Iron-chelating agents never suppress Fenton reaction but participate in quenching spin-trapped radicals.

PubMed

Li, Linxiang; Abe, Yoshihiro; Kanagawa, Kiyotada; Shoji, Tomoko; Mashino, Tadahiko; Mochizuki, Masataka; Tanaka, Miho; Miyata, Naoki

2007-09-19

Hydroxyl radical formation by Fenton reaction in the presence of an iron-chelating agent such as EDTA was traced by two different assay methods; an electron spin resonance (ESR) spin-trapping method with 5,5-dimethyl-1-pyrroline N-oxide (DMPO), and high Performance liquid chromatography (HPLC)-fluorescence detection with terephthalic acid (TPA), a fluorescent probe for hydroxyl radicals. From the ESR spin-trapping measurement, it was observed that EDTA seemed to suppress hydroxyl radical formation with the increase of its concentration. On the other hand, hydroxyl radical formation by Fenton reaction was not affected by EDTA monitored by HPLC assay. Similar inconsistent effects of other iron-chelating agents such as nitrylotriacetic acid (NTA), diethylenetriamine penta acetic acid (DTPA), oxalate and citrate were also observed. On the addition of EDTA solution to the reaction mixture 10 min after the Fenton reaction started, when hydroxyl radical formation should have almost ceased but the ESR signal of DMPO-OH radicals could be detected, it was observed that the DMPO-OH* signal disappeared rapidly. With the simultaneous addition of Fe(II) solution and EDTA after the Fenton reaction ceased, the DMPO-OH* signal disappeared more rapidly. The results indicated that these chelating agents should enhance the quenching of [DMPO-OH]* radicals by Fe(II), but they did not suppress Fenton reaction by forming chelates with iron ions.

Measurement of S-nitrosylated Proteins in Tissues of Rats Fed Diets with Differing Levels of Nitrite

DTIC Science & Technology

2011-12-01

project could be substantial. Excluding immediate deaths, 50 % of all battlefield deaths (KIAs) occur within 5 minutes of injury, prior to the...exists for quantitation of DMPO thioethers. Furthermore, DMPO exhibits low cytotoxicity. In spin trapping experiments, cells tolerate this nitrone in

Imaging free radicals in organelles, cells, tissue, and in vivo with immuno-spin trapping.

PubMed

Mason, Ronald Paul

2016-08-01

The accurate and sensitive detection of biological free radicals in a reliable manner is required to define the mechanistic roles of such species in biochemistry, medicine and toxicology. Most of the techniques currently available are either not appropriate to detect free radicals in cells and tissues due to sensitivity limitations (electron spin resonance, ESR) or subject to artifacts that make the validity of the results questionable (fluorescent probe-based analysis). The development of the immuno-spin trapping technique overcomes all these difficulties. This technique is based on the reaction of amino acid- and DNA base-derived radicals with the spin trap 5, 5-dimethyl-1-pyrroline N-oxide (DMPO) to form protein- and DNA-DMPO nitroxide radical adducts, respectively. These adducts have limited stability and decay to produce the very stable macromolecule-DMPO-nitrone product. This stable product can be detected by mass spectrometry, NMR or immunochemistry by the use of anti-DMPO nitrone antibodies. The formation of macromolecule-DMPO-nitrone adducts is based on the selective reaction of free radical addition to the spin trap and is thus not subject to artifacts frequently encountered with other methods for free radical detection. The selectivity of spin trapping for free radicals in biological systems has been proven by ESR. Immuno-spin trapping is proving to be a potent, sensitive (a million times higher sensitivity than ESR), and easy (not quantum mechanical) method to detect low levels of macromolecule-derived radicals produced in vitro and in vivo. Anti-DMPO antibodies have been used to determine the distribution of free radicals in cells and tissues and even in living animals. In summary, the invention of the immuno-spin trapping technique has had a major impact on the ability to accurately and sensitively detect biological free radicals and, subsequently, on our understanding of the role of free radicals in biochemistry, medicine and toxicology. Published by Elsevier B.V.

Adsorption of proteins at the solution/air interface influenced by added nonionic surfactants at very low concentrations for both components. 3. Dilational surface rheology.

PubMed

Fainerman, V B; Aksenenko, E V; Lylyk, S V; Lotfi, M; Miller, R

2015-03-05

The influence of the addition of the nonionic surfactants C12DMPO, C14DMPO, C10OH, and C10EO5 at concentrations between 10(-5) and 10(-1) mmol/L to solutions of β-casein (BCS) and β-lactoglobulin (BLG) at a fixed concentration of 10(-5) mmol/L on the dilational surface rheology is studied. A maximum in the viscoelasticity modulus |E| occurs at very low surfactant concentrations (10(-4) to 10(-3) mmol/L) for mixtures of BCS with C12DMPO and C14DMPO and for mixtures of BLG with C10EO5, while for mixture of BCS with C10EO5 the value of |E| only slightly increased. The |E| values calculated with a recently developed model, which assumes changes in the interfacial molar area of the protein molecules due to the interaction with the surfactants, are in satisfactory agreement with experimental data. A linear dependence exists between the ratio of the maximum modulus for the mixture to the modulus of the single protein solution and the coefficient reflecting the influence of the surfactants on the adsorption activity of the protein.

In Vivo and In Situ Detection of Macromolecular Free Radicals Using Immuno-Spin Trapping and Molecular Magnetic Resonance Imaging.

PubMed

Towner, Rheal A; Smith, Nataliya

2018-05-20

In vivo free radical imaging in preclinical models of disease has become a reality. Free radicals have traditionally been characterized by electron spin resonance (ESR) or electron paramagnetic resonance (EPR) spectroscopy coupled with spin trapping. The disadvantage of the ESR/EPR approach is that spin adducts are short-lived due to biological reductive and/or oxidative processes. Immuno-spin trapping (IST) involves the use of an antibody that recognizes macromolecular 5,5-dimethyl-pyrroline-N-oxide (DMPO) spin adducts (anti-DMPO antibody), regardless of the oxidative/reductive state of trapped radical adducts. Recent Advances: The IST approach has been extended to an in vivo application that combines IST with molecular magnetic resonance imaging (mMRI). This combined IST-mMRI approach involves the use of a spin-trapping agent, DMPO, to trap free radicals in disease models, and administration of an mMRI probe, an anti-DMPO probe, which combines an antibody against DMPO-radical adducts and an MRI contrast agent, resulting in targeted free radical adduct detection. The combined IST-mMRI approach has been used in several rodent disease models, including diabetes, amyotrophic lateral sclerosis (ALS), gliomas, and septic encephalopathy. The advantage of this approach is that heterogeneous levels of trapped free radicals can be detected directly in vivo and in situ to pin point where free radicals are formed in different tissues. The approach can also be used to assess therapeutic agents that are either free radical scavengers or generate free radicals. Smaller probe constructs and radical identification approaches are being considered. The focus of this review is on the different applications that have been studied, advantages and limitations, and future directions. Antioxid. Redox Signal. 28, 1404-1415.

Quantitative electron spin resonance (ESR) analysis of antioxidative properties using the acetaldehyde/xanthine oxidase system

NASA Astrophysics Data System (ADS)

Souchard, J.-P.; Nepveu, F.

1998-05-01

We present a method for the quantitative ESR analysis of the antioxidant properties of drugs using the acetaldhehyde/xanthine oxidase (AC/XOD) superoxide generating system and 5,5-dimethyl-l-pyrroline-N-oxide (DMPO) as spin trap. In stoichiometric conditions (AC/XOD, 60 mM/0.018 U), the resulting paramagnetic DMPO adduct disappeared with superoxide dismutase and remained when catalase or DMSO were used. That adduct was dependent only on superoxide and resulted from the trapping of a carboxyl radical by DMPO (aN = 15.2 G, aH = 18.9 G). Similar results were obtained using 4-pyridyl-l-oxide-N-t-butyl nitrone (POBN) as spin trap. The ESR signal of the DMPO-CO2- adduct was very stable and allowed quantitative analysis of the antioxidative activity of redox molecules from an IC{50} value representing the concentration causing 50% inhibition of its intensity. Among the tested compounds, manganese(II), complexes were the most effective, 25 times as active as ascorbic acid or (+)catechin and 500-fold more antioxidative than Trolox^R. Nous présentons une méthode d'analyse quantitative de l'activité antioxydante de composés d'intérêt pharmaceutique basée sur le système acétaldéhyde/xanthine oxydase (AC/XOD), l'utilisation de la RPE et du piégeage de spin avec le 5,5-diméthyl-l-pyrroline-N-oxyde (DMPO). Dans les conditions stoechiométriques {AC/XOD, 60 mM/0,018 U/ml}, l'adduit radicalaire résultant de ce système disparaît en présence de superoxyde dismutase et persiste en présence de catalase ou de DMSO. Cet adduit ne dépend que de la présence de l'anion superoxyde et provient du piégeage d'un radical carboxyle CO2- sur le DMPO (aN = 15.2 G, aH = 18.9 G). Des résultats similaires ont été obtenus avec le piégeur de spin 4-pyridyl-l-oxyde-N-t-butyl nitrone (POBN). Le signal RPE de l'adduit DMPO-CO2- est très stable et permet la quantification de l'activité antioxydante de pharmacophores redox par la détermination de la CI{50}, concentration qui diminue de 50 % son intensité. Parmi les composés testés, les complexes du manganèse sont les plus antioxydants, 25 fois plus actifs que la vitamine C ou la catéchine(+), 500 fois plus antioxydants que le Trolox^R.

Electron spin resonance detection of oxygen radicals released by UVA-irradiated human fibroblasts

NASA Astrophysics Data System (ADS)

Souchard, J. P.; Pierlot, G.; Barbacanne, M. A.; Charveron, M.; Bonafé, J.-L.; Nepveu, F.

1999-01-01

This work reports the electron spin resonance (ESR) detection of oxygenated radicals (OR) released by cultured human fibroblasts after UVA (365 nm) exposure. 5,5-dimethyl-pyrroline-N-oxide (DMPO) was used as spin trap. After a UVA irradiation of one hour, followed by a latent period of at least 45 min., and an incubation time of 30 min. in a trapping medium containing DMPO, glucose, Na^+, K+ and Ca2+ an ESR signal was recorded. By contrast, an ESR signal was produced after only 15 min. incubation when calcium ionophore A23187 was used. Although the ESR signal was characteristic of the hydroxyl adduct DMPO-OH, the use of catalase and superoxide dismutase (SOD) revealed that UVA stimulated fibroblasts released the superoxide anion O2- in the medium. SOD, vitamin C and (+)-catechin inhibited the release of superoxide generated by human fibroblasts stimulated with A23187 calcium ionophore at 5 units/ml, 10-5 M and 2× 10-4 M, respectively. Dans ce travail nous présentons la détection par résonance de spin électronique (RSE) de radicaux oxygénés (RO) libérés par des fibroblastes humains en culture après irradiation aux UVA (365 nm). Le 5,5-diméthyl-1-pyrroline-N-oxyde (DMPO) a été utilisé comme piégeur de spin. Après une irradiation aux UVA d'une heure, suivie d'une période de latence d'au moins 45 min. et d'une incubation de 30 min. dans un milieu de piégeage composé de DMPO, glucose, Na^+, K+ et Ca2+, un signal RPE est enregistré. L'ionophore calcique A23187 entraîne l'apparition d'un signal RPE après seulement 15 min. d'incubation. Bien que le signal RPE obtenu corresponde à l'adduit DMPO-OH du radical hydroxyle, l'utilisation de catalase et de superoxyde dismutase (SOD) a révélé que les fibroblastes libéraient l'anion superoxyde dans le milieu de culture. Sur ce modèle cellulaire la SOD, la vitamine C et la (+) catéchine inhibent la production du radical superoxyde aux concentrations respectivement de 5 unités/ml, 10-5 M et 2× 10-4M.

Production of hydroxyl radical by redox active flavonoids

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

Kalyanaraman, B.; Hodnick, W.F.; Pardini, R.S.

1986-05-01

The authors have previously shown that flavonoids autoxidize and generate superoxide (O/sub 2//sup -/) and hydrogen peroxide (H/sub 2/O/sub 2/), suggesting that hydroxyl radical (OH) could be formed via the metal-ion catalyzed Haber-Weiss reaction. In the presence of ethylenediamine tetraacetic acid (EDTA) and 5,5-dimethyl-1-pyrroline-1-oxide (DMPO), myricetin, quercetagetin and quercetin gave an ESR signal for the DMPO-OH spin adduct, and the DMPO-Eto adduct in the presence of excess ethanol, indicating the production of free OH. The addition of FeCl/sub 3/ to the reaction mixture resulted in a dramatic increase in the DMPO-OH signal. Without chelator (EDTA) there was no signal andmore » the presence of diethylenetriamine-pentaacetic acid (DETAPAC) greatly diminished the signal. The presence of superoxide dismutase (SOD) had no effect on the signal while catalase completely abrogated the signal. The addition of Fe (III)-EDTA to flavonoid solutions under anaerobic conditions produced time dependent auxochromic shifts in their absorption spectra and resulted in the reduction of Fe (III) to Fe (II). These data suggest that the flavonoids autoxidize to produce O/sub 2//sup -/ and H/sub 2/O/sub 2/ by dismutation and in the presence of Fe (III)-EDTA the flavonoid can directly reduce the Fe (III) to Fe (II) resulting in the production of OH through Fenton chemistry.« less

Influence of pH, buffers and role of quinolinic acid, a novel iron chelating agent, in the determination of hydroxyl radical scavenging activity of plant extracts by Electron Paramagnetic Resonance (EPR).

PubMed

Fadda, Angela; Barberis, Antonio; Sanna, Daniele

2018-02-01

The Fenton reaction is used to produce hydroxyl radicals for the evaluation of the antioxidant activity of plant extracts. In this paper the parameters affecting the production of hydroxyl radicals and their spin trapping with DMPO were studied. The use of quinolinic acid (Quin) as an Fe(II) ligand was proposed for antioxidant activity determination of Green tea, orange juice and asparagus extracts. Quin, buffers and pH affect the DMPO-OH signal intensity of the EPR spectra. Quin/Fe(II) and low pH enhance the OH generation. Phosphate and Tris-HCl buffers decrease the signal intensity measured in Fe(II)-sulfate and Fe(II)-Quin systems. The extracts were analyzed with Fenton systems containing Fe(II)-sulfate and Fe(II)-Quin with and without buffer. The highest activity was shown with Fe(II)-Quin without buffer, this system being less influenced by pH and chelating agents present in the extracts. This paper will help researchers to better design spin trapping experiments for food matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Site-specific detection of radicals on α-lactalbumin after a riboflavin-sensitized reaction, detected by immuno-spin trapping, ESR, and MS.

PubMed

Dalsgaard, Trine K; Triquigneaux, Mathilde; Deterding, Leesa; Summers, Fiona; Ranguelova, Kalina; Mortensen, Grith; Mason, Ronald P

2013-01-16

Free radicals and other oxidation products were characterized on α-lactalbumin with electron spin resonance (ESR), immuno-spin trapping, and mass spectrometry (MS) after riboflavin-mediated oxidation. Radicals were detected using the spin trap 5,5-dimethyl-1-pyrroline N-oxide (DMPO) in immuno-spin trapping with both enzyme-linked immunosorbent assay (ELISA) and Western blotting and further characterized with mass spectrometry. A DMPO-trapped radical was identified at His68 and another at one of the tyrosine residues, Tyr50 or Tyr36, respectively, generated by a type II or I mechanism. Not all tyrosyl radicals were trapped, as the secondary oxidation product, 3,4-dihydroxyphenylalanine (DOPA), was detected by mass spectrometry at Tyr18 and Tyr50. A further oxidation of DOPA resulted in the DOPA o-semiquinone radical, which was characterized by ESR. Both surface exposure and the neighboring residues in the local environment of the tertiary structure of α-lactalbumin seem to play a role in the generation of DMPO trapped radicals and secondary oxidation products.

Competitive adsorption from mixed hen egg-white lysozyme/surfactant solutions at the air-water interface studied by tensiometry, ellipsometry, and surface dilational rheology.

PubMed

Alahverdjieva, V S; Grigoriev, D O; Fainerman, V B; Aksenenko, E V; Miller, R; Möhwald, H

2008-02-21

The competitive adsorption at the air-water interface from mixed adsorption layers of hen egg-white lysozyme with a non-ionic surfactant (C10DMPO) was studied and compared to the mixture with an ionic surfactant (SDS) using bubble and drop shape analysis tensiometry, ellipsometry, and surface dilational rheology. The set of equilibrium and kinetic data of the mixed solutions is described by a thermodynamic model developed recently. The theoretical description of the mixed system is based on the model parameters for the individual components.

Measuring sunscreen protection against solar-simulated radiation-induced structural radical damage to skin using ESR/spin trapping: development of an ex vivo test method.

PubMed

Haywood, Rachel; Volkov, Arsen; Andrady, Carima; Sayer, Robert

2012-03-01

The in vitro star system used for sunscreen UVA-testing is not an absolute measure of skin protection being a ratio of the total integrated UVA/UVB absorption. The in vivo persistent-pigment-darkening method requires human volunteers. We investigated the use of the ESR-detectable DMPO protein radical-adduct in solar-simulator-irradiated skin substitutes for sunscreen testing. Sunscreens SPF rated 20+ with UVA protection, reduced this adduct by 40-65% when applied at 2 mg/cm(2). SPF 15 Organic UVA-UVB (BMDBM-OMC) and TiO(2)-UVB filters and a novel UVA-TiO(2) filter reduced it by 21, 31 and 70% respectively. Conventional broad-spectrum sunscreens do not fully protect against protein radical-damage in skin due to possible visible-light contributions to damage or UVA-filter degradation. Anisotropic spectra of DMPO-trapped oxygen-centred radicals, proposed intermediates of lipid-oxidation, were detected in irradiated sunscreen and DMPO. Sunscreen protection might be improved by the consideration of visible-light protection and the design of filters to minimise radical leakage and lipid-oxidation.

Equilibrium of adsorption of mixed milk protein/surfactant solutions at the water/air interface.

PubMed

Kotsmar, C; Grigoriev, D O; Xu, F; Aksenenko, E V; Fainerman, V B; Leser, M E; Miller, R

2008-12-16

Ellipsometry and surface profile analysis tensiometry were used to study and compare the adsorption behavior of beta-lactoglobulin (BLG)/C10DMPO, beta-casein (BCS)/C10DMPO and BCS/C12DMPO mixtures at the air/solution interface. The adsorption from protein/surfactant mixed solutions is of competitive nature. The obtained adsorption isotherms suggest a gradual replacement of the protein molecules at the interface with increasing surfactant concentration for all studied mixed systems. The thickness, refractive index, and the adsorbed amount of the respective adsorption layers, determined by ellipsometry, decrease monotonically and reach values close to those for a surface covered only by surfactant molecules, indicating the absence of proteins from a certain surfactant concentration on. These results correlate with the surface tension data. A continuous increase of adsorption layer thickness was observed up to this concentration, caused by the desorption of segments of the protein and transforming the thin surface layer into a rather diffuse and thick one. Replacement and structural changes of the protein molecules are discussed in terms of protein structure and surface activity of surfactant molecules. Theoretical models derived recently were used for the quantitative description of the equilibrium state of the mixed surface layers.

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

Paudel, Nava Raj, E-mail: nrpaudel@yahoo.com; Shvydka, Diana; Parsai, E. Ishmael

Purpose: Gold nanoparticles (GNPs) are known to be effective mediators in microwave hyperthermia. Interaction with an electromagnetic field, large surface to volume ratio, and size quantization of nanoparticles (NPs) can lead to increased cell killing beyond pure heating effects. The purpose of this study is to explore the possibility of free radical generation by GNPs in aqueous media when they are exposed to a microwave field. Methods: A number of samples with 500 mM 5,5-dimethyl-1-pyrroline N-oxide (DMPO) in 20 ppm GNP colloidal suspensions were scanned with an electron paramagnetic resonance (EPR)/electron spin resonance spectrometer to generate and detect free radicals.more » A fixed (9.68 GHz) frequency microwave from the spectrometer has served for both generation and detection of radicals. EPR spectra obtained as first derivatives of intensity with the spectrometer were double integrated to get the free radical signal intensities. Power dependence of radical intensity was studied by applying various levels of microwave power (12.5, 49.7, and 125 mW) while keeping all other scan parameters the same. Free radical signal intensities from initial and final scans, acquired at the same power levels, were compared. Results: Hydroxyl radical (OH⋅) signal was found to be generated due to the exposure of GNP–DMPO colloidal samples to a microwave field. Intensity of OH⋅ signal thus generated at 12.5 mW microwave power for 2.8 min was close to the intensity of OH⋅ signal obtained from a water–DMPO sample exposed to 1.5 Gy ionizing radiation dose. For repeated scans, higher OH⋅ intensities were observed in the final scan for higher power levels applied between the initial and the final scans. Final intensities were higher also for a shorter time interval between the initial and the final scans. Conclusions: Our results observed for the first time demonstrate that GNPs generate OH⋅ radicals in aqueous media when they are exposed to a microwave field. If OH⋅ radicals can be generated close to deoxyribonucleic acid of cells by proper localization of NPs, NP-aided microwave hyperthermia can yield cell killing via both elevated temperature and free radical generation.« less

A novel property of gold nanoparticles: Free radical generation under microwave irradiation.

PubMed

Paudel, Nava Raj; Shvydka, Diana; Parsai, E Ishmael

2016-04-01

Gold nanoparticles (GNPs) are known to be effective mediators in microwave hyperthermia. Interaction with an electromagnetic field, large surface to volume ratio, and size quantization of nanoparticles (NPs) can lead to increased cell killing beyond pure heating effects. The purpose of this study is to explore the possibility of free radical generation by GNPs in aqueous media when they are exposed to a microwave field. A number of samples with 500 mM 5,5-dimethyl-1-pyrroline N-oxide (DMPO) in 20 ppm GNP colloidal suspensions were scanned with an electron paramagnetic resonance (EPR)/electron spin resonance spectrometer to generate and detect free radicals. A fixed (9.68 GHz) frequency microwave from the spectrometer has served for both generation and detection of radicals. EPR spectra obtained as first derivatives of intensity with the spectrometer were double integrated to get the free radical signal intensities. Power dependence of radical intensity was studied by applying various levels of microwave power (12.5, 49.7, and 125 mW) while keeping all other scan parameters the same. Free radical signal intensities from initial and final scans, acquired at the same power levels, were compared. Hydroxyl radical (OH⋅) signal was found to be generated due to the exposure of GNP-DMPO colloidal samples to a microwave field. Intensity of OH⋅ signal thus generated at 12.5 mW microwave power for 2.8 min was close to the intensity of OH⋅ signal obtained from a water-DMPO sample exposed to 1.5 Gy ionizing radiation dose. For repeated scans, higher OH⋅ intensities were observed in the final scan for higher power levels applied between the initial and the final scans. Final intensities were higher also for a shorter time interval between the initial and the final scans. Our results observed for the first time demonstrate that GNPs generate OH⋅ radicals in aqueous media when they are exposed to a microwave field. If OH⋅ radicals can be generated close to deoxyribonucleic acid of cells by proper localization of NPs, NP-aided microwave hyperthermia can yield cell killing via both elevated temperature and free radical generation.

Hyperoxic sheep pulmonary microvascular endothelial cells generate free radicals via mitochondrial electron transport.

PubMed Central

Sanders, S P; Zweier, J L; Kuppusamy, P; Harrison, S J; Bassett, D J; Gabrielson, E W; Sylvester, J T

1993-01-01

Free radical generation by hyperoxic endothelial cells was studied using electron paramagnetic resonance (EPR) spectroscopy and the spin trap 5,5-dimethyl-1-pyrroline-N-oxide (DMPO). Studies were performed to determine the radical species produced, whether mitochondrial electron transport was involved, and the effect of the radical generation on cell mortality. Sheep pulmonary microvascular endothelial cell suspensions exposed to 100% O2 for 30 min exhibited prominent DMPO-OH and, occasionally, additional smaller DMPO-R signals thought to arise from the trapping of superoxide anion (O2-.), hydroxyl (.OH), and alkyl (.R) radicals. Superoxide dismutase (SOD) quenched both signals suggesting that the observed radicals were derived from O2-.. Studies with deferoxamine suggested that the generation of .R occurred secondary to the formation of .OH from O2-. via an iron-mediated Fenton reaction. Blocking mitochondrial electron transport with rotenone (20 microM) markedly decreased radical generation. Cell mortality increased slightly in oxygen-exposed cells. This increase was not significantly altered by SOD or deferoxamine, nor was it different from the mortality observed in air-exposed cells. These results suggest that endothelial cells exposed to hyperoxia for 30 min produce free radicals via mitochondrial electron transport, but under the conditions of these experiments, this radical generation did not appear cause cell death. PMID:8380815

Volumetric‐modulated arc therapy planning using multicriteria optimization for localized prostate cancer

PubMed Central

Ghandour, Sarah; Matzinger, Oscar

2015-01-01

The purpose of this work is to evaluate the volumetric‐modulated arc therapy (VMAT) multicriteria optimization (MCO) algorithm clinically available in the RayStation treatment planning system (TPS) and its ability to reduce treatment planning time while providing high dosimetric plan quality. Nine patients with localized prostate cancer who were previously treated with 78 Gy in 39 fractions using VMAT plans and rayArc system based on the direct machine parameter optimization (DMPO) algorithm were selected and replanned using the VMAT‐MCO system. First, the dosimetric quality of the plans was evaluated using multiple conformity metrics that account for target coverage and sparing of healthy tissue, used in our departmental clinical protocols. The conformity and homogeneity index, number of monitor units, and treatment planning time for both modalities were assessed. Next, the effects of the technical plan parameters, such as constraint leaf motion CLM (cm/°) and maximum arc delivery time T (s), on the accuracy of delivered dose were evaluated using quality assurance passing rates (QAs) measured using the Delta4 phantom from ScandiDos. For the dosimetric plan's quality analysis, the results show that the VMAT‐MCO system provides plans comparable to the rayArc system with no statistical difference for V95% (p<0.01), D1% (p<0.01), CI (p<0.01), and HI (p<0.01) of the PTV, bladder (p<0.01), and rectum (p<0.01) constraints, except for the femoral heads and healthy tissues, for which a dose reduction was observed using MCO compared with rayArc (p<0.01). The technical parameter study showed that a combination of CLM equal to 0.5 cm/degree and a maximum delivery time of 72 s allowed the accurate delivery of the VMAT‐MCO plan on the Elekta Versa HD linear accelerator. Planning evaluation and dosimetric measurements showed that VMAT‐MCO can be used clinically with the advantage of enhanced planning process efficiency by reducing the treatment planning time without impairing dosimetric quality. PACS numbers: 87.55.D, 87.55.de, 87.55.Qr PMID:26103500

Spin trapping combined with quantitative mass spectrometry defines free radical redistribution within the oxidized hemoglobin:haptoglobin complex.

PubMed

Vallelian, Florence; Garcia-Rubio, Ines; Puglia, Michele; Kahraman, Abdullah; Deuel, Jeremy W; Engelsberger, Wolfgang R; Mason, Ronald P; Buehler, Paul W; Schaer, Dominik J

2015-08-01

Extracellular or free hemoglobin (Hb) accumulates during hemolysis, tissue damage, and inflammation. Heme-triggered oxidative reactions can lead to diverse structural modifications of lipids and proteins, which contribute to the propagation of tissue damage. One important target of Hb׳s peroxidase reactivity is its own globin structure. Amino acid oxidation and crosslinking events destabilize the protein and ultimately cause accumulation of proinflammatory and cytotoxic Hb degradation products. The Hb scavenger haptoglobin (Hp) attenuates oxidation-induced Hb degradation. In this study we show that in the presence of hydrogen peroxide (H2O2), Hb and the Hb:Hp complex share comparable peroxidative reactivity and free radical generation. While oxidation of both free Hb and Hb:Hp complex generates a common tyrosine-based free radical, the spin-trapping reaction with 5,5-dimethyl-1-pyrroline N-oxide (DMPO) yields dissimilar paramagnetic products in Hb and Hb:Hp, suggesting that radicals are differently redistributed within the complex before reacting with the spin trap. With LC-MS(2) mass spectrometry we assigned multiple known and novel DMPO adduct sites. Quantification of these adducts suggested that the Hb:Hp complex formation causes extensive delocalization of accessible free radicals with drastic reduction of the major tryptophan and cysteine modifications in the β-globin chain of the Hb:Hp complex, including decreased βCys93 DMPO adduction. In contrast, the quantitative changes in DMPO adduct formation on Hb:Hp complex formation were less pronounced in the Hb α-globin chain. In contrast to earlier speculations, we found no evidence that free Hb radicals are delocalized to the Hp chain of the complex. The observation that Hb:Hp complex formation alters free radical distribution in Hb may help to better understand the structural basis for Hp as an antioxidant protein. Copyright © 2015 Elsevier Inc. All rights reserved.

Oxidation of spin-traps by chlorine dioxide (ClO2) radical in aqueous solutions: first ESR evidence of formation of new nitroxide radicals.

PubMed

Ozawa, T; Miura, Y; Ueda, J

1996-01-01

The reactivities of the chlorine dioxide (ClO2), which is a stable free radical towards some water-soluble spin-traps were investigated in aqueous solutions by an electron spin resonance (ESR) spectroscopy. The ClO2 radical was generated from the redox reaction of Ti3+ with potassium chlorate (KClO3) in aqueous solutions. When one of the spin-traps, 5,5-dimethyl-1-pyrroline N-oxide (DMPO), was included in the Ti3+-KClO3 reaction system, ESR spectrum due to the ClO2 radical completely disappeared and a new ESR spectrum [aN(1) = 0.72 mT, aH(2) = 0.41 mT], which is different from that of DMPO-ClO2 adduct, was observed. The ESR parameters of this new ESR signal was identical to those of 5,5-dimethylpyrrolidone-(2)-oxyl-(1) (DMPOX), suggesting the radical species giving the new ESR spectrum is assignable to DMPOX. The similar ESR spectrum consisting of a triplet [aN(1) = 0.69 mT] was observed when the derivative of DMPO, 3,3,5,5-tetramethyl-1-pyrroline N-oxide (M4PO) was included in the Ti3+-KClO3 reaction system. This radical species is attributed to the oxidation product of M4PO, 3,3,5,5-tetramethylpyrrolidone-(2)-oxyl-(1) (M4POX). When another nitrone spin-trap, alpha-(4-pyridyl-1-oxide)-N-t-butylnitrone (POBN) was used as a spin-trap, the ESR signal intensity due to the ClO2 radical decreased and a new ESR signal consisting of a triplet [aN(1) = 0.76 mT] was observed. The similar ESR spectrum was observed when N-t-butyl-alpha- nitrone (PBN) was used as a spin-trap. This ESR parameter [a(N)(1) = 0.85 mT] was identical to the oxidation product of PBN, PBNX. Thus, the new ESR signal observed from POBN may be assigned to the oxidation product of POBN, POBNX. These results suggest that the ClO2, radical does not form the stable spin adducts with nitrone spin-traps, but oxidizes these spin-traps to give the corresponding nitroxyl radicals. On the other hand, nitroso spin-traps, 5,5-dibromo-4-nitrosobenzenesulfonate (DBNBS), and 2-methyl-2-nitrosopropane (MNP) did not trap the ClO2 radical. This result indicates that an unpaired electron of the ClO2 radical is localized on oxygen atom, because nitroso spin-traps cannot form the stable spin adduct with oxygen-centered radical.

