 Gy. The treatment plans were optimized with the direct machine parameter optimization ("Direct Step & Shoot", DSS, Raysearch Laboratories, Sweden newly implemented in Masterplan v1.5 and the fluence modulation technique ("Intensity Modulation", IM which was available in previous versions of Masterplan already. The two techniques were compared with regard to compliance to the DVO, plan quality, and number of monitor units (MU required per fraction dose. Results The plans optimized with the DSS technique met the DVO for the PTV significantly better than the plans optimized with IM (p = 0.007 for the min DVO and p 0.05. Plan quality, target coverage and dose homogeneity inside the PTV were superior for the plans optimized with DSS for similar dose to the spinal cord and lower dose to the normal tissue. The mean dose to the parotids was lower for the plans optimized with IM. Treatment plan efficiency was higher for the DSS plans with (901 ± 160 MU compared to (1151 ± 157 MU for IM (p-value Renormalization of the IM plans to the mean of the dose to 95% of the PTV (D95 of the DSS plans, resulted in similar target coverage and dose to the parotids for both

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

    Science.gov (United States)

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

    2017-04-01

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

  2. Development of optimization model for sputtering process parameter based on gravitational search algorithm

    Science.gov (United States)

    Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.

    2016-07-01

    In the RF magnetron sputtering process, the desirable layer properties are largely influenced by the process parameters and conditions. If the quality of the thin film has not reached up to its intended level, the experiments have to be repeated until the desirable quality has been met. This research is proposing Gravitational Search Algorithm (GSA) as the optimization model to reduce the time and cost to be spent in the thin film fabrication. The optimization model's engine has been developed using Java. The model is developed based on GSA concept, which is inspired by the Newtonian laws of gravity and motion. In this research, the model is expected to optimize four deposition parameters which are RF power, deposition time, oxygen flow rate and substrate temperature. The results have turned out to be promising and it could be concluded that the performance of the model is satisfying in this parameter optimization problem. Future work could compare GSA with other nature based algorithms and test them with various set of data.

  3. Biodegradation of dye solution containing Malachite Green: optimization of effective parameters using Taguchi method.

    Science.gov (United States)

    Daneshvar, N; Khataee, A R; Rasoulifard, M H; Pourhassan, M

    2007-05-08

    In this paper, optimization of biological decolorization of synthetic dye solution containing Malachite Green was investigated. The effect of temperature, initial pH of the solution, type of algae, dye concentration and time of the reaction was studied and optimized using Taguchi method. Sixteen experiments were required to study the effect of parameters on biodegradation of the dye. Each of experiments was repeated three times to calculate signal/noise (S/N). Our results showed that initial pH of the solution was the most effective parameter in comparison with others and the basic pH was favorable. In this study, we also optimized the experimental parameters and chose the best condition by determination effective factors. Based on the S/N ratio, the optimized conditions for dye removal were temperature 25 degrees C, initial pH 10, dye concentration 5 ppm, algae type Chlorella and time 2.5h. The stability and efficiency of Chlorella sp. in long-term repetitive operations were also examined.

  4. [Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

    Science.gov (United States)

    Wu, Ling; Liu, Xiang-Nan; Zhou, Bo-Tian; Liu, Chuan-Hao; Li, Lu-Feng

    2012-12-01

    This study analyzed the sensitivities of three vegetation biochemical parameters [chlorophyll content (Cab), leaf water content (Cw), and leaf area index (LAI)] to the changes of canopy reflectance, with the effects of each parameter on the wavelength regions of canopy reflectance considered, and selected three vegetation indices as the optimization comparison targets of cost function. Then, the Cab, Cw, and LAI were estimated, based on the particle swarm optimization algorithm and PROSPECT + SAIL model. The results showed that retrieval efficiency with vegetation indices as the optimization comparison targets of cost function was better than that with all spectral reflectance. The correlation coefficients (R2) between the measured and estimated values of Cab, Cw, and LAI were 90.8%, 95.7%, and 99.7%, and the root mean square errors of Cab, Cw, and LAI were 4.73 microg x cm(-2), 0.001 g x cm(-2), and 0.08, respectively. It was suggested that to adopt vegetation indices as the optimization comparison targets of cost function could effectively improve the efficiency and precision of the retrieval of biochemical parameters based on PROSPECT + SAIL model.

  5. Optimization of pretreatments and process parameters for sorghum popping in microwave oven using response surface methodology.

    Science.gov (United States)

    Mishra, Gayatri; Joshi, Dinesh C; Mohapatra, Debabandya

    2015-12-01

    Sorghum is a popular healthy snack food. Popped sorghum was prepared in a domestic microwave oven. A 3 factor 3 level Box and Behneken design was used to optimize the pretreatment conditions. Grains were preconditioned to 12-20 % moisture content by the addition of 0-2 % salt solutions. Oil was applied (0-10 % w/w) to the preconditioned grains. Optimization of the pretreatments was based on popping yield, volume expansion ratio, and sensory score. The optimized condition was found at 16.62 % (wb), 0.55 % salt and 10 % oil with popping yield of 82.228 %, volume expansion ratio of 14.564 and overall acceptability of 8.495. Further, the microwave process parameters were optimized using a 2 factor 3 level design having microwave power density ranging from 9 to 18 W/g and residence time ranging from 100 to 180 s. For the production of superior quality pop sorghum, the optimized microwave process parameters were microwave power density of 18 Wg(-1) and residence time of 140 s.

  6. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection.

    Science.gov (United States)

    Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo

    2012-01-01

    Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods.

  7. Optimization of thread partitioning parameters in speculative multithreading based on artificial immune algorithm

    Institute of Scientific and Technical Information of China (English)

    Yu-xiang LI; Yin-liang ZHAO‡; Bin LIU; Shuo JI

    2015-01-01

    Thread partition plays an important role in speculative multithreading (SpMT) for automatic parallelization of ir-regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap-plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold (DT), lower limit of thread size (TSL), upper limit of thread size (TSU), lower limit of spawning distance (SDL), and upper limit of spawning distance (SDU). Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com-binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm (AIA) to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench-marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00%on a 4-core platform compared with a machine learning based approach, and 8.92%compared with a heuristics-based approach.

  8. Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

    Science.gov (United States)

    Oby, Emily R.; Perel, Sagi; Sadtler, Patrick T.; Ruff, Douglas A.; Mischel, Jessica L.; Montez, David F.; Cohen, Marlene R.; Batista, Aaron P.; Chase, Steven M.

    2016-06-01

    Objective. A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). Approach. We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. Main Results. The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. Significance. How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent

  9. An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI.

    Science.gov (United States)

    Churchill, Nathan W; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C

    2015-01-01

    BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline") significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.

  10. An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI.

    Directory of Open Access Journals (Sweden)

    Nathan W Churchill

    Full Text Available BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline" significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each, demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.

  11. Inflatable Wing Design Parameter Optimization Using Orthogonal Testing and Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    WANG Zhifei; WANG Hua

    2012-01-01

    The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels.To overcome some of the defects of the inflatable wing parameter design method,this paper proposes an optimization design scheme based on orthogonal testing and support vector machines (SVMs).Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency.Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure,uniform applied load and the number of chambers that affect the bending response of inflatable wings.An SVM intelligent model is established and limited orthogonal test swatches are studied.Thus,the precise relationships between each parameter and product quality features,as well the signal-to-noise ratio (SNR),can be obtained.This can guide general technological design optimization.

  12. Effect of experimental parameters on optimal reflection of light from opaque media

    Science.gov (United States)

    Anderson, Benjamin R.; Gunawidjaja, Ray; Eilers, Hergen

    2016-01-01

    Previously we considered the effect of experimental parameters on optimized transmission through opaque media using spatial light modulator (SLM)-based wavefront shaping. In this study we consider the opposite geometry, in which we optimize reflection from an opaque surface such that the backscattered light is focused onto a spot on an imaging detector. By systematically varying different experimental parameters (genetic algorithm iterations, bin size, SLM active area, target area, spot size, and sample angle with respect to the optical axis) and optimizing the reflected light we determine how each parameter affects the intensity enhancement. We find that the effects of the experimental parameters on the enhancement are similar to those measured for a transmissive geometry, but with the exact functional forms changed due to the different geometry and the use of a genetic algorithm instead of an iterative algorithm. Additionally, we find preliminary evidence of greater enhancements than predicted by random matrix theory, suggesting a possibly new physical mechanism to be investigated in future work.

  13. Laboratory automation in clinical bacteriology: what system to choose?

    Science.gov (United States)

    Croxatto, A; Prod'hom, G; Faverjon, F; Rochais, Y; Greub, G

    2016-03-01

    Automation was introduced many years ago in several diagnostic disciplines such as chemistry, haematology and molecular biology. The first laboratory automation system for clinical bacteriology was released in 2006, and it rapidly proved its value by increasing productivity, allowing a continuous increase in sample volumes despite limited budgets and personnel shortages. Today, two major manufacturers, BD Kiestra and Copan, are commercializing partial or complete laboratory automation systems for bacteriology. The laboratory automation systems are rapidly evolving to provide improved hardware and software solutions to optimize laboratory efficiency. However, the complex parameters of the laboratory and automation systems must be considered to determine the best system for each given laboratory. We address several topics on laboratory automation that may help clinical bacteriologists to understand the particularities and operative modalities of the different systems. We present (a) a comparison of the engineering and technical features of the various elements composing the two different automated systems currently available, (b) the system workflows of partial and complete laboratory automation, which define the basis for laboratory reorganization required to optimize system efficiency, (c) the concept of digital imaging and telebacteriology, (d) the connectivity of laboratory automation to the laboratory information system, (e) the general advantages and disadvantages as well as the expected impacts provided by laboratory automation and (f) the laboratory data required to conduct a workflow assessment to determine the best configuration of an automated system for the laboratory activities and specificities.

  14. Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

    Science.gov (United States)

    Morelli, Eugene A.; Klein, Vladislav

    1990-01-01

    A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

  15. Algorithms of D-optimal designs for Morgan Mercer Flodin (MMF) models with three parameters

    Science.gov (United States)

    Widiharih, Tatik; Haryatmi, Sri; Gunardi, Wilandari, Yuciana

    2016-02-01

    Morgan Mercer Flodin (MMF) model is used in many areas including biological growth studies, animal and husbandry, chemistry, finance, pharmacokinetics and pharmacodynamics. Locally D-optimal designs for Morgan Mercer Flodin (MMF) models with three parameters are investigated. We used the Generalized Equivalence Theorem of Kiefer and Wolvowitz to determine D-optimality criteria. Number of roots for standardized variance are determined using Tchebysheff system concept and it is used to decide that the design is minimally supported design. In these models, designs are minimally supported designs with uniform weight on its support, and the upper bound of the design region is a support point.