Inhibition of peroxynitrite-mediated DNA strand cleavage and hydroxyl radical formation by aspirin at pharmacologically relevant concentrations: implications for cancer intervention

PubMed Central

Chen, Wei; Zhu, Hong; Jia, Zhenquan; Li, Jianrong; Misra, Hara P.; Zhou, Kequan; Li, Yunbo

2009-01-01

Epidemiological studies have suggested that the long-term use of aspirin is associated with a decreased incidence of human malignancies, especially colorectal cancer. Since accumulating evidence indicates that peroxynitrite is critically involved in multistage carcinogenesis, this study was undertaken to investigate the ability of aspirin to inhibit peroxynitrite-mediated DNA damage. Peroxynitrite and its generator 3-morpholinosydnonimine (SIN-1) were used to cause DNA strand breaks in φX-174 plasmid DNA. We demonstrated that the presence of aspirin at concentrations (0.25 -2 mM) compatible with amounts in plasma during chronic anti-inflammatory therapy resulted in a significant inhibition of DNA cleavage induced by both peroxynitrite and SIN-1. Moreover, the consumption of oxygen caused by 250 µM SIN-1 was found to be decreased in the presence of aspirin, indicating that aspirin might affect the auto-oxidation of SIN-1. Furthermore, EPR spectroscopy using 5,5-dimethylpyrroline-N-oxide (DMPO) as a spin trap demonstrated the formation of DMPO-hydroxyl radical adduct (DMPO-OH) from authentic peroxynitrite, and that aspirin at 0.25 - 2 mM potently diminished the radical adduct formation in a concentration-dependent manner. Taken together, these results demonstrate for the first time that aspirin at pharmacologically relevant concentrations can inhibit peroxynitrite-mediated DNA strand breakage and hydroxyl radical formation. These results may have implications for cancer intervention by aspirin. PMID:19785994

Inhibition of peroxynitrite-mediated DNA strand cleavage and hydroxyl radical formation by aspirin at pharmacologically relevant concentrations: Implications for cancer intervention

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

Chen, Wei; College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310035; Department of Food Science and Technology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061

Epidemiological studies have suggested that the long-term use of aspirin is associated with a decreased incidence of human malignancies, especially colorectal cancer. Since accumulating evidence indicates that peroxynitrite is critically involved in multistage carcinogenesis, this study was undertaken to investigate the ability of aspirin to inhibit peroxynitrite-mediated DNA damage. Peroxynitrite and its generator 3-morpholinosydnonimine (SIN-1) were used to cause DNA strand breaks in {phi}X-174 plasmid DNA. We demonstrated that the presence of aspirin at concentrations (0.25-2 mM) compatible with amounts in plasma during chronic anti-inflammatory therapy resulted in a significant inhibition of DNA cleavage induced by both peroxynitrite and SIN-1.more » Moreover, the consumption of oxygen caused by 250 {mu}M SIN-1 was found to be decreased in the presence of aspirin, indicating that aspirin might affect the auto-oxidation of SIN-1. Furthermore, EPR spectroscopy using 5,5-dimethylpyrroline-N-oxide (DMPO) as a spin trap demonstrated the formation of DMPO-hydroxyl radical adduct (DMPO-OH) from authentic peroxynitrite, and that aspirin at 0.25-2 mM potently diminished the radical adduct formation in a concentration-dependent manner. Taken together, these results demonstrate for the first time that aspirin at pharmacologically relevant concentrations can inhibit peroxynitrite-mediated DNA strand breakage and hydroxyl radical formation. These results may have implications for cancer intervention by aspirin.« less

Characteristics of the spin-trapping reaction of a free radical derived from AAPH: further development of the ORAC-ESR assay.

PubMed

Nakajima, A; Matsuda, E; Masuda, Y; Sameshima, H; Ikenoue, T

2012-06-01

The characteristics of the spin-trapping reaction in the oxygen radical absorbance capacity (ORAC)-electron spin resonance (ESR) assay were examined, focusing on the kind of spin traps. 2,2-Azobis(2-amidinopropane) dihydrochloride (AAPH) was used as a free radical initiator. The spin adducts of the AAPH-derived free radical were assigned as those of the alkoxyl radical, RO· (R=H(2)N(HN)C-C(CH(3))(2)). Among the spin traps tested, 5,5-dimethyl-1-pyrroline N-oxide (DMPO), 5,5-dimethyl-4-phenyl-1-pyrroline N-oxide (4PDMPO), 5-(2,2-dimethyl-1,3-propoxycyclophosphoryl)-5-methyl-1-pyrroline N-oxide (CYPMPO), and 5-diethoxyphosphoryl-5-methyl-1-pyrroline N-oxide (DEPMPO) were applicable to the ORAC-ESR assay. Optimal formation of spin-trapped radical adduct was observed with 1 mM AAPH, 10 mM spin trap, and 5 s UV irradiation. The calibration curve (the Stern-Volmer's plot) for each spin trap showed good linearity, and their slopes, k (SB)/k (ST), were estimated to be 87.7±2.3, 267±15, 228±9, and 213±16 for DMPO, 4PDMPO, CYPMPO, and DEPMPO, respectively. Though the k (SB)/k (ST) values for selected biosubstances varied with various spin traps, their ratios to Trolox (the relative ORAC values) were almost the same for all spin traps tested. The ORAC-ESR assay also had a very good reproducibility. The ORAC-ESR assay was conducted under stoichiometric experimental conditions. The present results demonstrate the superiority of the ORAC-ESR assay.

Myeloperoxidase-produced Genomic DNA-centered Radicals and Protection by Resveratrol

EPA Science Inventory

Myeloperoxidase (MPO) released by activated neutrophils, production of hypochlorous acid (HOCI) and oxidation of the genomic DNA in epithelial cells is thought to initiate and promote carcinogenesis. In this study we applied the 5,5-dimethyl-l-pyrroline N-oxide (DMPO)-based i;nmu...

Comparison between modelling and experimental measurements of Criegee intermediates from the ozonolysis of biogenic and anthropogenic VOCs

NASA Astrophysics Data System (ADS)

Giorio, Chiara; Campbell, Steven; Bruschi, Maurizio; Archibald, Alexander; Kalberer, Markus

2017-04-01

One of the most important reactions in the troposphere is ozonolysis of alkenes contributing to local photochemical smog and global climate change (Vereecken, 2013). Ozonolysis of alkenes occurs with a generally accepted mechanism, in which ozone adds to the double bond of alkenes forming a primary ozonide, which promptly decomposes to form a carbonyl compound and a biradical/zwitterion called Criegee intermediate (CI) (Criegee, 1975). CIs are highly reactive and short-lived and therefore their analysis represents an analytical challenge. We generated CIs in a flow tube by reacting olefinic compounds with ozone and we stabilised them with the volatile spin trap 5,5-dimethyl-pyrroline N-oxide (DMPO) prior to analysis with proton transfer reaction mass spectrometry (PTR-MS). In a recent study we unambiguously identified the structure of the CI-spin trap adducts formed in the ozonolysis of α-pinene (Giorio et al, submitted). Identification was performed and molecular structures of the adducts were determined with mass spectrometry techniques and nuclear magnetic resonance and supported by density functional theory (DFT) calculations (Giorio et al., submitted). We have now expanded the study to the ozonolysis of various biogenic alkenes, including β-pinene, limonene and methacrolein, as well as anthropogenic alkenes, including cis-2-hexene and styrene. As an example, for the ozonolysis of β-pinene both of the expected C1 and C9 CIs have been detected. These measurements indicate that the ratio between the yields of the C9 CI- and the C1 CI- DMPO adducts formed in this system is about 0.1, while theoretical estimates with the model "Master Chemical Mechanism" (MCM) 3.3.1 suggest a ratio of 0.7 (considering the stabilised CIs) (Saunders et al., 2003). This difference is likely due to different reaction rates of the two CIs with the spin trap DMPO. Similarly, for limonene, all three masses corresponding to the CIs from ozone attack to both the endo and the exo double bonds have been detected. For all the tested VOCs, experimental measurements were compared with MCM modelling results and discrepancies discussed in terms of stability of the CI-DMPO adducts derived from DFT calculations. R. Criegee, Angew. Chemie Int. Ed. English, 1975, 14, 745-752. C. Giorio, et al., J. Am. Chem. Soc., submitted. S. M. Saunders, et al., Atmos. Chem. Phys., 2003, 3, 161-180. L. Vereecken, Science, 2013 340 (6129), 154-155.

Generation of hydroxyl radicals and singlet oxygen during oxidation of rhododendrol and rhododendrol-catechol.

PubMed

Miyaji, Akimitsu; Gabe, Yu; Kohno, Masahiro; Baba, Toshihide

2017-03-01

The generation of hydroxyl radicals and singlet oxygen during the oxidation of 4-(4-hydroxyphenyl)-2-butanol (rhododendrol) and 4-(3,4-dihydroxyphenyl)-2-butanol (rhododendrol-catechol) with mushroom tyrosinase in a phosphate buffer (pH 7.4) was examined as the model for the reactive oxygen species generation via the two rhododendrol compounds in melanocytes. The reaction was performed in the presence of 5,5-dimethyl-1-pyrroline- N -oxide (DMPO) spin trap reagents for hydroxyl radical or 2,2,6,6-tetramethyl-4-piperidone (4-oxo-TEMP), an acceptor of singlet oxygen, and their electron spin resonances were measured. An increase in the electron spin resonances signal attributable to the adduct of DMPO reacting with the hydroxyl radical and that of 4-oxo-TEMP reacting with singlet oxygen was observed during the tyrosinase-catalyzed oxidation of rhododendrol and rhododendrol-catechol, indicating the generation of hydroxyl radical and singlet oxygen. Moreover, hydroxyl radical generation was also observed in the autoxidation of rhododendrol-catechol. We show that generation of intermediates during tyrosinase-catalyzed oxidation of rhododendrol enhances oxidative stress in melanocytes.

Radical scavenging ability of some compounds isolated from Piper cubeba towards free radicals.

PubMed

Aboul-Enein, Hassan Y; Kładna, Aleksandra; Kruk, Irena

2011-01-01

The purpose of this study was to identify the antioxidant activity of 16 compounds isolated from Piper cubeba (CNCs) through the extent of their capacities to scavenge free radicals, hydroxyl radical (HO(•)), superoxide anion radical O•(2)(-) and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH(•)), in different systems. Electron paramagnetic resonance (EPR) and 5,5-dimethyl-1-pyrroline-N-oxide, DMPO, as the spin trap, and chemiluminescence techniques were applied. Using the Fenton-like reaction [Fe(II) + H(2)O(2)], CNCs were found to inhibit DMPO-OH radical formation ranging from 5 to 57% at 1.25 mmol L(-1) concentration. The examined CNCs also showed a high DPPH antiradical activity (ranging from 15 to 99% at 5 mmol L(-1) concentration). Furthermore, the results indicated that seven of the 16 tested compounds may catalyse the conversion of superoxide radicals generated in the potassium superoxide/18-crown-6 ether system, thus showing superoxide dismutase-like activity. The data obtained suggest that radical scavenging properties of CNCs might have potential application in many plant medicines. Copyright © 2010 John Wiley & Sons, Ltd.

Effect of water content on thermal oxidation of oleic acid investigated by combination of EPR spectroscopy and SPME-GC-MS/MS.

PubMed

Chen, Hongjian; Cao, Peirang; Li, Bo; Sun, Dewei; Wang, Yong; Li, Jinwei; Liu, Yuanfa

2017-04-15

Promotion of water to the thermal oxidation of oleic acid was detected by the combination of EPR, SPME-GC-MS/MS and GC. Spin-trapping technique was used to identify and quantify the radical species formed during thermal oxidation of oleic acid by using DMPO as electron spin trap. The most abundant radical species were identified as DMPO-alkyl radical adducts. EPR intensity plateau of the samples with 5% water content was 140% higher than the samples without water. It implies oleic acid samples with high water content had high level of oxidation rates. The proportion of aldehydes of the samples with 2% water content was the maximum about 59.97%. Among the formed products, (E,E)-2,4-decadienal has genotoxic and cytotoxic effects, whose percentage was nearly twice comparing with that of 5-0% water content. This study demonstrated that higher water content in frying systems would contribute to seriously oxidation and degradation of oleic acids. Copyright © 2016 Elsevier Ltd. All rights reserved.

Free radical generation induced by ultrasound in red wine and model wine: An EPR spin-trapping study.

PubMed

Zhang, Qing-An; Shen, Yuan; Fan, Xue-Hui; Martín, Juan Francisco García; Wang, Xi; Song, Yun

2015-11-01

Direct evidence for the formation of 1-hydroxylethyl radicals by ultrasound in red wine and air-saturated model wine is presented in this paper. Free radicals are thought to be the key intermediates in the ultrasound processing of wine, but their nature has not been established yet. Electron paramagnetic resonance (EPR) spin trapping with 5,5-dimethyl-l-pyrrolin N-oxide (DMPO) was used for the detection of hydroxyl free radicals and 1-hydroxylethyl free radicals. Spin adducts of hydroxyl free radicals were detected in DMPO aqueous solution after sonication while 1-hydroxylethyl free radical adducts were observed in ultrasound-processed red wine and model wine. The latter radical arose from ethanol oxidation via the hydroxyl radical generated by ultrasound in water, thus providing the first direct evidence of the formation of 1-hydroxylethyl free radical in red wine exposed to ultrasound. Finally, the effects of ultrasound frequency, ultrasound power, temperature and ultrasound exposure time were assessed on the intensity of 1-hydroxylethyl radical spin adducts in model wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Absence of an effect of vitamin E on protein and lipid radical formation during lipoperoxidation of LDL by lipoxygenase

PubMed Central

Ganini, Douglas; Mason, Ronald P.

2014-01-01

LDL oxidation is the primary event in atherosclerosis, where LDL lipoperoxidation leads to modifications in the apolipoprotein B-100 (apo B-100) and lipids. Intermediate species of lipoperoxidation are known to be able to generate amino acid-centered radicals. Thus, we hypothesized that lipoperoxidation intermediates induce protein-derived free radical formation during LDL oxidation. Using DMPO and immuno spin-trapping, we detected the formation of protein free radicals on LDL incubated with Cu2+ or the soybean lipoxidase (LPOx)/phospholipase A2 (PLA2). With low concentrations of DMPO (1 mM), Cu2+ dose-dependently induced oxidation of LDL and easily detected apo B-100 radicals. Protein radical formation in LDL incubated with Cu2+ showed maximum yields after 30 minutes. In contrast, the yields of apo B-100-radicals formed by LPOx/PLA2 followed a typical enzyme-catalyzed kinetics that was unaffected by DMPO concentrations of up to 50 mM. Furthermore, when we analyzed the effect of antioxidants on protein radical formation during LDL oxidation, we found that ascorbate, urate and Trolox dose-dependently reduced apo B-100-free radical formation in LDL exposed to Cu2+. In contrast, Trolox was the only antioxidant that even partially protected LDL from LPOx/PLA2. We also examined the kinetics of lipid radical formation and protein radical formation induced by Cu2+ or LPOx/PLA2 for LDL supplemented with α-tocopherol. In contrast to the potent antioxidant effect of α-tocopherol on the delay of LDL oxidation induced by Cu2+, when we used the oxidizing system LPOx/PLA2, no significant protection was detected. The lack of protection of α-tocopherol on the apo B-100 and lipid free radical formation by LPOx may explain the failure of vitamin E as a cardiovascular protective agent for humans. PMID:25091900

Photochemical sensitization by azathioprine and its metabolites. Part 3. A direct EPR and spin-trapping study of light-induced free radicals from 6-mercaptopurine and its oxidation products.

PubMed

Moore, D E; Sik, R H; Bilski, P; Chignell, C F; Reszka, K J

1994-12-01

Sunlight has been implicated in the high incidence of skin cancer found in patients receiving 6-mercaptopurine (PSH) in the form of its pro-drug azathioprine. In this study we have used EPR spectroscopy in conjunction with the spin-trapping technique to determine whether PSH and its metabolic or photochemical oxidation products generate highly reactive free radicals upon UV irradiation. When an aqueous anaerobic solution (pH 5 or 9) of PSH (pKa = 7.7) and either 2-methyl-2-nitrosopropane (MNP) or nitromethane (NM) were irradiated (lambda > 300 nm) with a Xe arc lamp, the corresponding purine-6-thiyl (PS.) radical adduct and the reduced form of the spin trap (MNP/H. or CH3NO2.-) were observed. However, no radical adducts were detected when PSH and 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) were irradiated (lambda = 320 nm) in oxygen-free buffer. These findings suggest that PSH does not photoionize but that instead MNP and NM are reduced by direct electron transfer from excited state PSH, 1.3(PSH)*. In aerobic solution, oxygen can act as an electron acceptor and the O2.- and PS. radicals are formed and trapped by DMPO. 6-Mercaptopurine did photoionize when irradiated with a Nd:YAG laser at 355 nm as evidenced by the appearance of the DMPO/H.(eq- + H+) adduct, which decreased in intensity in the presence of N2O. 1.3(6-Mercaptopurine)* oxidized ascorbate, formate and reduced glutathione to the corresponding ascorbyl, CO2.- or glutathiyl radicals. The photochemical behavior of 6-thioxanthine and 6-thiouric acid was similar to PSH. However, the excited states of these metabolic oxidation products exhibited stronger reducing properties than 1.3(PSH)*.(ABSTRACT TRUNCATED AT 250 WORDS)

Simplified Synthesis of Isotopically Labeled 5,5-Dimethyl-pyrroline N-Oxide

PubMed Central

Leinisch, Fabian; Jiang, JinJie; Deterding, Leesa J.; Mason, Ronald P.

2011-01-01

5,5-Dimethylpyrroline N-oxide (15N) and 5,5-di(trideuteromethyl)pyrroline N-oxide were synthesized from the respective isotopically labeled 2-nitropropane analogs obtained from the reaction of sodium nitrate with 2-halopropanes. This facile, straightforward process allows synthesizing isotopically labeled DMPO analogs in a 4-step reaction without special equipment. PMID:21986521

Generation of hydroxyl radicals by urban suspended particulate air matter. The role of iron ions

NASA Astrophysics Data System (ADS)

Valavanidis, Athanasios; Salika, Anastasia; Theodoropoulou, Anna

Recent epidemiologic studies showed statistical associations between particulate air pollution in urban areas and increased morbidity and mortality, even at levels well within current national air quality standards. Inhalable particulate matter (PM 10) can penetrate into the lower airways where they can cause acute and chronic lung injury by generating toxic oxygen free radicals. We tested inhalable total suspended particulates (TSP) from the Athens area, diesel and gasoline exhaust particles (DEP and GED), and urban street dusts, by Electron Paramagnetic Resonance (EPR). All particulates can generate hydroxyl radicals (HO ṡ), in aqueous buffered solutions, in the presence of hydrogen peroxide. Results showed that oxidant generating activity is related with soluble iron ions. Leaching studies showed that urban particulate matter can release large amounts of Fe 3+ and lesser amounts of Fe 2+, as it was shown from other studies. Direct evidence of HO ṡ was confirmed by spin trapping with DMPO and measurement of DMPO-OH adduct by EPR. Evidence was supported with the use of chelator (EDTA), which increases the EPR signal, and the inhibition of the radical generating activity by desferrioxamine or/and antioxidants ( D-mannitol, sodium benzoate).

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

Shepherd, R.E.; Myser, T.K.; Elliott, M.G.

The kinetics of the reaction of O/sub 2/ and Fe/sub 2/(ttha)/sup 2/minus// (ttha/sup 6/minus// = triethylenetetraminehexaacetate) are reported. The mechanism of oxidation is discussed in terms of the equilibrium binding of O/sub 2/ forming an ((Fe/sup III/O/sub 2//sup /minus//)ttha(Fe/sup II/)) intermediate in an open-chain configuration. When Fe/sub 2/(ttha)/sup 2/minus// is oxidized by H/sub 2/O/sub 2/, the HODMPO/sup /center dot// (DMPO = 5,5-dimethyl-1-pyrrolis-N-oxide) adduct is readily detected by its characteristic four-line 1:2:2:1 pattern (a/sub N/ = a/sub H/ = 15.0 G) in its ESR spectrum. The bound O/sub 2//sup /minus// intermediate in the Fe/sub 2/(ttha)/sup 2/minus// autoxidation scheme will extract anmore » H atom from C/sub 2/H/sub 5/OH in its solvent cage; the DMPO adduct of CH/sub 3/CHOH is trapped, giving a pattern of six equal lines (a/sub N/ = 16.0 G, a/sub H/ = 22.8 G). A maximum of 6% of the pathway results in a carbon-centered radial; the remainder of the reduction events produce Fe/sub 2/O(ttha)/sup 2/minus// by rapid reduction of the (Fe/sup III//sub 2/(O/sub 2//sup 2/minus//)(ttha))/sup 2/minus// intermediate. The Fe/sub 2/O(ttha)/sup 2/minus// ion was precipitated as its Me/sub 2/Dabco/sup 2+/ salt. This compound was shown to have ferric ions in an /sup 6/A/sub 1/ state; the Moessbauer spectrum yielded 0.63 mm/s for the isomer shift and 1.56 mm/s for the quadrupole splitting parameter vs sodium nitroprusside. The solid exhibited an Fe-O-Fe asymmetric stretch at 833 cm/sup /minus/1/ for /sup 16/O and 846 cm/sup /minus/1/ for the /sup 18/O-labeled complex. The uv-visible spectrum is very similar to that of the Fe/sub 2/(hedta)/sub 2//sup 2/minus// complex. 62 references, 7 figures, 3 tables.« less

Site-specific radical formation in DNA induced by Cu(II)-H2O2 oxidizing system, using ESR, Immuno-spin trapping, LC/MS and MS/MS

PubMed Central

Bhattacharjee, Suchandra; Deterding, Leesa J.; Chatterjee, Saurabh; Jiang, JinJie; Ehrenshaft, Marilyn; Lardinois, Olivier; Ramirez, Dario C.; Tomer, Kenneth B.; Mason, Ronald P.

2011-01-01

Oxidative stress-related damage to the DNA macromolecule produces a multitude of lesions that are implicated in mutagenesis, carcinogenesis, reproductive cell death and aging. Many of these lesions have been studied and characterized by various techniques. Of the techniques that are available, the comet assay, HPLC-EC, GC-MS, HPLC-MS and especially HPLC-MS/MS remain the most widely used and have provided invaluable information on these lesions. However, accurate measurement of DNA damage has been a matter of debate. In particular, there have been reports of artifactual oxidation leading to erroneously high damage estimates. Further, most of these techniques measure the end product of a sequence of events and thus provide only limited information on the initial radical mechanism. We report here a qualitative measurement of DNA damage induced by a Cu(II)-H2O2 oxidizing system using immuno spin-trapping (IST) with EPR, MS and MS/MS. The radical generated is trapped by DMPO immediately upon formation. The DMPO adduct formed is initially EPR active but subsequently is oxidized to the stable nitrone, which can then be detected by IST and further characterized by MS and MS/MS. PMID:21382477

Endotoxin-induced mortality in rats is reduced by nitrones

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

Hamburger, S.A.; McCay, P.B.

The goal of these investigations was to determine if nitrone spin-trapping agents can alter mortality associated with endotoxemia in the rat. Reactive free radicals attack nitrone spin-trapping agents forming relatively reactive, persistent free radical spin adducts. We administered 85 mM (10 ml/kg) of alpha-phenyl N-tert-butyl nitrone (PBN), alpha-4-pyridyl-N-oxide N-tert-butyl nitrone (4-POBN), 5,5-dimethyl-1-pyrroline-N-oxide (DMPO), or vehicle (saline i.p.) 30 min before endotoxin (25 mg/kg i.p.) or vehicle to Sprague-Dawley (SD) or Holtzman virus-free (HVF) rats (n = 10-17/group). All vehicle-treated rats receiving endotoxin were dead by 1 day. At 7 days, 83% of PBN-treated SD, 42% of PBN- or POBN-treated HVF,more » and 25% of DMPO-treated HVF rats were alive. The difference in survival of PBN-treated animals between strains may reflect the higher susceptibility of HVF rats to endotoxin. The observed reduction in mortality may be related to the well-established capacity of spin-trapping agents to capture reactive free radicals that may be generated in target tissues in response to endotoxin, and that would otherwise react with cell components and produce tissue injury.« less

Demonstration of the production of oxygen-centered free radicals during electrolysis using E.S.R. spin-trapping techniques: effects on cardiac function in the isolated rat heart.

PubMed

Lecour, S; Baouali, A B; Maupoil, V; Chahine, R; Abadie, C; Javouhey-Donzel, A; Rochette, L; Nadeau, R

1998-03-01

The present study was designed to identify the free radicals generated during the electrolysis of the solution used to perfuse isolated rat heart Langendorff preparations. The high reactivity and very short half-life of oxygen free radicals make their detection and identification difficult. A diamagnetic organic molecule (spin trap) can be used to react with a specific radical to produce a more stable secondary radical or "spin adduct" detected by electron spin resonance (ESR). Isovolumic left ventricular systolic pressure (LVSP) and left ventricular end diastolic pressure (LVEDP) were measured by a fluid-filled latex balloon inserted into the left ventricle. The coronary flow was measured by effluent collection. Electrolysis was performed with constant currents of 0.5, 1, 1.5, 3, 5, 7.5, and 10 mA generated by a Grass stimulator and applied to the perfusion solution for 1 min. A group of experiments was done using a 1.5 mA current and a Krebs-Henseleit (K-H) solution containing free radical scavengers (superoxide dismutase (SOD): 100 IU/ml or mannitol: 50 mM). Heart function rapidly declined in hearts perfused with K-H buffer that had been electrolyzed for 1 min. The addition of mannitol (50 mM) to the perfusion solution had no effect on baseline cardiac function before electrolysis while SOD (100 IU/ml) increased the coronary flow. However, SOD was more effective than the mannitol in protecting the heart against decreased of cardiac function, 5 min after the end of electrolysis. Samples of the K-H medium subjected to electrolysis were collected in cuvettes containing a final concentration of 125 mM 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) and analyzed by spectroscopy. The ESR spectrum consisted of a quartet signal (hyperfine couplings aN = aH = 14.9 G) originating from the hydroxyl adduct signal, DMPO-OH. The intensity of the DMPO-OH signal remained stable during the 60 s of electrolysis and the quantity of free radicals induced by electrolysis was directly proportional to the intensity of the current. The addition of mannitol and SOD to the perfusate scavenged the hydroxyl radicals present in the solution, suggesting that both hydroxyl and superoxide radicals were formed during electrolysis.

Chromate enhanced visible light driven TiO₂ photocatalytic mechanism on Acid Orange 7 photodegradation.

PubMed

Wang, Yeoung-Sheng; Shen, Jyun-Hong; Horng, Jao-Jia

2014-06-15

When hexavalent chromium (Cr(VI)) is added to a TiO2 photocatalytic reaction, the decolorization and mineralization efficiencies of azo dyes Acid Orange 7 (AO7) are enhanced even though the mechanism is unclear. This study used 5,5-dimethyl-l-pyrroline-N-oxide (DMPO) as the scavenger and the analysis of Electron Spin Resonance (ESR) to investigate this enhancement effect by observing the hydroxyl radical (OH) generation of the Cr(VI)/TiO2 system under UV and visible light (Vis) irradiation. With Cr(VI), the decolorization efficiencies were approximately 95% and 62% under UV and Vis, and those efficiencies were 25% less in the absence of Cr(VI). The phenomena of the DMPO-OH signals during the ESR analysis under Vis 405 and 550 nm irradiation were obviously the enhancement effects of Cr(VI) in aerobic conditions. In anoxic conditions, the catalytic effects of Cr(VI) could not be achieved due to the lack of a redox reaction between Cr(VI) and the adsorbed oxygen at the oxygen vacancy sites on the TiO2 surfaces. The results suggest that by introducing the agents of redox reactions such as chromate ions, we could lower the photoenergy of TiO2 needed and allow Vis irradiation to activate photocatalysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Adsorption of proteins at the solution/air interface influenced by added nonionic surfactants at very low concentrations for both components. 2. Effect of different surfactants and theoretical model.

PubMed

Fainerman, V B; Lotfi, M; Javadi, A; Aksenenko, E V; Tarasevich, Yu I; Bastani, D; Miller, R

2014-11-04

The influence of the addition of the nonionic surfactants dodecyl dimethyl phosphine oxide (C12DMPO), tetradecyl dimethyl phosphine oxide (C14DMPO), decyl alcohol (C10OH), and C10EO5 at concentrations between 10(-5) and 10(-1) mmol/L to solutions of β-casein (BCS) and β-lactoglobulin (BLG) at a fixed concentration of 10(-5) mmol/L on the surface tension is studied. It is shown that a significant decrease of the water/air surface tension occurs for all the surfactants studied at very low concentrations (10(-5)-10(-3) mmol/L). All measurements were performed with the buoyant bubble profile method. The dynamics of the surface tension was simulated using the Fick and Ward-Tordai equations. The calculation results agree well with the experimental data, indicating that the equilibration times in the system studied do not exceed 30 000 s, while the time required to attain the equilibrium on a plane surface is by one order of magnitude higher. To achieve agreement between theory and experiment for the mixtures, a supposition was made about the influence of the concentration of nonionic surfactant on the adsorption activity of the protein. The adsorption isotherm equation of the protein was modified accordingly, and this corrected model agrees well with all experimental data.

Photodegradation of environmental mutagens by visible irradiation in the presence of xanthene dyes as photosensitizers.

PubMed

Odo, Junichi; Torimoto, Sei-ichi; Nakanishi, Suguru; Niitani, Tomoya; Aoki, Hiroyuki; Inoguchi, Masahiko; Yamasaki, Yu

2012-01-01

The photodegradation of environmental mutagens, such as 3-amino-1,4-dimethyl-5H-pyrido[4,3-b]indole (Trp-P-1), 3-amino-1-methyl-5H-pyrido[4,3-b]indole (Trp-P-2), 2-amino-3-methyl-9H-pyrido[2,3-b]indole (MeAαC), and 2-amino-3-methyl-imidazo[4,5-f]quinoline (IQ), was investigated by visible irradiation in the presence of xanthene dyes as photosensitizers. Although the environmental mutagens themselves were very stable during visible irradiation under the conditions in this study, they were effectively photodegraded in the presence of the xanthene dyes (erythrosine, rose bengal, and phloxine). Moreover, photodegradation of the mutagens was further enhanced for xanthene dyes loaded onto a water-soluble diethylaminoethyl (DEAE)-dextran anion-exchanger via ionic interactions (xanthene-dyeDEX). Photodegradation was inhibited by O2 removal from the reaction solution. In ESR spin-trapping experiments using 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) as a trapping reagent, signals characteristic of DMPO-•OH (hydroxyl radical) were observed in the presence of xanthene-dyeDEX. These results suggest that reactive oxygen species derived from O2, such as singlet molecular oxygen (•1O2) and/or •OH, were active participants in photodegradation of the mutagens in the presence of xanthene dyes or xanthene-dyeDEX.

Antioxidative effects of a processed grain food.

PubMed

Minamiyama, Y; Yoshikawa, T; Tanigawa, T; Takahashi, S; Naito, Y; Ichikawa, H; Kondo, M

1994-10-01

Antioxidant biofactor: AOB is a unique processed grain food. It is a yellow-green powder. It contains the following extracts: germ extracts, soybean, rice bran, tear grass, sesame, wheat, citron, green tea, green leaf extract, and malted rice. These materials were slowly roasted under a powdered oure at less than 60 degrees C and fermented with Aspergillus oryzae over 3 days to transform each ingredient into low molecular weight substances. These conditions were different by each material, environmental humidity and temperature. It probably contains a variety of substances having antioxidant activity including flavonoids, alpha-tocopherol, vitamin C, and tannins. We investigated its antioxidative properties using electron spin resonance (ESR) and autoxidation of rat brain homogenates. The superoxide, hydroxyl radical, and the stable free radical, diphenyl-p-picrylhydrazyl (DPPH) radical scavenging activity of AOB was investigated using ESR spectrometry. In an in vitro study, a suspension of AOB was added directly to a superoxide generating system (hypoxanthine-xanthine oxidase; HX/XO) and investigated using 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) as a spin trapping agent. At final concentrations of 0.01, 0.05, and 0.1 mg/ml, AOB dose-dependent scavenging activity was observed as 0.103, 0.619, and 1.369 U/ml, respectively. A concentration of 1.0 mg/ml completely scavenged DMPO-OOH signals; 1.0 mg/ml of AOB inhibited the DMPO-OH signal generated by Fenton's reaction, but its inhibitory effect was not competitive, and was inhibition of the Fenton's reaction. 1.0, 3.0, and 5.0 mg/ml of AOB were significantly inhibited the DPPH radical. In an in vivo study, rats were fed AOB orally at doses of 1 or 5 g/day for 24 h or for 3 days and the superoxide scavenging activity was measured in plasma. With the administration of 1 g/day for 3 days, the superoxide scavenging activity was about 1.8 times that of the control group fed a basal diet; 1.5 times the control with 5 g/day for 1 day, and 2.6 times the control with 5 g/day for 3 days, all of which represented significant increases in superoxide scavenging activity. AOB strongly inhibited the autoxidation of rat brain homogenates in vitro in a dose-dependent manner. However, each ingredient before roast and fermentation little inhibited lipid peroxidation. Roasting and fermentation with A. oryzae way be important to transform each ingredient into low molecular weight substances. Therefore, it was suggested that AOB possesses strong antioxidant and free radical scavenging activities.

Effect of temperature on thermal oxidation of palmitic acid studied by combination of EPR spin trapping technique and SPME-GC-MS/MS.

PubMed

Chen, Hongjian; Wang, Yong; Cao, Peirang; Liu, Yuanfa

2017-11-01

Effect of temperatures on thermal oxidation of palmitic acid was studied by the combination of EPR and GC-MS/MS. DMPO was used as the spin trap. The experimental spectrum was simulated with alkyl and alkoxyl spin adducts. Total amount of spins, a parameter to indicate radical concentrations, detected at 180°C was nearly 10 times higher than that at 175°C. Besides, total amounts of spins detected at 180°C decreased rapidly because of the reaction between radical adducts and newly formed radicals. Signal intensities of alkyl radical adducts increased rapidly from 0.405 to 4.785 from 175°C to 180°C. Besides, more palmitic acid degraded to oxidized compounds from 175°C to 180°C than that of other temperature ranges. The C-C linkages between carbons 2 to 6 were easier to be oxidized at 180°C. The results all implied that oxidation rates of palmitic acid samples increased rapidly from 175°C to 180°C. Copyright © 2017 Elsevier Ltd. All rights reserved.

Antioxidant activities of nano-bubble hydrogen-dissolved water assessed by ESR and 2,2'-bipyridyl methods.

PubMed

Kato, Shinya; Matsuoka, Daigo; Miwa, Nobuhiko

2015-08-01

We prepared nano-bubble hydrogen-dissolved water (nano-H water) which contained hydrogen nano-bubbles of <717-nm diameter for 54% of total bubbles. In the DMPO-spin trap electron spin resonance (ESR) method, the DMPO-OH:MnO ratio, being attributed to amounts of hydroxyl radicals (OH), was 2.78 for pure water (dissolved hydrogen [DH]≤0.01 ppm, oxidation-reduction potential [ORP]=+324 mV), 2.73 for tap water (0.01 ppm, +286 mV), 2.93 for commercially available hydrogen water (0.075 ppm, +49 mV), and 2.66 for manufactured hydrogen water (0.788 ppm, -614 mV), whereas the nano-H water (0.678 ppm, -644 mV) exhibited 2.05, showing the superiority of nano-H water to other types of hydrogen water in terms of OH-scavenging activity. Then, the reduction activity of nano-H water was assessed spectrophotometrically by the 2,2'-bipyridyl method. Differential absorbance at 530 nm was in the order: 0.018 for pure water, 0.055 for tap water, 0.079 for nano-H water, 0.085 for commercially available hydrogen water, and 0.090 for manufactured hydrogen water, indicating a prominent reduction activity of hydrogen water and nano-H water against oxidation in ascorbate-coupled ferric ion-bipyridyl reaction. Thus, nano-H water has an improved antioxidant activity as compared to hydrogen water of similar DH-level, indicating the more marked importance of nano-bubbles rather than the concentration of hydrogen in terms of OH-scavenging. Copyright © 2015 Elsevier B.V. All rights reserved.

Free radical mediated formation of 3-monochloropropanediol (3-MCPD) fatty acid diesters.

PubMed

Zhang, Xiaowei; Gao, Boyan; Qin, Fang; Shi, Haiming; Jiang, Yuangrong; Xu, Xuebing; Yu, Liangli Lucy

2013-03-13

The present study was conducted to test the hypothesis that a free radical was formed and mediated the formation of 3-monochloropropanediol (3-MCPD) fatty acid diesters, a group of food contaminants, from diacylglycerols at high temperature under a low-moisture condition for the first time. The presence of free radicals in a vegetable oil kept at 120 °C for 20 min was demonstrated using an electron spin resonance (ESR) spectroscopy examination with 5,5-dimethylpyrroline-N-oxide (DMPO) as the spin trap agent. ESR investigation also showed an association between thermal treatment degree and the concentration of free radicals. A Fourier transform infrared spectroscopy (FT-IR) analysis of sn-1,2-stearoylglycerol (DSG) at 25 and 120 °C suggested the possible involvement of an ester carbonyl group in forming 3-MCPD diesters. On the basis of these results, a novel free radical mediated chemical mechanism was proposed for 3-MCPD diester formation. Furthermore, a quadrupole-time of flight (Q-TOF) MS/MS investigation was performed and detected the DMPO adducts with the cyclic acyloxonium free radical (CAFR) and its product MS ions, proving the presence of CAFR. Furthermore, the free radical mechanism was validated by the formation of 3-MCPD diesters through reacting DSG with a number of organic and inorganic chlorine sources including chlorine gas at 120 and 240 °C. The findings of this study might lead to the improvement of oil and food processing conditions to reduce the level of 3-MCPD diesters in foods and enhance food safety.

Redox Regulation of NF-κB p50 and M1 Polarization in Microglia

PubMed Central

Taetzsch, Thomas; Levesque, Shannon; McGraw, Constance; Brookins, Savannah; Luqa, Rafy; Bonini, Marcelo G.; Mason, Ronald P.; Oh, Unsong; Block, Michelle L.

2014-01-01

Redox-signaling is implicated in deleterious microglial activation underlying CNS disease, but how ROS program aberrant microglial function is unknown. Here, the oxidation of NF-κB p50 to a free radical intermediate is identified as a marker of dysfunctional M1 (pro-inflammatory) polarization in microglia. Microglia exposed to steady fluxes of H2O2 showed altered NF-κB p50 protein-protein interactions, decreased NF-κB p50 DNA binding, and augmented late-stage TNFα expression, indicating that H2O2 impairs NF-κB p50 function and prolongs amplified M1 activation. NF-κB p50−/− mice and cultures exhibited a disrupted M2 (alternative) response and impaired resolution of the M1 response. Persistent neuroinflammation continued 1 week after LPS (1mg/kg, IP) administration in the NF-κB p50−/− mice. However, peripheral inflammation had already resolved in both strains of mice. Treatment with the spin-trap DMPO mildly reduced LPS-induced 22 h TNFα in the brain in NF-κB p50+/+ mice. Interestingly, DMPO failed to reduce and strongly augmented brain TNFα production in NF-κB p50−/− mice, implicating a fundamental role for NF-κB p50 in the regulation of chronic neuroinflammation by free radicals. These data identify NF-κB p50 as a key redox-signaling mechanism regulating the M1/M2 balance in microglia, where loss of function leads to a CNS-specific vulnerability to chronic inflammation. PMID:25331559

Ambient Particulate Matter Induces Oxidative Dna Damage in Lung Epithelial Cells.

PubMed

Knaapen, A M; Schins, R P; Steinfartz, Y; Doris, H; Dunemann, L; Borm, P J

2000-01-01

Although epidemiological studies have established a correlation between PMIO levels and acute cardiovascular and respiratory complications, hardly any data is available on possible chronic effects such as cancer. The purpose of this study was to investigate the production of free radicals by ambient particulate matter (TSP) and to link these data to oxidative DNA damage in lung epithelial cells. In line with previous findings on PMIO, supercoiled plasmid DNA was depleted by JSP as well as JSP supernatant (p < .001), and this effect was reduced in the presence of mannitol (5 mM). Using electron spin resonance (ESR) and the spin trap dimethyl-1-pyrroline N-oxide (DMPO) we were able to show that hydroxy/radicals ('OH) are formed from both JSP and JSP supernatant. The DMPO-OH signal was completely abrogated when TSP was preincubated with deferoxamine (5 mM), showing the importance of iron and other soluble metals in this process. Atomic absorption spectroscopy (AAS) analysis of the TSP supernatant showed the presence of soluble Fe, V, and Ni (respectively 253.0, 14.7, and 76.0 µ/g insoluble TSP). To investigate the biological significance of OH formation by TSP, 8-hydroxydeoxyguanosine (8-oxodC) was measured in a rat type II cell line by immunocytochemistry. The formation of this hydroxyl-radical-specific DNA adduct was increased twofold (p < .01) after incubation with TSP supernatants, and this effect was inhibited by deferoxamine (p < .01). In summary, our results provide direct evidence that ambient particulate matter generates hydroxyI radicals in acellular systems. Furthermore, we showed that these particulates induce the hydroxyl-radical-specific DNA lesion 8-oxodC in lung target cells via an iron-mediated mechanism.

Adsorption of β-casein-surfactant mixed layers at the air-water interface evaluated by interfacial rheology.

PubMed

Maestro, Armando; Kotsmar, Csaba; Javadi, Aliyar; Miller, Reinhard; Ortega, Francisco; Rubio, Ramón G

2012-04-26

This work presents a detailed study of the dilational viscoelastic moduli of the adsorption layers of the milk protein β-casein (BCS) and a surfactant at the liquid/air interface, over a broad frequency range. Two complementary techniques have been used: a drop profile tensiometry technique and an excited capillary wave method, ECW. Two different surfactants were studied: the nonionic dodecyldimethylphosphine oxide (C12DMPO) and the cationic dodecyltrimethylammonium bromide (DoTAB). The interfacial dilational elasticity and viscosity are very sensitive to the composition of protein-surfactant mixed adsorption layers at the air/water interface. Two different dynamic processes have been observed for the two systems studied, whose characteristic frequencies are close to 0.01 and 100 Hz. In both systems, the surface elasticity was found to show a maximum when plotted versus the surfactant concentration. However, at frequencies above 50 Hz the surface elasticity of BCS + C12DMPO is higher than the one of the aqueous BCS solution over most of the surfactant concentration range, whereas for the BCS + DoTAB it is smaller for high surfactant concentrations and higher at low concentrations. The BCS-surfactant interaction modifies the BCS random coil structure via electrostatic and/or hydrophobic interactions, leading to a competitive adsorption of the BCS-surfactant complexes with the free, unbound surfactant molecules. Increasing the surfactant concentration decreases the adsorbed proteins. However, the BCS molecules are rather strongly bound to the interface due to their large adsorption energy. The results have been fitted to the model proposed by C. Kotsmar et al. ( J. Phys. Chem. B 2009 , 113 , 103 ). Even though the model describes well the concentration dependence of the limiting elasticity, it does not properly describe its frequency dependence.

Dosimetric effect of beam arrangement for intensity-modulated radiation therapy in the treatment of upper thoracic esophageal carcinoma

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

Fu, Yuchuan; Deng, Min; Zhou, Xiaojuan

To evaluate the lung sparing in intensity-modulated radiation therapy (IMRT) for patients with upper thoracic esophageal tumors extending inferiorly to the thorax by different beam arrangement. Overall, 15 patient cases with cancer of upper thoracic esophagus were selected for a retrospective treatment-planning study. Intensity-modulated radiation therapy plans using 4, 5, and 7 beams (4B, 5B, and 7B) were developed for each patient by direct machine parameter optimization (DMPO). All plans were evaluated with respect to dose volumes to irradiated targets and normal structures, with statistical comparisons made between 4B with 5B and 7B intensity-modulated radiation therapy plans. Differences among plansmore » were evaluated using a two-tailed Friedman test at a statistical significance of p < 0.05. The maximum dose, average dose, and the conformity index (CI) of planning target volume 1 (PTV1) were similar for 3 plans for each case. No significant difference of coverage for planning target volume 1 and maximum dose for spinal cords were observed among 3 plans in present study (p > 0.05). The average V{sub 5}, V{sub 13}, V{sub 20}, mean lung dose, and generalized equivalent uniform dose (gEUD) for the total lung were significantly lower in 4B-plans than those data in 5B-plans and 7B-plans (p < 0.01). Although the average V{sub 30} for the total lung were significantly higher in 4B-plans than those in 5B-plans and 7B-plans (p < 0.05). In addition, when comparing with the 4B-plans, the conformity/heterogeneity index of the 5B- and 7B-plans were significantly superior (p < 0.05). The 4B-intensity-modulated radiation therapy plan has advantage to address the specialized problem of lung sparing to low- and intermediate-dose exposure in the thorax when dealing with relative long tumors extended inferiorly to the thoracic esophagus for upper esophageal carcinoma with the cost for less conformity. Studies are needed to compare the superiority of volumetric modulated arc therapy with intensity-modulated radiation therapy technique.« less

1,4-Diamino-2-butanone, a wide-spectrum microbicide, yields reactive species by metal-catalyzed oxidation.

PubMed

Soares, Chrislaine O; Alves, Maria Julia M; Bechara, Etelvino J H

2011-06-15

The α-aminoketone 1,4-diamino-2-butanone (DAB), a putrescine analogue, is highly toxic to various microorganisms, including Trypanosoma cruzi. However, little is known about the molecular mechanisms underlying DAB's cytotoxic properties. We report here that DAB (pK(a) 7.5 and 9.5) undergoes aerobic oxidation in phosphate buffer, pH 7.4, at 37°C, catalyzed by Fe(II) and Cu(II) ions yielding NH(4)(+) ion, H(2)O(2), and 4-amino-2-oxobutanal (oxoDAB). OxoDAB, like methylglyoxal and other α-oxoaldehydes, is expected to cause protein aggregation and nucleobase lesions. Propagation of DAB oxidation by superoxide radical was confirmed by the inhibitory effect of added SOD (50 U ml-1) and stimulatory effect of xanthine/xanthine oxidase, a source of superoxide radical. EPR spin trapping studies with 5,5-dimethyl-1-pyrroline-1-oxide (DMPO) revealed an adduct attributable to DMPO-HO(•), and those with α-(4-pyridyl-1-oxide)-N-tert-butylnitrone or 3,5-dibromo-4-nitrosobenzenesulfonic acid, a six-line adduct assignable to a DAB(•) resonant enoyl radical adduct. Added horse spleen ferritin (HoSF) and bovine apo-transferrin underwent oxidative changes in tryptophan residues in the presence of 1.0-10 mM DAB. Iron release from HoSF was observed as well. Assays performed with fluorescein-encapsulated liposomes of cardiolipin and phosphatidylcholine (20:80) incubated with DAB resulted in extensive lipid peroxidation and consequent vesicle permeabilization. DAB (0-10 mM) administration to cultured LLC-MK2 epithelial cells caused a decline in cell viability, which was inhibited by preaddition of either catalase (4.5 μM) or aminoguanidine (25 mM). Our findings support the hypothesis that DAB toxicity to several pathogenic microorganisms previously described may involve not only reported inhibition of polyamine metabolism but also DAB pro-oxidant activity. Copyright © 2011 Elsevier Inc. All rights reserved.

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

Paudel, N; Shvydka, D; Karpov, V

Purpose: Hyperthermia, an established method of cancer treatment used in adjuvant to radiation and chemotherapy, can utilize metallic nanoparticles (NPs) for tumor heating with a microwave electromagnetic field. The high surface-area-to-volume ratio of nanoparticles makes them effective catalysts for free radical generation, thus amplifying the cell-killing effect of hyperthermia. We explore the effect of gold and platinum NPs in generating free radicals in aqueous media under a microwave field. Methods: Spin trap 5,5-Dimethyl-1-pyrroline-N-oxide (DMPO) was mixed separately with 3.2 nm Mesogold and Mesoplatinum colloidal nanoparticle suspensions in deionized water to trap radicals. The mixtures were injected into a number ofmore » glass capillaries and exposed to the 9.68GHz microwave field of an electron paramagnetic resonance (EPR) spectrometer. The microwave radiation from the spectrometer served to both generate and detect the trapped radicals. Each sample was scanned at 12mW microwave power to obtain the initial signal of hydroxyl radicals (OH.), then at 39.8mW followed by 79.8 or 125mW, and finally re-scanned at 12mW. Radical signal intensities obtained by double integration of EPR spectra from the initial and the final scans were then compared. Results: Nanoparticle samples had no intentionally-added free radicals before the initial measurement. While samples with DMPO-water solution showed no OH. signal, all those with AuNPs or PtNPs developed an OH. signal during their first exposure to the microwave field. Depending upon the applied microwave power and time interval between the initial and the final EPR scans, an OH. intensity increase of ∼10-60% was found. This contradicts the typical trend of exponential decay of the OH. signal with time. Conclusion: The consistent increase in OH. intensity establishes that gold and platinum nanoparticles facilitate free radical generation under microwave irradiation. Our results suggest that NP-aided hyperthermia is accompanied by the generation of free radicals, which enhance the cell-killing effects of hyperthermia.« less

PrP{sup C} displays an essential protective role from oxidative stress in an astrocyte cell line derived from PrP{sup C} knockout mice

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

Bertuchi, Fernanda R.; Bourgeon, Dominique M.G.; Landemberger, Michele C.

2012-02-03

Highlights: Black-Right-Pointing-Pointer PrP{sup C} in solution acts as a radical scavenger. Black-Right-Pointing-Pointer PrP{sup C} reduces hydrogen peroxide toxicity in astrocytes. Black-Right-Pointing-Pointer Increase in ROS disrupted the cell cycle in the PrP{sup C}-knockout astrocytes. Black-Right-Pointing-Pointer PrP{sup C} prevents the cell death independently of an SOD-like activity. -- Abstract: The PrP{sup C} protein, which is especially present in the cellular membrane of nervous system cells, has been extensively studied for its controversial antioxidant activity. In this study, we elucidated the free radical scavenger activity of purified murine PrP{sup C} in solution and its participation as a cell protector in astrocytes that weremore » subjected to treatment with an oxidant. In vitro and using an EPR spin-trapping technique, we observed that PrP{sup C} decreased the oxidation of the DMPO trap in a Fenton reaction system (Cu{sup 2+}/ascorbate/H{sub 2}O{sub 2}), which was demonstrated by approximately 70% less DMPO/OH{sup {center_dot}}. In cultured PrP{sup C}-knockout astrocytes from mice, the absence of PrP{sup C} caused an increase in intracellular ROS (reactive oxygen species) generation during the first 3 h of H{sub 2}O{sub 2} treatment. This rapid increase in ROS disrupted the cell cycle in the PrP{sup C}-knockout astrocytes, which increased the population of cells in the sub-G1 phase when compared with cultured wild-type astrocytes. We conclude that PrP{sup C} in solution acts as a radical scavenger, and in astrocytes, it is essential for protection from oxidative stress caused by an external chemical agent, which is a likely condition in human neurodegenerative CNS disorders and pathological conditions such as ischemia.« less

Phosphine oxide surfactants revisited.

PubMed

Stubenrauch, Cosima; Preisig, Natalie; Laughlin, Robert G

2016-04-01

This review summarizes everything we currently know about the nonionic surfactants alkyl dimethyl (C(n)DMPO) and alkyl diethyl (C(n)DEPO) phosphine oxide (PO surfactants). The review starts with the synthesis and the general properties (Section 2) of these compounds and continues with their interfacial properties (Section 3) such as surface tension, surface rheology, interfacial tension and adsorption at solid surfaces. We discuss studies on thin liquid films and foams stabilized by PO surfactants (Section 4) as well as studies on their self-assembly into lyotropic liquid crystals and microemulsions, respectively (Section 5). We aim at encouraging colleagues from both academia and industry to take on board PO surfactants whenever possible and feasible because of their broad variety of excellent properties. Copyright © 2016 Elsevier B.V. All rights reserved.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

Quasi-VMAT in high-grade glioma radiation therapy.

PubMed

Fadda, G; Massazza, G; Zucca, S; Durzu, S; Meleddu, G; Possanzini, M; Farace, P

2013-05-01

To compare a quasi-volumetric modulated arc therapy (qVMAT) with three-dimensional conformal radiation therapy (3D-CRT) and intensity-modulated radiation therapy (IMRT) for the treatment of high-grade gliomas. The qVMAT technique is a fast method of radiation therapy in which multiple equispaced beams analogous to those in rotation therapy are radiated in succession. This study included 12 patients with a planning target volume (PTV) that overlapped at least one organ at risk (OAR). 3D-CRT was planned using 2-3 non-coplanar beams, whereby the field-in-field technique (FIF) was used to divide each field into 1-3 subfields to shield the OAR. The qVMAT strategy was planned with 15 equispaced beams and IMRT was planned using 9 beams with a total of 80 segments. Inverse planning for qVMAT and IMRT was performed by direct machine parameter optimization (DMPO) to deliver a homogenous dose distribution of 60 Gy within the PTV and simultaneously limit the dose received by the OARs to the recommended values. Finally, the effect of introducing a maximum dose objective (max. dose < 54 Gy) for a virtual OAR in the form of a 0.5 cm ring around the PTV was investigated. The qVMAT method gave rise to significantly improved PTV95% and conformity index (CI) values in comparison to 3D-CRT (PTV95% = 90.7 % vs. 82.0 %; CI = 0.79 vs. 0.74, respectively). A further improvement was achieved by IMRT (PTV95% = 94.4 %, CI = 0.78). In qVMAT and IMRT, the addition of a 0.5 cm ring around the PTV produced a significant increase in CI (0.87 and 0.88, respectively), but dosage homogeneity within the PTV was considerably reduced (PTV95% = 88.5 % and 92.3 %, respectively). The time required for qVMAT dose delivery was similar to that required using 3D-CRT. These findings suggest that qVMAT should be preferred to 3D-CRT for the treatment of high-grade gliomas. The qVMAT method could be applied in hospitals, for example, which have limited departmental resources and are not equipped with systems capable of VMAT delivery.

Dosimetric effect of beam arrangement for intensity-modulated radiation therapy in the treatment of upper thoracic esophageal carcinoma.

PubMed

Fu, Yuchuan; Deng, Min; Zhou, Xiaojuan; Lin, Qiang; Du, Bin; Tian, Xue; Xu, Yong; Wang, Jin; Lu, You; Gong, Youling

2017-01-01

To evaluate the lung sparing in intensity-modulated radiation therapy (IMRT) for patients with upper thoracic esophageal tumors extending inferiorly to the thorax by different beam arrangement. Overall, 15 patient cases with cancer of upper thoracic esophagus were selected for a retrospective treatment-planning study. Intensity-modulated radiation therapy plans using 4, 5, and 7 beams (4B, 5B, and 7B) were developed for each patient by direct machine parameter optimization (DMPO). All plans were evaluated with respect to dose volumes to irradiated targets and normal structures, with statistical comparisons made between 4B with 5B and 7B intensity-modulated radiation therapy plans. Differences among plans were evaluated using a two-tailed Friedman test at a statistical significance of p < 0.05. The maximum dose, average dose, and the conformity index (CI) of planning target volume 1 (PTV1) were similar for 3 plans for each case. No significant difference of coverage for planning target volume 1 and maximum dose for spinal cords were observed among 3 plans in present study (p > 0.05). The average V 5 , V 13 , V 20 , mean lung dose, and generalized equivalent uniform dose (gEUD) for the total lung were significantly lower in 4B-plans than those data in 5B-plans and 7B-plans (p < 0.01). Although the average V 30 for the total lung were significantly higher in 4B-plans than those in 5B-plans and 7B-plans (p < 0.05). In addition, when comparing with the 4B-plans, the conformity/heterogeneity index of the 5B- and 7B-plans were significantly superior (p < 0.05). The 4B-intensity-modulated radiation therapy plan has advantage to address the specialized problem of lung sparing to low- and intermediate-dose exposure in the thorax when dealing with relative long tumors extended inferiorly to the thoracic esophagus for upper esophageal carcinoma with the cost for less conformity. Studies are needed to compare the superiority of volumetric modulated arc therapy with intensity-modulated radiation therapy technique. Copyright © 2017 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.

Determination of the optimal mesh parameters for Iguassu centrifuge flow and separation calculations

NASA Astrophysics Data System (ADS)

Romanihin, S. M.; Tronin, I. V.

2016-09-01

We present the method and the results of the determination for optimal computational mesh parameters for axisymmetric modeling of flow and separation in the Iguasu gas centrifuge. The aim of this work was to determine the mesh parameters which provide relatively low computational cost whithout loss of accuracy. We use direct search optimization algorithm to calculate optimal mesh parameters. Obtained parameters were tested by the calculation of the optimal working regime of the Iguasu GC. Separative power calculated using the optimal mesh parameters differs less than 0.5% from the result obtained on the detailed mesh. Presented method can be used to determine optimal mesh parameters of the Iguasu GC with different rotor speeds.

Scavenging of free-radical metabolites of aniline xenobiotics and drugs by amino acid derivatives: toxicological implications of radical-transfer reactions.

PubMed

Michail, Karim; Baghdasarian, Argishti; Narwaley, Malyaj; Aljuhani, Naif; Siraki, Arno G

2013-12-16

We investigated a novel scavenging mechanism of arylamine free radicals by poly- and monoaminocarboxylates. Free radicals of arylamine xenobiotics and drugs did not react with oxygen in peroxidase-catalyzed reactions; however, they showed marked oxygen uptake in the presence of an aminocarboxylate. These free-radical intermediates were identified using the spin trap 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) and electron paramagnetic resonance (EPR) spectrometry. Diethylenetriaminepentaacetic acid (DTPA), a polyaminocarboxylate, caused a concentration-dependent attenuation of N-centered radicals produced by the peroxidative metabolism of arylamines with the subsequent formation of secondary aliphatic carbon-centered radicals stemming from the cosubstrate molecule. Analogously, N,N-dimethylglycine (DMG) and N-methyliminodiacetate (MIDA), but not iminodiacetic acid (IDA), demonstrated a similar scavenging effect of arylamine-derived free radicals in a horseradish peroxidase/H2O2 system. Using human promyelocytic leukemia (HL-60) cell lysate as a model of human neutrophils, DTPA, MIDA, and DMG readily reduced anilinium cation radicals derived from the arylamines and gave rise to the corresponding carbon radicals. The rate of peroxidase-triggered polymerization of aniline was studied as a measure of nitrogen-radical scavenging. Although, IDA had no effect on the rate of aniline polymerization, this was almost nullified in the presence of DTPA and MIDA at half of the molar concentration of the aniline substrate, whereas a 20 molar excess of DMPO caused only a partial inhibition. Furthermore, the yield of formaldehyde, a specific reaction endproduct of the oxidation of aminocarboxylates by aniline free-radical metabolites, was quantitatively determined. Azobenzene, a specific reaction product of peroxidase-catalyzed free-radical dimerization of aniline, was fully abrogated in the presence of DTPA, as confirmed by GC/MS. Under aerobic conditions, a radical-transfer reaction is proposed between aminocarboxylates and arylamine free radicals via the carboxylic group-linked tertiary nitrogen of the deprotonated amino acid derivatives. These findings may have significant implications for the biological fate of arylamine xenobiotic and drug free-radical metabolites.

Control and optimization system

DOEpatents

Xinsheng, Lou

2013-02-12

A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

NASA Astrophysics Data System (ADS)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

PubMed

Van Derlinden, E; Bernaerts, K; Van Impe, J F

2010-05-21

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Multi-parameter optimization of piezoelectric actuators for multi-mode active vibration control of cylindrical shells

NASA Astrophysics Data System (ADS)

Hu, K. M.; Li, Hua

2018-07-01

A novel technique for the multi-parameter optimization of distributed piezoelectric actuators is presented in this paper. The proposed method is designed to improve the performance of multi-mode vibration control in cylindrical shells. The optimization parameters of actuator patch configuration include position, size, and tilt angle. The modal control force of tilted orthotropic piezoelectric actuators is derived and the multi-parameter cylindrical shell optimization model is established. The linear quadratic energy index is employed as the optimization criterion. A geometric constraint is proposed to prevent overlap between tilted actuators, which is plugged into a genetic algorithm to search the optimal configuration parameters. A simply-supported closed cylindrical shell with two actuators serves as a case study. The vibration control efficiencies of various parameter sets are evaluated via frequency response and transient response simulations. The results show that the linear quadratic energy indexes of position and size optimization decreased by 14.0% compared to position optimization; those of position and tilt angle optimization decreased by 16.8%; and those of position, size, and tilt angle optimization decreased by 25.9%. It indicates that, adding configuration optimization parameters is an efficient approach to improving the vibration control performance of piezoelectric actuators on shells.

Optimization of a centrifugal compressor impeller using CFD: the choice of simulation model parameters

NASA Astrophysics Data System (ADS)

Neverov, V. V.; Kozhukhov, Y. V.; Yablokov, A. M.; Lebedev, A. A.

2017-08-01

Nowadays the optimization using computational fluid dynamics (CFD) plays an important role in the design process of turbomachines. However, for the successful and productive optimization it is necessary to define a simulation model correctly and rationally. The article deals with the choice of a grid and computational domain parameters for optimization of centrifugal compressor impellers using computational fluid dynamics. Searching and applying optimal parameters of the grid model, the computational domain and solver settings allows engineers to carry out a high-accuracy modelling and to use computational capability effectively. The presented research was conducted using Numeca Fine/Turbo package with Spalart-Allmaras and Shear Stress Transport turbulence models. Two radial impellers was investigated: the high-pressure at ψT=0.71 and the low-pressure at ψT=0.43. The following parameters of the computational model were considered: the location of inlet and outlet boundaries, type of mesh topology, size of mesh and mesh parameter y+. Results of the investigation demonstrate that the choice of optimal parameters leads to the significant reduction of the computational time. Optimal parameters in comparison with non-optimal but visually similar parameters can reduce the calculation time up to 4 times. Besides, it is established that some parameters have a major impact on the result of modelling.