  16. Multi-parameter optimization of a nanomagnetic system for spintronic applications

    Energy Technology Data Exchange (ETDEWEB)

    Morales Meza, Mishel [Centro de Investigación en Materiales Avanzados, S.C. (CIMAV), Chihuahua/Monterrey, 120 Avenida Miguel de Cervantes, 31109 Chihuahua (Mexico); Zubieta Rico, Pablo F. [Centro de Investigación en Materiales Avanzados, S.C. (CIMAV), Chihuahua/Monterrey, 120 Avenida Miguel de Cervantes, 31109 Chihuahua (Mexico); Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV) Querétaro, Libramiento Norponiente 2000, Fracc. Real de Juriquilla, 76230 Querétaro (Mexico); Horley, Paul P., E-mail: paul.horley@cimav.edu.mx [Centro de Investigación en Materiales Avanzados, S.C. (CIMAV), Chihuahua/Monterrey, 120 Avenida Miguel de Cervantes, 31109 Chihuahua (Mexico); Sukhov, Alexander [Institut für Physik, Martin-Luther Universität Halle-Wittenberg, 06120 Halle (Saale) (Germany); Vieira, Vítor R. [Centro de Física das Interacções Fundamentais (CFIF), Instituto Superior Técnico, Universidade Técnica de Lisboa, Avenida Rovisco Pais, 1049-001 Lisbon (Portugal)

    2014-11-15

    Magnetic properties of nano-particles feature many interesting physical phenomena that are essentially important for the creation of a new generation of spin-electronic devices. The magnetic stability of the nano-particles can be improved by formation of ordered particle arrays, which should be optimized over several parameters. Here we report successful optimization regarding inter-particle distance and applied field frequency allowing to obtain about three-times reduction of coercivity of a particle array compared to that of a single particle, which opens new perspectives for development of new spintronic devices.

  17. Parameter estimation and uncertainty quantification in a biogeochemical model using optimal experimental design methods

    Science.gov (United States)

    Reimer, Joscha; Piwonski, Jaroslaw; Slawig, Thomas

    2016-04-01

    The statistical significance of any model-data comparison strongly depends on the quality of the used data and the criterion used to measure the model-to-data misfit. The statistical properties (such as mean values, variances and covariances) of the data should be taken into account by choosing a criterion as, e.g., ordinary, weighted or generalized least squares. Moreover, the criterion can be restricted onto regions or model quantities which are of special interest. This choice influences the quality of the model output (also for not measured quantities) and the results of a parameter estimation or optimization process. We have estimated the parameters of a three-dimensional and time-dependent marine biogeochemical model describing the phosphorus cycle in the ocean. For this purpose, we have developed a statistical model for measurements of phosphate and dissolved organic phosphorus. This statistical model includes variances and correlations varying with time and location of the measurements. We compared the obtained estimations of model output and parameters for different criteria. Another question is if (and which) further measurements would increase the model's quality at all. Using experimental design criteria, the information content of measurements can be quantified. This may refer to the uncertainty in unknown model parameters as well as the uncertainty regarding which model is closer to reality. By (another) optimization, optimal measurement properties such as locations, time instants and quantities to be measured can be identified. We have optimized such properties for additional measurement for the parameter estimation of the marine biogeochemical model. For this purpose, we have quantified the uncertainty in the optimal model parameters and the model output itself regarding the uncertainty in the measurement data using the (Fisher) information matrix. Furthermore, we have calculated the uncertainty reduction by additional measurements depending on time

  18. Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery

    Directory of Open Access Journals (Sweden)

    Brian A. Johnson

    2015-10-01

    Full Text Available Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA methods are becoming widely-used in remote sensing because single-scale/single-level (SS-GEOBIA methods are often unable to obtain an accurate segmentation and classification of all land use/land cover (LULC types in an image. However, there have been few comparisons between SS-GEOBIA and MS-GEOBIA approaches for the purpose of mapping a specific LULC type, so it is not well understood which is more appropriate for this task. In addition, there are few methods for automating the selection of segmentation parameters for MS-GEOBIA, while manual selection (i.e., trial-and-error approach of parameters can be quite challenging and time-consuming. In this study, we examined SS-GEOBIA and MS-GEOBIA approaches for extracting residential areas in Landsat 8 imagery, and compared naïve and parameter-optimized segmentation approaches to assess whether unsupervised segmentation parameter optimization (USPO could improve the extraction of residential areas. Our main findings were: (i the MS-GEOBIA approaches achieved higher classification accuracies than the SS-GEOBIA approach, and (ii USPO resulted in more accurate MS-GEOBIA classification results while reducing the number of segmentation levels and classification variables considerably.

  19. Application of Powell's optimization method to surge arrester circuit models' parameters

    Energy Technology Data Exchange (ETDEWEB)

    Christodoulou, C.A.; Stathopulos, I.A. [National Technical University of Athens, School of Electrical and Computer Engineering, 9 Iroon Politechniou St., Zografou Campus, 157 80 Athens (Greece); Vita, V.; Ekonomou, L.; Chatzarakis, G.E. [A.S.PE.T.E. - School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece)

    2010-08-15

    Powell's optimization method has been used for the evaluation of the surge arrester models parameters. The proper modelling of metal-oxide surge arresters and the right selection of equivalent circuit parameters are very significant issues, since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters' dynamic behavior. The proposed approach selects optimum arrester model equivalent circuit parameter values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer. Application of the method in performed on a 120 kV metal oxide arrester. The use of the obtained optimum parameter values reduces significantly the relative error between the simulated and manufacturer's peak residual voltage value, presenting the effectiveness of the method. (author)

  20. IDENTIFICATION OF OPTIMAL PARAMETERS OF REINFORCED CONCRETE STRUCTURES WITH ACCOUNT FOR THE PROBABILITY OF FAILURE

    Directory of Open Access Journals (Sweden)

    Filimonova Ekaterina Aleksandrovna

    2012-10-01

    The author suggests splitting the aforementioned parameters into the two groups, namely, natural parameters and value-related parameters that are introduced to assess the costs of development, transportation, construction and operation of a structure, as well as the costs of its potential failure. The author proposes a new improved methodology for the identification of the above parameters that ensures optimal solutions to non-linear objective functions accompanied by non-linear restrictions that are critical to the design of reinforced concrete structures. Any structural failure may be interpreted as the bounce of a random process associated with the surplus bearing capacity into the negative domain. Monte Carlo numerical methods make it possible to assess these bounces into the unacc eptable domain.

  1. OPTIMIZATION OF PROCESS PARAMETERS TO MINIMIZE ANGULAR DISTORTION IN GAS TUNGSTEN ARC WELDED STAINLESS STEEL 202 GRADE PLATES USING PARTICLE SWARM OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    R. SUDHAKARAN

    2012-04-01

    Full Text Available This paper presents a study on optimization of process parameters using particle swarm optimization to minimize angular distortion in 202 grade stainless steel gas tungsten arc welded plates. Angular distortion is a major problem and most pronounced among different types of distortion in butt welded plates. The process control parameters chosen for the study are welding gun angle, welding speed, plate length, welding current and gas flow rate. The experiments were conducted using design of experiments technique with five factor five level central composite rotatable design with full replication technique. A mathematical model was developed correlating the process parameters with angular distortion. A source code was developed in MATLAB 7.6 to do the optimization. The optimal process parameters gave a value of 0.0305° for angular distortion which demonstrates the accuracy of the model developed. The results indicate that the optimized values for the process parameters are capable of producing weld with minimum distortion.

  2. A comparison between gradient descent and stochastic approaches for parameter optimization of a coupled ocean-sea ice model

    Science.gov (United States)

    Sumata, H.; Kauker, F.; Gerdes, R.; Köberle, C.; Karcher, M.

    2012-11-01

    Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean-sea ice model, and applicability and efficiency of the respective methods were examined. One is a finite difference method based on a traditional gradient descent approach, while the other adopts genetic algorithms as an example of stochastic approaches. Several series of parameter optimization experiments were performed by minimizing a cost function composed of model-data misfit of ice concentration, ice drift velocity and ice thickness. The finite difference method fails to estimate optimal parameters due to an ill-shaped nature of the cost function, whereas the genetic algorithms can effectively estimate near optimal parameters with a practical number of iterations. The results of the study indicate that a sophisticated stochastic approach is of practical use to a parameter optimization of a coupled ocean-sea ice model.

  3. An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

    Directory of Open Access Journals (Sweden)

    M. Anitha

    2012-04-01

    Full Text Available Settings White Matter lesions (WMLs are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM and Grey Matter (GM. The aim of this paper is to
    automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means clustering, Geostatistical Possibilistic clustering and Geostatistical Fuzzy clustering and 3.Optimization using Particle Swarm Optimization (PSO. The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 89%, specificity of 94% and overall accuracy of 93% over FCM and GPC. The clustered brain images are then subjected to Particle Swarm Optimization (PSO. The optimized result obtained from GFCM-PSO provides sensitivity of 90%, specificity of 94% and accuracy of 95%. The detection results reveals that GFCM and GFCMPSO better localizes the large regions of lesions and gives less false positive rate when compared to GPC and GPC-PSO which captures the largest loads of WMLs only in the upper ventral horns of the brain.

  4. SWANS: A Prototypic SCALE Criticality Sequence for Automated Optimization Using the SWAN Methodology

    Energy Technology Data Exchange (ETDEWEB)

    Greenspan, E.

    2001-01-11

    SWANS is a new prototypic analysis sequence that provides an intelligent, semi-automatic search for the maximum k{sub eff} of a given amount of specified fissile material, or of the minimum critical mass. It combines the optimization strategy of the SWAN code with the composition-dependent resonance self-shielded cross sections of the SCALE package. For a given system composition arrived at during the iterative optimization process, the value of k{sub eff} is as accurate and reliable as obtained using the CSAS1X Sequence of SCALE-4.4. This report describes how SWAN is integrated within the SCALE system to form the new prototypic optimization sequence, describes the optimization procedure, provides a user guide for SWANS, and illustrates its application to five different types of problems. In addition, the report illustrates that resonance self-shielding might have a significant effect on the maximum k{sub eff} value a given fissile material mass can have.

  5. An automated optimization tool for high-dose-rate (HDR) prostate brachytherapy with divergent needle pattern

    NARCIS (Netherlands)

    Borot, Maxence; Maenhout, M.; de Senneville, B. Denis; Hautvast, G.; Binnekamp, D.; Lagendijk, J. J. W.; van Vulpen, M.; Moerland, M. A.

    2015-01-01

    Focal high-dose-rate (HDR) for prostate cancer has gained increasing interest as an alternative to whole gland therapy as it may contribute to the reduction of treatment related toxicity. For focal treatment, optimal needle guidance and placement is warranted. This can be achieved under MR guidance.

  6. Optimization of Process Parameters with Minimum Thrust Force and Torque in Drilling Operation Using Taguchi Method

    Directory of Open Access Journals (Sweden)

    Suleyman Neseli

    2014-10-01

    Full Text Available This research outlines the Taguchi optimization methodology, which is applied to optimize cutting parameters in drilling of AISI 1040 steel. The drilling parameters evaluated are cutting speed, feed rate, and helix angle. Series of experiments are conducted to relate the cutting parameters on the thrust force and torque. L27(313 orthogonal array, signal-to-noise ratio is employed to analyze the influence of these parameters on thrust force and torque during drilling. Analysis of variance (ANOVA is used to study the effect of process parameters on machining process. The study shows that the Taguchi method is suitable to solve the stated problem with the minimum number of trials. The main objective is to find the important factors and combination of factors that influence the machining process to achieve low thrust force and torque. The analysis of the Taguchi method indicates that the feed rate is the most significant factor affecting the thrust force, while the cutting speed contributes the most to the torque.

  7. A novel optimization approach to estimating kinetic parameters of the enzymatic hydrolysis of corn stover

    Directory of Open Access Journals (Sweden)

    Fenglei Qi

    2016-01-01

    Full Text Available Enzymatic hydrolysis is an integral step in the conversion of lignocellulosic biomass to ethanol. The conversion of cellulose to fermentable sugars in the presence of inhibitors is a complex kinetic problem. In this study, we describe a novel approach to estimating the kinetic parameters underlying this process. This study employs experimental data measuring substrate and enzyme loadings, sugar and acid inhibitions for the production of glucose. Multiple objectives to minimize the difference between model predictions and experimental observations are developed and optimized by adopting multi-objective particle swarm optimization method. Model reliability is assessed by exploring likelihood profile in each parameter space. Compared to previous studies, this approach improved the prediction of sugar yields by reducing the mean squared errors by 34% for glucose and 2.7% for cellobiose, suggesting improved agreement between model predictions and the experimental data. Furthermore, kinetic parameters such as K2IG2, K1IG, K2IG, K1IA, and K3IA are identified as contributors to the model non-identifiability and wide parameter confidence intervals. Model reliability analysis indicates possible ways to reduce model non-identifiability and tighten parameter confidence intervals. These results could help improve the design of lignocellulosic biorefineries by providing higher fidelity predictions of fermentable sugars under inhibitory conditions.

  8. Parameter Estimation for Coupled Hydromechanical Simulation of Dynamic Compaction Based on Pareto Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2015-01-01

    Full Text Available This paper presented a parameter estimation method based on a coupled hydromechanical model of dynamic compaction and the Pareto multiobjective optimization technique. The hydromechanical model of dynamic compaction is established in the FEM program LS-DYNA. The multiobjective optimization algorithm, Nondominated Sorted Genetic Algorithm (NSGA-IIa, is integrated with the numerical model to identify soil parameters using multiple sources of field data. A field case study is used to demonstrate the capability of the proposed method. The observed pore water pressure and crater depth at early blow of dynamic compaction are simultaneously used to estimate the soil parameters. Robustness of the back estimated parameters is further illustrated by a forward prediction. Results show that the back-analyzed soil parameters can reasonably predict lateral displacements and give generally acceptable predictions of dynamic compaction for an adjacent location. In addition, for prediction of ground response of the dynamic compaction at continuous blows, the prediction based on the second blow is more accurate than the first blow due to the occurrence of the hardening and strengthening of soil during continuous compaction.

  9. The scheme of combined application of optimization and simulation models for formation of an optimum structure of an automated control system of space systems

    Science.gov (United States)

    Chernigovskiy, A. S.; Tsarev, R. Yu; Nikiforov, A. Yu; Zelenkov, P. V.

    2016-11-01

    With the development of automated control systems of space systems, there are new classes of spacecraft that requires improvement of their structure and expand their functions. When designing the automated control system of space systems occurs various tasks such as: determining location of elements and subsystems in the space, hardware selection, the distribution of the set of functions performed by the system units, all of this under certain conditions on the quality of control and connectivity of components. The problem of synthesis of structure of automated control system of space systems formalized using discrete variables at various levels of system detalization. A sequence of tasks and stages of the formation of automated control system of space systems structure is developed. The authors have developed and proposed a scheme of the combined implementation of optimization and simulation models to ensure rational distribution of functions between the automated control system complex and the rest of the system units. The proposed approach allows to make reasonable hardware selection, taking into account the different requirements for the operation of automated control systems of space systems.

  10. Optimization of Squeeze Casting Parameters for 2017 A Wrought Al Alloy Using Taguchi Method

    Directory of Open Access Journals (Sweden)

    Najib Souissi

    2014-04-01

    Full Text Available This study applies the Taguchi method to investigate the relationship between the ultimate tensile strength, hardness and process variables in a squeeze casting 2017 A wrought aluminium alloy. The effects of various casting parameters including squeeze pressure, melt temperature and die temperature were studied. Therefore, the objectives of the Taguchi method for the squeeze casting process are to establish the optimal combination of process parameters and to reduce the variation in quality between only a few experiments. The experimental results show that the squeeze pressure significantly affects the microstructure and the mechanical properties of 2017 A Al alloy.

  11. Parameter Optimization of a 9 × 9 Polymer Arrayed Waveguide Grating Multiplexer

    Institute of Scientific and Technical Information of China (English)

    郭文滨; 马春生; 陈维友; 张大明; 陈开鑫; 崔战臣; 赵禹; 刘式墉

    2002-01-01

    Some important parameters are optimized for a 9 × 9 polymer arrayed waveguide grating multiplexer around the central wavelength of 1.55μm with the wavelength spacing of 1.6nm. These parameters include the thickness and width of the guide core, diffraction order, pitch of adjacent waveguides, path length difference of adjacent arrayed waveguides, focal length of slab waveguides, free spectral range, the number of input/output channels and the number of arrayed waveguides. Finally, a schematic waveguide layout of this device is presented, which contains 2 slabs, 9 input and 9 output channels, and 91 arrayed waveguides.

  12. Optimization of constitutive parameters of foundation soils k-means clustering analysis

    Institute of Scientific and Technical Information of China (English)

    Muge Elif Orakoglu; Cevdet Emin Ekinci

    2013-01-01

    The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits:raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un-confined compression strengths and other parameters of the different soils.

  13. Selection of the Optimal Cooling Parameters to the Multi-Cavity Die

    Institute of Scientific and Technical Information of China (English)

    Chiaming; Yen; Jui-Cheng; Lin; Wujeng; Li

    2002-01-01

    This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he Taguchi's method and the abductive network are used. These methods are appli ed to create an efficient model with functional nodes for the considered problem . Once the cooling system parameters are developed, this network can be used to predict the warp for the die-casting die accurately. A ...

  14. Optimization of struvite precipitation in synthetic biologically treated swine wastewater--determination of the optimal process parameters.

    Science.gov (United States)

    Capdevielle, Aurélie; Sýkorová, Eva; Biscans, Béatrice; Béline, Fabrice; Daumer, Marie-Line

    2013-01-15

    A sustainable way to recover phosphorus (P) in swine wastewater involves a preliminary step of P dissolution followed by the separation of particulate organic matter. The next two steps are firstly the precipitation of struvite crystals done by adding a crystallization reagent (magnesia) and secondly the filtration of the crystals. A design of experiments with five process parameters was set up to optimize the size of the struvite crystals in a synthetic swine wastewater. More than 90% of P was recovered as large crystals of struvite in optimal conditions which were: low Mg:Ca ratio (2.25:1), the leading parameter, high N:P ratio (3:1), moderate stirring rate (between 45 and 90 rpm) and low temperature (below 20 °C).These results were obtained despite the presence of a large amount of calcium and using a cheap reactant (MgO). The composition of the precipitates was identified by Raman analysis and solid dissolution. Results showed that amorphous calcium phosphate (ACP) co-precipitated with struvite and that carbonates were incorporated with solid fractions.

  15. Process optimization and biocompatibility of cell carriers suitable for automated magnetic manipulation.

    Science.gov (United States)

    Krejci, I; Piana, C; Howitz, S; Wegener, T; Fiedler, S; Zwanzig, M; Schmitt, D; Daum, N; Meier, K; Lehr, C M; Batista, U; Zemljic, S; Messerschmidt, J; Franzke, J; Wirth, M; Gabor, F

    2012-03-01

    There is increasing demand for automated cell reprogramming in the fields of cell biology, biotechnology and the biomedical sciences. Microfluidic-based platforms that provide unattended manipulation of adherent cells promise to be an appropriate basis for cell manipulation. In this study we developed a magnetically driven cell carrier to serve as a vehicle within an in vitro environment. To elucidate the impact of the carrier on cells, biocompatibility was estimated using the human adenocarcinoma cell line Caco-2. Besides evaluation of the quality of the magnetic carriers by field emission scanning electron microscopy, the rate of adherence, proliferation and differentiation of Caco-2 cells grown on the carriers was quantified. Moreover, the morphology of the cells was monitored by immunofluorescent staining. Early generations of the cell carrier suffered from release of cytotoxic nickel from the magnetic cushion. Biocompatibility was achieved by complete encapsulation of the nickel bulk within galvanic gold. The insulation process had to be developed stepwise and was controlled by parallel monitoring of the cell viability. The final carrier generation proved to be a proper support for cell manipulation, allowing proliferation of Caco-2 cells equal to that on glass or polystyrene as a reference for up to 10 days. Functional differentiation was enhanced by more than 30% compared with the reference. A flat, ferromagnetic and fully biocompatible carrier for cell manipulation was developed for application in microfluidic systems. Beyond that, this study offers advice for the development of magnetic cell carriers and the estimation of their biocompatibility.

  16. Optimization and Quality Control of Automated Quantitative Mineralogy Analysis for Acid Rock Drainage Prediction

    Directory of Open Access Journals (Sweden)

    Robert Pooler

    2017-01-01

    Full Text Available Low ore-grade waste samples from the Codelco Andina mine that were analyzed in an environmental and mineralogical test program for acid rock drainage prediction, revealed inconsistencies between the quantitative mineralogical data (QEMSCAN® and the results of geochemical characterizations by atomic absorption spectroscopy (AAS, LECO® furnace, and sequential extractions. For the QEMSCAN® results, biases were observed in the proportions of pyrite and calcium sulfate minerals detected. An analysis of the results indicated that the problems observed were likely associated with polished section preparation. Therefore, six different sample preparation protocols were tested and evaluated using three samples from the previous study. One of the methods, which involved particle size reduction and transverse section preparation, was identified as having the greatest potential for correcting the errors observed in the mineralogical analyses. Further, the biases in the quantities of calcium sulfate minerals detected were reduced through the use of ethylene glycol as a polishing lubricant. It is recommended that the sample preparation methodology described in this study be used in order to accurately quantify percentages of pyrite and calcium sulfate minerals in environmental mineralogical studies which use automated mineralogical analysis.

  17. The effect of key parameters on the design of an optimized caes power plant

    Directory of Open Access Journals (Sweden)

    Assadi M. Khalaji

    2016-01-01

    Full Text Available Due to the significant variations in electricity generation and its demand, the power plant owners are encountered with challenges of economic operation. Among all, Compressed air energy storage (CAES technology has proposed itself as a reliable and efficient solution to match the two sides. This paper deals with a modeled compressed air energy storage power plant which has been optimized thermodynamically through an efficient genetic algorithm code. The results of this optimized model, considered as the base case, show that the power plant is technically and financially justifiable. In order to obtain a more tangible realization, it is necessary to verify the results against the variation of key parameters. In this study, the sensitivity analysis is performed based on main parameters including plant loading and ambient condition and the resultant trends of each case are presented. This approach will help the designers to analyze the quality of their designs in different situations.

  18. Optimization of the Geometrical Parameters of a Solar Bubble Pump for Absorption-Diffusion Cooling Systems

    Directory of Open Access Journals (Sweden)

    N. Dammak

    2010-01-01

    Full Text Available Problem statement: The objective of this study was to optimize the geometrical parameters of a bubble pump integrated in a solar flat plate collector. Approach: This solar bubble pump was part of an ammonia/water/helium (NH3/H2O/He absorption-diffusion cooling system. Results: An empirical model was developed on the basis of momentum, mass, material equations and energy balances. The mathematical model was solved using the simulation tool “Engineering Equation Solver (EES”. Conclusion/Recommendations: Using metrological data from Gabes (Tunisia various parameters were geometrically optimized for maximum bubble pump efficiency which was best for a bubble pump tube diameter of 6 mm, a tube length of 1.5 m, an inclination to the horizontal between 30 and 50° of the solar flat plate collector and a submergence ratio between 0.2 and 0.3.