Numerical optimization methods for controlled systems with parameters

NASA Astrophysics Data System (ADS)

Tyatyushkin, A. I.

2017-10-01

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

Formation of methemoglobin and phenoxyl radicals from p-hydroxyanisole and oxyhemoglobin.

PubMed

Stolze, K; Nohl, H

1991-01-01

The reaction of p-hydroxyanisole with oxyhemoglobin was investigated using electron spin resonance spectroscopy (ESR) and visible spectroscopy. As a reactive reaction intermediate we found the p-methoxyphenoxyl radical, the one-electron oxidation product of p-hydroxyanisole. Detection of this species required the rapid flow device elucidating the instability of this radical intermediate. The second reaction product formed is methemoglobin. Catalase or SOD had no effect upon the reaction kinetics. Accordingly, reactive oxygen species such as hydroxyl radicals or superoxide could not be observed although the spin trapping agent DMPO was used to make these short-lived species detectable. When the sulfhydryl blocking agents N-ethylmaleimide or mersalyl acid were used, an increase of the methemoglobin formation rate and of the phenoxyl radical concentration were observed. We have interpreted this observation in terms of a side reaction of free radical intermediates with thiol groups.

Rare earth ions doped ZnO: Synthesis, characterization and preliminary photoactivity assessment

NASA Astrophysics Data System (ADS)

Cerrato, Erik; Gionco, Chiara; Berruti, Ilaria; Sordello, Fabrizio; Calza, Paola; Paganini, Maria Cristina

2018-08-01

This work reports the effect of doping zinc oxide with lanthanide ions on structural, EPR and UV visible properties. Bare and doped samples were synthesized using the simple and green hydrothermal process. Different rare earth ions (RE = La, Ce, Pr, Er and Yb) with 1% molar ratio RE/Zn were used. The samples have been studied using X Ray Diffraction, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and UV visible diffuse reflectance spectroscopy. Finally, electron paramagnetic resonance (EPR) spectroscopy, was used to assess the materials photoactivity under UV irradiation, both in solid state, to see the charge carriers' generation and in solution, evaluating the OH• radical formation using the DMPO (5,5-Dimethyl-1-Pyrroline-N-Oxide) spin trapping technique. The results suggest that the synthesized materials could be interesting systems for the photocatalytic abatement of emerging organic persistent pollutants in wastewater treatment plants.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Simultaneous Intrinsic and Extrinsic Parameter Identification of a Hand-Mounted Laser-Vision Sensor

PubMed Central

Lee, Jong Kwang; Kim, Kiho; Lee, Yongseok; Jeong, Taikyeong

2011-01-01

In this paper, we propose a simultaneous intrinsic and extrinsic parameter identification of a hand-mounted laser-vision sensor (HMLVS). A laser-vision sensor (LVS), consisting of a camera and a laser stripe projector, is used as a sensor component of the robotic measurement system, and it measures the range data with respect to the robot base frame using the robot forward kinematics and the optical triangulation principle. For the optimal estimation of the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. Best-fit parameters, including both the intrinsic and extrinsic parameters of the HMLVS, are simultaneously obtained based on the least-squares criterion. From the simulation and experimental results, it is shown that the parameter identification problem considered was characterized by a highly multimodal landscape; thus, the global optimization technique such as a particle swarm optimization can be a promising tool to identify the model parameters for a HMLVS, while the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum. The proposed optimization method does not require good initial guesses of the system parameters to converge at a very stable solution and it could be applied to a kinematically dissimilar robot system without loss of generality. PMID:22164104

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Optimal Designs for the Rasch Model

ERIC Educational Resources Information Center

Grasshoff, Ulrike; Holling, Heinz; Schwabe, Rainer

2012-01-01

In this paper, optimal designs will be derived for estimating the ability parameters of the Rasch model when difficulty parameters are known. It is well established that a design is locally D-optimal if the ability and difficulty coincide. But locally optimal designs require that the ability parameters to be estimated are known. To attenuate this…

Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...

Study on loading path optimization of internal high pressure forming process

NASA Astrophysics Data System (ADS)

Jiang, Shufeng; Zhu, Hengda; Gao, Fusheng

2017-09-01

In the process of internal high pressure forming, there is no formula to describe the process parameters and forming results. The article use numerical simulation to obtain several input parameters and corresponding output result, use the BP neural network to found their mapping relationship, and with weighted summing method make each evaluating parameters to set up a formula which can evaluate quality. Then put the training BP neural network into the particle swarm optimization, and take the evaluating formula of the quality as adapting formula of particle swarm optimization, finally do the optimization and research at the range of each parameters. The results show that the parameters obtained by the BP neural network algorithm and the particle swarm optimization algorithm can meet the practical requirements. The method can solve the optimization of the process parameters in the internal high pressure forming process.

Removal of H2O2 and generation of superoxide radical: Role of cytochrome c and NADH

PubMed Central

Velayutham, Murugesan; Hemann, Craig; Zweier, Jay L.

2011-01-01

In cells, mitochondria, endoplasmic reticulum, and peroxisomes are the major sources of reactive oxygen species (ROS) under physiological and pathophysiological conditions. Cytochrome c (cyt c) is known to participate in mitochondrial electron transport and has antioxidant and peroxidase activities. Under oxidative or nitrative stress, the peroxidase activity of Fe3+cyt c is increased. The level of NADH is also increased under pathophysiological conditions such as ischemia and diabetes and a concurrent increase in hydrogen peroxide (H2O2) production occurs. Studies were performed to understand the related mechanisms of radical generation and NADH oxidation by Fe3+cyt c in the presence of H2O2. Electron paramagnetic resonance (EPR) spin trapping studies using 5,5-dimethyl-1-pyrroline-N-oxide (DMPO) were performed with NADH, Fe3+cyt c, and H2O2 in the presence of methyl-β-cyclodextrin. An EPR spectrum corresponding to the superoxide radical adduct of DMPO encapsulated in methyl-β-cyclodextrin was obtained. This EPR signal was quenched by the addition of the superoxide scavenging enzyme Cu,Zn-superoxide dismutase (SOD1). The amount of superoxide radical adduct formed from the oxidation of NADH by the peroxidase activity of Fe3+cyt c increased with NADH and H2O2 concentration. From these results, we propose a mechanism in which the peroxidase activity of Fe3+cyt c oxidizes NADH to NAD•, which in turn donates an electron to O2 resulting in superoxide radical formation. A UV-visible spectroscopic study shows that Fe3+cyt c is reduced in the presence of both NADH and H2O2. Our results suggest that Fe3+cyt c could have a novel role in the deleterious effects of ischemia/reperfusion and diabetes due to increased production of superoxide radical. In addition, Fe3+cyt c may play a key role in the mitochondrial “ROS-induced ROS-release (RIRR)” signaling and in mitochondrial and cellular injury/death. The increased oxidation of NADH and generation of superoxide radical by this mechanism may have implications for the regulation of apoptotic cell death, endothelial dysfunction, and neurological diseases. We also propose an alternative electron transfer pathway, which may protect mitochondria and mitochondrial proteins from oxidative damage. PMID:21545835

Chronic Endurance Exercise Impairs Cardiac Structure and Function in Middle-Aged Mice with Impaired Nrf2 Signaling

PubMed Central

Shanmugam, Gobinath; Narasimhan, Madhusudhanan; Conley, Robbie L.; Sairam, Thiagarajan; Kumar, Ashutosh; Mason, Ronald P.; Sankaran, Ramalingam; Hoidal, John R.; Rajasekaran, Namakkal S.

2017-01-01

Nuclear factor erythroid 2 related factor 2 (Nrf2) signaling maintains the redox homeostasis and its activation is shown to suppress cardiac maladaptation. Earlier we reported that acute endurance exercise (2 days) evoked antioxidant cytoprotection in young WT animals but not in aged WT animals. However, the effect of repeated endurance exercise during biologic aging (WT) characterized by an inherent deterioration in Nrf2 signaling and pathological aging (pronounced oxidative susceptibility—Nrf2 absence) in the myocardium remains elusive. Thus, the purpose of our study was to determine the effect of chronic endurance exercise-induced cardiac adaptation in aged mice with and without Nrf2. Age-matched WT and Nrf2-null mice (Nrf2−/−) (>22 months) were subjected to 6 weeks chronic endurance exercise (25 meter/min, 12% grade). The myocardial redox status was assessed by expression of antioxidant defense genes and proteins along with immunochemical detection of DMPO-radical adduct, GSH-NEM, and total ubiquitination. Cardiac functions were assessed by echocardiography and electrocardiogram. At sedentary state, loss of Nrf2 resulted in significant downregulation of antioxidant gene expression (Nqo1, Ho1, Gclm, Cat, and Gst-α) with decreased GSH-NEM immuno-fluorescence signals. While Nrf2−/− mice subjected to CEE showed an either similar or more pronounced reduction in the transcript levels of Gclc, Nqo1, Gsr, and Gst-α in relation to WT littermates. In addition, the hearts of Nrf2−/− on CEE showed a substantial reduction in specific antioxidant proteins, G6PD and CAT along with decreased GSH, a pronounced increase in DMPO-adduct and the total ubiquitination levels. Further, CEE resulted in a significant upregulation of hypertrophy genes (Anf, Bnf, and β-Mhc) (p < 0.05) in the Nrf2−/− hearts in relation to WT mice. Moreover, the aged Nrf2−/− mice exhibited a higher degree of cardiac remodeling in association with a significant decrease in fractional shortening, pronounced ST segment, and J wave elevation upon CEE compared to age-matched WT littermates. In conclusion, our findings indicate that while the aged WT and Nrf2 knockout animals both exhibit hypertrophy after CEE, the older Nrf2 knockouts showed ventricular remodeling coupled with profound cardiac functional abnormalities and diastolic dysfunction. PMID:28515695

Chaos minimization in DC-DC boost converter using circuit parameter optimization

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Natarajan, Rajasekar; Gourav, Kumar; Padmavathi, P.

2017-11-01

DC-DC converters are prone to several types of nonlinear phenomena including bifurcation, quasi periodicity, intermittency and chaos. These undesirable effects must be controlled for periodic operation of the converter to ensure the stability. In this paper an effective solution to control of chaos in solar fed DC-DC boost converter is proposed. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The obtained results are compared with the operation of traditional boost converter. Further the obtained results with BFA optimized parameter ensures the operations of the converter are within the controllable region. To elaborate the study of bifurcation analysis with optimized and unoptimized parameters are also presented.

Optimal Bayesian Adaptive Design for Test-Item Calibration.

PubMed

van der Linden, Wim J; Ren, Hao

2015-06-01

An optimal adaptive design for test-item calibration based on Bayesian optimality criteria is presented. The design adapts the choice of field-test items to the examinees taking an operational adaptive test using both the information in the posterior distributions of their ability parameters and the current posterior distributions of the field-test parameters. Different criteria of optimality based on the two types of posterior distributions are possible. The design can be implemented using an MCMC scheme with alternating stages of sampling from the posterior distributions of the test takers' ability parameters and the parameters of the field-test items while reusing samples from earlier posterior distributions of the other parameters. Results from a simulation study demonstrated the feasibility of the proposed MCMC implementation for operational item calibration. A comparison of performances for different optimality criteria showed faster calibration of substantial numbers of items for the criterion of D-optimality relative to A-optimality, a special case of c-optimality, and random assignment of items to the test takers.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

NASA Astrophysics Data System (ADS)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

ConvAn: a convergence analyzing tool for optimization of biochemical networks.

PubMed

Kostromins, Andrejs; Mozga, Ivars; Stalidzans, Egils

2012-01-01

Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable. Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces. A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale. By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters. The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties. The software ConvAn is freely available on www.biosystems.lv/convan. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process.

PubMed

Deng, Bo; Shi, Yaoyao; Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-31

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process

PubMed Central

Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-01

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048

Optimization of microphysics in the Unified Model, using the Micro-genetic algorithm.

NASA Astrophysics Data System (ADS)

Jang, J.; Lee, Y.; Lee, H.; Lee, J.; Joo, S.

2016-12-01

This study focuses on parameter optimization of microphysics in the Unified Model (UM) using the Micro-genetic algorithm (Micro-GA). We need the optimization of microphysics in UM. Because, Microphysics in the Numerical Weather Prediction (NWP) model is important to Quantitative Precipitation Forecasting (QPF). The Micro-GA searches for optimal parameters on the basis of fitness function. The five parameters are chosen. The target parameters include x1, x2 related to raindrop size distribution, Cloud-rain correlation coefficient, Surface droplet number and Droplet taper height. The fitness function is based on the skill score that is BIAS and Critical Successive Index (CSI). An interface between UM and Micro-GA is developed and applied to three precipitation cases in Korea. The cases are (ⅰ) heavy rainfall in the Southern area because of typhoon NAKRI, (ⅱ) heavy rainfall in the Youngdong area, and (ⅲ) heavy rainfall in the Seoul metropolitan area. When the optimized result is compared to the control result (using the UM default value, CNTL), the optimized result leads to improvements in precipitation forecast, especially for heavy rainfall of the late forecast time. Also, we analyze the skill score of precipitation forecasts in terms of various thresholds of CNTL, Optimized result, and experiments on each optimized parameter for five parameters. Generally, the improvement is maximized when the five optimized parameters are used simultaneously. Therefore, this study demonstrates the ability to improve Korean precipitation forecasts by optimizing microphysics in UM.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

PubMed

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

Constraining neutron guide optimizations with phase-space considerations

NASA Astrophysics Data System (ADS)

Bertelsen, Mads; Lefmann, Kim

2016-09-01

We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

Real-time parameter optimization based on neural network for smart injection molding

NASA Astrophysics Data System (ADS)

Lee, H.; Liau, Y.; Ryu, K.

2018-03-01

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.

Optimization of Gas Metal Arc Welding Process Parameters

NASA Astrophysics Data System (ADS)

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.

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, Chuan; Zhang, Rong-Hua; Wu, Xinrong; Sun, Jichang

2018-04-01

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer ( T e), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as "the thermocline effect") is represented by an introduced parameter, α Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Using a 4D-Variational Method to Optimize Model Parameters in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, C.; Zhang, R. H.

2017-12-01

Large biases exist in real-time ENSO prediction, which is attributed to uncertainties in initial conditions and model parameters. Previously, a four dimentional variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation, written as Te=αTe×FTe (SL). The introduced parameter, αTe, represents the strength of the thermocline effect on sea surface temperature (SST; referred as the thermocline effect). A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having initial condition optimized only and having initial condition plus this additional model parameter optimized both are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameter and initial condition together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Flight Test Validation of Optimal Input Design and Comparison to Conventional Inputs

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1997-01-01

A technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Angle of Attack Research Vehicle (HARV). Model parameter accuracies calculated from flight test data were compared on an equal basis for optimal input designs and conventional inputs at the same flight condition. In spite of errors in the a priori input design models and distortions of the input form by the feedback control system, the optimal inputs increased estimated parameter accuracies compared to conventional 3-2-1-1 and doublet inputs. In addition, the tests using optimal input designs demonstrated enhanced design flexibility, allowing the optimal input design technique to use a larger input amplitude to achieve further increases in estimated parameter accuracy without departing from the desired flight test condition. This work validated the analysis used to develop the optimal input designs, and demonstrated the feasibility and practical utility of the optimal input design technique.

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Hybrid computer optimization of systems with random parameters

NASA Technical Reports Server (NTRS)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

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

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

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

Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.

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

NASA Astrophysics Data System (ADS)

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.

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

NASA Astrophysics Data System (ADS)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Selecting and optimizing eco-physiological parameters of Biome-BGC to reproduce observed woody and leaf biomass growth of Eucommia ulmoides plantation in China using Dakota optimizer

NASA Astrophysics Data System (ADS)

Miyauchi, T.; Machimura, T.

2013-12-01

In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the field survey) were weighted for priority. We compared some gradient-based global optimization methods of Dakota starting with the default parameters of Biome-BGC. In the result of sensitive analysis, carbon allocation parameters between coarse root and leaf, between stem and leaf, and SLA had high contribution on both leaf and woody biomass changes. These parameters were selected to be optimized. The measured leaf, above- and below-ground woody biomass carbon density at the last year were 0.22, 1.81 and 0.86 kgC m-2, respectively, whereas those simulated in the non-optimized control case using all default parameters were 0.12, 2.26 and 0.52 kgC m-2, respectively. After optimizing the parameters, the simulated values were improved to 0.19, 1.81 and 0.86 kgC m-2, respectively. The coliny global optimization method gave the better fitness than efficient global and ncsu direct method. The optimized parameters showed the higher carbon allocation rates to coarse roots and leaves and the lower SLA than the default parameters, which were consistent to the general water physiological response in a dry climate. The simulation using the weighted object function resulted in the closer simulations to the measurements at the last year with the lower fitness during the previous years.

Method for Household Refrigerators Efficiency Increasing

NASA Astrophysics Data System (ADS)

Lebedev, V. V.; Sumzina, L. V.; Maksimov, A. V.

2017-11-01

The relevance of working processes parameters optimization in air conditioning systems is proved in the work. The research is performed with the use of the simulation modeling method. The parameters optimization criteria are considered, the analysis of target functions is given while the key factors of technical and economic optimization are considered in the article. The search for the optimal solution at multi-purpose optimization of the system is made by finding out the minimum of the dual-target vector created by the Pareto method of linear and weight compromises from target functions of the total capital costs and total operating costs. The tasks are solved in the MathCAD environment. The research results show that the values of technical and economic parameters of air conditioning systems in the areas relating to the optimum solutions’ areas manifest considerable deviations from the minimum values. At the same time, the tendencies for significant growth in deviations take place at removal of technical parameters from the optimal values of both the capital investments and operating costs. The production and operation of conditioners with the parameters which are considerably deviating from the optimal values will lead to the increase of material and power costs. The research allows one to establish the borders of the area of the optimal values for technical and economic parameters at air conditioning systems’ design.

A parameters optimization method for planar joint clearance model and its application for dynamics simulation of reciprocating compressor

NASA Astrophysics Data System (ADS)

Hai-yang, Zhao; Min-qiang, Xu; Jin-dong, Wang; Yong-bo, Li

2015-05-01

In order to improve the accuracy of dynamics response simulation for mechanism with joint clearance, a parameter optimization method for planar joint clearance contact force model was presented in this paper, and the optimized parameters were applied to the dynamics response simulation for mechanism with oversized joint clearance fault. By studying the effect of increased clearance on the parameters of joint clearance contact force model, the relation of model parameters between different clearances was concluded. Then the dynamic equation of a two-stage reciprocating compressor with four joint clearances was developed using Lagrange method, and a multi-body dynamic model built in ADAMS software was used to solve this equation. To obtain a simulated dynamic response much closer to that of experimental tests, the parameters of joint clearance model, instead of using the designed values, were optimized by genetic algorithms approach. Finally, the optimized parameters were applied to simulate the dynamics response of model with oversized joint clearance fault according to the concluded parameter relation. The dynamics response of experimental test verified the effectiveness of this application.

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

PubMed

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

Comparison of Optimal Design Methods in Inverse Problems

PubMed Central

Banks, H. T.; Holm, Kathleen; Kappel, Franz

2011-01-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher Information Matrix (FIM). A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criteria with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model [13], the standard harmonic oscillator model [13] and a popular glucose regulation model [16, 19, 29]. PMID:21857762

Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

PubMed Central

Parida, Arun Kumar; Routara, Bharat Chandra

2014-01-01

Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (R a). The machining is conducted with Taguchi L9 orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v 3-f 2-d 3. Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v 1-f 1-d 3. Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach. PMID:27437503

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2015-10-01

Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception.

PubMed

Shimansky, Y P

2011-05-01

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

PubMed

He, L; Huang, G H; Lu, H W

2010-04-15

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

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.

New generation photoelectric converter structure optimization using nano-structured materials

NASA Astrophysics Data System (ADS)

Dronov, A.; Gavrilin, I.; Zheleznyakova, A.

2014-12-01

In present work the influence of anodizing process parameters on PAOT geometric parameters for optimizing and increasing ETA-cell efficiency was studied. During the calculations optimal geometrical parameters were obtained. Parameters such as anodizing current density, electrolyte composition and temperature, as well as the anodic oxidation process time were selected for this investigation. Using the optimized TiO2 photoelectrode layer with 3,6 μm porous layer thickness and pore diameter more than 80 nm the ETA-cell efficiency has been increased by 3 times comparing to not nanostructured TiO2 photoelectrode.

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

PubMed

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

2017-01-25

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

Optimal Control Method of Robot End Position and Orientation Based on Dynamic Tracking Measurement

NASA Astrophysics Data System (ADS)

Liu, Dalong; Xu, Lijuan

2018-01-01

In order to improve the accuracy of robot pose positioning and control, this paper proposed a dynamic tracking measurement robot pose optimization control method based on the actual measurement of D-H parameters of the robot, the parameters is taken with feedback compensation of the robot, according to the geometrical parameters obtained by robot pose tracking measurement, improved multi sensor information fusion the extended Kalan filter method, with continuous self-optimal regression, using the geometric relationship between joint axes for kinematic parameters in the model, link model parameters obtained can timely feedback to the robot, the implementation of parameter correction and compensation, finally we can get the optimal attitude angle, realize the robot pose optimization control experiments were performed. 6R dynamic tracking control of robot joint robot with independent research and development is taken as experimental subject, the simulation results show that the control method improves robot positioning accuracy, and it has the advantages of versatility, simplicity, ease of operation and so on.

Design of Experiments for the Thermal Characterization of Metallic Foam

NASA Technical Reports Server (NTRS)

Crittenden, Paul E.; Cole, Kevin D.

2003-01-01

Metallic foams are being investigated for possible use in the thermal protection systems of reusable launch vehicles. As a result, the performance of these materials needs to be characterized over a wide range of temperatures and pressures. In this paper a radiation/conduction model is presented for heat transfer in metallic foams. Candidates for the optimal transient experiment to determine the intrinsic properties of the model are found by two methods. First, an optimality criterion is used to find an experiment to find all of the parameters using one heating event. Second, a pair of heating events is used to determine the parameters in which one heating event is optimal for finding the parameters related to conduction, while the other heating event is optimal for finding the parameters associated with radiation. Simulated data containing random noise was analyzed to determine the parameters using both methods. In all cases the parameter estimates could be improved by analyzing a larger data record than suggested by the optimality criterion.

Optimization of seismic isolation systems via harmony search

NASA Astrophysics Data System (ADS)

Melih Nigdeli, Sinan; Bekdaş, Gebrail; Alhan, Cenk

2014-11-01

In this article, the optimization of isolation system parameters via the harmony search (HS) optimization method is proposed for seismically isolated buildings subjected to both near-fault and far-fault earthquakes. To obtain optimum values of isolation system parameters, an optimization program was developed in Matlab/Simulink employing the HS algorithm. The objective was to obtain a set of isolation system parameters within a defined range that minimizes the acceleration response of a seismically isolated structure subjected to various earthquakes without exceeding a peak isolation system displacement limit. Several cases were investigated for different isolation system damping ratios and peak displacement limitations of seismic isolation devices. Time history analyses were repeated for the neighbouring parameters of optimum values and the results proved that the parameters determined via HS were true optima. The performance of the optimum isolation system was tested under a second set of earthquakes that was different from the first set used in the optimization process. The proposed optimization approach is applicable to linear isolation systems. Isolation systems composed of isolation elements that are inherently nonlinear are the subject of a future study. Investigation of the optimum isolation system parameters has been considered in parametric studies. However, obtaining the best performance of a seismic isolation system requires a true optimization by taking the possibility of both near-fault and far-fault earthquakes into account. HS optimization is proposed here as a viable solution to this problem.

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

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

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

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

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

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

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

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

PubMed

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

2017-03-01

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

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

PubMed Central

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

2017-01-01

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

Application of the advanced engineering environment for optimization energy consumption in designed vehicles

NASA Astrophysics Data System (ADS)

Monica, Z.; Sękala, A.; Gwiazda, A.; Banaś, W.

2016-08-01

Nowadays a key issue is to reduce the energy consumption of road vehicles. In particular solution one could find different strategies of energy optimization. The most popular but not sophisticated is so called eco-driving. In this strategy emphasized is particular behavior of drivers. In more sophisticated solution behavior of drivers is supported by control system measuring driving parameters and suggesting proper operation of the driver. The other strategy is concerned with application of different engineering solutions that aid optimization the process of energy consumption. Such systems take into consideration different parameters measured in real time and next take proper action according to procedures loaded to the control computer of a vehicle. The third strategy bases on optimization of the designed vehicle taking into account especially main sub-systems of a technical mean. In this approach the optimal level of energy consumption by a vehicle is obtained by synergetic results of individual optimization of particular constructional sub-systems of a vehicle. It is possible to distinguish three main sub-systems: the structural one the drive one and the control one. In the case of the structural sub-system optimization of the energy consumption level is related with the optimization or the weight parameter and optimization the aerodynamic parameter. The result is optimized body of a vehicle. Regarding the drive sub-system the optimization of the energy consumption level is related with the fuel or power consumption using the previously elaborated physical models. Finally the optimization of the control sub-system consists in determining optimal control parameters.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

PubMed

Gomez-Cardona, Daniel; Hayes, John W; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong

2018-05-01

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs. The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object. © 2018 American Association of Physicists in Medicine.

Recursive Branching Simulated Annealing Algorithm

NASA Technical Reports Server (NTRS)

Bolcar, Matthew; Smith, J. Scott; Aronstein, David

2012-01-01

This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.

Optimizing Photosynthetic and Respiratory Parameters Based on the Seasonal Variation Pattern in Regional Net Ecosystem Productivity Obtained from Atmospheric Inversion

NASA Astrophysics Data System (ADS)

Chen, Z.; Chen, J.; Zheng, X.; Jiang, F.; Zhang, S.; Ju, W.; Yuan, W.; Mo, G.

2014-12-01

In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation pattern of the net carbon flux. An optimization scheme is proposed to estimate two key parameters (Vcmax and Q10) by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate Vcmax and Q10 of the Boreal Ecosystem Productivity Simulator (BEPS) to improve its NEP simulation in the Boreal North America (BNA) region. Simultaneously, in-situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results have the implication on using atmospheric CO2 data for optimizing ecosystem parameters through atmospheric inversion or data assimilation techniques.

Gaussian mass optimization for kernel PCA parameters

NASA Astrophysics Data System (ADS)

Liu, Yong; Wang, Zulin

2011-10-01

This paper proposes a novel kernel parameter optimization method based on Gaussian mass, which aims to overcome the current brute force parameter optimization method in a heuristic way. Generally speaking, the choice of kernel parameter should be tightly related to the target objects while the variance between the samples, the most commonly used kernel parameter, doesn't possess much features of the target, which gives birth to Gaussian mass. Gaussian mass defined in this paper has the property of the invariance of rotation and translation and is capable of depicting the edge, topology and shape information. Simulation results show that Gaussian mass leads a promising heuristic optimization boost up for kernel method. In MNIST handwriting database, the recognition rate improves by 1.6% compared with common kernel method without Gaussian mass optimization. Several promising other directions which Gaussian mass might help are also proposed at the end of the paper.

Feasibility of employing model-based optimization of pulse amplitude and electrode distance for effective tumor electropermeabilization.

PubMed

Sel, Davorka; Lebar, Alenka Macek; Miklavcic, Damijan

2007-05-01

In electrochemotherapy (ECT) electropermeabilization, parameters (pulse amplitude, electrode setup) need to be customized in order to expose the whole tumor to electric field intensities above permeabilizing threshold to achieve effective ECT. In this paper, we present a model-based optimization approach toward determination of optimal electropermeabilization parameters for effective ECT. The optimization is carried out by minimizing the difference between the permeabilization threshold and electric field intensities computed by finite element model in selected points of tumor. We examined the feasibility of model-based optimization of electropermeabilization parameters on a model geometry generated from computer tomography images, representing brain tissue with tumor. Continuous parameter subject to optimization was pulse amplitude. The distance between electrode pairs was optimized as a discrete parameter. Optimization also considered the pulse generator constraints on voltage and current. During optimization the two constraints were reached preventing the exposure of the entire volume of the tumor to electric field intensities above permeabilizing threshold. However, despite the fact that with the particular needle array holder and pulse generator the entire volume of the tumor was not permeabilized, the maximal extent of permeabilization for the particular case (electrodes, tissue) was determined with the proposed approach. Model-based optimization approach could also be used for electro-gene transfer, where electric field intensities should be distributed between permeabilizing threshold and irreversible threshold-the latter causing tissue necrosis. This can be obtained by adding constraints on maximum electric field intensity in optimization procedure.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Optimization and Simulation of SLM Process for High Density H13 Tool Steel Parts

NASA Astrophysics Data System (ADS)

Laakso, Petri; Riipinen, Tuomas; Laukkanen, Anssi; Andersson, Tom; Jokinen, Antero; Revuelta, Alejandro; Ruusuvuori, Kimmo

This paper demonstrates the successful printing and optimization of processing parameters of high-strength H13 tool steel by Selective Laser Melting (SLM). D-Optimal Design of Experiments (DOE) approach is used for parameter optimization of laser power, scanning speed and hatch width. With 50 test samples (1×1×1cm) we establish parameter windows for these three parameters in relation to part density. The calculated numerical model is found to be in good agreement with the density data obtained from the samples using image analysis. A thermomechanical finite element simulation model is constructed of the SLM process and validated by comparing the calculated densities retrieved from the model with the experimentally determined densities. With the simulation tool one can explore the effect of different parameters on density before making any printed samples. Establishing a parameter window provides the user with freedom for parameter selection such as choosing parameters that result in fastest print speed.

Probability distribution functions for unit hydrographs with optimization using genetic algorithm

NASA Astrophysics Data System (ADS)

Ghorbani, Mohammad Ali; Singh, Vijay P.; Sivakumar, Bellie; H. Kashani, Mahsa; Atre, Atul Arvind; Asadi, Hakimeh

2017-05-01

A unit hydrograph (UH) of a watershed may be viewed as the unit pulse response function of a linear system. In recent years, the use of probability distribution functions (pdfs) for determining a UH has received much attention. In this study, a nonlinear optimization model is developed to transmute a UH into a pdf. The potential of six popular pdfs, namely two-parameter gamma, two-parameter Gumbel, two-parameter log-normal, two-parameter normal, three-parameter Pearson distribution, and two-parameter Weibull is tested on data from the Lighvan catchment in Iran. The probability distribution parameters are determined using the nonlinear least squares optimization method in two ways: (1) optimization by programming in Mathematica; and (2) optimization by applying genetic algorithm. The results are compared with those obtained by the traditional linear least squares method. The results show comparable capability and performance of two nonlinear methods. The gamma and Pearson distributions are the most successful models in preserving the rising and recession limbs of the unit hydographs. The log-normal distribution has a high ability in predicting both the peak flow and time to peak of the unit hydrograph. The nonlinear optimization method does not outperform the linear least squares method in determining the UH (especially for excess rainfall of one pulse), but is comparable.

Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

Attitude determination and parameter estimation using vector observations - Theory

NASA Technical Reports Server (NTRS)

Markley, F. Landis

1989-01-01

Procedures for attitude determination based on Wahba's loss function are generalized to include the estimation of parameters other than the attitude, such as sensor biases. Optimization with respect to the attitude is carried out using the q-method, which does not require an a priori estimate of the attitude. Optimization with respect to the other parameters employs an iterative approach, which does require an a priori estimate of these parameters. Conventional state estimation methods require a priori estimates of both the parameters and the attitude, while the algorithm presented in this paper always computes the exact optimal attitude for given values of the parameters. Expressions for the covariance of the attitude and parameter estimates are derived.

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

NASA Astrophysics Data System (ADS)

Grippa, Tais; Georganos, Stefanos; Lennert, Moritz; Vanhuysse, Sabine; Wolff, Eléonore

2017-10-01

Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

Genetic algorithm optimization of transcutaneous energy transmission systems for implantable ventricular assist devices.

PubMed

Byron, Kelly; Bluvshtein, Vlad; Lucke, Lori

2013-01-01

Transcutaneous energy transmission systems (TETS) wirelessly transmit power through the skin. TETS is particularly desirable for ventricular assist devices (VAD), which currently require cables through the skin to power the implanted pump. Optimizing the inductive link of the TET system is a multi-parameter problem. Most current techniques to optimize the design simplify the problem by combining parameters leading to sub-optimal solutions. In this paper we present an optimization method using a genetic algorithm to handle a larger set of parameters, which leads to a more optimal design. Using this approach, we were able to increase efficiency while also reducing power variability in a prototype, compared to a traditional manual design method.

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.

Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter

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

Kao, Jim; Flicker, Dawn; Ide, Kayo

2006-05-20

This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from amore » single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand.« less

TH-E-BRF-06: Kinetic Modeling of Tumor Response to Fractionated Radiotherapy

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

Zhong, H; Gordon, J; Chetty, I

2014-06-15

Purpose: Accurate calibration of radiobiological parameters is crucial to predicting radiation treatment response. Modeling differences may have a significant impact on calibrated parameters. In this study, we have integrated two existing models with kinetic differential equations to formulate a new tumor regression model for calibrating radiobiological parameters for individual patients. Methods: A system of differential equations that characterizes the birth-and-death process of tumor cells in radiation treatment was analytically solved. The solution of this system was used to construct an iterative model (Z-model). The model consists of three parameters: tumor doubling time Td, half-life of dying cells Tr and cellmore » survival fraction SFD under dose D. The Jacobian determinant of this model was proposed as a constraint to optimize the three parameters for six head and neck cancer patients. The derived parameters were compared with those generated from the two existing models, Chvetsov model (C-model) and Lim model (L-model). The C-model and L-model were optimized with the parameter Td fixed. Results: With the Jacobian-constrained Z-model, the mean of the optimized cell survival fractions is 0.43±0.08, and the half-life of dying cells averaged over the six patients is 17.5±3.2 days. The parameters Tr and SFD optimized with the Z-model differ by 1.2% and 20.3% from those optimized with the Td-fixed C-model, and by 32.1% and 112.3% from those optimized with the Td-fixed L-model, respectively. Conclusion: The Z-model was analytically constructed from the cellpopulation differential equations to describe changes in the number of different tumor cells during the course of fractionated radiation treatment. The Jacobian constraints were proposed to optimize the three radiobiological parameters. The developed modeling and optimization methods may help develop high-quality treatment regimens for individual patients.« less

Optimization of GATE and PHITS Monte Carlo code parameters for uniform scanning proton beam based on simulation with FLUKA general-purpose code

NASA Astrophysics Data System (ADS)

Kurosu, Keita; Takashina, Masaaki; Koizumi, Masahiko; Das, Indra J.; Moskvin, Vadim P.

2014-10-01

Although three general-purpose Monte Carlo (MC) simulation tools: Geant4, FLUKA and PHITS have been used extensively, differences in calculation results have been reported. The major causes are the implementation of the physical model, preset value of the ionization potential or definition of the maximum step size. In order to achieve artifact free MC simulation, an optimized parameters list for each simulation system is required. Several authors have already proposed the optimized lists, but those studies were performed with a simple system such as only a water phantom. Since particle beams have a transport, interaction and electromagnetic processes during beam delivery, establishment of an optimized parameters-list for whole beam delivery system is therefore of major importance. The purpose of this study was to determine the optimized parameters list for GATE and PHITS using proton treatment nozzle computational model. The simulation was performed with the broad scanning proton beam. The influences of the customizing parameters on the percentage depth dose (PDD) profile and the proton range were investigated by comparison with the result of FLUKA, and then the optimal parameters were determined. The PDD profile and the proton range obtained from our optimized parameters list showed different characteristics from the results obtained with simple system. This led to the conclusion that the physical model, particle transport mechanics and different geometry-based descriptions need accurate customization in planning computational experiments for artifact-free MC simulation.

Non-adaptive and adaptive hybrid approaches for enhancing water quality management

NASA Astrophysics Data System (ADS)

Kalwij, Ineke M.; Peralta, Richard C.

2008-09-01

SummaryUsing optimization to help solve groundwater management problems cost-effectively is becoming increasingly important. Hybrid optimization approaches, that combine two or more optimization algorithms, will become valuable and common tools for addressing complex nonlinear hydrologic problems. Hybrid heuristic optimizers have capabilities far beyond those of a simple genetic algorithm (SGA), and are continuously improving. SGAs having only parent selection, crossover, and mutation are inefficient and rarely used for optimizing contaminant transport management. Even an advanced genetic algorithm (AGA) that includes elitism (to emphasize using the best strategies as parents) and healing (to help assure optimal strategy feasibility) is undesirably inefficient. Much more efficient than an AGA is the presented hybrid (AGCT), which adds comprehensive tabu search (TS) features to an AGA. TS mechanisms (TS probability, tabu list size, search coarseness and solution space size, and a TS threshold value) force the optimizer to search portions of the solution space that yield superior pumping strategies, and to avoid reproducing similar or inferior strategies. An AGCT characteristic is that TS control parameters are unchanging during optimization. However, TS parameter values that are ideal for optimization commencement can be undesirable when nearing assumed global optimality. The second presented hybrid, termed global converger (GC), is significantly better than the AGCT. GC includes AGCT plus feedback-driven auto-adaptive control that dynamically changes TS parameters during run-time. Before comparing AGCT and GC, we empirically derived scaled dimensionless TS control parameter guidelines by evaluating 50 sets of parameter values for a hypothetical optimization problem. For the hypothetical area, AGCT optimized both well locations and pumping rates. The parameters are useful starting values because using trial-and-error to identify an ideal combination of control parameter values for a new optimization problem can be time consuming. For comparison, AGA, AGCT, and GC are applied to optimize pumping rates for assumed well locations of a complex large-scale contaminant transport and remediation optimization problem at Blaine Naval Ammunition Depot (NAD). Both hybrid approaches converged more closely to the optimal solution than the non-hybrid AGA. GC averaged 18.79% better convergence than AGCT, and 31.9% than AGA, within the same computation time (12.5 days). AGCT averaged 13.1% better convergence than AGA. The GC can significantly reduce the burden of employing computationally intensive hydrologic simulation models within a limited time period and for real-world optimization problems. Although demonstrated for a groundwater quality problem, it is also applicable to other arenas, such as managing salt water intrusion and surface water contaminant loading.

Application of Layered Perforation Profile Control Technique to Low Permeable Reservoir

NASA Astrophysics Data System (ADS)

Wei, Sun

2018-01-01

it is difficult to satisfy the demand of profile control of complex well section and multi-layer reservoir by adopting the conventional profile control technology, therefore, a research is conducted on adjusting the injection production profile with layered perforating parameters optimization. i.e. in the case of coproduction for multi-layer, water absorption of each layer is adjusted by adjusting the perforating parameters, thus to balance the injection production profile of the whole well section, and ultimately enhance the oil displacement efficiency of water flooding. By applying the relationship between oil-water phase percolation theory/perforating damage and capacity, a mathematic model of adjusting the injection production profile with layered perforating parameters optimization, besides, perforating parameters optimization software is programmed. Different types of optimization design work are carried out according to different geological conditions and construction purposes by using the perforating optimization design software; furthermore, an application test is done for low permeable reservoir, and the water injection profile tends to be balanced significantly after perforation with optimized parameters, thereby getting a good application effect on site.

Regenerative Medicine and Restoration of Joint Function

DTIC Science & Technology

2012-10-01

identify the parameters that generate anatomically shaped bone substitutes of optimal composition and structure with an articulating profile. 2) to develop...strengths. An in vivo study in rabbits to evaluate these materials are ongoing. Task 2. Optimization of SFF Rolling Compaction Parameters : The work is...ongoing related to optimizing SFF rolling compaction parameters to control the density of green samples. We have used CPP powders for these studies

Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

DTIC Science & Technology

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and

Utility of coupling nonlinear optimization methods with numerical modeling software

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

Murphy, M.J.

1996-08-05

Results of using GLO (Global Local Optimizer), a general purpose nonlinear optimization software package for investigating multi-parameter problems in science and engineering is discussed. The package consists of the modular optimization control system (GLO), a graphical user interface (GLO-GUI), a pre-processor (GLO-PUT), a post-processor (GLO-GET), and nonlinear optimization software modules, GLOBAL & LOCAL. GLO is designed for controlling and easy coupling to any scientific software application. GLO runs the optimization module and scientific software application in an iterative loop. At each iteration, the optimization module defines new values for the set of parameters being optimized. GLO-PUT inserts the new parametermore » values into the input file of the scientific application. GLO runs the application with the new parameter values. GLO-GET determines the value of the objective function by extracting the results of the analysis and comparing to the desired result. GLO continues to run the scientific application over and over until it finds the ``best`` set of parameters by minimizing (or maximizing) the objective function. An example problem showing the optimization of material model is presented (Taylor cylinder impact test).« less

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

PubMed

Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki

2015-01-01

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.

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

PubMed

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-08-14

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

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

PubMed Central

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-01-01

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

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming.

PubMed

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

2008-10-01

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

SU-D-12A-06: A Comprehensive Parameter Analysis for Low Dose Cone-Beam CT Reconstruction

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

Lu, W; Southern Medical University, Guangzhou; Yan, H

Purpose: There is always a parameter in compressive sensing based iterative reconstruction (IR) methods low dose cone-beam CT (CBCT), which controls the weight of regularization relative to data fidelity. A clear understanding of the relationship between image quality and parameter values is important. The purpose of this study is to investigate this subject based on experimental data and a representative advanced IR algorithm using Tight-frame (TF) regularization. Methods: Three data sets of a Catphan phantom acquired at low, regular and high dose levels are used. For each tests, 90 projections covering a 200-degree scan range are used for reconstruction. Threemore » different regions-of-interest (ROIs) of different contrasts are used to calculate contrast-to-noise ratios (CNR) for contrast evaluation. A single point structure is used to measure modulation transfer function (MTF) for spatial-resolution evaluation. Finally, we analyze CNRs and MTFs to study the relationship between image quality and parameter selections. Results: It was found that: 1) there is no universal optimal parameter. The optimal parameter value depends on specific task and dose level. 2) There is a clear trade-off between CNR and resolution. The parameter for the best CNR is always smaller than that for the best resolution. 3) Optimal parameters are also dose-specific. Data acquired under a high dose protocol require less regularization, yielding smaller optimal parameter values. 4) Comparing with conventional FDK images, TF-based CBCT images are better under a certain optimally selected parameters. The advantages are more obvious for low dose data. Conclusion: We have investigated the relationship between image quality and parameter values in the TF-based IR algorithm. Preliminary results indicate optimal parameters are specific to both the task types and dose levels, providing guidance for selecting parameters in advanced IR algorithms. This work is supported in part by NIH (1R01CA154747-01)« less

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

NASA Astrophysics Data System (ADS)

Bukhari, Hassan J.

2017-12-01

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

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

NASA Astrophysics Data System (ADS)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

Vascular protective actions of a nitric oxide aspirin analog in both in vitro and in vivo models of diabetes mellitus.

PubMed

Pieper, Galen M; Siebeneich, Wolfgang; Olds, Cara L; Felix, Christopher C; Del Soldato, Piero

2002-06-01

Defective endothelium-dependent relaxation is observed in experimental and human diabetes mellitus. The nature of this defect is not fully understood but may involve decreased nitric oxide (NO) bioactivity due to enhanced production of reactive oxygen species (ROS). In this paper, we examine the benefits and actions of a novel NO-donating, antioxidant called 2-acetoxybenzoic acid 2-(2-nitrooxymethyl) phenyl ester, and denoted as NCX4016, on NO-mediated endothelium-dependent relaxation in normal arteries exposed to acute elevations in glucose or in arteries derived from chronic diabetic animals. Intrinsic free radical scavenging by NO-NSAIDs in solution were evaluated using electron paramagnetic resonance (EPR) spectroscopy and spin trapping with 5,5-dimethyl-1-pyrroline-N-oxide (DMPO). In acute studies, normal rat aortas were exposed in tissue culture for 18 h to 5.5 mM or 40 mM in the presence or absence of NCX4016, a NO-donating NSAID unrelated to aspirin (NCX2216) or aspirin. Vascular reactivity of thoracic aortic rings to endothelium-dependent relaxation to acetylcholine in vitro was determined. For chronic hyperglycemia, diabetes was induced in rats by intravenous injection with streptozotocin. Vascular reactivity of thoracic aortic rings to endothelium-dependent relaxation to acetylcholine in vitro was determined after 8 wks in untreated animals or animals chronically-treated with NCX4016. Antioxidant efficacy in vivo was determined by measurement of plasma isoprostanes and by nuclear binding activity of NF-kappaB in nuclear fractions of aortae. Incubation with NCX4016 and NCX2216 produced a concentration-dependent inhibition of DMPO-OH formation indicating scavenging of hydroxyl radicals (HO(*)). In contrast, little efficacy to scavenge superoxide anion radicals was noted. Acute incubation of normal arteries with elevated glucose concentration caused inhibition of normal relaxation to acetylcholine. This impairment was prevented by co-incubation with NCX4106 but not by mannitol, the parent compound (aspirin) or by NCX2216. In addition, chronic treatment with NCX4016 prevented the development of defective endothelium-dependent relaxation to acetylcholine. This protection did not occur as a result to any changes in blood glucose concentration or hemoglobin glycation. Treatment with NCX4016 did decrease the elevation in plasma isoprostanes and normalized the diabetes-induced increase in NF-kappaB binding activity in nuclear fractions derived from aortic tissue. Collectively, these studies suggest that antioxidant interventions using NO-donating NSAIDs may provide an important novel therapeutic strategy to protect the diabetic endothelium.

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

NASA Astrophysics Data System (ADS)

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.

Optimization of parameters of special asynchronous electric drives

NASA Astrophysics Data System (ADS)

Karandey, V. Yu; Popov, B. K.; Popova, O. B.; Afanasyev, V. L.

2018-03-01

The article considers the solution of the problem of parameters optimization of special asynchronous electric drives. The solution of the problem will allow one to project and create special asynchronous electric drives for various industries. The created types of electric drives will have optimum mass-dimensional and power parameters. It will allow one to realize and fulfill the set characteristics of management of technological processes with optimum level of expenses of electric energy, time of completing the process or other set parameters. The received decision allows one not only to solve a certain optimizing problem, but also to construct dependences between the optimized parameters of special asynchronous electric drives, for example, with the change of power, current in a winding of the stator or rotor, induction in a gap or steel of magnetic conductors and other parameters. On the constructed dependences, it is possible to choose necessary optimum values of parameters of special asynchronous electric drives and their components without carrying out repeated calculations.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

PubMed

Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

2013-01-01

Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis

PubMed Central

Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

2013-01-01

Introduction: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. Methods: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. Results: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. Conclusion: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum. PMID:23766941

Application of Differential Evolutionary Optimization Methodology for Parameter Structure Identification in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.

2013-12-01

With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

NASA Astrophysics Data System (ADS)

Belwanshi, Vinod; Topkar, Anita

2016-05-01

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

IPO: a tool for automated optimization of XCMS parameters.

PubMed

Libiseller, Gunnar; Dvorzak, Michaela; Kleb, Ulrike; Gander, Edgar; Eisenberg, Tobias; Madeo, Frank; Neumann, Steffen; Trausinger, Gert; Sinner, Frank; Pieber, Thomas; Magnes, Christoph

2015-04-16

Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .

Optimal design of monitoring networks for multiple groundwater quality parameters using a Kalman filter: application to the Irapuato-Valle aquifer.

PubMed

Júnez-Ferreira, H E; Herrera, G S; González-Hita, L; Cardona, A; Mora-Rodríguez, J

2016-01-01

A new method for the optimal design of groundwater quality monitoring networks is introduced in this paper. Various indicator parameters were considered simultaneously and tested for the Irapuato-Valle aquifer in Mexico. The steps followed in the design were (1) establishment of the monitoring network objectives, (2) definition of a groundwater quality conceptual model for the study area, (3) selection of the parameters to be sampled, and (4) selection of a monitoring network by choosing the well positions that minimize the estimate error variance of the selected indicator parameters. Equal weight for each parameter was given to most of the aquifer positions and a higher weight to priority zones. The objective for the monitoring network in the specific application was to obtain a general reconnaissance of the water quality, including water types, water origin, and first indications of contamination. Water quality indicator parameters were chosen in accordance with this objective, and for the selection of the optimal monitoring sites, it was sought to obtain a low-uncertainty estimate of these parameters for the entire aquifer and with more certainty in priority zones. The optimal monitoring network was selected using a combination of geostatistical methods, a Kalman filter and a heuristic optimization method. Results show that when monitoring the 69 locations with higher priority order (the optimal monitoring network), the joint average standard error in the study area for all the groundwater quality parameters was approximately 90 % of the obtained with the 140 available sampling locations (the set of pilot wells). This demonstrates that an optimal design can help to reduce monitoring costs, by avoiding redundancy in data acquisition.

Optimal Design of Material and Process Parameters in Powder Injection Molding

NASA Astrophysics Data System (ADS)

Ayad, G.; Barriere, T.; Gelin, J. C.; Song, J.; Liu, B.

2007-04-01

The paper is concerned with optimization and parametric identification for the different stages in Powder Injection Molding process that consists first in injection of powder mixture with polymer binder and then to the sintering of the resulting powders part by solid state diffusion. In the first part, one describes an original methodology to optimize the process and geometry parameters in injection stage based on the combination of design of experiments and an adaptive Response Surface Modeling. Then the second part of the paper describes the identification strategy that one proposes for the sintering stage, using the identification of sintering parameters from dilatometeric curves followed by the optimization of the sintering process. The proposed approaches are applied to the optimization of material and process parameters for manufacturing a ceramic femoral implant. One demonstrates that the proposed approach give satisfactory results.

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2016-01-01

Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

Warpage improvement on wheel caster by optimizing the process parameters using genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, the defects will always encountered and affected the final product shape and functionality. This study is concerning on minimizing warpage and optimizing the process parameter of injection moulding part. Apart from eliminating product wastes, this project also giving out best recommended parameters setting. This research studied on five parameters. The optimization showed that warpage have been improved 42.64% from 0.6524 mm to 0.30879 mm in Autodesk Moldflow Insight (AMI) simulation result and Genetic Algorithm (GA) respectively.

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

NASA Technical Reports Server (NTRS)

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.

An automated analysis workflow for optimization of force-field parameters using neutron scattering data

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

Lynch, Vickie E.; Borreguero, Jose M.; Bhowmik, Debsindhu

Graphical abstract: - Highlights: • An automated workflow to optimize force-field parameters. • Used the workflow to optimize force-field parameter for a system containing nanodiamond and tRNA. • The mechanism relies on molecular dynamics simulation and neutron scattering experimental data. • The workflow can be generalized to any other experimental and simulation techniques. - Abstract: Large-scale simulations and data analysis are often required to explain neutron scattering experiments to establish a connection between the fundamental physics at the nanoscale and data probed by neutrons. However, to perform simulations at experimental conditions it is critical to use correct force-field (FF) parametersmore » which are unfortunately not available for most complex experimental systems. In this work, we have developed a workflow optimization technique to provide optimized FF parameters by comparing molecular dynamics (MD) to neutron scattering data. We describe the workflow in detail by using an example system consisting of tRNA and hydrophilic nanodiamonds in a deuterated water (D{sub 2}O) environment. Quasi-elastic neutron scattering (QENS) data show a faster motion of the tRNA in the presence of nanodiamond than without the ND. To compare the QENS and MD results quantitatively, a proper choice of FF parameters is necessary. We use an efficient workflow to optimize the FF parameters between the hydrophilic nanodiamond and water by comparing to the QENS data. Our results show that we can obtain accurate FF parameters by using this technique. The workflow can be generalized to other types of neutron data for FF optimization, such as vibrational spectroscopy and spin echo.« less

Identification of vehicle suspension parameters by design optimization

NASA Astrophysics Data System (ADS)

Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.

2014-05-01

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.

Optimization of Parameter Ranges for Composite Tape Winding Process Based on Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Yu, Tao; Shi, Yaoyao; He, Xiaodong; Kang, Chao; Deng, Bo; Song, Shibo

2017-08-01

This study is focus on the parameters sensitivity of winding process for composite prepreg tape. The methods of multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis are proposed. The polynomial empirical model of interlaminar shear strength is established by response surface experimental method. Using this model, the relative sensitivity of key process parameters including temperature, tension, pressure and velocity is calculated, while the single-parameter sensitivity curves are obtained. According to the analysis of sensitivity curves, the stability and instability range of each parameter are recognized. Finally, the optimization method of winding process parameters is developed. The analysis results show that the optimized ranges of the process parameters for interlaminar shear strength are: temperature within [100 °C, 150 °C], tension within [275 N, 387 N], pressure within [800 N, 1500 N], and velocity within [0.2 m/s, 0.4 m/s], respectively.

Photodynamic therapy of Curcuma longa extract stimulated with blue light against Aggregatibacter actinomycetemcomitans.

PubMed

Saitawee, Darika; Teerakapong, Aroon; Morales, Noppawan Phumala; Jitprasertwong, Paiboon; Hormdee, Doosadee

2018-06-01

Curcumin, one of an established curcuminoid substances extracted from Curcuma longa, has been used as a photosensitizer (PS) in photodynamic therapy (PDT). Curcuminoid substances has been reported to have benefits in treating dental chronic infection and inflammation diseases, such as chronic periodontitis. The purpose of this study was to find the optimum concentration of Curcuma longa (CL) extract, containing all curcuminoid substances, and the power density of blue light (BL) in photodynamic therapy against periodontally pathogenic bacteria, A. actinomycetemcomitans. Antibacterial activity of various concentrations of CL extract against A. actinomycetemcomitans was determined. Exponentially growing bacteria were combined with 2-fold dilution of CL extract solution ranging from 25 to 0.098 μg/ml. Co-culture bacteria treated with 0.12% chlorhexidine (CHX) served as the positive control. The effect of photostimulation with light emitting diode (LED) 420-480 nm at 16.8 J/cm 2 for 1 min on the selected concentration of CL extract was examined. Bacteria viability was determined by plate counting technique. In addition, production of free radicals was tested by electron spin resonance spectroscope (ESR) with 5,5-dimethyl-1-pyrroline N-oxide (DMPO). The antibacterial activity of CL extract was dose dependent. Without BL, 25 μg/ml CL extract showed 6.03 ± 0.39 log 10 A. actinomycetemcomitans. Interestingly, the combination of BL and 0.78 μg/ml CL extract solution showed complete absence of A. actinomycetemcomitans. Peak signal intensity of hydroxyl radical production was also detected with the combination of BL and CL. CL extract not only had antimicrobial activity but also could be used as an effective PS when stimulated with BL in PDT. The optimal antibacterial effect of CL extract with BL was equal to the standard oral disinfectant, 0.12% CHX. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimization of parameter values for complex pulse sequences by simulated annealing: application to 3D MP-RAGE imaging of the brain.

PubMed

Epstein, F H; Mugler, J P; Brookeman, J R

1994-02-01

A number of pulse sequence techniques, including magnetization-prepared gradient echo (MP-GRE), segmented GRE, and hybrid RARE, employ a relatively large number of variable pulse sequence parameters and acquire the image data during a transient signal evolution. These sequences have recently been proposed and/or used for clinical applications in the brain, spine, liver, and coronary arteries. Thus, the need for a method of deriving optimal pulse sequence parameter values for this class of sequences now exists. Due to the complexity of these sequences, conventional optimization approaches, such as applying differential calculus to signal difference equations, are inadequate. We have developed a general framework for adapting the simulated annealing algorithm to pulse sequence parameter value optimization, and applied this framework to the specific case of optimizing the white matter-gray matter signal difference for a T1-weighted variable flip angle 3D MP-RAGE sequence. Using our algorithm, the values of 35 sequence parameters, including the magnetization-preparation RF pulse flip angle and delay time, 32 flip angles in the variable flip angle gradient-echo acquisition sequence, and the magnetization recovery time, were derived. Optimized 3D MP-RAGE achieved up to a 130% increase in white matter-gray matter signal difference compared with optimized 3D RF-spoiled FLASH with the same total acquisition time. The simulated annealing approach was effective at deriving optimal parameter values for a specific 3D MP-RAGE imaging objective, and may be useful for other imaging objectives and sequences in this general class.

Multi-Criteria Optimization of Regulation in Metabolic Networks

PubMed Central

Higuera, Clara; Villaverde, Alejandro F.; Banga, Julio R.; Ross, John; Morán, Federico

2012-01-01

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks. PMID:22848435

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

EPA Pesticide Factsheets

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

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

Optimal robust control strategy of a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

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

Autopilot for frequency-modulation atomic force microscopy.

PubMed

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

NASA Astrophysics Data System (ADS)

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Autopilot for frequency-modulation atomic force microscopy

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

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri, E-mail: phsivan@tx.technion.ac.il

2015-10-15

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loopsmore » require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.« less

Estimation of the discharges of the multiple water level stations by multi-objective optimization

NASA Astrophysics Data System (ADS)

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

2016-04-01

This presentation shows two aspects of the parameter identification to estimate the discharges of the multiple water level stations by multi-objective optimization. One is how to adjust the parameters to estimate the discharges accurately. The other is which optimization algorithms are suitable for the parameter identification. Regarding the previous studies, there is a study that minimizes the weighted error of the discharges of the multiple water level stations by single-objective optimization. On the other hand, there are some studies that minimize the multiple error assessment functions of the discharge of a single water level station by multi-objective optimization. This presentation features to simultaneously minimize the errors of the discharges of the multiple water level stations by multi-objective optimization. Abe River basin in Japan is targeted. The basin area is 567.0km2. There are thirteen rainfall stations and three water level stations. Nine flood events are investigated. They occurred from 2005 to 2012 and the maximum discharges exceed 1,000m3/s. The discharges are calculated with PWRI distributed hydrological model. The basin is partitioned into the meshes of 500m x 500m. Two-layer tanks are placed on each mesh. Fourteen parameters are adjusted to estimate the discharges accurately. Twelve of them are the hydrological parameters and two of them are the parameters of the initial water levels of the tanks. Three objective functions are the mean squared errors between the observed and calculated discharges at the water level stations. Latin Hypercube sampling is one of the uniformly sampling algorithms. The discharges are calculated with respect to the parameter values sampled by a simplified version of Latin Hypercube sampling. The observed discharge is surrounded by the calculated discharges. It suggests that it might be possible to estimate the discharge accurately by adjusting the parameters. In a sense, it is true that the discharge of a water level station can be accurately estimated by setting the parameter values optimized to the responding water level station. However, there are some cases that the calculated discharge by setting the parameter values optimized to one water level station does not meet the observed discharge at another water level station. It is important to estimate the discharges of all the water level stations in some degree of accuracy. It turns out to be possible to select the parameter values from the pareto optimal solutions by the condition that all the normalized errors by the minimum error of the responding water level station are under 3. The optimization performance of five implementations of the algorithms and a simplified version of Latin Hypercube sampling are compared. Five implementations are NSGA2 and PAES of an optimization software inspyred and MCO_NSGA2R, MOPSOCD and NSGA2R_NSGA2R of a statistical software R. NSGA2, PAES and MOPSOCD are the optimization algorithms of a genetic algorithm, an evolution strategy and a particle swarm optimization respectively. The number of the evaluations of the objective functions is 10,000. Two implementations of NSGA2 of R outperform the others. They are promising to be suitable for the parameter identification of PWRI distributed hydrological model.

Parameter optimization of fusion splicing of photonic crystal fibers and conventional fibers to increase strength

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Zhang, Zuchen; Song, Jingming; Wu, Chunxiao; Song, Ningfang

2015-03-01

A splicing parameter optimization method to increase the tensile strength of splicing joint between photonic crystal fiber (PCF) and conventional fiber is demonstrated. Based on the splicing recipes provided by splicer or fiber manufacturers, the optimal values of some major splicing parameters are obtained in sequence, and a conspicuous improvement in the mechanical strength of splicing joints between PCFs and conventional fibers is validated through experiments.

Design of compact long-period gratings imprinted in optimized photonic crystal fibers

NASA Astrophysics Data System (ADS)

Seraji, F. E.; Chehreghani Anzabi, L.; Farsinezhad, S.

2009-10-01

To imprint a long-period grating (LPG) in a photonic crystal fiber (PCF) with an optimum response, first the parameters of the PCF should be optimized. In this paper, by using a semi-analytical enhanced improved vectorial effective index method, the optimized PCF parameters are determined by dividing the single-mode operation of the PCF into two regions in terms of air-hole spacing Λ ( Λ>3 μm and Λ≤3 μm). For each region appropriate expressions are suggested to evaluate the PCF parameters. By calculating the effective refractive index difference between the optimized core and cladding of the PCF under a phase-matching condition, the optimum grating period in terms of the PCF parameters is obtained.

Case study: Optimizing fault model input parameters using bio-inspired algorithms

NASA Astrophysics Data System (ADS)

Plucar, Jan; Grunt, Onřej; Zelinka, Ivan

2017-07-01

We present a case study that demonstrates a bio-inspired approach in the process of finding optimal parameters for GSM fault model. This model is constructed using Petri Nets approach it represents dynamic model of GSM network environment in the suburban areas of Ostrava city (Czech Republic). We have been faced with a task of finding optimal parameters for an application that requires high amount of data transfers between the application itself and secure servers located in datacenter. In order to find the optimal set of parameters we employ bio-inspired algorithms such as Differential Evolution (DE) or Self Organizing Migrating Algorithm (SOMA). In this paper we present use of these algorithms, compare results and judge their performance in fault probability mitigation.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

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

PubMed

Prakash, Jaya; Yalavarthy, Phaneendra K

2013-03-01

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

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

PubMed Central

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. PMID:27362762

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

PubMed

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.

A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

DOE PAGES

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; ...

2016-01-01

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

A quasi-dense matching approach and its calibration application with Internet photos.