  19. Optimization of Cylindrical Grinding Process Parameters on C40E Steel Using Taguchi Technique

    Directory of Open Access Journals (Sweden)

    Naresh Kumar

    2015-01-01

    Full Text Available Surface finish and dimensional accuracy play a vital role in the today’s engineering industry. There are several methods used to achieve good surface finish like burnishing, honing and lapping, and grinding. Grinding is one of these ways that improves the surface finish and dimensional accuracy simultaneously. C40E steel has good industrial application in manufacturing of shafts, axles, spindles, studs, etc. In the present work the cylindrical grinding of C40E steel is done for the optimization of grinding process parameters. During this experimental work input process parameters i.e. speed, feed, depth of cut is optimized using Taguchi L9 orthogonal array. Analysis of variance (ANOVA concluded that surface roughness is minimum at the 210 rpm, 0.11mm/rev feed, and 0.04mm depth of penetration.

  20. Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems

    Directory of Open Access Journals (Sweden)

    Erdem Demircioglu

    2015-01-01

    Full Text Available This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN. To achieve the best ANN performance, Particle Swarm Optimization (PSO and Differential Evolution (DE are applied with ANN’s conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.

  1. Performance Evaluation and Parameter Optimization of SoftCast Wireless Video Broadcast

    Directory of Open Access Journals (Sweden)

    Dongxue Yang

    2015-08-01

    Full Text Available Wireless video broadcast plays an imp ortant role in multimedia communication with the emergence of mobile video applications. However, conventional video broadcast designs suffer from a cliff effect due to separated source and channel encoding. The newly prop osed SoftCast scheme employs a cross-layer design, whose reconstructed video quality is prop ortional to the channel condition. In this pap er, we provide the p erformance evaluation and the parameter optimization of the SoftCast system. Optimization principles on parameter selection are suggested to obtain a b etter video quality, o ccupy less bandwidth and/or utilize lower complexity. In addition, we compare SoftCast with H.264 in the LTE EPA scenario. The simulation results show that SoftCast provides a b etter p erformance in the scalability to channel conditions and the robustness to packet losses.

  2. Multi-objective process parameter optimization for energy saving in injection molding process

    Institute of Scientific and Technical Information of China (English)

    Ning-yun LU; Gui-xia GONG; Yi YANG; Jian-hua LU

    2012-01-01

    This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of variance (ANOVA),a process modeling algorithm by artificial neural network (ANN),and a multi-objective parameter optimization algorithm by genetic algorithm (GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.

  3. Optimization of Bending Process Parameters for Seamless Tubes Using Taguchi Method and Finite Element Method

    Directory of Open Access Journals (Sweden)

    Jui-Chang Lin

    2015-01-01

    Full Text Available The three-dimensional tube (or pipe is manufactured by CNC tube bending machine. The key techniques are determined by tube diameter, wall thickness, material, and bending radius. The obtained technique through experience and the trial and error method is unreliable. Finite element method (FEM simulation for the tube bending process before production can avoid wasting manpower and raw materials. The computer-aided engineering (CAE software ABAQUS 6.12 is applied to simulate bending characteristics and to explore the maximum stress and strain conditions. The Taguchi method is used to find the optimal parameters of bending. The confirmation experiment is performed according to optimal parameters. Results indicate that the strain error between CAE simulation and bending experiments is within 6.39%.

  4. Multi-criteria optimization of chassis parameters of Nissan 200 SX for drifting competitions

    Science.gov (United States)

    Maniowski, M.

    2016-09-01

    The work objective is to increase performance of Nissan 200sx S13 prepared for a quasi-static state of drifting on a circular path with given constant radius (R=15 m) and tyre-road friction coefficient (μ = 0.9). First, a high fidelity “miMA” multibody model of the vehicle is formulated. Then, a multicriteria optimization problem is solved with one of the goals to maximize a stable drift angle (β) of the vehicle. The decision variables contain 11 parameters of the vehicle chassis (describing the wheel suspension stiffness and geometry) and 2 parameters responsible for a driver steering and accelerator actions, that control this extreme closed-loop manoeuvre. The optimized chassis setup results in the drift angle increase by 14% from 35 to 40 deg.

  5. Cellular scanning strategy for selective laser melting: Generating reliable, optimized scanning paths and processing parameters

    DEFF Research Database (Denmark)

    Mohanty, Sankhya; Hattel, Jesper Henri

    2015-01-01

    Selective laser melting is yet to become a standardized industrial manufacturing technique. The process continues to suffer from defects such as distortions, residual stresses, localized deformations and warpage caused primarily due to the localized heating, rapid cooling and high temperature...... gradients that occur during the process. While process monitoring and control of selective laser melting is an active area of research, establishing the reliability and robustness of the process still remains a challenge.In this paper, a methodology for generating reliable, optimized scanning paths...... and process parameters for selective laser melting of a standard sample is introduced. The processing of the sample is simulated by sequentially coupling a calibrated 3D pseudo-analytical thermal model with a 3D finite element mechanical model.The optimized processing parameters are subjected to a Monte Carlo...

  6. Parameter Optimization for Enhancement of Ethanol Yield by Atmospheric Pressure DBD-Treated Saccharomyces cerevisiae

    Science.gov (United States)

    Dong, Xiaoyu; Yuan, Yulian; Tang, Qian; Dou, Shaohua; Di, Lanbo; Zhang, Xiuling

    2014-01-01

    In this study, Saccharomyces cerevisiae (S. cerevisiae) was exposed to dielectric barrier discharge plasma (DBD) to improve its ethanol production capacity during fermentation. Response surface methodology (RSM) was used to optimize the discharge-associated parameters of DBD for the purpose of maximizing the ethanol yield achieved by DBD-treated S. cerevisiae. According to single factor experiments, a mathematical model was established using Box-Behnken central composite experiment design, with plasma exposure time, power supply voltage, and exposed-sample volume as impact factors and ethanol yield as the response. This was followed by response surface analysis. Optimal experimental parameters for plasma discharge-induced enhancement in ethanol yield were plasma exposure time of 1 min, power voltage of 26 V, and an exposed sample volume of 9 mL. Under these conditions, the resulting yield of ethanol was 0.48 g/g, representing an increase of 33% over control.

  7. Experimental and numerical analysis for optimal design parameters of a falling film evaporator

    Indian Academy of Sciences (India)

    RAJNEESH KAUSHAL; RAJ KUMAR; GAURAV VATS

    2016-06-01

    Present study exhibits an experimental examination of mass transfer coefficient and evaporative effectiveness of a falling film evaporator. Further, a statistical replica is extended in order to have optimal controlling parameters viz. non-dimensional enthalpy potential, film Reynolds number of cooling water, Reynolds number of air and relative humidity of up-streaming air. The models not only give an optimal solution but also help in establishing a correlation among controlling parameters. In this context, response surface methodology is employed by aid of design of experiment approach. Later, the response surface curves are studied using ANOVA. Finally, the relations established are confirmed experimentally to validate the models. The relations thus established are beneficent in furtherance of designing evaporators. Additionally, the presentstudy is among the first attempts to reveal the effect of humidity on the performance of falling film evaporator.

  8. Physiochemical parameters optimization for enhanced nisin production by Lactococcus lactis (MTCC 440

    Directory of Open Access Journals (Sweden)

    Puspadhwaja Mall

    2010-02-01

    Full Text Available The influence of various physiochemical parameters on the growth of Lactococcus lactis sub sp. lactis MTCC 440 was studied at shake flask level for 20 h. Media optimization (MRS broth was studied to achieve enhanced growth of the organism and also nisin production. Bioassay of nisin was done with agar diffusion method using Streptococcus agalactae NCIM 2401 as indicator strain. MRS broth (6%, w/v with 0.15μg/ml of nisin supplemented with 0.5% (v/v skimmed milk was found to be the best for nisin production as well as for growth of L lactis. The production of nisin was strongly influenced by the presence of skimmed milk and nisin in MRS broth. The production of nisin was affected by the physical parameters and maximum nisin production was at 30(0C while the optimal temperature for biomass production was 37(0C.

  9. Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Gao Fei; Li Zhuo-Qiu; Tong Heng-Qing

    2008-01-01

    This paper proposes a novel quantum-behaved particle swarm optimization (NQPSO) for the estimation of chaos'unknown parameters by transforming them into nonlinear functions' optimization. By means of the techniques in the following three aspects: contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles' convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems axe given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.

  10. A novel membrane-based process to isolate peroxidase from horseradish roots: optimization of operating parameters.

    Science.gov (United States)

    Liu, Jianguo; Yang, Bo; Chen, Changzhen

    2013-02-01

    The optimization of operating parameters for the isolation of peroxidase from horseradish (Armoracia rusticana) roots with ultrafiltration (UF) technology was systemically studied. The effects of UF operating conditions on the transmission of proteins were quantified using the parameter scanning UF. These conditions included solution pH, ionic strength, stirring speed and permeate flux. Under optimized conditions, the purity of horseradish peroxidase (HRP) obtained was greater than 84 % after a two-stage UF process and the recovery of HRP from the feedstock was close to 90 %. The resulting peroxidase product was then analysed by isoelectric focusing, SDS-PAGE and circular dichroism, to confirm its isoelectric point, molecular weight and molecular secondary structure. The effects of calcium ion on HRP specific activities were also experimentally determined.

  11. A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Aijia Ouyang

    2015-01-01

    Full Text Available Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter θ to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if θ≠1/3, but interestingly when θ=1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.

  12. Determination of optimal parameters of basil supercritical fluid extraction by response surface methodology

    Directory of Open Access Journals (Sweden)

    Filip Snežana Đ.

    2016-01-01

    Full Text Available The supercritical fluid extraction of aroma compounds from basil (Ocimum basilicum L. was studied. Response surface methodology was used to optimize the parameters of the process. Full factorial design was applied to evaluate the effects of two independent variables (pressure and temperature on the extraction yield and linalool yield. From the response surface plots, pressure and temperature exhibited independent and interactive effect on the extraction yield. The optimal conditions to obtain the highest extraction yield (1.91% of O. basilicum were the pressure of 29.7 MPa and temperature of 59.2oC, whereas the highest yield of linalool (1.998 g•kg-1 was obtained at the pressure of 20 MPa and temperature of 40oC. The experimental values agreed with the predicted ones, indicating suitability of the response surface methodology for optimizing the extraction process. [Projekat Ministarstva nauke Republike Srbije, br. TR 31013

  13. SVM classification model in depression recognition based on mutation PSO parameter optimization

    Directory of Open Access Journals (Sweden)

    Zhang Ming

    2017-01-01

    Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

  14. Optimization of Process Parameters and Kinetic Model of Enzymatic Extraction of Polyphenols from Lonicerae Flos

    OpenAIRE

    Kong,Fansheng; Yu, Shujuan; Bi, Yongguang; Huang, Xiaojun; Huang, Mengqian

    2016-01-01

    Objective: To optimize and verify the cellulase extraction of polyphenols from honeysuckle and provide a reference for enzymatic extracting polyphenols from honeysuckle. Materials and Methods: The uniform design was used According to Fick's first law and kinetic model, fitting analysis of the dynamic process of enzymatic extracting polyphenols was conducted. Results: The optimum enzymatic extraction parameters for polyphenols from honeysuckle are found to be 80% (v/v) of alcohol, 35:1 (mL/g) ...

  15. The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller

    Institute of Scientific and Technical Information of China (English)

    CHENQing-Geng; WANGNing; HUANGShao-Feng

    2005-01-01

    Enlightened by distribution of creatures in natural ecology environment, the distribution population-based genetic algorithm (DPGA) is presented in this paper. The searching capability of the algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and the simulation results show that satisfactory performances are obtained.

  16. CFD analysis and experimental investigations towards optimizing the parameters of Ranque-Hilsch vortex tube

    OpenAIRE

    Behera, Upendra; Paul, PJ; Kasthurirengan, S; Karunanithi, R.; Ram, SN; Dinesh, K; Jacob, S

    2005-01-01

    Computational fluid dynamics (CFD) and experimental studies are conducted towards the optimization of the Ranque-Hilsch vortex tubes.Different types of nozzle profiles and number of nozzles are evaluated by CFD analysis. The swirl velocity, axial velocity and radial velocity components as well as the flow patterns including secondary circulation flow have been evaluated. The optimum cold end diameter (d(c)) and the length to diameter (L/D) ratios and optimum parameters for obtaining the maxim...

  17. A Genetic Algorithm Approach to Optimize Parameters in Infrared Guidance System

    Institute of Scientific and Technical Information of China (English)

    周德俊

    2001-01-01

    In the infrared guidance system, the gray level threshold is key for target recognition. After thresholding, a target in the binary image is distinguished from the complex background by three recognition features. Using a genetic algorithm, this paper seeks to find the optimal parameters varied with different sub-images to compute the adaptive segmentation threshold. The experimental results reveal that the GA paradigm is an efficient and effective method of search.

  18. Response of a quarter car model with optimal magnetorheological damper parameters

    Science.gov (United States)

    Prabakar, R. S.; Sujatha, C.; Narayanan, S.

    2013-04-01

    In this paper, the control of the stationary response of a quarter car model to random road excitation with a Magnetorheological (MR) damper as a semi-active suspension device is considered. The MR damper is a hypothetical analytical damper whose parameters are determined optimally using a multi-objective optimization technique Non-dominated Sorting Genetic Algorithm II (NSGA II). The hysteretic behaviour of the MR damper is characterized using Bingham and modified Bouc-Wen models. The multi-objective optimization problem is solved by minimizing the difference between the root mean square (rms) sprung mass acceleration, suspension stroke and the road holding responses of the quarter car model with the MR damper and those of the active suspension system based on linear quadratic regulator (LQR) control with the constraint that the MR damper control force lies between ±5 percent of the LQR control force. It is observed that the MR damper suspension systems with optimal parameters perform an order of magnitude better than the passive suspension and perform as well as active suspensions with limited state feedback and closer to the performance of fully active suspensions.

  19. Performance Optimization of Spin-Torque Microwave Detectors with Material and Operational Parameters

    Directory of Open Access Journals (Sweden)

    X. Li

    2016-01-01

    Full Text Available Sensitivity, bandwidth, and noise equivalent power (NEP are important indicators of the performance of microwave detectors. The previous reports on spin-torque microwave detectors (STMDs have proposed various approaches to increase the sensitivity. However, the effects of these methods on the other two indicators remain unclear. In this work, macrospin simulation is developed to evaluate how the performance can be optimized through changing the material (tilt angle of reference-layer magnetization and operational parameters (the direction of magnetic field and working temperature. The study on the effect of magnetic field reveals that the driving force behind the performance tuning is the effective field and the equilibrium angle between the magnetization of the free layer and that of the reference layer. The material that offers the optimal tilt angle in reference-layer magnetization is determined. The sensitivity can be further increased by changing the direction of the applied magnetic field and the operation temperature. Although the optimized sensitivity is accompanied by a reduction in bandwidth or an increase in NEP, a balance among these performance indicators can be reached through optimal tuning of the corresponding influencing parameters.

  20. Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system

    Institute of Scientific and Technical Information of China (English)

    贺建军; 喻寿益; 钟掘

    2003-01-01

    A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings. The difference from GA is that AGA takes objective function as adaptability function directly, so it cuts down some unnecessary time expense because of float-point calculation of function conversion. The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented. It can be applied to a wide class of problems. The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA. The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.

  1. Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2012-01-01

    Full Text Available In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell by introducing differential evolution strategy into the canonical cellular genetic algorithm for tackling this problem. For DECell, the gained Pareto front could be as close as possible to the exact Pareto front, and also the diversity of nondominated individuals could be better maintained. The experiments on the test functions reveal that DECell is of good performance in solving high-dimension nonlinear multiobjective problems. And the results of optimizing the new brake model indicate that DECell obviously outperforms the compared popular algorithm NSGA-II concerning the number of obtained brake design parameter sets, the speed, and stability for finding them.

  2. Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network

    Institute of Scientific and Technical Information of China (English)

    LIU Gu; WANG Liu-ying; CHEN Gui-ming; HUA Shao-chun

    2011-01-01

    Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel.Experiments were carried out to characterize the hardening qualities.A predicting and optimizing model using genetic algorithm-back propagation neural network(GA-BP) was developed based on the experimental results.The non-linear relationship between properties of hardening layers and process parameters was established.The results show that the GA-BP predicting model is reliable since prediction results are in rather good agreement with measured results.The optimal properties of the hardened layer were deduced from GA.And through multi optimizations,the optimum comprehensive performances of the hardened layer were as follows:plasma arc current is 90 A,hardening speed is 2.2 m/min,plasma gas flow rate is 6.0 L/min and hardening distance is 4.3 mm.It concludes that GA-BP mode developed in this study provides a promising method for plasma hardening parameters prediction and optimization.

  3. Mechanism and optimization of fuel injection parameters on combustion noise of DI diesel engine

    Institute of Scientific and Technical Information of China (English)

    张庆辉; 郝志勇; 郑旭; 杨文英; 毛杰

    2016-01-01

    Combustion noise takes large proportion in diesel engine noise and the studies of its influence factors play an important role in noise reduction. Engine noise and cylinder pressure measurement experiments were carried out. And the improved attenuation curves were obtained, by which the engine noise was predicted. The effect of fuel injection parameters in combustion noise was investigated during the combustion process. At last, the method combining single variable optimization and multivariate combination was introduced to online optimize the combustion noise. The results show that injection parameters can affect the cylinder pressure rise rate and heat release rate, and consequently affect the cylinder pressure load and pressure oscillation to influence the combustion noise. Among these parameters, main injection advance angle has the greatest influence on the combustion noise, while the pilot injection interval time takes the second place, and the pilot injection quantity is of minimal impact. After the optimal design of the combustion noise, the average sound pressure level of the engine is distinctly reduced by 1.0 dB(A) generally. Meanwhile, the power, emission and economy performances are ensured.

  4. Evaluation of Anaerobic Biofilm Reactor Kinetic Parameters Using Ant Colony Optimization.

    Science.gov (United States)

    Satya, Eswari Jujjavarapu; Venkateswarlu, Chimmiri

    2013-09-01

    Fixed bed reactors with naturally attached biofilms are increasingly used for anaerobic treatment of industry wastewaters due their effective treatment performance. The complex nature of biological reactions in biofilm processes often poses difficulty in analyzing them experimentally, and mathematical models could be very useful for their design and analysis. However, effective application of biofilm reactor models to practical problems suffers due to the lack of knowledge of accurate kinetic models and uncertainty in model parameters. In this work, an inverse modeling approach based on ant colony optimization is proposed and applied to estimate the kinetic and film thickness model parameters of wastewater treatment process in an anaerobic fixed bed biofilm reactor. Experimental data of pharmaceutical industry wastewater treatment process are used to determine the model parameters as a consequence of the solution of the rigorous mathematical models of the process. Results were evaluated for different modeling configurations derived from the combination of mathematical models, kinetic expressions, and optimization algorithms. Analysis of results showed that the two-dimensional mathematical model with Haldane kinetics better represents the pharmaceutical wastewater treatment in the biofilm reactor. The mathematical and kinetic modeling of this work forms a useful basis for the design and optimization of industry wastewater treating biofilm reactors.

  5. Biological optimization of simultaneous boost on intra-prostatic lesions (DILs): sensitivity to TCP parameters.

    Science.gov (United States)

    Azzeroni, R; Maggio, A; Fiorino, C; Mangili, P; Cozzarini, C; De Cobelli, F; Di Muzio, N G; Calandrino, R

    2013-11-01

    The aim of this investigation was to explore the potential of biological optimization in the case of simultaneous integrated boost on intra-prostatic dominant lesions (DIL) and evaluating the impact of TCP parameters uncertainty. Different combination of TCP parameters (TD50 and γ50 in the Poisson-like model), were considered for DILs and the prostate outside DILs (CTV) for 7 intermediate/high-risk prostate patients. The aim was to maximize TCP while constraining NTCPs below 5% for all organs at risk. TCP values were highly depending on the parameters used and ranged between 38.4% and 99.9%; the optimized median physical doses were in the range 94-116 Gy and 69-77 Gy for DIL and CTV respectively. TCP values were correlated with the overlap PTV-rectum and the minimum distance between rectum and DIL. In conclusion, biological optimization for selective dose escalation is feasible and suggests prescribed dose around 90-120 Gy to the DILs. The obtained result is critically depending on the assumptions concerning the higher radioresistence in the DILs. In case of very resistant clonogens into the DIL, it may be difficult to maximize TCP to acceptable levels without violating NTCP constraints.

  6. Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Naveed Khan

    2016-10-01

    Full Text Available In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA algorithm by using a genetic algorithm (GA to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%.

  7. Parameters Identification of Fluxgate Magnetic Core Adopting the Biogeography-Based Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wenjuan Jiang

    2016-06-01

    Full Text Available The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA, Particle Swarm Optimization (PSO algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core.

  8. Parameter optimization of the fungicide (Vapam) sorption onto soil modified with clinoptilolite by Taguchi method.

    Science.gov (United States)

    Azizi, Seyed N; Asemi, Neda

    2010-11-01

    This study employs the Taguchi optimization methodology to optimize the effective parameters for the pesticide (Vapam) sorption onto soil modified with natural zeolite (clinoptilolite). The experimental factors and their ranges chosen for determination of the effective parameters were: initial Vapam concentration (0.4-1.6 mg/L), initial pH of the pesticide solution (2-12), the percentage of clinoptilolite in the modified soil (0-6 %), temperature (15-35°C) and shaking time (2-24 h). The orthogonal array (OA) L(16) and the bigger the better response category of the Taguchi method were selected to determine the optimum conditions: initial Vapam concentration (1.2 mg/L), initial pH of the pesticide solution (2), the percentage of clinoptilolite in the modified soil (4 %), temperature (15°C) and shaking time (2 h). The results showed that in comparison with other parameters, the initial Vapam concentration was the most effective one for the sorption of this pesticide onto soil, modified with clinoptilolite. Moreover, after determining the optimum levels of the sorption process parameters, confirmation experiments were performed to prove the effectiveness of the Taguchi's experimental design methodology.