PubMed

Wan, Yanli; Miao, Zhenjiang; Wu, Q M Jonathan; Wang, Xifu; Tang, Zhen; Wang, Zhifei

2015-03-01

This paper proposes a quasi-dense matching approach to the automatic acquisition of camera parameters, which is required for recovering 3-D information from 2-D images. An affine transformation-based optimization model and a new matching cost function are used to acquire quasi-dense correspondences with high accuracy in each pair of views. These correspondences can be effectively detected and tracked at the sub-pixel level in multiviews with our neighboring view selection strategy. A two-layer iteration algorithm is proposed to optimize 3-D quasi-dense points and camera parameters. In the inner layer, different optimization strategies based on local photometric consistency and a global objective function are employed to optimize the 3-D quasi-dense points and camera parameters, respectively. In the outer layer, quasi-dense correspondences are resampled to guide a new estimation and optimization process of the camera parameters. We demonstrate the effectiveness of our algorithm with several experiments.

Optimization of hydraulic turbine governor parameters based on WPA

NASA Astrophysics Data System (ADS)

Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao

2018-01-01

The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.

Optimal critic learning for robot control in time-varying environments.

PubMed

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Optimization of injection molding process parameters for a plastic cell phone housing component

NASA Astrophysics Data System (ADS)

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.

Definitive screening design enables optimization of LC-ESI-MS/MS parameters in proteomics.

PubMed

Aburaya, Shunsuke; Aoki, Wataru; Minakuchi, Hiroyoshi; Ueda, Mitsuyoshi

2017-12-01

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC-ESI-MS/MS to comprehensively identify these peptides. However, there are many parameters for LC-ESI-MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC-ESI-MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC-ESI-MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC-ESI-MS/MS systems.

General equations for optimal selection of diagnostic image acquisition parameters in clinical X-ray imaging.

PubMed

Zheng, Xiaoming

2017-12-01

The purpose of this work was to examine the effects of relationship functions between diagnostic image quality and radiation dose on the governing equations for image acquisition parameter variations in X-ray imaging. Various equations were derived for the optimal selection of peak kilovoltage (kVp) and exposure parameter (milliAmpere second, mAs) in computed tomography (CT), computed radiography (CR), and direct digital radiography. Logistic, logarithmic, and linear functions were employed to establish the relationship between radiation dose and diagnostic image quality. The radiation dose to the patient, as a function of image acquisition parameters (kVp, mAs) and patient size (d), was used in radiation dose and image quality optimization. Both logistic and logarithmic functions resulted in the same governing equation for optimal selection of image acquisition parameters using a dose efficiency index. For image quality as a linear function of radiation dose, the same governing equation was derived from the linear relationship. The general equations should be used in guiding clinical X-ray imaging through optimal selection of image acquisition parameters. The radiation dose to the patient could be reduced from current levels in medical X-ray imaging.

Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4.

PubMed

Bouillon-Pichault, Marion; Jullien, Vincent; Bazzoli, Caroline; Pons, Gérard; Tod, Michel

2011-02-01

The aim of this work was to determine whether optimizing the study design in terms of ages and sampling times for a drug eliminated solely via cytochrome P450 3A4 (CYP3A4) would allow us to accurately estimate the pharmacokinetic parameters throughout the entire childhood timespan, while taking into account age- and weight-related changes. A linear monocompartmental model with first-order absorption was used successively with three different residual error models and previously published pharmacokinetic parameters ("true values"). The optimal ages were established by D-optimization using the CYP3A4 maturation function to create "optimized demographic databases." The post-dose times for each previously selected age were determined by D-optimization using the pharmacokinetic model to create "optimized sparse sampling databases." We simulated concentrations by applying the population pharmacokinetic model to the optimized sparse sampling databases to create optimized concentration databases. The latter were modeled to estimate population pharmacokinetic parameters. We then compared true and estimated parameter values. The established optimal design comprised four age ranges: 0.008 years old (i.e., around 3 days), 0.192 years old (i.e., around 2 months), 1.325 years old, and adults, with the same number of subjects per group and three or four samples per subject, in accordance with the error model. The population pharmacokinetic parameters that we estimated with this design were precise and unbiased (root mean square error [RMSE] and mean prediction error [MPE] less than 11% for clearance and distribution volume and less than 18% for k(a)), whereas the maturation parameters were unbiased but less precise (MPE < 6% and RMSE < 37%). Based on our results, taking growth and maturation into account a priori in a pediatric pharmacokinetic study is theoretically feasible. However, it requires that very early ages be included in studies, which may present an obstacle to the use of this approach. First-pass effects, alternative elimination routes, and combined elimination pathways should also be investigated.

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Optimizing pulsed Nd:YAG laser beam welding process parameters to attain maximum ultimate tensile strength for thin AISI316L sheet using response surface methodology and simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Torabi, Amir; Kolahan, Farhad

2018-07-01

Pulsed laser welding is a powerful technique especially suitable for joining thin sheet metals. In this study, based on experimental data, pulsed laser welding of thin AISI316L austenitic stainless steel sheet has been modeled and optimized. The experimental data required for modeling are gathered as per Central Composite Design matrix in Response Surface Methodology (RSM) with full replication of 31 runs. Ultimate Tensile Strength (UTS) is considered as the main quality measure in laser welding. Furthermore, the important process parameters including peak power, pulse duration, pulse frequency and welding speed are selected as input process parameters. The relation between input parameters and the output response is established via full quadratic response surface regression with confidence level of 95%. The adequacy of the regression model was verified using Analysis of Variance technique results. The main effects of each factor and the interactions effects with other factors were analyzed graphically in contour and surface plot. Next, to maximum joint UTS, the best combinations of parameters levels were specified using RSM. Moreover, the mathematical model is implanted into a Simulated Annealing (SA) optimization algorithm to determine the optimal values of process parameters. The results obtained by both SA and RSM optimization techniques are in good agreement. The optimal parameters settings for peak power of 1800 W, pulse duration of 4.5 ms, frequency of 4.2 Hz and welding speed of 0.5 mm/s would result in a welded joint with 96% of the base metal UTS. Computational results clearly demonstrate that the proposed modeling and optimization procedures perform quite well for pulsed laser welding process.

Parameter Optimization for Feature and Hit Generation in a General Unknown Screening Method-Proof of Concept Study Using a Design of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data Independent Acquisition.

PubMed

Elmiger, Marco P; Poetzsch, Michael; Steuer, Andrea E; Kraemer, Thomas

2018-03-06

High resolution mass spectrometry and modern data independent acquisition (DIA) methods enable the creation of general unknown screening (GUS) procedures. However, even when DIA is used, its potential is far from being exploited, because often, the untargeted acquisition is followed by a targeted search. Applying an actual GUS (including untargeted screening) produces an immense amount of data that must be dealt with. An optimization of the parameters regulating the feature detection and hit generation algorithms of the data processing software could significantly reduce the amount of unnecessary data and thereby the workload. Design of experiment (DoE) approaches allow a simultaneous optimization of multiple parameters. In a first step, parameters are evaluated (crucial or noncrucial). Second, crucial parameters are optimized. The aim in this study was to reduce the number of hits, without missing analytes. The obtained parameter settings from the optimization were compared to the standard settings by analyzing a test set of blood samples spiked with 22 relevant analytes as well as 62 authentic forensic cases. The optimization lead to a marked reduction of workload (12.3 to 1.1% and 3.8 to 1.1% hits for the test set and the authentic cases, respectively) while simultaneously increasing the identification rate (68.2 to 86.4% and 68.8 to 88.1%, respectively). This proof of concept study emphasizes the great potential of DoE approaches to master the data overload resulting from modern data independent acquisition methods used for general unknown screening procedures by optimizing software parameters.

Global Parameter Optimization of CLM4.5 Using Sparse-Grid Based Surrogates

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Gu, L.

2016-12-01

Calibration of the Community Land Model (CLM) is challenging because of its model complexity, large parameter sets, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the CLM parameters in order to improve model projection of carbon fluxes. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the actual CLM model, and then we calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated with sufficiently large number of times in the optimization, which facilitates the global search. We calibrate five parameters against 12 months of GPP, NEP, and TLAI data from the U.S. Missouri Ozark (US-MOz) tower. The results indicate that an accurate surrogate model can be created for the CLM4.5 with a relatively small number of SG points (i.e., CLM4.5 simulations), and the application of the optimized parameters leads to a higher predictive capacity than the default parameter values in the CLM4.5 for the US-MOz site.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

An effective parameter optimization with radiation balance constraints in the CAM5

NASA Astrophysics Data System (ADS)

Wu, L.; Zhang, T.; Qin, Y.; Lin, Y.; Xue, W.; Zhang, M.

2017-12-01

Uncertain parameters in physical parameterizations of General Circulation Models (GCMs) greatly impact model performance. Traditional parameter tuning methods are mostly unconstrained optimization, leading to the simulation results with optimal parameters may not meet the conditions that models have to keep. In this study, the radiation balance constraint is taken as an example, which is involved in the automatic parameter optimization procedure. The Lagrangian multiplier method is used to solve this optimization problem with constrains. In our experiment, we use CAM5 atmosphere model under 5-yr AMIP simulation with prescribed seasonal climatology of SST and sea ice. We consider the synthesized metrics using global means of radiation, precipitation, relative humidity, and temperature as the goal of optimization, and simultaneously consider the conditions that FLUT and FSNTOA should satisfy as constraints. The global average of the output variables FLUT and FSNTOA are set to be approximately equal to 240 Wm-2 in CAM5. Experiment results show that the synthesized metrics is 13.6% better than the control run. At the same time, both FLUT and FSNTOA are close to the constrained conditions. The FLUT condition is well satisfied, which is obviously better than the average annual FLUT obtained with the default parameters. The FSNTOA has a slight deviation from the observed value, but the relative error is less than 7.7‰.

Parameter optimization of a hydrologic model in a snow-dominated basin using a modular Python framework

NASA Astrophysics Data System (ADS)

Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.

2016-12-01

Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.

Optimized microsystems-enabled photovoltaics

DOEpatents

Cruz-Campa, Jose Luis; Nielson, Gregory N.; Young, Ralph W.; Resnick, Paul J.; Okandan, Murat; Gupta, Vipin P.

2015-09-22

Technologies pertaining to designing microsystems-enabled photovoltaic (MEPV) cells are described herein. A first restriction for a first parameter of an MEPV cell is received. Subsequently, a selection of a second parameter of the MEPV cell is received. Values for a plurality of parameters of the MEPV cell are computed such that the MEPV cell is optimized with respect to the second parameter, wherein the values for the plurality of parameters are computed based at least in part upon the restriction for the first parameter.

Launch Vehicle Propulsion Design with Multiple Selection Criteria

NASA Technical Reports Server (NTRS)

Shelton, Joey D.; Frederick, Robert A.; Wilhite, Alan W.

2005-01-01

The approach and techniques described herein define an optimization and evaluation approach for a liquid hydrogen/liquid oxygen single-stage-to-orbit system. The method uses Monte Carlo simulations, genetic algorithm solvers, a propulsion thermo-chemical code, power series regression curves for historical data, and statistical models in order to optimize a vehicle system. The system, including parameters for engine chamber pressure, area ratio, and oxidizer/fuel ratio, was modeled and optimized to determine the best design for seven separate design weight and cost cases by varying design and technology parameters. Significant model results show that a 53% increase in Design, Development, Test and Evaluation cost results in a 67% reduction in Gross Liftoff Weight. Other key findings show the sensitivity of propulsion parameters, technology factors, and cost factors and how these parameters differ when cost and weight are optimized separately. Each of the three key propulsion parameters; chamber pressure, area ratio, and oxidizer/fuel ratio, are optimized in the seven design cases and results are plotted to show impacts to engine mass and overall vehicle mass.

Thermofluid Analysis of Magnetocaloric Refrigeration

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

Abdelaziz, Omar; Gluesenkamp, Kyle R; Vineyard, Edward Allan

While there have been extensive studies on thermofluid characteristics of different magnetocaloric refrigeration systems, a conclusive optimization study using non-dimensional parameters which can be applied to a generic system has not been reported yet. In this study, a numerical model has been developed for optimization of active magnetic refrigerator (AMR). This model is computationally efficient and robust, making it appropriate for running the thousands of simulations required for parametric study and optimization. The governing equations have been non-dimensionalized and numerically solved using finite difference method. A parametric study on a wide range of non-dimensional numbers has been performed. While themore » goal of AMR systems is to improve the performance of competitive parameters including COP, cooling capacity and temperature span, new parameters called AMR performance index-1 have been introduced in order to perform multi objective optimization and simultaneously exploit all these parameters. The multi-objective optimization is carried out for a wide range of the non-dimensional parameters. The results of this study will provide general guidelines for designing high performance AMR systems.« less

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.

PubMed

Ismail, Ahmad Muhaimin; Mohamad, Mohd Saberi; Abdul Majid, Hairudin; Abas, Khairul Hamimah; Deris, Safaai; Zaki, Nazar; Mohd Hashim, Siti Zaiton; Ibrahim, Zuwairie; Remli, Muhammad Akmal

2017-12-01

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Design of robust systems by means of the numerical optimization with harmonic changing of the model parameters

NASA Astrophysics Data System (ADS)

Zhmud, V. A.; Reva, I. L.; Dimitrov, L. V.

2017-01-01

The design of robust feedback systems by means of the numerical optimization method is mostly accomplished with modeling of the several systems simultaneously. In each such system, regulators are similar. But the object models are different. It includes all edge values from the possible variants of the object model parameters. With all this, not all possible sets of model parameters are taken into account. Hence, the regulator can be not robust, i. e. it can not provide system stability in some cases, which were not tested during the optimization procedure. The paper proposes an alternative method. It consists in sequent changing of all parameters according to harmonic low. The frequencies of changing of each parameter are aliquant. It provides full covering of the parameters space.

Design Optimization of a Hybrid Electric Vehicle Powertrain

NASA Astrophysics Data System (ADS)

Mangun, Firdause; Idres, Moumen; Abdullah, Kassim

2017-03-01

This paper presents an optimization work on hybrid electric vehicle (HEV) powertrain using Genetic Algorithm (GA) method. It focused on optimization of the parameters of powertrain components including supercapacitors to obtain maximum fuel economy. Vehicle modelling is based on Quasi-Static-Simulation (QSS) backward-facing approach. A combined city (FTP-75)-highway (HWFET) drive cycle is utilized for the design process. Seeking global optimum solution, GA was executed with different initial settings to obtain sets of optimal parameters. Starting from a benchmark HEV, optimization results in a smaller engine (2 l instead of 3 l) and a larger battery (15.66 kWh instead of 2.01 kWh). This leads to a reduction of 38.3% in fuel consumption and 30.5% in equivalent fuel consumption. Optimized parameters are also compared with actual values for HEV in the market.

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

PubMed

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

2009-01-01

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

Assessing FPAR Source and Parameter Optimization Scheme in Application of a Diagnostic Carbon Flux Model

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

Turner, D P; Ritts, W D; Wharton, S

2009-02-26

The combination of satellite remote sensing and carbon cycle models provides an opportunity for regional to global scale monitoring of terrestrial gross primary production, ecosystem respiration, and net ecosystem production. FPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) is a critical input to diagnostic models, however little is known about the relative effectiveness of FPAR products from different satellite sensors nor about the sensitivity of flux estimates to different parameterization approaches. In this study, we used multiyear observations of carbon flux at four eddy covariance flux tower sites within the conifer biome to evaluate these factors.more » FPAR products from the MODIS and SeaWiFS sensors, and the effects of single site vs. cross-site parameter optimization were tested with the CFLUX model. The SeaWiFs FPAR product showed greater dynamic range across sites and resulted in slightly reduced flux estimation errors relative to the MODIS product when using cross-site optimization. With site-specific parameter optimization, the flux model was effective in capturing seasonal and interannual variation in the carbon fluxes at these sites. The cross-site prediction errors were lower when using parameters from a cross-site optimization compared to parameter sets from optimization at single sites. These results support the practice of multisite optimization within a biome for parameterization of diagnostic carbon flux models.« less

Comparison of optimal design methods in inverse problems

NASA Astrophysics Data System (ADS)

Banks, H. T.; Holm, K.; Kappel, F.

2011-07-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667-77 De Gaetano A and Arino O 2000 J. Math. Biol. 40 136-68 Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979-90).

Error propagation of partial least squares for parameters optimization in NIR modeling.

PubMed

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Error propagation of partial least squares for parameters optimization in NIR modeling

NASA Astrophysics Data System (ADS)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm

PubMed Central

Tamjidy, Mehran; Baharudin, B. T. Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-01-01

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon’s entropy. PMID:28772893

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm.

PubMed

Tamjidy, Mehran; Baharudin, B T Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-05-15

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon's entropy.

Optimization and experimental validation of a thermal cycle that maximizes entropy coefficient fisher identifiability for lithium iron phosphate cells

NASA Astrophysics Data System (ADS)

Mendoza, Sergio; Rothenberger, Michael; Hake, Alison; Fathy, Hosam

2016-03-01

This article presents a framework for optimizing the thermal cycle to estimate a battery cell's entropy coefficient at 20% state of charge (SOC). Our goal is to maximize Fisher identifiability: a measure of the accuracy with which a parameter can be estimated. Existing protocols in the literature for estimating entropy coefficients demand excessive laboratory time. Identifiability optimization makes it possible to achieve comparable accuracy levels in a fraction of the time. This article demonstrates this result for a set of lithium iron phosphate (LFP) cells. We conduct a 24-h experiment to obtain benchmark measurements of their entropy coefficients. We optimize a thermal cycle to maximize parameter identifiability for these cells. This optimization proceeds with respect to the coefficients of a Fourier discretization of this thermal cycle. Finally, we compare the estimated parameters using (i) the benchmark test, (ii) the optimized protocol, and (iii) a 15-h test from the literature (by Forgez et al.). The results are encouraging for two reasons. First, they confirm the simulation-based prediction that the optimized experiment can produce accurate parameter estimates in 2 h, compared to 15-24. Second, the optimized experiment also estimates a thermal time constant representing the effects of thermal capacitance and convection heat transfer.

A Systematic Approach to Sensor Selection for Aircraft Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2009-01-01

A systematic approach for selecting an optimal suite of sensors for on-board aircraft gas turbine engine health estimation is presented. The methodology optimally chooses the engine sensor suite and the model tuning parameter vector to minimize the Kalman filter mean squared estimation error in the engine s health parameters or other unmeasured engine outputs. This technique specifically addresses the underdetermined estimation problem where there are more unknown system health parameters representing degradation than available sensor measurements. This paper presents the theoretical estimation error equations, and describes the optimization approach that is applied to select the sensors and model tuning parameters to minimize these errors. Two different model tuning parameter vector selection approaches are evaluated: the conventional approach of selecting a subset of health parameters to serve as the tuning parameters, and an alternative approach that selects tuning parameters as a linear combination of all health parameters. Results from the application of the technique to an aircraft engine simulation are presented, and compared to those from an alternative sensor selection strategy.

Strategie de commande optimale de la production electrique dans un site isole

NASA Astrophysics Data System (ADS)

Barris, Nicolas

Hydro-Quebec manages more than 20 isolated power grids all over the province. The grids are located in small villages where the electricity demand is rather small. Those villages being far away from each other and from the main electricity production facilities, energy is produced locally using diesel generators. Electricity production costs at the isolated power grids are very important due to elevated diesel prices and transportation costs. However, the price of electricity is the same for the entire province, with no regards to the production costs of the electricity consumed. These two factors combined result in yearly exploitation losses for Hydro-Quebec. For any given village, several diesel generators are required to satisfy the demand. When the load increases, it becomes necessary to increase the capacity either by adding a generator to the production or by switching to a more powerful generator. The same thing happens when the load decreases. Every decision regarding changes in the production is included in the control strategy, which is based on predetermined parameters. These parameters were specified according to empirical studies and the knowledge base of the engineers managing the isolated power grids, but without any optimisation approach. The objective of the presented work is to minimize the diesel consumption by optimizing the parameters included in the control strategy. Its impact would be to limit the exploitation losses generated by the isolated power grids and the CO2 equivalent emissions without adding new equipment or completely changing the nature of the strategy. To satisfy this objective, the isolated power grid simulator OPERA is used along with the optimization library NOMAD and the data of three villages in northern Quebec. The preliminary optimization instance for the first village showed that some modifications to the existing control strategy must be done to better achieve the minimization objective. The main optimization processes consist of three different optimization approaches: the optimization of one set of parameters for all the villages, the optimization of one set of parameters per village, and the optimization of one set of parameters per diesel generator configuration per village. In the first scenario, the optimization of one set of parameters for all the villages leads to compromises for all three villages without allowing a full potential reduction for any village. Therefore, it is proven that applying one set of parameters to all the villages is not suitable for finding an optimal solution. In the second scenario, the optimization of one set of parameters per village allows an improvement over the previous results. At this point, it is shown that it is crucial to remove from the production the less efficient configurations when they are next to more efficient configurations. In the third scenario, the optimization of one set of parameters per configuration per village requires a very large number of function evaluations but does not result in any satisfying solution. In order to improve the performance of the optimization, it has been decided that the problem structure would be used. Two different approaches are considered: optimizing one set of parameters at a time and optimizing different rules included in the control strategy one at a time. In both cases, results are similar but calculation costs differ, the second method being much more cost efficient. The optimal values of the ultimate rules parameters can be directly linked to the efficient transition points that favor an efficient operation of the isolated power grids. Indeed, these transition points are defined in such a way that the high efficiency zone of every configuration is used. Therefore, it seems possible to directly identify on the graphs these optimal transition points and define the parameters in the control strategy without even having to run any optimization process. The diesel consumption reduction for all three villages is about 1.9%. Considering elevated diesel costs and the existence of about 20 other isolated power grids, the use of the developed methods together with a calibration of OPERA would allow a substantial reduction of Hydro-Quebec's annual deficit. Also, since one of the developed methods is very cost effective and produces equivalent results, it could be possible to use it during other processes; for example, when buying new equipment for the grid it could be possible to assess its full potential, under an optimized control strategy, and improve the net present value.

Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

2014-03-01

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

GA-optimization for rapid prototype system demonstration

NASA Technical Reports Server (NTRS)

Kim, Jinwoo; Zeigler, Bernard P.

1994-01-01

An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was developed to solve complicated engineering problems which require optimization of a large number of parameters with high precision. High level GAs search for few parameters which are much more sensitive to the system performance. Low level GAs search in more detail and employ a greater number of parameters for further optimization. Therefore, the complexity of the search is decreased and the computing resources are used more efficiently.

Results of an integrated structure/control law design sensitivity analysis

NASA Technical Reports Server (NTRS)

Gilbert, Michael G.

1989-01-01

A design sensitivity analysis method for Linear Quadratic Cost, Gaussian (LQG) optimal control laws, which predicts change in the optimal control law due to changes in fixed problem parameters using analytical sensitivity equations is discussed. Numerical results of a design sensitivity analysis for a realistic aeroservoelastic aircraft example are presented. In this example, the sensitivity of the optimally controlled aircraft's response to various problem formulation and physical aircraft parameters is determined. These results are used to predict the aircraft's new optimally controlled response if the parameter was to have some other nominal value during the control law design process. The sensitivity results are validated by recomputing the optimal control law for discrete variations in parameters, computing the new actual aircraft response, and comparing with the predicted response. These results show an improvement in sensitivity accuracy for integrated design purposes over methods which do not include changes in the optimal control law. Use of the analytical LQG sensitivity expressions is also shown to be more efficient than finite difference methods for the computation of the equivalent sensitivity information.

Modelling and multi objective optimization of WEDM of commercially Monel super alloy using evolutionary algorithms

NASA Astrophysics Data System (ADS)

Varun, Sajja; Reddy, Kalakada Bhargav Bal; Vardhan Reddy, R. R. Vishnu

2016-09-01

In this research work, development of a multi response optimization technique has been undertaken, using traditional desirability analysis and non-traditional particle swarm optimization techniques (for different customer's priorities) in wire electrical discharge machining (WEDM). Monel 400 has been selected as work material for experimentation. The effect of key process parameters such as pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF) were on material removal rate (MRR) and surface roughness(SR) in WEDM operation were investigated. Further, the responses such as MRR and SR were modelled empirically through regression analysis. The developed models can be used by the machinists to predict the MRR and SR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users by using Taguchi-desirability function method and particle swarm optimization technique. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. Finally, the confirmation experiments were conducted for the optimal set of machining parameters, and the betterment has been proved.

Multi objective optimization model for minimizing production cost and environmental impact in CNC turning process

NASA Astrophysics Data System (ADS)

Widhiarso, Wahyu; Rosyidi, Cucuk Nur

2018-02-01

Minimizing production cost in a manufacturing company will increase the profit of the company. The cutting parameters will affect total processing time which then will affect the production cost of machining process. Besides affecting the production cost and processing time, the cutting parameters will also affect the environment. An optimization model is needed to determine the optimum cutting parameters. In this paper, we develop an optimization model to minimize the production cost and the environmental impact in CNC turning process. The model is used a multi objective optimization. Cutting speed and feed rate are served as the decision variables. Constraints considered are cutting speed, feed rate, cutting force, output power, and surface roughness. The environmental impact is converted from the environmental burden by using eco-indicator 99. Numerical example is given to show the implementation of the model and solved using OptQuest of Oracle Crystal Ball software. The results of optimization indicate that the model can be used to optimize the cutting parameters to minimize the production cost and the environmental impact.

Inverse design of bulk morphologies in block copolymers using particle swarm optimization

NASA Astrophysics Data System (ADS)

Khadilkar, Mihir; Delaney, Kris; Fredrickson, Glenn

Multiblock polymers are a versatile platform for creating a large range of nanostructured materials with novel morphologies and properties. However, achieving desired structures or property combinations is difficult due to a vast design space comprised of parameters including monomer species, block sequence, block molecular weights and dispersity, copolymer architecture, and binary interaction parameters. Navigating through such vast design spaces to achieve an optimal formulation for a target structure or property set requires an efficient global optimization tool wrapped around a forward simulation technique such as self-consistent field theory (SCFT). We report on such an inverse design strategy utilizing particle swarm optimization (PSO) as the global optimizer and SCFT as the forward prediction engine. To avoid metastable states in forward prediction, we utilize pseudo-spectral variable cell SCFT initiated from a library of defect free seeds of known block copolymer morphologies. We demonstrate that our approach allows for robust identification of block copolymers and copolymer alloys that self-assemble into a targeted structure, optimizing parameters such as block fractions, blend fractions, and Flory chi parameters.

Optimization for minimum sensitivity to uncertain parameters

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

The Quantum Approximation Optimization Algorithm for MaxCut: A Fermionic View

NASA Technical Reports Server (NTRS)

Wang, Zhihui; Hadfield, Stuart; Jiang, Zhang; Rieffel, Eleanor G.

2017-01-01

Farhi et al. recently proposed a class of quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA), for approximately solving combinatorial optimization problems. A level-p QAOA circuit consists of steps in which a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2p times for which these two Hamiltonians are applied are the parameters of the algorithm. As p increases, however, the parameter search space grows quickly. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here, we analytically and numerically study parameter setting for QAOA applied to MAXCUT. For level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MAXCUT, the Ring of Disagrees, or the 1D antiferromagnetic ring, we provide an analysis for arbitrarily high level. Using a Fermionic representation, the evolution of the system under QAOA translates into quantum optimal control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of QAOA for any p. It also greatly simplifies numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional sub-manifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Optimization of the blade trailing edge geometric parameters for a small scale ORC turbine

NASA Astrophysics Data System (ADS)

Zhang, L.; Zhuge, W. L.; Peng, J.; Liu, S. J.; Zhang, Y. J.

2013-12-01

In general, the method proposed by Whitfield and Baines is adopted for the turbine preliminary design. In this design procedure for the turbine blade trailing edge geometry, two assumptions (ideal gas and zero discharge swirl) and two experience values (WR and γ) are used to get the three blade trailing edge geometric parameters: relative exit flow angle β6, the exit tip radius R6t and hub radius R6h for the purpose of maximizing the rotor total-to-static isentropic efficiency. The method above is established based on the experience and results of testing using air as working fluid, so it does not provide a mathematical optimal solution to instruct the optimization of geometry parameters and consider the real gas effects of the organic, working fluid which must be taken into consideration for the ORC turbine design procedure. In this paper, a new preliminary design and optimization method is established for the purpose of reducing the exit kinetic energy loss to improve the turbine efficiency ηts, and the blade trailing edge geometric parameters for a small scale ORC turbine with working fluid R123 are optimized based on this method. The mathematical optimal solution to minimize the exit kinetic energy is deduced, which can be used to design and optimize the exit shroud/hub radius and exit blade angle. And then, the influence of blade trailing edge geometric parameters on turbine efficiency ηts are analysed and the optimal working ranges of these parameters for the equations are recommended in consideration of working fluid R123. This method is used to modify an existing ORC turbine exit kinetic energy loss from 11.7% to 7%, which indicates the effectiveness of the method. However, the internal passage loss increases from 7.9% to 9.4%, so the only way to consider the influence of geometric parameters on internal passage loss is to give the empirical ranges of these parameters, such as the recommended ranges that the value of γ is at 0.3 to 0.4, and the value of τ is at 0.5 to 0.6.

Use of multilevel modeling for determining optimal parameters of heat supply systems

NASA Astrophysics Data System (ADS)

Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.

2017-07-01

The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in St. Petersburg, the city of Bratsk, and the Magistral'nyi settlement.

Parameter Optimization of PAL-XFEL Injector

NASA Astrophysics Data System (ADS)

Lee, Jaehyun; Ko, In Soo; Han, Jang-Hui; Hong, Juho; Yang, Haeryong; Min, Chang Ki; Kang, Heung-Sik

2018-05-01

A photoinjector is used as the electron source to generate a high peak current and low emittance beam for an X-ray free electron laser (FEL). The beam emittance is one of the critical parameters to determine the FEL performance together with the slice energy spread and the peak current. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) was constructed in 2015, and the beam commissioning was carried out in spring 2016. The injector is running routinely for PAL-XFEL user operation. The operational parameters of the injector have been optimized experimentally, and these are somewhat different from the originally designed ones. Therefore, we study numerically the injector parameters based on the empirically optimized parameters and review the present operating condition.

Genetic algorithm parameters tuning for resource-constrained project scheduling problem

NASA Astrophysics Data System (ADS)

Tian, Xingke; Yuan, Shengrui

2018-04-01

Project Scheduling Problem (RCPSP) is a kind of important scheduling problem. To achieve a certain optimal goal such as the shortest duration, the smallest cost, the resource balance and so on, it is required to arrange the start and finish of all tasks under the condition of satisfying project timing constraints and resource constraints. In theory, the problem belongs to the NP-hard problem, and the model is abundant. Many combinatorial optimization problems are special cases of RCPSP, such as job shop scheduling, flow shop scheduling and so on. At present, the genetic algorithm (GA) has been used to deal with the classical RCPSP problem and achieved remarkable results. Vast scholars have also studied the improved genetic algorithm for the RCPSP problem, which makes it to solve the RCPSP problem more efficiently and accurately. However, for the selection of the main parameters of the genetic algorithm, there is no parameter optimization in these studies. Generally, we used the empirical method, but it cannot ensure to meet the optimal parameters. In this paper, the problem was carried out, which is the blind selection of parameters in the process of solving the RCPSP problem. We made sampling analysis, the establishment of proxy model and ultimately solved the optimal parameters.

Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring

PubMed Central

Liu, Xiaoli; Chen, Hong-Xin; Kim, Jinxie

2014-01-01

A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline. PMID:25025089

Application of the Response Surface Methodology to Optimize the Fermentation Parameters for Enhanced Docosahexaenoic Acid (DHA) Production by Thraustochytrium sp. ATCC 26185.