  9. Optimization of burnishing parameters and determination of select surface characteristics in engineering materials

    Indian Academy of Sciences (India)

    P Ravindra Babu; K Ankamma; T Siva Prasad; A V S Raju; N Eswara Prasad

    2012-08-01

    The present study is aimed at filling the gaps in scientific understanding of the burnishing process, and also to aid and arrive at technological solutions for the surface modifications based on burnishing of some of the commonly employed engineering materials. The effects of various burnishing parameters on the surface characteristics, surface microstructure, micro hardness are evaluated, reported and discussed in the case of EN Series steels (EN 8, EN 24 and EN 31), Aluminum alloy (AA6061) and Alpha-beta brass. The burnishing parameters considered for studies principally are burnishing speed, burnishing force, burnishing feed and number of passes. Taguchi technique is employed in the present investigation to identify the most influencing parameters on surface roughness. Effort is also made to identify the optimal burnishing parameters and the factors for scientific basis of such optimization. Finally, a brief attempt is made to construct the Burnishing maps with respect to strength level (in this case, average micro hardness of unburnished material).

  10. Fast and Efficient Black Box Optimization Using the Parameter-less Population Pyramid.

    Science.gov (United States)

    Goldman, B W; Punch, W F

    2015-01-01

    The parameter-less population pyramid (P3) is a recently introduced method for performing evolutionary optimization without requiring any user-specified parameters. P3's primary innovation is to replace the generational model with a pyramid of multiple populations that are iteratively created and expanded. In combination with local search and advanced crossover, P3 scales to problem difficulty, exploiting previously learned information before adding more diversity. Across seven problems, each tested using on average 18 problem sizes, P3 outperformed all five advanced comparison algorithms. This improvement includes requiring fewer evaluations to find the global optimum and better fitness when using the same number of evaluations. Using both algorithm analysis and comparison, we find P3's effectiveness is due to its ability to properly maintain, add, and exploit diversity. Unlike the best comparison algorithms, P3 was able to achieve this quality without any problem-specific tuning. Thus, unlike previous parameter-less methods, P3 does not sacrifice quality for applicability. Therefore we conclude that P3 is an efficient, general, parameter-less approach to black box optimization which is more effective than existing state-of-the-art techniques.

  11. An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

    Science.gov (United States)

    Jia, Liyuan; Gong, Wenyin; Wu, Hongbin

    Differential evolution (DE), a fast and robust evolutionary algorithm for global optimization, has been widely used in many areas. However, the success of DE for solving different problems mainly depends on properly choosing the control parameter values. On the other hand, DE is good at exploring the search space and locating the region of global minimum, but it is slow at exploiting the solution. In order to alleviate these drawbacks of DE, this paper proposes an improved self-adaptive control parameter of DE, referred to as ISADE, for global numerical optimization. The proposed approach employs the individual fitness information to adapt the parameter settings. Hence, it can exploit the information of the individual and generate the promising offspring efficiently. To verify the viability of the proposed ISADE, 10 high-dimensional benchmark problems are chosen from literature. Experiment results indicate that this approach is efficient and effective. It is proved that this approach performs better than the original DE in terms of the convergence rate and the quality of the final solutions. Moreover, ISADE obtains faster convergence than the original self-adaptive control parameter of DE (SADE).

  12. Multiobjective Optimization of Turning Cutting Parameters for J-Steel Material

    Directory of Open Access Journals (Sweden)

    Adel T. Abbas

    2016-01-01

    Full Text Available This paper presents a multiobjective optimization study of cutting parameters in turning operation for a heat-treated alloy steel material (J-Steel with Vickers hardness in the range of HV 365–395 using uncoated, unlubricated Tungsten-Carbide tools. The primary aim is to identify proper settings of the cutting parameters (cutting speed, feed rate, and depth of cut that lead to reasonable compromises between good surface quality and high material removal rate. Thorough exploration of the range of cutting parameters was conducted via a five-level full-factorial experimental matrix of samples and the Pareto trade-off frontier is identified. The trade-off among the objectives was observed to have a “knee” shape, in which certain settings for the cutting parameters can achieve both good surface quality and high material removal rate within certain limits. However, improving one of the objectives beyond these limits can only happen at the expense of a large compromise in the other objective. An alternative approach for identifying the trade-off frontier was also tested via multiobjective implementation of the Efficient Global Optimization (m-EGO algorithm. The m-EGO algorithm was successful in identifying two points within the good range of the trade-off frontier with 36% fewer experimental samples.

  13. Optimization of flapping-wing micro aircrafts based on the kinematic parameters using genetic algorithm method

    Directory of Open Access Journals (Sweden)

    Ebrahim BARATI

    2013-03-01

    Full Text Available In this paper the optimization of kinematics, which has great influence in performance of flapping foil propulsion, is investigated. The purpose of optimization is to design a flapping-wing micro aircraft with appropriate kinematics and aerodynamics features, making the micro aircraft suitable for transportation over large distance with minimum energy consumption. On the point of optimal design, the pitch amplitude, wing reduced frequency and phase difference between plunging and pitching are considered as given parameters and consumed energy, generated thrust by wings and lost power are computed using the 2D quasi-steady aerodynamic model and multi-objective genetic algorithm. Based on the thrust optimization, the increase in pitch amplitude reduces the power consumption. In this case the lost power increases and the maximum thrust coefficient is computed of 2.43. Based on the power optimization, the results show that the increase in pitch amplitude leads to power consumption increase. Additionally, the minimum lost power obtained in this case is 23% at pitch amplitude of 25°, wing reduced frequency of 0.42 and phase angle difference between plunging and pitching of 77°. Furthermore, the wing reduced frequency can be estimated using regression with respect to pitch amplitude, because reduced frequency variations with pitch amplitude is approximately a linear function.

  14. Statistical optimization of process parameters on biohydrogen production from glucose by Clostridium sp. Fanp2.

    Science.gov (United States)

    Pan, C M; Fan, Y T; Xing, Y; Hou, H W; Zhang, M L

    2008-05-01

    Statistically based experimental designs were applied to optimizing process parameters for hydrogen production from glucose by Clostridium sp. Fanp2 which was isolated from effluent sludge of anaerobic hydrogen-producing bioreactor. The important factors influencing hydrogen production, which identified by initial screening method of Plackett-Burman, were glucose, phosphate buffer and vitamin solution. The path of steepest ascent was undertaken to approach the optimal region of the three significant factors. Box-Behnken design and response surface analysis were adopted to further investigate the mutual interaction between the variables and identify optimal values that bring maximum hydrogen production. Experimental results showed that glucose, vitamin solution and phosphate buffer concentration all had an individual significant influence on the specific hydrogen production potential (Ps). Simultaneously, glucose and vitamin solution, glucose and phosphate buffer were interdependent. The optimal conditions for the maximal Ps were: glucose 23.75 g/l, phosphate buffer 0.159 M and vitamin solution 13.3 ml/l. Using this statistical optimization method, the hydrogen production from glucose was increased from 2248.5 to 4165.9 ml H2/l.

  15. Parameter optimization of pulse compression in ultrasound imaging systems with coded excitation.

    Science.gov (United States)

    Behar, Vera; Adam, Dan

    2004-08-01

    A linear array imaging system with coded excitation is considered, where the proposed excitation/compression scheme maximizes the signal-to-noise ratio (SNR) and minimizes sidelobes at the output of the compression filter. A pulse with linear frequency modulation (LFM) is used for coded excitation. The excitation/compression scheme is based on the fast digital mismatched filtering. The parameter optimization of the excitation/compression scheme includes (i) choice of an optimal filtering function for the mismatched filtering; (ii) choice of an optimal window function for tapering of the chirp amplitude; (iii) optimization of a chirp-to-transducer bandwidth ratio; (iv) choice of an appropriate n-bit quantizer. The simulation results show that the excitation/compression scheme can be implemented as a Dolph-Chebyshev filter including amplitude tapering of the chirp with a Lanczos window. An example of such an optimized system is given where the chirp bandwidth is chosen to be 2.5 times the transducer bandwidth and equals 6 MHz: The sidelobes are suppressed to -80 dB, for a central frequency of 4 MHz, and to -94 dB, for a central frequency of 8 MHz. The corresponding improvement of the SNR is 18 and 21 dB, respectively, when compared to a conventional short pulse imaging system. Simulation of B-mode images demonstrates the advantage of coded excitation systems of detecting regions with low contrast.

  16. Optimization of model parameters and experimental designs with the Optimal Experimental Design Toolbox (v1.0) exemplified by sedimentation in salt marshes

    Science.gov (United States)

    Reimer, J.; Schuerch, M.; Slawig, T.

    2015-03-01

    The geosciences are a highly suitable field of application for optimizing model parameters and experimental designs especially because many data are collected. In this paper, the weighted least squares estimator for optimizing model parameters is presented together with its asymptotic properties. A popular approach to optimize experimental designs called local optimal experimental designs is described together with a lesser known approach which takes into account the potential nonlinearity of the model parameters. These two approaches have been combined with two methods to solve their underlying discrete optimization problem. All presented methods were implemented in an open-source MATLAB toolbox called the Optimal Experimental Design Toolbox whose structure and application is described. In numerical experiments, the model parameters and experimental design were optimized using this toolbox. Two existing models for sediment concentration in seawater and sediment accretion on salt marshes of different complexity served as an application example. The advantages and disadvantages of these approaches were compared based on these models. Thanks to optimized experimental designs, the parameters of these models could be determined very accurately with significantly fewer measurements compared to unoptimized experimental designs. The chosen optimization approach played a minor role for the accuracy; therefore, the approach with the least computational effort is recommended.

  17. Castor oil transesterification reaction: A kinetic study and optimization of parameters

    Energy Technology Data Exchange (ETDEWEB)

    Ramezani, K. [Fuel Cell Research Laboratory, Green Research Center, Iran University of Science and Technology, Tehran (Iran); Rowshanzamir, S. [Fuel Cell Research Laboratory, Green Research Center, Iran University of Science and Technology, Tehran (Iran); School of Chemical Engineering, Iran University of Science and Technology, Tehran (Iran); Eikani, M.H. [Department of Chemical Industries, Iranian Research Organization for Science and Technology (IROST), Tehran (Iran)

    2010-10-15

    In this paper, parameters affecting castor oil transesterification reaction were investigated. Applying four basic catalysts including NaOCH{sub 3}, NaOH, KOCH{sub 3} and KOH the best one with maximum biodiesel yield was identified. Using Taguchi method consisting four parameters and three levels, the best experimental conditions were determined. Reaction temperature (25, 65 and 80 C), mixing intensity (250, 400 and 600 rpm), alcohol/oil ratio (4:1, 6:1 and 8:1) and catalyst concentration (0.25, 0.35 and 0.5%) were selected as experimental parameters. It was concluded that reaction temperature and mixing intensity can be optimized. Using the optimum results, we proposed a kinetic model which resulted in establishing an equation for the beginning rate of transesterification reaction. Furthermore, applying ASTM D 976 correlation, minimum cetane number of produced biodiesel was determined as 37.1. (author)

  18. Diagnosis parameters of mold filling pattern for optimization of a casting system

    Directory of Open Access Journals (Sweden)

    Jun-Ho Hong

    2012-11-01

    Full Text Available For optimal design of a gating system, the setting of diagnosis parameters is very important. In this study, the permanent mold casting process was selected because most of the other casting processes have more complicated factors that influence the mold filling pattern compared to the permanent mold casting process, such as the surface roughness of mold, gas generation from the mold wash and binder of sand mold, and the gas permeability through a sand mold, etc. Two diagnosis parameters (flow rate difference and arrival time difference of molten metal flow pattern in the numerical simulation are suggested for design of an optimum casting system with a permanent mold. The results show that the arrival time difference can be used as one important diagnosis parameter of the complexity of the runner system and its usefulness has been verified via making aluminum parts using permanent mold casting (Fig. 9.

  19. Parameter Estimation of a Ground Moving Target Using Image Sharpness Optimization.

    Science.gov (United States)

    Yu, Jing; Li, Yaan

    2016-06-30

    Motion parameter estimation of a ground moving target is an important issue in synthetic aperture radar ground moving target indication (SAR-GMTI) which has significant applications for civilian and military. The SAR image of a moving target may be displaced and defocused due to the radial and along-track velocity components, respectively. The sharpness cost function presents a measure of the degree of focus of the image. In this work, a new ground moving target parameter estimation algorithm based on the sharpness optimization criterion is proposed. The relationships between the quadratic phase errors and the target's velocity components are derived. Using two-dimensional searching of the sharpness cost function, we can obtain the velocity components of the target and the focused target image simultaneously. The proposed moving target parameter estimation method and image sharpness metrics are analyzed in detail. Finally, numerical results illustrate the effective and superior velocity estimation performance of the proposed method when compared to existing algorithms.

  20. The Use of Response Surface Methodology to Optimize Parameter Adjustments in CNC Machine Tools

    Directory of Open Access Journals (Sweden)

    Shao-Hsien Chen

    2014-01-01

    Full Text Available This paper mainly covers a research intended to improve the circular accuracy of CNC machine tools and the adjustment and analysis of the main controller parameters applied to improve accuracy. In this study, controller analysis software was used to detect the adjustment status of the servo parameters of the feed axis. According to the FANUC parameter manual, the parameter address, frequency, response measurements, and the one-fourth corner acceleration and deceleration measurements of the machine tools were adjusted. The experimental design (DOE was adopted in this study for taking circular measurements and engaging in the planning and selection of important parameter data. The Minitab R15 software was adopted to predict the experimental data analysis, while the seminormal probability map, Plato, and analysis of variance (ANOVA were adopted to determine the impacts of the significant parameter factors and the interactions among them. Additionally, based on the response surface map and contour plot, the optimal values were obtained. In addition, comparison and verification were conducted through the Taguchi method, regression analysis to improved machining accuracy and efficiency. The unadjusted error was 7.8 μm; through the regression analysis method, the error was 5.8 μm and through the Taguchi analysis method, the error was 6.4 μm.

  1. Optimization Of Blasting Design Parameters On Open Pit Bench A Case Study Of Nchanga Open Pits

    Directory of Open Access Journals (Sweden)

    Victor Mwango Bowa

    2015-08-01

    Full Text Available Abstract In hard rock mining blasting is the most productive excavation technique applied to fragment insitu rock to the required size for efficient loading and crushing. In order to blast the insitu rock to the desired fragment size blast design parameter such as bench height hole diameter spacing burden hole length bottom charge specific charge and rock factor are considered. The research was carried out as a practical method on Nchanga Open Pits NOP ore Bench to optimize the blasting design parameters that can yield the required fragmentation size thereby reducing the shovel loading times and maximizing efficiency of the subsequent mining unit operations such as hauling and crushing. Fragmentation characteristics such as the mean fragment size were measured by means of a digital measuring tape and predicated using the Kuznetsov equation and rock factor value of ore bench was calculated using Lilly 1986 equations by means of rock characteristics. Traditional blasting design parameters were acquired for NOP and modified using Langerfors and Sharma P.A approaches. Several blast operations were conducted using both traditional and modified blasting design parameters on the same ore bench with the same geological conditions. Loading times of the shovel and fragment sizes were obtained after the blasts from ore bench where both the traditional and modified blasting design parameters were applied. Results show that mean fragment size and loading times were reduced from 51cm and 12minutes to 22cm and 3minutes where traditional and modified blasting design parameters were applied respectively.

  2. Experiments for practical education in process parameter optimization for selective laser sintering to increase workpiece quality

    Science.gov (United States)

    Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas

    2016-04-01

    Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.

  3. Optimization of the Automated Spray Layer-by-Layer Technique for Thin Film Deposition

    Science.gov (United States)

    2010-06-01

    she was extremely busy running our household of five, homeschooling our son and volunteering. She amazes me every single day. I know that her... benefit to using solutions in excess of 80 mmol of PAA. However, at concentrations less than 80 mmol for the other standard parameter values it is...maximum film thickness. These results demonstrate that faster film deposition time is not the only benefit of Spray-LbL’s shorter polyelectrolyte to

  4. Parameter uncertainty-based pattern identification and optimization for robust decision making on watershed load reduction

    Science.gov (United States)

    Jiang, Qingsong; Su, Han; Liu, Yong; Zou, Rui; Ye, Rui; Guo, Huaicheng

    2017-04-01

    Nutrients loading reduction in watershed is essential for lake restoration from eutrophication. The efficient and optimal decision-making on loading reduction is generally based on water quality modeling and the quantitative identification of nutrient sources at the watershed scale. The modeling process is influenced inevitably by inherent uncertainties, especially by uncertain parameters due to equifinality. Therefore, the emerging question is: if there is parameter uncertainty, how to ensure the robustness of the optimal decisions? Based on simulation-optimization models, an integrated approach of pattern identification and analysis of robustness was proposed in this study that focuses on the impact of parameter uncertainty in water quality modeling. Here the pattern represents the discernable regularity of solutions for load reduction under multiple parameter sets. Pattern identification is achieved by using a hybrid clustering analysis (i.e., Ward-Hierarchical and K-means), which was flexible and efficient in analyzing Lake Bali near the Yangtze River in China. The results demonstrated that urban domestic nutrient load is the most potential source that should be reduced, and there are two patterns for Total Nitrogen (TN) reduction and three patterns for Total Phosphorus (TP) reduction. The patterns indicated different total reduction of nutrient loads, which reflect diverse decision preferences. The robust solution was identified by the highest accomplishment with the water quality at monitoring stations that were improved uniformly with this solution. We conducted a process analysis of robust decision-making that was based on pattern identification and uncertainty, which provides effective support for decision-making with preference under uncertainty.

  5. Optimization of Operating Parameters for Minimum Mechanical Specific Energy in Drilling

    Energy Technology Data Exchange (ETDEWEB)

    Hamrick, Todd [West Virginia Univ., Morgantown, WV (United States)

    2011-01-01

    Efficiency in drilling is measured by Mechanical Specific Energy (MSE). MSE is the measure of the amount of energy input required to remove a unit volume of rock, expressed in units of energy input divided by volume removed. It can be expressed mathematically in terms of controllable parameters; Weight on Bit, Torque, Rate of Penetration, and RPM. It is well documented that minimizing MSE by optimizing controllable factors results in maximum Rate of Penetration. Current methods for computing MSE make it possible to minimize MSE in the field only through a trial-and-error process. This work makes it possible to compute the optimum drilling parameters that result in minimum MSE. The parameters that have been traditionally used to compute MSE are interdependent. Mathematical relationships between the parameters were established, and the conventional MSE equation was rewritten in terms of a single parameter, Weight on Bit, establishing a form that can be minimized mathematically. Once the optimum Weight on Bit was determined, the interdependent relationship that Weight on Bit has with Torque and Penetration per Revolution was used to determine optimum values for those parameters for a given drilling situation. The improved method was validated through laboratory experimentation and analysis of published data. Two rock types were subjected to four treatments each, and drilled in a controlled laboratory environment. The method was applied in each case, and the optimum parameters for minimum MSE were computed. The method demonstrated an accurate means to determine optimum drilling parameters of Weight on Bit, Torque, and Penetration per Revolution. A unique application of micro-cracking is also presented, which demonstrates that rock failure ahead of the bit is related to axial force more than to rotation speed.

  6. Optimization of iterative reconstruction parameters with attenuation correction, scatter correction and resolution recovery in myocardial perfusion SPECT/CT

    OpenAIRE

    Okuda, Koichi; Nakajima, Kenichi; Yamada, Masato; Wakabayashi, Hiroshi; Ichikawa, Hajime; Arai, Hiroyuki; Matsuo, Shinro; Taki, Junichi; Hashimoto, Mitsumasa; Kinuya, Seigo

    2014-01-01

    Objective: The aim of this study was to characterize the optimal reconstruction parameters for ordered-subset expectation maximization (OSEM) with attenuation correction, scatter correction, and depth-dependent resolution recovery (OSEMACSCRR). We assessed the optimal parameters for OSEMACSCRR in an anthropomorphic torso phantom study, and evaluated the validity of the reconstruction parameters in the groups of normal volunteers and patients with abnormal perfusion. Methods: Images of the ant...

  7. Optimization of operating parameters of internally cooled superconducting cables; Optimale Betriebsparameter fuer intern gekuehlte Supraleiterkabel

    Energy Technology Data Exchange (ETDEWEB)

    Katheder, H. [Max-Planck-Inst. fuer Plasmaphysik, Garching (Germany). NET Team

    1995-12-31

    Large superconducting coils such as are used for fusion experiments (Tokamak or Stellarator confiurations) are best equipped with internally cooled superconducting cables. These cables, which are cooled with helium at 4 Koptimal values for the operating parameters inlet temperature, pressure drop, mass flow, and inlet pressure. These operating parameters are optimally adjusted when the cable can be cooled with minimum mass flow. The present paper deals with the thermodynamic behaviour of flowing helium in conduits and with the thermal load which may occur in a cable. It describes a mehtod of optimising the operating paramters and gives a numerical calculation using typical cable data. (orig.) [Deutsch] Bei grossen supraleitenden Spulen z.B. fuer Fusionsexperimente (Tokamak- oder Stellarator Anordnungen) werden mit Vorteil intern gekuehlte Supraleiterkabel eingesetzt. Seit einigen Jahren werden solche Kabel, gekuehlt mit Helium bei 4Optimal sind diese Betriebsparameter dann, wenn das Kabel mit minimalem Massenstrom gekuehlt werden kann. Der Bericht befasst sich mit dem thermodynamischen Verhalten von stroemenden Helium in Kanaelen und der moeglichen Waermelast in einem Kabel. Es wird eine Mehtode gezeigt die optimalen Betriebsparameter zu finden und mit tyischen Kabeldaten eine numerische Berechnung durchgefuehrt. (orig.)

  8. Feedback optimal control of distributed parameter systems by using finite-dimensional approximation schemes.

    Science.gov (United States)

    Alessandri, Angelo; Gaggero, Mauro; Zoppoli, Riccardo

    2012-06-01

    Optimal control for systems described by partial differential equations is investigated by proposing a methodology to design feedback controllers in approximate form. The approximation stems from constraining the control law to take on a fixed structure, where a finite number of free parameters can be suitably chosen. The original infinite-dimensional optimization problem is then reduced to a mathematical programming one of finite dimension that consists in optimizing the parameters. The solution of such a problem is performed by using sequential quadratic programming. Linear combinations of fixed and parameterized basis functions are used as the structure for the control law, thus giving rise to two different finite-dimensional approximation schemes. The proposed paradigm is general since it allows one to treat problems with distributed and boundary controls within the same approximation framework. It can be applied to systems described by either linear or nonlinear elliptic, parabolic, and hyperbolic equations in arbitrary multidimensional domains. Simulation results obtained in two case studies show the potentials of the proposed approach as compared with dynamic programming.