PubMed

Wu, Kang; Ding, Lijian; Zhu, Peng; Li, Shuang; He, Shan

2018-04-22

The aim of this study was to determine the cumulative effect of fermentation parameters and enhance the production of docosahexaenoic acid (DHA) by Thraustochytrium sp. ATCC 26185 using response surface methodology (RSM). Among the eight variables screened for effects of fermentation parameters on DHA production by Plackett-Burman design (PBD), the initial pH, inoculum volume, and fermentation volume were found to be most significant. The Box-Behnken design was applied to derive a statistical model for optimizing these three fermentation parameters for DHA production. The optimal parameters for maximum DHA production were initial pH: 6.89, inoculum volume: 4.16%, and fermentation volume: 140.47 mL, respectively. The maximum yield of DHA production was 1.68 g/L, which was in agreement with predicted values. An increase in DHA production was achieved by optimizing the initial pH, fermentation, and inoculum volume parameters. This optimization strategy led to a significant increase in the amount of DHA produced, from 1.16 g/L to 1.68 g/L. Thraustochytrium sp. ATCC 26185 is a promising resource for microbial DHA production due to the high-level yield of DHA that it produces, and the capacity for large-scale fermentation of this organism.

Real data assimilation for optimization of frictional parameters and prediction of afterslip in the 2003 Tokachi-oki earthquake inferred from slip velocity by an adjoint method

NASA Astrophysics Data System (ADS)

Kano, Masayuki; Miyazaki, Shin'ichi; Ishikawa, Yoichi; Hiyoshi, Yoshihisa; Ito, Kosuke; Hirahara, Kazuro

2015-10-01

Data assimilation is a technique that optimizes the parameters used in a numerical model with a constraint of model dynamics achieving the better fit to observations. Optimized parameters can be utilized for the subsequent prediction with a numerical model and predicted physical variables are presumably closer to observations that will be available in the future, at least, comparing to those obtained without the optimization through data assimilation. In this work, an adjoint data assimilation system is developed for optimizing a relatively large number of spatially inhomogeneous frictional parameters during the afterslip period in which the physical constraints are a quasi-dynamic equation of motion and a laboratory derived rate and state dependent friction law that describe the temporal evolution of slip velocity at subduction zones. The observed variable is estimated slip velocity on the plate interface. Before applying this method to the real data assimilation for the afterslip of the 2003 Tokachi-oki earthquake, a synthetic data assimilation experiment is conducted to examine the feasibility of optimizing the frictional parameters in the afterslip area. It is confirmed that the current system is capable of optimizing the frictional parameters A-B, A and L by adopting the physical constraint based on a numerical model if observations capture the acceleration and decaying phases of slip on the plate interface. On the other hand, it is unlikely to constrain the frictional parameters in the region where the amplitude of afterslip is less than 1.0 cm d-1. Next, real data assimilation for the 2003 Tokachi-oki earthquake is conducted to incorporate slip velocity data inferred from time dependent inversion of Global Navigation Satellite System time-series. The optimized values of A-B, A and L are O(10 kPa), O(102 kPa) and O(10 mm), respectively. The optimized frictional parameters yield the better fit to the observations and the better prediction skill of slip velocity afterwards. Also, further experiment shows the importance of employing a fine-mesh model. It will contribute to the further understanding of the frictional properties on plate interfaces and lead to the forecasting system that provides useful information on the possibility of consequent earthquakes.

Development of optimization model for sputtering process parameter based on gravitational search algorithm

NASA Astrophysics Data System (ADS)

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.

Optimization and analysis of large chemical kinetic mechanisms using the solution mapping method - Combustion of methane

NASA Technical Reports Server (NTRS)

Frenklach, Michael; Wang, Hai; Rabinowitz, Martin J.

1992-01-01

A method of systematic optimization, solution mapping, as applied to a large-scale dynamic model is presented. The basis of the technique is parameterization of model responses in terms of model parameters by simple algebraic expressions. These expressions are obtained by computer experiments arranged in a factorial design. The developed parameterized responses are then used in a joint multiparameter multidata-set optimization. A brief review of the mathematical background of the technique is given. The concept of active parameters is discussed. The technique is applied to determine an optimum set of parameters for a methane combustion mechanism. Five independent responses - comprising ignition delay times, pre-ignition methyl radical concentration profiles, and laminar premixed flame velocities - were optimized with respect to thirteen reaction rate parameters. The numerical predictions of the optimized model are compared to those computed with several recent literature mechanisms. The utility of the solution mapping technique in situations where the optimum is not unique is also demonstrated.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Preparation and Optimization of Vanadium Titanomagnetite Carbon Composite Hot Briquette: A New Type of Blast Furnace Burden

NASA Astrophysics Data System (ADS)

Zhao, W.; Wang, H. T.; Liu, Z. G.; Chu, M. S.; Ying, Z. W.; Tang, J.

2017-10-01

A new type of blast furnace burden, named VTM-CCB (vanadium titanomagnetite carbon composite hot briquette), is proposed and optimized in this paper. The preparation process of VTM-CCB includes two components, hot briquetting and heat treatment. The hot-briquetting and heat-treatment parameters are systematically optimized based on the Taguchi method and single-factor experiment. The optimized preparation parameters of VTM-CCB include a hot-briquetting temperature of 300°C, a coal particle size of <0.075 mm, a vanadium titanomagnetite particle size of <0.075 mm, a coal-added ratio of 28.52%, a heat-treatment temperature of 500°C and a heat-treatment time of 3 h. The compressive strength of VTM-CCB, based on the optimized parameters, reaches 2450 N, which meets the requirement of blast furnace ironmaking. These integrated parameters provide a theoretical basis for the production and application of a blast furnace smelting VTM-CCB.

On the optimal use of fictitious time in variation of parameters methods with application to BG14

NASA Technical Reports Server (NTRS)

Gottlieb, Robert G.

1991-01-01

The optimal way to use fictitious time in variation of parameter methods is presented. Setting fictitious time to zero at the end of each step is shown to cure the instability associated with some types of problems. Only some parameters are reinitialized, thereby retaining redundant information.

Generalized massive optimal data compression

NASA Astrophysics Data System (ADS)

Alsing, Justin; Wandelt, Benjamin

2018-05-01

In this paper, we provide a general procedure for optimally compressing N data down to n summary statistics, where n is equal to the number of parameters of interest. We show that compression to the score function - the gradient of the log-likelihood with respect to the parameters - yields n compressed statistics that are optimal in the sense that they preserve the Fisher information content of the data. Our method generalizes earlier work on linear Karhunen-Loéve compression for Gaussian data whilst recovering both lossless linear compression and quadratic estimation as special cases when they are optimal. We give a unified treatment that also includes the general non-Gaussian case as long as mild regularity conditions are satisfied, producing optimal non-linear summary statistics when appropriate. As a worked example, we derive explicitly the n optimal compressed statistics for Gaussian data in the general case where both the mean and covariance depend on the parameters.

Multi-objective optimization model of CNC machining to minimize processing time and environmental impact

NASA Astrophysics Data System (ADS)

Hamada, Aulia; Rosyidi, Cucuk Nur; Jauhari, Wakhid Ahmad

2017-11-01

Minimizing processing time in a production system can increase the efficiency of a manufacturing company. Processing time are influenced by application of modern technology and machining parameter. Application of modern technology can be apply by use of CNC machining, one of the machining process can be done with a CNC machining is turning. However, the machining parameters not only affect the processing time but also affect the environmental impact. Hence, optimization model is needed to optimize the machining parameters to minimize the processing time and environmental impact. This research developed a multi-objective optimization to minimize the processing time and environmental impact in CNC turning process which will result in optimal decision variables of cutting speed and feed rate. Environmental impact is converted from environmental burden through the use of eco-indicator 99. The model were solved by using OptQuest optimization software from Oracle Crystal Ball.

Optimal radiotherapy dose schedules under parametric uncertainty

NASA Astrophysics Data System (ADS)

Badri, Hamidreza; Watanabe, Yoichi; Leder, Kevin

2016-01-01

We consider the effects of parameter uncertainty on the optimal radiation schedule in the context of the linear-quadratic model. Our interest arises from the observation that if inter-patient variability in normal and tumor tissue radiosensitivity or sparing factor of the organs-at-risk (OAR) are not accounted for during radiation scheduling, the performance of the therapy may be strongly degraded or the OAR may receive a substantially larger dose than the allowable threshold. This paper proposes a stochastic radiation scheduling concept to incorporate inter-patient variability into the scheduling optimization problem. Our method is based on a probabilistic approach, where the model parameters are given by a set of random variables. Our probabilistic formulation ensures that our constraints are satisfied with a given probability, and that our objective function achieves a desired level with a stated probability. We used a variable transformation to reduce the resulting optimization problem to two dimensions. We showed that the optimal solution lies on the boundary of the feasible region and we implemented a branch and bound algorithm to find the global optimal solution. We demonstrated how the configuration of optimal schedules in the presence of uncertainty compares to optimal schedules in the absence of uncertainty (conventional schedule). We observed that in order to protect against the possibility of the model parameters falling into a region where the conventional schedule is no longer feasible, it is required to avoid extremal solutions, i.e. a single large dose or very large total dose delivered over a long period. Finally, we performed numerical experiments in the setting of head and neck tumors including several normal tissues to reveal the effect of parameter uncertainty on optimal schedules and to evaluate the sensitivity of the solutions to the choice of key model parameters.

Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

PubMed Central

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. PMID:25603158

Optimization of Maghemite (γ-Fe2O3) Nano-Powder Mixed micro-EDM of CoCrMo with Multiple Responses Using Gray Relational Analysis (GRA)

NASA Astrophysics Data System (ADS)

Mejid Elsiti, Nagwa; Noordin, M. Y.; Idris, Ani; Saed Majeed, Faraj

2017-10-01

This paper presents an optimization of process parameters of Micro-Electrical Discharge Machining (EDM) process with (γ-Fe2O3) nano-powder mixed dielectric using multi-response optimization Grey Relational Analysis (GRA) method instead of single response optimization. These parameters were optimized based on 2-Level factorial design combined with Grey Relational Analysis. The machining parameters such as peak current, gap voltage, and pulse on time were chosen for experimentation. The performance characteristics chosen for this study are material removal rate (MRR), tool wear rate (TWR), Taper and Overcut. Experiments were conducted using electrolyte copper as the tool and CoCrMo as the workpiece. Experimental results have been improved through this approach.

Post-Optimality Analysis In Aerospace Vehicle Design

NASA Technical Reports Server (NTRS)

Braun, Robert D.; Kroo, Ilan M.; Gage, Peter J.

1993-01-01

This analysis pertains to the applicability of optimal sensitivity information to aerospace vehicle design. An optimal sensitivity (or post-optimality) analysis refers to computations performed once the initial optimization problem is solved. These computations may be used to characterize the design space about the present solution and infer changes in this solution as a result of constraint or parameter variations, without reoptimizing the entire system. The present analysis demonstrates that post-optimality information generated through first-order computations can be used to accurately predict the effect of constraint and parameter perturbations on the optimal solution. This assessment is based on the solution of an aircraft design problem in which the post-optimality estimates are shown to be within a few percent of the true solution over the practical range of constraint and parameter variations. Through solution of a reusable, single-stage-to-orbit, launch vehicle design problem, this optimal sensitivity information is also shown to improve the efficiency of the design process, For a hierarchically decomposed problem, this computational efficiency is realized by estimating the main-problem objective gradient through optimal sep&ivity calculations, By reducing the need for finite differentiation of a re-optimized subproblem, a significant decrease in the number of objective function evaluations required to reach the optimal solution is obtained.

Quantum Parameter Estimation: From Experimental Design to Constructive Algorithm

NASA Astrophysics Data System (ADS)

Yang, Le; Chen, Xi; Zhang, Ming; Dai, Hong-Yi

2017-11-01

In this paper we design the following two-step scheme to estimate the model parameter ω 0 of the quantum system: first we utilize the Fisher information with respect to an intermediate variable v=\\cos ({ω }0t) to determine an optimal initial state and to seek optimal parameters of the POVM measurement operators; second we explore how to estimate ω 0 from v by choosing t when a priori information knowledge of ω 0 is available. Our optimal initial state can achieve the maximum quantum Fisher information. The formulation of the optimal time t is obtained and the complete algorithm for parameter estimation is presented. We further explore how the lower bound of the estimation deviation depends on the a priori information of the model. Supported by the National Natural Science Foundation of China under Grant Nos. 61273202, 61673389, and 61134008

Numerical Parameter Optimization of the Ignition and Growth Model for HMX Based Plastic Bonded Explosives

NASA Astrophysics Data System (ADS)

Gambino, James; Tarver, Craig; Springer, H. Keo; White, Bradley; Fried, Laurence

2017-06-01

We present a novel method for optimizing parameters of the Ignition and Growth reactive flow (I&G) model for high explosives. The I&G model can yield accurate predictions of experimental observations. However, calibrating the model is a time-consuming task especially with multiple experiments. In this study, we couple the differential evolution global optimization algorithm to simulations of shock initiation experiments in the multi-physics code ALE3D. We develop parameter sets for HMX based explosives LX-07 and LX-10. The optimization finds the I&G model parameters that globally minimize the difference between calculated and experimental shock time of arrival at embedded pressure gauges. This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. LLNS, LLC LLNL-ABS- 724898.

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

PubMed

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

2016-12-01

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

On the optimization of Gaussian basis sets

NASA Astrophysics Data System (ADS)

Petersson, George A.; Zhong, Shijun; Montgomery, John A.; Frisch, Michael J.

2003-01-01

A new procedure for the optimization of the exponents, αj, of Gaussian basis functions, Ylm(ϑ,φ)rle-αjr2, is proposed and evaluated. The direct optimization of the exponents is hindered by the very strong coupling between these nonlinear variational parameters. However, expansion of the logarithms of the exponents in the orthonormal Legendre polynomials, Pk, of the index, j: ln αj=∑k=0kmaxAkPk((2j-2)/(Nprim-1)-1), yields a new set of well-conditioned parameters, Ak, and a complete sequence of well-conditioned exponent optimizations proceeding from the even-tempered basis set (kmax=1) to a fully optimized basis set (kmax=Nprim-1). The error relative to the exact numerical self-consistent field limit for a six-term expansion is consistently no more than 25% larger than the error for the completely optimized basis set. Thus, there is no need to optimize more than six well-conditioned variational parameters, even for the largest sets of Gaussian primitives.

Estimation of beech pyrolysis kinetic parameters by Shuffled Complex Evolution.

PubMed

Ding, Yanming; Wang, Changjian; Chaos, Marcos; Chen, Ruiyu; Lu, Shouxiang

2016-01-01

The pyrolysis kinetics of a typical biomass energy feedstock, beech, was investigated based on thermogravimetric analysis over a wide heating rate range from 5K/min to 80K/min. A three-component (corresponding to hemicellulose, cellulose and lignin) parallel decomposition reaction scheme was applied to describe the experimental data. The resulting kinetic reaction model was coupled to an evolutionary optimization algorithm (Shuffled Complex Evolution, SCE) to obtain model parameters. To the authors' knowledge, this is the first study in which SCE has been used in the context of thermogravimetry. The kinetic parameters were simultaneously optimized against data for 10, 20 and 60K/min heating rates, providing excellent fits to experimental data. Furthermore, it was shown that the optimized parameters were applicable to heating rates (5 and 80K/min) beyond those used to generate them. Finally, the predicted results based on optimized parameters were contrasted with those based on the literature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Carnivorous pitcher plant uses free radicals in the digestion of prey.

PubMed

Chia, Tet Fatt; Aung, Hnin Hnin; Osipov, Anatoly N; Goh, Ngoh Khang; Chia, Lian Sai

2004-01-01

A study of the involvement of free oxygen radicals in trapping and digestion of insects by carnivorous plants was the main goal of the present investigation. We showed that the generation of oxygen free radicals by pitcher fluid of Nepenthes is the first step of the digestion process, as seen by EPR spin trapping assay and gel-electrophoresis. The EPR spectrum of N. gracilis fluid in the presence of DMPO spin trap showed the superposition of the hydroxyl radical spin adduct signal and of the ascorbyl radical signal. Catalase addition decreased the generation of hydroxyl radicals showing that hydroxyl radicals are generated from hydrogen peroxide, which can be derived from superoxide radicals. Gel-electrophoresis data showed that myosin, an abundant protein component of insects, can be rapidly broken down by free radicals and protease inhibitors do not inhibit this process. Addition of myoglobin to the pitcher plant fluid decreased the concentration of detectable radicals. Based on these observations, we conclude that oxygen free radicals produced by the pitcher plant aid in the digestion of the insect prey.

Antioxidant Activity and Total Phenolic and Flavonoid Contents of Hieracium pilosella L. Extracts

PubMed Central

Stanojević, Ljiljana; Stanković, Mihajlo; Nikolić, Vesna; Nikolić, Ljubiša; Ristić, Dušica; Čanadanovic-Brunet, Jasna; Tumbas, Vesna

2009-01-01

The antioxidant activity of water, ethanol and methanol Hieracium pilosella L. extracts is reported. The antioxidative activity was tested by spectrophotometrically measuring their ability to scavenge a stable DPPH• free radical and a reactive hydroxyl radical trapped by DMPO during the Fenton reaction, using the ESR spectroscopy. Total phenolic content and total flavonoid content were evaluated according to the Folin-Ciocalteu procedure, and a colorimetric method, respectively. A HPLC method was used for identification of some phenolic compounds (chlorogenic acid, apigenin-7-O-glucoside and umbelliferone). The antioxidant activity of the investigated extracts slightly differs depending on the solvent used. The concentration of 0.30 mg/mL of water, ethanol and methanol extract is less effective in scavenging hydroxyl radicals (56.35, 58.73 and 54.35%, respectively) in comparison with the DPPH• radical scavenging activity (around 95% for all extracts). The high contents of total phenolic compounds (239.59–244.16 mg GAE/g of dry extract) and total flavonoids (79.13–82.18 mg RE/g of dry extract) indicated that these compounds contribute to the antioxidative activity. PMID:22346723

In situ generation of a hydroxyl radical by nanoporous activated carbon derived from rice husk for environmental applications: kinetic and thermodynamic constants.

PubMed

Karthikeyan, S; Sekaran, G

2014-03-07

The objective of this investigation is to evaluate the hydroxyl radical (˙OH) generation using nanoporous activated carbon (NPAC), derived from rice husk, and dissolved oxygen in water. The in situ production of the ˙OH radical was confirmed through the DMPO spin trapping method in EPR spectroscopy and quantitative determination by a deoxyribose assay procedure. NPAC served as a heterogeneous catalyst to degrade 2-deoxy-d-ribose (a reference compound) using hydroxyl radical generated from dissolved oxygen in water at temperatures in the range 313-373 K and pH 6, with first order rate constants (k = 9.2 × 10(-2) min(-1), k = 1.2 × 10(-1) min(-1), k = 1.3 × 10(-1) min(-1) and k = 1.68 × 10(-1) min(-1)). The thermodynamic constants for the generation of hydroxyl radicals by NPAC and dissolved oxygen in water were ΔG -1.36 kJ mol(-1) at 313 K, ΔH 17.73 kJ mol(-1) and ΔS 61.01 J mol(-1) K(-1).

Optimal control problems of epidemic systems with parameter uncertainties: application to a malaria two-age-classes transmission model with asymptomatic carriers.

PubMed

Mwanga, Gasper G; Haario, Heikki; Capasso, Vicenzo

2015-03-01

The main scope of this paper is to study the optimal control practices of malaria, by discussing the implementation of a catalog of optimal control strategies in presence of parameter uncertainties, which is typical of infectious diseases data. In this study we focus on a deterministic mathematical model for the transmission of malaria, including in particular asymptomatic carriers and two age classes in the human population. A partial qualitative analysis of the relevant ODE system has been carried out, leading to a realistic threshold parameter. For the deterministic model under consideration, four possible control strategies have been analyzed: the use of Long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic and asymptomatic individuals. The numerical results show that using optimal control the disease can be brought to a stable disease free equilibrium when all four controls are used. The Incremental Cost-Effectiveness Ratio (ICER) for all possible combinations of the disease-control measures is determined. The numerical simulations of the optimal control in the presence of parameter uncertainty demonstrate the robustness of the optimal control: the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the designing of cost-effective strategies for disease controls with multiple interventions, even under considerable uncertainty of model parameters. Copyright © 2014 Elsevier Inc. All rights reserved.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Experimental Investigation and Optimization of Response Variables in WEDM of Inconel - 718

NASA Astrophysics Data System (ADS)

Karidkar, S. S.; Dabade, U. A.

2016-02-01

Effective utilisation of Wire Electrical Discharge Machining (WEDM) technology is challenge for modern manufacturing industries. Day by day new materials with high strengths and capabilities are being developed to fulfil the customers need. Inconel - 718 is similar kind of material which is extensively used in aerospace applications, such as gas turbine, rocket motors, and spacecraft as well as in nuclear reactors and pumps etc. This paper deals with the experimental investigation of optimal machining parameters in WEDM for Surface Roughness, Kerf Width and Dimensional Deviation using DoE such as Taguchi methodology, L9 orthogonal array. By keeping peak current constant at 70 A, the effect of other process parameters on above response variables were analysed. Obtained experimental results were statistically analysed using Minitab-16 software. Analysis of Variance (ANOVA) shows pulse on time as the most influential parameter followed by wire tension whereas spark gap set voltage is observed to be non-influencing parameter. Multi-objective optimization technique, Grey Relational Analysis (GRA), shows optimal machining parameters such as pulse on time 108 Machine unit, spark gap set voltage 50 V and wire tension 12 gm for optimal response variables considered for the experimental analysis.

Inverse modeling of rainfall infiltration with a dual permeability approach using different matrix-fracture coupling variants.

NASA Astrophysics Data System (ADS)

Blöcher, Johanna; Kuraz, Michal

2017-04-01

In this contribution we propose implementations of the dual permeability model with different inter-domain exchange descriptions and metaheuristic optimization algorithms for parameter identification and mesh optimization. We compare variants of the coupling term with different numbers of parameters to test if a reduction of parameters is feasible. This can reduce parameter uncertainty in inverse modeling, but also allow for different conceptual models of the domain and matrix coupling. The different variants of the dual permeability model are implemented in the open-source objective library DRUtES written in FORTRAN 2003/2008 in 1D and 2D. For parameter identification we use adaptations of the particle swarm optimization (PSO) and Teaching-learning-based optimization (TLBO), which are population-based metaheuristics with different learning strategies. These are high-level stochastic-based search algorithms that don't require gradient information or a convex search space. Despite increasing computing power and parallel processing, an overly fine mesh is not feasible for parameter identification. This creates the need to find a mesh that optimizes both accuracy and simulation time. We use a bi-objective PSO algorithm to generate a Pareto front of optimal meshes to account for both objectives. The dual permeability model and the optimization algorithms were tested on virtual data and field TDR sensor readings. The TDR sensor readings showed a very steep increase during rapid rainfall events and a subsequent steep decrease. This was theorized to be an effect of artificial macroporous envelopes surrounding TDR sensors creating an anomalous region with distinct local soil hydraulic properties. One of our objectives is to test how well the dual permeability model can describe this infiltration behavior and what coupling term would be most suitable.

Automatic genetic optimization approach to two-dimensional blade profile design for steam turbines

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

Trigg, M.A.; Tubby, G.R.; Sheard, A.G.

1999-01-01

In this paper a systematic approach to the optimization of two-dimensional blade profiles is presented. A genetic optimizer has been developed that modifies the blade profile and calculates its profile loss. This process is automatic, producing profile designs significantly faster and with significantly lower loss than has previously been possible. The optimizer developed uses a genetic algorithm to optimize a two-dimensional profile, defined using 17 parameters, for minimum loss with a given flow condition. The optimizer works with a population of two-dimensional profiles with varied parameters. A CFD mesh is generated for each profile, and the result is analyzed usingmore » a two-dimensional blade-to-blade solver, written for steady viscous compressible flow, to determine profile loss. The loss is used as the measure of a profile`s fitness. The optimizer uses this information to select the members of the next population, applying crossovers, mutations, and elitism in the process. Using this method, the optimizer tends toward the best values for the parameters defining the profile with minimum loss.« less

Parameter assessment for virtual Stackelberg game in aerodynamic shape optimization

NASA Astrophysics Data System (ADS)

Wang, Jing; Xie, Fangfang; Zheng, Yao; Zhang, Jifa

2018-05-01

In this paper, parametric studies of virtual Stackelberg game (VSG) are conducted to assess the impact of critical parameters on aerodynamic shape optimization, including design cycle, split of design variables and role assignment. Typical numerical cases, including the inverse design and drag reduction design of airfoil, have been carried out. The numerical results confirm the effectiveness and efficiency of VSG. Furthermore, the most significant parameters are identified, e.g. the increase of design cycle can improve the optimization results but it will also add computational burden. These studies will maximize the productivity of the effort in aerodynamic optimization for more complicated engineering problems, such as the multi-element airfoil and wing-body configurations.

Chickpea seeds germination rational parameters optimization

NASA Astrophysics Data System (ADS)

Safonova, Yu A.; Ivliev, M. N.; Lemeshkin, A. V.

2018-05-01

The paper presents the influence of chickpea seeds bioactivation parameters on their enzymatic activity experimental results. Optimal bioactivation process modes were obtained by regression-factor analysis: process temperature - 13.6 °C, process duration - 71.5 h. It was found that in the germination process, the proteolytic, amylolytic and lipolytic enzymes activity increased, and the urease enzyme activity is reduced. The dependences of enzyme activity on chickpea seeds germination conditions were obtained by mathematical processing of experimental data. The calculated data are in good agreement with the experimental ones. This confirms the optimization efficiency based on experiments mathematical planning in order to determine the enzymatic activity of chickpea seeds germination optimal parameters of bioactivated seeds.

Research on Optimization of GLCM Parameter in Cell Classification

NASA Astrophysics Data System (ADS)

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.

Co-Optimization of Blunt Body Shapes for Moving Vehicles

NASA Technical Reports Server (NTRS)

Kinney, David J. (Inventor); Mansour, Nagi N (Inventor); Brown, James L. (Inventor); Garcia, Joseph A (Inventor); Bowles, Jeffrey V (Inventor)

2014-01-01

A method and associated system for multi-disciplinary optimization of various parameters associated with a space vehicle that experiences aerocapture and atmospheric entry in a specified atmosphere. In one embodiment, simultaneous maximization of a ratio of landed payload to vehicle atmospheric entry mass, maximization of fluid flow distance before flow separation from vehicle, and minimization of heat transfer to the vehicle are performed with respect to vehicle surface geometric parameters, and aerostructure and aerothermal vehicle response for the vehicle moving along a specified trajectory. A Pareto Optimal set of superior performance parameters is identified.

A Minimum (Delta)V Orbit Maintenance Strategy for Low-Altitude Missions Using Burn Parameter Optimization

NASA Technical Reports Server (NTRS)

Brown, Aaron J.

2011-01-01

Orbit maintenance is the series of burns performed during a mission to ensure the orbit satisfies mission constraints. Low-altitude missions often require non-trivial orbit maintenance (Delta)V due to sizable orbital perturbations and minimum altitude thresholds. A strategy is presented for minimizing this (Delta)V using impulsive burn parameter optimization. An initial estimate for the burn parameters is generated by considering a feasible solution to the orbit maintenance problem. An example demonstrates the dV savings from the feasible solution to the optimal solution.

Design optimization for permanent magnet machine with efficient slot per pole ratio

NASA Astrophysics Data System (ADS)

Potnuru, Upendra Kumar; Rao, P. Mallikarjuna

2018-04-01

This paper presents a methodology for the enhancement of a Brush Less Direct Current motor (BLDC) with 6Poles and 8slots. In particular; it is focused on amulti-objective optimization using a Genetic Algorithmand Grey Wolf Optimization developed in MATLAB. The optimization aims to maximize the maximum output power value and minimize the total losses of a motor. This paper presents an application of the MATLAB optimization algorithms to brushless DC (BLDC) motor design, with 7 design parameters chosen to be free. The optimal design parameters of the motor derived by GA are compared with those obtained by Grey Wolf Optimization technique. A comparative report on the specified enhancement approaches appearsthat Grey Wolf Optimization technique has a better convergence.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2011-12-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model

NASA Astrophysics Data System (ADS)

Qian, Y.; Yang, B.; Lin, G.; Leung, R.; Zhang, Y.

2012-04-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. The latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2012-03-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Stepped MS(All) Relied Transition (SMART): An approach to rapidly determine optimal multiple reaction monitoring mass spectrometry parameters for small molecules.

PubMed

Ye, Hui; Zhu, Lin; Wang, Lin; Liu, Huiying; Zhang, Jun; Wu, Mengqiu; Wang, Guangji; Hao, Haiping

2016-02-11

Multiple reaction monitoring (MRM) is a universal approach for quantitative analysis because of its high specificity and sensitivity. Nevertheless, optimization of MRM parameters remains as a time and labor-intensive task particularly in multiplexed quantitative analysis of small molecules in complex mixtures. In this study, we have developed an approach named Stepped MS(All) Relied Transition (SMART) to predict the optimal MRM parameters of small molecules. SMART requires firstly a rapid and high-throughput analysis of samples using a Stepped MS(All) technique (sMS(All)) on a Q-TOF, which consists of serial MS(All) events acquired from low CE to gradually stepped-up CE values in a cycle. The optimal CE values can then be determined by comparing the extracted ion chromatograms for the ion pairs of interest among serial scans. The SMART-predicted parameters were found to agree well with the parameters optimized on a triple quadrupole from the same vendor using a mixture of standards. The parameters optimized on a triple quadrupole from a different vendor was also employed for comparison, and found to be linearly correlated with the SMART-predicted parameters, suggesting the potential applications of the SMART approach among different instrumental platforms. This approach was further validated by applying to simultaneous quantification of 31 herbal components in the plasma of rats treated with a herbal prescription. Because the sMS(All) acquisition can be accomplished in a single run for multiple components independent of standards, the SMART approach are expected to find its wide application in the multiplexed quantitative analysis of complex mixtures. Copyright © 2015 Elsevier B.V. All rights reserved.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

A two-step parameter optimization algorithm for improving estimation of optical properties using spatial frequency domain imaging

NASA Astrophysics Data System (ADS)

Hu, Dong; Lu, Renfu; Ying, Yibin

2018-03-01

This research was aimed at optimizing the inverse algorithm for estimating the optical absorption (μa) and reduced scattering (μs‧) coefficients from spatial frequency domain diffuse reflectance. Studies were first conducted to determine the optimal frequency resolution and start and end frequencies in terms of the reciprocal of mean free path (1/mfp‧). The results showed that the optimal frequency resolution increased with μs‧ and remained stable when μs‧ was larger than 2 mm-1. The optimal end frequency decreased from 0.3/mfp‧ to 0.16/mfp‧ with μs‧ ranging from 0.4 mm-1 to 3 mm-1, while the optimal start frequency remained at 0 mm-1. A two-step parameter estimation method was proposed based on the optimized frequency parameters, which improved estimation accuracies by 37.5% and 9.8% for μa and μs‧, respectively, compared with the conventional one-step method. Experimental validations with seven liquid optical phantoms showed that the optimized algorithm resulted in the mean absolute errors of 15.4%, 7.6%, 5.0% for μa and 16.4%, 18.0%, 18.3% for μs‧ at the wavelengths of 675 nm, 700 nm, and 715 nm, respectively. Hence, implementation of the optimized parameter estimation method should be considered in order to improve the measurement of optical properties of biological materials when using spatial frequency domain imaging technique.