  9. Optimization of process parameters for the rapid biosynthesis of hematite nanoparticles.

    Science.gov (United States)

    Rajendran, Kumar; Sen, Shampa

    2016-06-01

    Hematite (α-Fe2O3) nanoparticles are widely used in various applications including gas sensors, pigments owing to its low cost, environmental friendliness, non-toxicity and high resistance to corrosion. These nanoparticles were generally synthesized by different chemical methods. In the present study, nanoparticles were synthesized rapidly without heat treatment by biosynthesis approach using culture supernatant of Bacillus cereus SVK1. The physiochemical parameters for rapid synthesis were optimized by using UV-visible spectroscopy. The time taken for hematite nanoparticle synthesis was found to increase with the increasing concentration of the precursor. This might be due to the inadequate proportion of quantity of biomolecules present in the culture supernatant to the precursor which led to delayed bioreduction. Greater quantities of culture supernatant with respect to precursor lead to rapid synthesis of hematite nanoparticles. The nucleation of the hematite nucleus happens more easily when the solution pH was less than 10. The optimum parameters identified for the rapid biosynthesis of hematite nanoparticles were pH9, 37°C (temperature) and 1mM ferric chloride as precursor. The particles were well crystallized hexagonal structured hematite nanoparticles and are predominantly (110)-oriented. The synthesized nanoparticles were found to contain predominantly iron (73.47%) and oxygen (22.58%) as evidenced by Energy Dispersive X-ray analysis. Hematite nanoparticles of 15-40nm diameters were biosynthesized in 48h under optimized conditions, compared to 21days before optimization.

  10. Multicriteria Optimization of Gasification Operational Parameters Using a Pareto Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Miguel Caldas

    2005-04-01

    Full Text Available Gasification is a well-known technology that allows for a combustible gas to be obtained from a carbonaceous fuel by a partial oxidation process (POX. The resulting gas (synthesis gas or syngas can be used either as a fuel or as a feedstock for chemical production. Recently, gasification has also received a great deal of attention concerning power production possibilities through IGCC process (Integrated Gasification Combined Cycle, which is currently the most environmentally friendly and efficient method for the production of electricity. Gasification allows for low grade fuels, or dirty fuels, to be used in an environmental acceptable way. Amongst these fuels are wastes from the petrochemical and other industries, which vary in composition from shipment to shipment, and from lot to lot. If operating conditions are kept constant this could result in lose of efficiency. This paper presents an application of Genetic Algorithms to optimize the operating parameters of a gasifier processing a given fuel, so that the system achieves maximum efficiency for each particular fuel composition. A Pareto multiobjective optimization method, combined with a Genetic Algorithm, is applied to the simultaneous maximization of two different objective functions: Cold Gas Efficiency and Hydrogen Contents of the syngas. Results show that the optimization method developed is fast and simple enough to be used for on-line adjustment of the gasification operating parameters for each fuel composition and aim of gasification, thus improving overall performance of the industrial process.

  11. Multi-response optimization of WEDM Process Parameters using the AHP and TOPSIS method

    Directory of Open Access Journals (Sweden)

    B.B. Nayak

    2013-10-01

    Full Text Available The work presents a multi response optimization approach to determine the optimal process parameters in wire electrical discharge machining process. Experiments have been conducted using six process parameters such as discharge current, pulse duration, pulse frequency, wire speed, wire tension and dielectric flow rate each at three levels for obtaining the responses like material removal rate, roughness value of the worked surface (a measure of surface finish, and kerf. Taguchi L27 orthogonal array is used to gather information regarding the process with less number of experimental runs. Traditional Taguchi approach is insufficient to solve a multi response optimization problem. In order to overcome this limitation, a multi criteria decision making method, techniques for order preference by similarity to idealsolution (TOPSIS is applied in the present study. In order to consider experimental uncertainty, the responses are expressed in linguistic terms rather than crisp values. The weight for each criterion (response is obtained by analytical hierarchy process instead of using intuition and judgement of the decision maker.

  12. Distributed bees algorithm parameters optimization for a cost efficient target allocation in swarms of robots.

    Science.gov (United States)

    Jevtić, Aleksandar; Gutiérrez, Alvaro

    2011-01-01

    Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.

  13. Process Parameters Optimization of 14nm MOSFET Using 2-D Analytical Modelling

    Directory of Open Access Journals (Sweden)

    Noor Faizah Z.A.

    2016-01-01

    Full Text Available This paper presents the modeling and optimization of 14nm gate length CMOS transistor which is down-scaled from previous 32nm gate length. High-k metal gate material was used in this research utilizing Hafnium Dioxide (HfO2 as dielectric and Tungsten Silicide (WSi2 and Titanium Silicide (TiSi2 as a metal gate for NMOS and PMOS respectively. The devices are fabricated virtually using ATHENA module and characterized its performance evaluation via ATLAS module; both in Virtual Wafer Fabrication (VWF of Silvaco TCAD Tools. The devices were then optimized through a process parameters variability using L9 Taguchi Method. There were four process parameter with two noise factor of different values were used to analyze the factor effect. The results show that the optimal value for both transistors are well within ITRS 2013 prediction where VTH and IOFF are 0.236737V and 6.995705nA/um for NMOS device and 0.248635 V and 5.26nA/um for PMOS device respectively.

  14. Multiobjective adaptive surrogate modeling-based optimization for parameter estimation of large, complex geophysical models

    Science.gov (United States)

    Gong, Wei; Duan, Qingyun; Li, Jianduo; Wang, Chen; Di, Zhenhua; Ye, Aizhong; Miao, Chiyuan; Dai, Yongjiu

    2016-03-01

    Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model - the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO.

  15. Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots

    Directory of Open Access Journals (Sweden)

    Álvaro Gutiérrez

    2011-11-01

    Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.

  16. Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

    Directory of Open Access Journals (Sweden)

    Jiang Tieying

    2015-06-01

    Full Text Available This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle (UAV through modified particle swam optimization (PSO. The procedure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems (MEMS inertial measuring element and a global positioning system (GPS receiver to provide test information. A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration (SRPSO. Once modified PSO is applied to the mathematical model, the simulation results show that the mathematical model is correct, and aerodynamic parameters and coefficients of the propeller can be identified accurately. Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV. Some parameter identification results are affected slightly by noise, but the identification results are very good overall. Eventually, experimental validation is employed to test the proposed method, which demonstrates the usefulness of this method.

  17. Inverse parameter determination in the development of an optimized lithium iron phosphate - Graphite battery discharge model

    Science.gov (United States)

    Maheshwari, Arpit; Dumitrescu, Mihaela Aneta; Destro, Matteo; Santarelli, Massimo

    2016-03-01

    Battery models are riddled with incongruous values of parameters considered for validation. In this work, thermally coupled electrochemical model of the pouch is developed and discharge tests on a LiFePO4 pouch cell at different discharge rates are used to optimize the LiFePO4 battery model by determining parameters for which there is no consensus in literature. A discussion on parameter determination, selection and comparison with literature values has been made. The electrochemical model is a P2D model, while the thermal model considers heat transfer in 3D. It is seen that even with no phase change considered for LiFePO4 electrode, the model is able to simulate the discharge curves over a wide range of discharge rates with a single set of parameters provided a dependency of the radius of the LiFePO4 electrode on discharge rate. The approach of using a current dependent radius is shown to be equivalent to using a current dependent diffusion coefficient. Both these modelling approaches are a representation of the particle size distribution in the electrode. Additionally, the model has been thermally validated, which increases the confidence level in the selection of values of parameters.

  18. Optimization of CVD parameters for long ZnO NWs grown on ITO/glass substrate

    Indian Academy of Sciences (India)

    ABDULQADER D FAISAL

    2016-12-01

    The optimization of chemical vapour deposition (CVD) parameters for long and vertically aligned (VA) ZnO nanowires (NWs) were investigated. Typical ZnO NWs as a single crystal grown on indium tin oxide (ITO)-coated glass substrate were successfully synthesized. First, the conducted side of ITO–glass substrate was coated with zinc acetate dihydrate to form seed layer of ZnO nanocrystals. Double zone tube furnace connected to vacuum pump was used for ZnO growth process. Zn metal powder was positioned at the first zone at temperature 900$^{\\circ}$C. The ITO–glass substrate with pre-coated seed layer was then located in the second zone of tube furnace at growth temperature of 550$^{\\circ}$C. The growth of ZnO NWs was controlled under constant concentration of seed layer, while other parameters such as argon and oxygen flow rates, substrate position, time and oxygen flow rate were varied.The VA ZnO NWs were finally characterized by scanning electron microscopy, X-ray diffractometer and high-resolution transmission electron microscope equipped with energy-dispersive X-ray spectroscopy. The results showthat long and VA ZnO NWs were single crystalline with hexagonal wurtzite structure. The ultimate length and average diameter of ZnO NWs were 10 $\\mu$m and 50–100 nm, respectively. These were achieved under optimized CVD growth parameters. The mechanism of vertical growth model of ZnO NWs is also discussed.

  19. Optimization of control parameters for SR in EDM injection flushing type on stainless steel 304 workpiece

    Science.gov (United States)

    Reza, M. S.; Yusoff, A. R.; Shaharun, M. A.

    2012-09-01

    The operating control parameters of injection flushing type of electrical discharge machining process on stainless steel 304 workpiece with copper tools are being optimized according to its individual machining characteristic i.e. surface roughness (SR). Higher SR during EDM machining process results for poor surface integrity of the workpiece. Hence, the quality characteristic for SR is set to lower-the-better to achieve the optimum surface integrity. Taguchi method has been used for the construction, layout and analysis of the experiment for each of the machining characteristic for the SR. The use of Taguchi method in the experiment saves a lot of time and cost of machining the experiment samples. Therefore, an L18 Orthogonal array which was the fundamental component in the statistical design of experiments has been used to plan the experiments and Analysis of Variance (ANOVA) is used to determine the optimum machining parameters for this machining characteristic. The control parameters selected for this optimization experiments are polarity, pulse on duration, discharge current, discharge voltage, machining depth, machining diameter and dielectric liquid pressure. The result had shown that the lower the machining diameter, the lower will be the SR.

  20. Laser beam micro-milling of nickel alloy: dimensional variations and RSM optimization of laser parameters

    Science.gov (United States)

    Ahmed, Naveed; Alahmari, Abdulrahman M.; Darwish, Saied; Naveed, Madiha

    2016-12-01

    Micro-channels are considered as the integral part of several engineering devices such as micro-channel heat exchangers, micro-coolers, micro-pulsating heat pipes and micro-channels used in gas turbine blades for aerospace applications. In such applications, a fluid flow is required to pass through certain micro-passages such as micro-grooves and micro-channels. The fluid flow characteristics (flow rate, turbulence, pressure drop and fluid dynamics) are mainly established based on the size and accuracy of micro-passages. Variations (oversizing and undersizing) in micro-passage's geometry directly affect the fluid flow characteristics. In this study, the micro-channels of several sizes are fabricated in well-known aerospace nickel alloy (Inconel 718) through laser beam micro-milling. The variations in geometrical characteristics of different-sized micro-channels are studied under the influences of different parameters of Nd:YAG laser. In order to have a minimum variation in the machined geometries of each size of micro-channel, the multi-objective optimization of laser parameters has been carried out utilizing the response surface methodology approach. The objective was set to achieve the targeted top widths and depths of micro-channels with minimum degree of taperness associated with the micro-channel's sidewalls. The optimized sets of laser parameters proposed for each size of micro-channel can be used to fabricate the micro-channels in Inconel 718 with minimum amount of geometrical variations.