Analysis of multidimensional difference-of-Gaussians filters in terms of directly observable parameters.

PubMed

Cope, Davis; Blakeslee, Barbara; McCourt, Mark E

2013-05-01

The difference-of-Gaussians (DOG) filter is a widely used model for the receptive field of neurons in the retina and lateral geniculate nucleus (LGN) and is a potential model in general for responses modulated by an excitatory center with an inhibitory surrounding region. A DOG filter is defined by three standard parameters: the center and surround sigmas (which define the variance of the radially symmetric Gaussians) and the balance (which defines the linear combination of the two Gaussians). These parameters are not directly observable and are typically determined by nonlinear parameter estimation methods applied to the frequency response function. DOG filters show both low-pass (optimal response at zero frequency) and bandpass (optimal response at a nonzero frequency) behavior. This paper reformulates the DOG filter in terms of a directly observable parameter, the zero-crossing radius, and two new (but not directly observable) parameters. In the two-dimensional parameter space, the exact region corresponding to bandpass behavior is determined. A detailed description of the frequency response characteristics of the DOG filter is obtained. It is also found that the directly observable optimal frequency and optimal gain (the ratio of the response at optimal frequency to the response at zero frequency) provide an alternate coordinate system for the bandpass region. Altogether, the DOG filter and its three standard implicit parameters can be determined by three directly observable values. The two-dimensional bandpass region is a potential tool for the analysis of populations of DOG filters (for example, populations of neurons in the retina or LGN), because the clustering of points in this parameter space may indicate an underlying organizational principle. This paper concentrates on circular Gaussians, but the results generalize to multidimensional radially symmetric Gaussians and are given as an appendix.

Application of nonlinear least-squares regression to ground-water flow modeling, west-central Florida

USGS Publications Warehouse

Yobbi, D.K.

2000-01-01

A nonlinear least-squares regression technique for estimation of ground-water flow model parameters was applied to an existing model of the regional aquifer system underlying west-central Florida. The regression technique minimizes the differences between measured and simulated water levels. Regression statistics, including parameter sensitivities and correlations, were calculated for reported parameter values in the existing model. Optimal parameter values for selected hydrologic variables of interest are estimated by nonlinear regression. Optimal estimates of parameter values are about 140 times greater than and about 0.01 times less than reported values. Independently estimating all parameters by nonlinear regression was impossible, given the existing zonation structure and number of observations, because of parameter insensitivity and correlation. Although the model yields parameter values similar to those estimated by other methods and reproduces the measured water levels reasonably accurately, a simpler parameter structure should be considered. Some possible ways of improving model calibration are to: (1) modify the defined parameter-zonation structure by omitting and/or combining parameters to be estimated; (2) carefully eliminate observation data based on evidence that they are likely to be biased; (3) collect additional water-level data; (4) assign values to insensitive parameters, and (5) estimate the most sensitive parameters first, then, using the optimized values for these parameters, estimate the entire data set.

Quantum approximate optimization algorithm for MaxCut: A fermionic view

NASA Astrophysics Data System (ADS)

Wang, Zhihui; Hadfield, Stuart; Jiang, Zhang; Rieffel, Eleanor G.

2018-02-01

Farhi et al. recently proposed a class of quantum algorithms, the quantum approximate optimization algorithm (QAOA), for approximately solving combinatorial optimization problems (E. Farhi et al., arXiv:1411.4028; arXiv:1412.6062; arXiv:1602.07674). A level-p QAOA circuit consists of p steps; in each step a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2 p times for which these two Hamiltonians are applied are the parameters of the algorithm, which are to be optimized classically for the best performance. As p increases, parameter optimization becomes inefficient due to the curse of dimensionality. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here we analytically and numerically study parameter setting for the QAOA applied to MaxCut. For the level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MaxCut, the "ring of disagrees," or the one-dimensional antiferromagnetic ring, we provide an analysis for an arbitrarily high level. Using a fermionic representation, the evolution of the system under the QAOA translates into quantum control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of the QAOA for any p . It also greatly simplifies the numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional submanifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.

Particle Swarm Learning Algorithm Based on Adjustment of Parameter and its Applications Assessment of Agricultural Projects

NASA Astrophysics Data System (ADS)

Yang, Shanlin; Zhu, Weidong; Chen, Li

The particle swarm, which optimizes neural networks, has overcome its disadvantage of slow convergent speed and shortcoming of local optimum. The parameter that the particle swarm optimization relates to is not much. But it has strongly sensitivity to the parameter. In this paper, we applied PSO-BP to evaluate the environmental effect of an agricultural project, and researched application and Particle Swarm learning algorithm based on adjustment of parameter. This paper, we use MATLAB language .The particle number is 5, 30, 50, 90, and the inertia weight is 0.4, 0.6, and 0.8 separately. Calculate 10 times under each same parameter, and analyze the influence under the same parameter. Result is indicated that the number of particles is in 25 ~ 30 and the inertia weight is in 0.6 ~ 0.7, and the result of optimization is satisfied.

Tuning Parameters in Heuristics by Using Design of Experiments Methods

NASA Technical Reports Server (NTRS)

Arin, Arif; Rabadi, Ghaith; Unal, Resit

2010-01-01

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.

Minimal residual method provides optimal regularization parameter for diffuse optical tomography

NASA Astrophysics Data System (ADS)

Jagannath, Ravi Prasad K.; Yalavarthy, Phaneendra K.

2012-10-01

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter.

Minimal residual method provides optimal regularization parameter for diffuse optical tomography.

PubMed

Jagannath, Ravi Prasad K; Yalavarthy, Phaneendra K

2012-10-01

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter.

Optimization design of LED heat dissipation structure based on strip fins

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Streamflow Prediction based on Chaos Theory

NASA Astrophysics Data System (ADS)

Li, X.; Wang, X.; Babovic, V. M.

2015-12-01

Chaos theory is a popular method in hydrologic time series prediction. Local model (LM) based on this theory utilizes time-delay embedding to reconstruct the phase-space diagram. For this method, its efficacy is dependent on the embedding parameters, i.e. embedding dimension, time lag, and nearest neighbor number. The optimal estimation of these parameters is thus critical to the application of Local model. However, these embedding parameters are conventionally estimated using Average Mutual Information (AMI) and False Nearest Neighbors (FNN) separately. This may leads to local optimization and thus has limitation to its prediction accuracy. Considering about these limitation, this paper applies a local model combined with simulated annealing (SA) to find the global optimization of embedding parameters. It is also compared with another global optimization approach of Genetic Algorithm (GA). These proposed hybrid methods are applied in daily and monthly streamflow time series for examination. The results show that global optimization can contribute to the local model to provide more accurate prediction results compared with local optimization. The LM combined with SA shows more advantages in terms of its computational efficiency. The proposed scheme here can also be applied to other fields such as prediction of hydro-climatic time series, error correction, etc.

Parameter estimation using meta-heuristics in systems biology: a comprehensive review.

PubMed

Sun, Jianyong; Garibaldi, Jonathan M; Hodgman, Charlie

2012-01-01

This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

Concerning the Development of the Wide-Field Optics for WFXT Including Methods of Optimizing X-Ray Optical Prescriptions for Wide-Field Applications

NASA Technical Reports Server (NTRS)

Weisskopf, M. C.; Elsner, R. F.; O'Dell, S. L.; Ramsey, B. D.

2010-01-01

We present a progress report on the various endeavors we are undertaking at MSFC in support of the Wide Field X-Ray Telescope development. In particular we discuss assembly and alignment techniques, in-situ polishing corrections, and the results of our efforts to optimize mirror prescriptions including polynomial coefficients, relative shell displacements, detector placements and tilts. This optimization does not require a blind search through the multi-dimensional parameter space. Under the assumption that the parameters are small enough so that second order expansions are valid, we show that the performance at the detector can be expressed as a quadratic function with numerical coefficients derived from a ray trace through the underlying Wolter I optic. The optimal values for the parameters are found by solving the linear system of equations creating by setting derivatives of this function with respect to each parameter to zero.

Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel

NASA Astrophysics Data System (ADS)

Deris, A. M.; Zain, A. M.; Sallehuddin, R.; Sharif, S.

2017-09-01

Electric discharge machine (EDM) is one of the widely used nonconventional machining processes for hard and difficult to machine materials. Due to the large number of machining parameters in EDM and its complicated structural, the selection of the optimal solution of machining parameters for obtaining minimum machining performance is remain as a challenging task to the researchers. This paper proposed experimental investigation and optimization of machining parameters for EDM process on stainless steel 316L work piece using Harmony Search (HS) algorithm. The mathematical model was developed based on regression approach with four input parameters which are pulse on time, peak current, servo voltage and servo speed to the output response which is dimensional accuracy (DA). The optimal result of HS approach was compared with regression analysis and it was found HS gave better result y giving the most minimum DA value compared with regression approach.

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.

2018-01-01

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

Optimal design criteria - prediction vs. parameter estimation

NASA Astrophysics Data System (ADS)

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.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Optimization of bone drilling parameters using Taguchi method based on finite element analysis

NASA Astrophysics Data System (ADS)

Rosidi, Ayip; Lenggo Ginta, Turnad; Rani, Ahmad Majdi Bin Abdul

2017-05-01

Thermal necrosis results fracture problems and implant failure if temperature exceeds 47 °C for one minute during bone drilling. To solve this problem, this work studied a new thermal model by using three drilling parameters: drill diameter, feed rate and spindle speed. Effects of those parameters to heat generation were studied. The drill diameters were 4 mm, 6 mm and 6 mm; the feed rates were 80 mm/min, 100 mm/min and 120 mm/min whereas the spindle speeds were 400 rpm, 500 rpm and 600 rpm then an optimization was done by Taguchi method to which combination parameter can be used to prevent thermal necrosis during bone drilling. The results showed that all the combination of parameters produce confidence results which were below 47 °C and finite element analysis combined with Taguchi method can be used for predicting temperature generation and optimizing bone drilling parameters prior to clinical bone drilling. All of the combination parameters can be used for surgeon to achieve sustainable orthopaedic surgery.

Situational reaction and planning

NASA Technical Reports Server (NTRS)

Yen, John; Pfluger, Nathan

1994-01-01

One problem faced in designing an autonomous mobile robot system is that there are many parameters of the system to define and optimize. While these parameters can be obtained for any given situation determining what the parameters should be in all situations is difficult. The usual solution is to give the system general parameters that work in all situations, but this does not help the robot to perform its best in a dynamic environment. Our approach is to develop a higher level situation analysis module that adjusts the parameters by analyzing the goals and history of sensor readings. By allowing the robot to change the system parameters based on its judgement of the situation, the robot will be able to better adapt to a wider set of possible situations. We use fuzzy logic in our implementation to reduce the number of basic situations the controller has to recognize. For example, a situation may be 60 percent open and 40 percent corridor, causing the optimal parameters to be somewhere between the optimal settings for the two extreme situations.

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

PubMed Central

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. PMID:26943630

Production of fibrinolytic protease from Streptomyces lusitanus isolated from marine sediments

NASA Astrophysics Data System (ADS)

SudeshWarma, S.; Merlyn keziah, S.; Subathra Devi, C.

2017-11-01

This study aim was to isolate, screen, characterize and optimize marine Streptomyces for fibrinolytic enzyme production. The potent actinomycete isolate was subjected to optimization. The parameters for optimization included pH, temperature, carbon, nitrogen sources. The crude supernatant produced was purified using size exclusion gel filtration chromatography. The optimized parameters for maximum productivity were found to be pH 7, 37°C, maltose and peptone respectively. The molecular weight of the purified enzyme was found to be 21kDa.

Cost Minimization for Joint Energy Management and Production Scheduling Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Shah, Rahul H.

Production costs account for the largest share of the overall cost of manufacturing facilities. With the U.S. industrial sector becoming more and more competitive, manufacturers are looking for more cost and resource efficient working practices. Operations management and production planning have shown their capability to dramatically reduce manufacturing costs and increase system robustness. When implementing operations related decision making and planning, two fields that have shown to be most effective are maintenance and energy. Unfortunately, the current research that integrates both is limited. Additionally, these studies fail to consider parameter domains and optimization on joint energy and maintenance driven production planning. Accordingly, production planning methodology that considers maintenance and energy is investigated. Two models are presented to achieve well-rounded operating strategy. The first is a joint energy and maintenance production scheduling model. The second is a cost per part model considering maintenance, energy, and production. The proposed methodology will involve a Time-of-Use electricity demand response program, buffer and holding capacity, station reliability, production rate, station rated power, and more. In practice, the scheduling problem can be used to determine a joint energy, maintenance, and production schedule. Meanwhile, the cost per part model can be used to: (1) test the sensitivity of the obtained optimal production schedule and its corresponding savings by varying key production system parameters; and (2) to determine optimal system parameter combinations when using the joint energy, maintenance, and production planning model. Additionally, a factor analysis on the system parameters is conducted and the corresponding performance of the production schedule under variable parameter conditions, is evaluated. Also, parameter optimization guidelines that incorporate maintenance and energy parameter decision making in the production planning framework are discussed. A modified Particle Swarm Optimization solution technique is adopted to solve the proposed scheduling problem. The algorithm is described in detail and compared to Genetic Algorithm. Case studies are presented to illustrate the benefits of using the proposed model and the effectiveness of the Particle Swarm Optimization approach. Numerical Experiments are implemented and analyzed to test the effectiveness of the proposed model. The proposed scheduling strategy can achieve savings of around 19 to 27 % in cost per part when compared to the baseline scheduling scenarios. By optimizing key production system parameters from the cost per part model, the baseline scenarios can obtain around 20 to 35 % in savings for the cost per part. These savings further increase by 42 to 55 % when system parameter optimization is integrated with the proposed scheduling problem. Using this method, the most influential parameters on the cost per part are the rated power from production, the production rate, and the initial machine reliabilities. The modified Particle Swarm Optimization algorithm adopted allows greater diversity and exploration compared to Genetic Algorithm for the proposed joint model which results in it being more computationally efficient in determining the optimal scheduling. While Genetic Algorithm could achieve a solution quality of 2,279.63 at an expense of 2,300 seconds in computational effort. In comparison, the proposed Particle Swarm Optimization algorithm achieved a solution quality of 2,167.26 in less than half the computation effort which is required by Genetic Algorithm.

Deploying response surface methodology (RSM) and glowworm swarm optimization (GSO) in optimizing warpage on a mobile phone cover

NASA Astrophysics Data System (ADS)

Lee, X. N.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.; Shazzuan, S.

2017-09-01

Plastic injection moulding is a popular manufacturing method not only it is reliable, but also efficient and cost saving. It able to produce plastic part with detailed features and complex geometry. However, defects in injection moulding process degrades the quality and aesthetic of the injection moulded product. The most common defect occur in the process is warpage. Inappropriate process parameter setting of injection moulding machine is one of the reason that leads to the occurrence of warpage. The aims of this study were to improve the quality of injection moulded part by investigating the optimal parameters in minimizing warpage using Response Surface Methodology (RSM) and Glowworm Swarm Optimization (GSO). Subsequent to this, the most significant parameter was identified and recommended parameters setting was compared with the optimized parameter setting using RSM and GSO. In this research, the mobile phone case was selected as case study. The mould temperature, melt temperature, packing pressure, packing time and cooling time were selected as variables whereas warpage in y-direction was selected as responses in this research. The simulation was carried out by using Autodesk Moldflow Insight 2012. In addition, the RSM was performed by using Design Expert 7.0 whereas the GSO was utilized by using MATLAB. The warpage in y direction recommended by RSM were reduced by 70 %. The warpages recommended by GSO were decreased by 61 % in y direction. The resulting warpages under optimal parameter setting by RSM and GSO were validated by simulation in AMI 2012. RSM performed better than GSO in solving warpage issue.

Reducing the uncertainty of parameters controlling seasonal carbon and water fluxes in Chinese forests and its implication for simulated climate sensitivities

NASA Astrophysics Data System (ADS)

Li, Yue; Yang, Hui; Wang, Tao; MacBean, Natasha; Bacour, Cédric; Ciais, Philippe; Zhang, Yiping; Zhou, Guangsheng; Piao, Shilong

2017-08-01

Reducing parameter uncertainty of process-based terrestrial ecosystem models (TEMs) is one of the primary targets for accurately estimating carbon budgets and predicting ecosystem responses to climate change. However, parameters in TEMs are rarely constrained by observations from Chinese forest ecosystems, which are important carbon sink over the northern hemispheric land. In this study, eddy covariance data from six forest sites in China are used to optimize parameters of the ORganizing Carbon and Hydrology In Dynamics EcosystEms TEM. The model-data assimilation through parameter optimization largely reduces the prior model errors and improves the simulated seasonal cycle and summer diurnal cycle of net ecosystem exchange, latent heat fluxes, and gross primary production and ecosystem respiration. Climate change experiments based on the optimized model are deployed to indicate that forest net primary production (NPP) is suppressed in response to warming in the southern China but stimulated in the northeastern China. Altered precipitation has an asymmetric impact on forest NPP at sites in water-limited regions, with the optimization-induced reduction in response of NPP to precipitation decline being as large as 61% at a deciduous broadleaf forest site. We find that seasonal optimization alters forest carbon cycle responses to environmental change, with the parameter optimization consistently reducing the simulated positive response of heterotrophic respiration to warming. Evaluations from independent observations suggest that improving model structure still matters most for long-term carbon stock and its changes, in particular, nutrient- and age-related changes of photosynthetic rates, carbon allocation, and tree mortality.

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems.

PubMed

Ihme, Matthias; Marsden, Alison L; Pitsch, Heinz

2008-02-01

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.

Optimization of ecosystem model parameters with different temporal variabilities using tower flux data and an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

He, L.; Chen, J. M.; Liu, J.; Mo, G.; Zhen, T.; Chen, B.; Wang, R.; Arain, M.

2013-12-01

Terrestrial ecosystem models have been widely used to simulate carbon, water and energy fluxes and climate-ecosystem interactions. In these models, some vegetation and soil parameters are determined based on limited studies from literatures without consideration of their seasonal variations. Data assimilation (DA) provides an effective way to optimize these parameters at different time scales . In this study, an ensemble Kalman filter (EnKF) is developed and applied to optimize two key parameters of an ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS): (1) the maximum photosynthetic carboxylation rate (Vcmax) at 25 °C, and (2) the soil water stress factor (fw) for stomatal conductance formulation. These parameters are optimized through assimilating observations of gross primary productivity (GPP) and latent heat (LE) fluxes measured in a 74 year-old pine forest, which is part of the Turkey Point Flux Station's age-sequence sites. Vcmax is related to leaf nitrogen concentration and varies slowly over the season and from year to year. In contrast, fw varies rapidly in response to soil moisture dynamics in the root-zone. Earlier studies suggested that DA of vegetation parameters at daily time steps leads to Vcmax values that are unrealistic. To overcome the problem, we developed a three-step scheme to optimize Vcmax and fw. First, the EnKF is applied daily to obtain precursor estimates of Vcmax and fw. Then Vcmax is optimized at different time scales assuming fw is unchanged from first step. The best temporal period or window size is then determined by analyzing the magnitude of the minimized cost-function, and the coefficient of determination (R2) and Root-mean-square deviation (RMSE) of GPP and LE between simulation and observation. Finally, the daily fw value is optimized for rain free days corresponding to the Vcmax curve from the best window size. The optimized fw is then used to model its relationship with soil moisture. We found that the optimized fw is best correlated linearly to soil water content at 5 to 10 cm depth. We also found that both the temporal scale or window size and the priori uncertainty of Vcmax (given as its standard deviation) are important in determining the seasonal trajectory of Vcmax. During the leaf expansion stage, an appropriate window size leads to reasonable estimate of Vcmax. In the summer, the fluctuation of optimized Vcmax is mainly caused by the uncertainties in Vcmax but not the window size. Our study suggests that a smooth Vcmax curve optimized from an optimal time window size is close to the reality though the RMSE of GPP at this window is not the minimum. It also suggests that for the accurate optimization of Vcmax, it is necessary to set appropriate levels of uncertainty of Vcmax in the spring and summer because the rate of leaf nitrogen concentration change is different over the season. Parameter optimizations for more sites and multi-years are in progress.

Use of constrained optimization in the conceptual design of a medium-range subsonic transport

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1980-01-01

Constrained parameter optimization was used to perform the optimal conceptual design of a medium range transport configuration. The impact of choosing a given performance index was studied, and the required income for a 15 percent return on investment was proposed as a figure of merit. A number of design constants and constraint functions were systematically varied to document the sensitivities of the optimal design to a variety of economic and technological assumptions. A comparison was made for each of the parameter variations between the baseline configuration and the optimally redesigned configuration.

Taguchi's off line method and Multivariate loss function approach for quality management and optimization of process parameters -A review

NASA Astrophysics Data System (ADS)

Bharti, P. K.; Khan, M. I.; Singh, Harbinder

2010-10-01

Off-line quality control is considered to be an effective approach to improve product quality at a relatively low cost. The Taguchi method is one of the conventional approaches for this purpose. Through this approach, engineers can determine a feasible combination of design parameters such that the variability of a product's response can be reduced and the mean is close to the desired target. The traditional Taguchi method was focused on ensuring good performance at the parameter design stage with one quality characteristic, but most products and processes have multiple quality characteristics. The optimal parameter design minimizes the total quality loss for multiple quality characteristics. Several studies have presented approaches addressing multiple quality characteristics. Most of these papers were concerned with maximizing the parameter combination of signal to noise (SN) ratios. The results reveal the advantages of this approach are that the optimal parameter design is the same as the traditional Taguchi method for the single quality characteristic; the optimal design maximizes the amount of reduction of total quality loss for multiple quality characteristics. This paper presents a literature review on solving multi-response problems in the Taguchi method and its successful implementation in various industries.

Parameter optimization for the visco-hyperelastic constitutive model of tendon using FEM.

PubMed

Tang, C Y; Ng, G Y F; Wang, Z W; Tsui, C P; Zhang, G

2011-01-01

Numerous constitutive models describing the mechanical properties of tendons have been proposed during the past few decades. However, few were widely used owing to the lack of implementation in the general finite element (FE) software, and very few systematic studies have been done on selecting the most appropriate parameters for these constitutive laws. In this work, the visco-hyperelastic constitutive model of the tendon implemented through the use of three-parameter Mooney-Rivlin form and sixty-four-parameter Prony series were firstly analyzed using ANSYS FE software. Afterwards, an integrated optimization scheme was developed by coupling two optimization toolboxes (OPTs) of ANSYS and MATLAB for estimating these unknown constitutive parameters of the tendon. Finally, a group of Sprague-Dawley rat tendons was used to execute experimental and numerical simulation investigation. The simulated results showed good agreement with the experimental data. An important finding revealed that too many Maxwell elements was not necessary for assuring accuracy of the model, which is often neglected in most open literatures. Thus, all these proved that the constitutive parameter optimization scheme was reliable and highly efficient. Furthermore, the approach can be extended to study other tendons or ligaments, as well as any visco-hyperelastic solid materials.

Multi-parameter geometrical scaledown study for energy optimization of MTJ and related spintronics nanodevices

NASA Astrophysics Data System (ADS)

Farhat, I. A. H.; Alpha, C.; Gale, E.; Atia, D. Y.; Stein, A.; Isakovic, A. F.

The scaledown of magnetic tunnel junctions (MTJ) and related nanoscale spintronics devices poses unique challenges for energy optimization of their performance. We demonstrate the dependence of the switching current on the scaledown variable, while considering the influence of geometric parameters of MTJ, such as the free layer thickness, tfree, lateral size of the MTJ, w, and the anisotropy parameter of the MTJ. At the same time, we point out which values of the saturation magnetization, Ms, and anisotropy field, Hk, can lead to lowering the switching current and overall decrease of the energy needed to operate an MTJ. It is demonstrated that scaledown via decreasing the lateral size of the MTJ, while allowing some other parameters to be unconstrained, can improve energy performance by a measurable factor, shown to be the function of both geometric and physical parameters above. Given the complex interdependencies among both families of parameters, we developed a particle swarm optimization (PSO) algorithm that can simultaneously lower energy of operation and the switching current density. Results we obtained in scaledown study and via PSO optimization are compared to experimental results. Support by Mubadala-SRC 2012-VJ-2335 is acknowledged, as are staff at Cornell-CNF and BNL-CFN.

Investigation into the influence of laser energy input on selective laser melted thin-walled parts by response surface method

NASA Astrophysics Data System (ADS)

Liu, Yang; Zhang, Jian; Pang, Zhicong; Wu, Weihui

2018-04-01

Selective laser melting (SLM) provides a feasible way for manufacturing of complex thin-walled parts directly, however, the energy input during SLM process, namely derived from the laser power, scanning speed, layer thickness and scanning space, etc. has great influence on the thin wall's qualities. The aim of this work is to relate the thin wall's parameters (responses), namely track width, surface roughness and hardness to the process parameters considered in this research (laser power, scanning speed and layer thickness) and to find out the optimal manufacturing conditions. Design of experiment (DoE) was used by implementing composite central design to achieve better manufacturing qualities. Mathematical models derived from the statistical analysis were used to establish the relationships between the process parameters and the responses. Also, the effects of process parameters on each response were determined. Then, a numerical optimization was performed to find out the optimal process set at which the quality features are at their desired values. Based on this study, the relationship between process parameters and SLMed thin-walled structure was revealed and thus, the corresponding optimal process parameters can be used to manufactured thin-walled parts with high quality.

Optimizing chirped laser pulse parameters for electron acceleration in vacuum

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

Akhyani, Mina; Jahangiri, Fazel; Niknam, Ali Reza

2015-11-14

Electron dynamics in the field of a chirped linearly polarized laser pulse is investigated. Variations of electron energy gain versus chirp parameter, time duration, and initial phase of laser pulse are studied. Based on maximizing laser pulse asymmetry, a numerical optimization procedure is presented, which leads to the elimination of rapid fluctuations of gain versus the chirp parameter. Instead, a smooth variation is observed that considerably reduces the accuracy required for experimentally adjusting the chirp parameter.

Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity

NASA Astrophysics Data System (ADS)

Shutov, A. V.; Kaygorodtseva, A. A.; Dranishnikov, N. S.

2017-10-01

A problem of parameter identification for a model of finite strain elasto-plasticity is discussed. The utilized phenomenological material model accounts for nonlinear isotropic and kinematic hardening; the model kinematics is described by a nested multiplicative split of the deformation gradient. A hierarchy of optimization problems is considered. First, following the standard procedure, the material parameters are identified through minimization of a certain least square error functional. Next, the focus is placed on finding optimal weighting coefficients which enter the error functional. Toward that end, a stochastic noise with systematic and non-systematic components is introduced to the available measurement results; a superordinate optimization problem seeks to minimize the sensitivity of the resulting material parameters to the introduced noise. The advantage of this approach is that no additional experiments are required; it also provides an insight into the robustness of the identification procedure. As an example, experimental data for the steel 42CrMo4 are considered and a set of weighting coefficients is found, which is optimal in a certain class.

Robust optimal design of diffusion-weighted magnetic resonance experiments for skin microcirculation

NASA Astrophysics Data System (ADS)

Choi, J.; Raguin, L. G.

2010-10-01

Skin microcirculation plays an important role in several diseases including chronic venous insufficiency and diabetes. Magnetic resonance (MR) has the potential to provide quantitative information and a better penetration depth compared with other non-invasive methods such as laser Doppler flowmetry or optical coherence tomography. The continuous progress in hardware resulting in higher sensitivity must be coupled with advances in data acquisition schemes. In this article, we first introduce a physical model for quantifying skin microcirculation using diffusion-weighted MR (DWMR) based on an effective dispersion model for skin leading to a q-space model of the DWMR complex signal, and then design the corresponding robust optimal experiments. The resulting robust optimal DWMR protocols improve the worst-case quality of parameter estimates using nonlinear least squares optimization by exploiting available a priori knowledge of model parameters. Hence, our approach optimizes the gradient strengths and directions used in DWMR experiments to robustly minimize the size of the parameter estimation error with respect to model parameter uncertainty. Numerical evaluations are presented to demonstrate the effectiveness of our approach as compared to conventional DWMR protocols.

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

NASA Technical Reports Server (NTRS)

Schmidt, Phillip; Garg, Sanjay; Holowecky, Brian

1992-01-01

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

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

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Mathematical simulation and optimization of cutting mode in turning of workpieces made of nickel-based heat-resistant alloy

NASA Astrophysics Data System (ADS)

Bogoljubova, M. N.; Afonasov, A. I.; Kozlov, B. N.; Shavdurov, D. E.

2018-05-01

A predictive simulation technique of optimal cutting modes in the turning of workpieces made of nickel-based heat-resistant alloys, different from the well-known ones, is proposed. The impact of various factors on the cutting process with the purpose of determining optimal parameters of machining in concordance with certain effectiveness criteria is analyzed in the paper. A mathematical model of optimization, algorithms and computer programmes, visual graphical forms reflecting dependences of the effectiveness criteria – productivity, net cost, and tool life on parameters of the technological process - have been worked out. A nonlinear model for multidimensional functions, “solution of the equation with multiple unknowns”, “a coordinate descent method” and heuristic algorithms are accepted to solve the problem of optimization of cutting mode parameters. Research shows that in machining of workpieces made from heat-resistant alloy AISI N07263, the highest possible productivity will be achieved with the following parameters: cutting speed v = 22.1 m/min., feed rate s=0.26 mm/rev; tool life T = 18 min.; net cost – 2.45 per hour.

Parameters optimization for magnetic resonance coupling wireless power transmission.

PubMed

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.

Process optimization of rolling for zincked sheet technology using response surface methodology and genetic algorithm

NASA Astrophysics Data System (ADS)

Ji, Liang-Bo; Chen, Fang

2017-07-01

Numerical simulation and intelligent optimization technology were adopted for rolling and extrusion of zincked sheet. By response surface methodology (RSM), genetic algorithm (GA) and data processing technology, an efficient optimization of process parameters for rolling of zincked sheet was investigated. The influence trend of roller gap, rolling speed and friction factor effects on reduction rate and plate shortening rate were analyzed firstly. Then a predictive response surface model for comprehensive quality index of part was created using RSM. Simulated and predicted values were compared. Through genetic algorithm method, the optimal process parameters for the forming of rolling were solved. They were verified and the optimum process parameters of rolling were obtained. It is feasible and effective.

Automated optimization of water-water interaction parameters for a coarse-grained model.

PubMed

Fogarty, Joseph C; Chiu, See-Wing; Kirby, Peter; Jakobsson, Eric; Pandit, Sagar A

2014-02-13

We have developed an automated parameter optimization software framework (ParOpt) that implements the Nelder-Mead simplex algorithm and applied it to a coarse-grained polarizable water model. The model employs a tabulated, modified Morse potential with decoupled short- and long-range interactions incorporating four water molecules per interaction site. Polarizability is introduced by the addition of a harmonic angle term defined among three charged points within each bead. The target function for parameter optimization was based on the experimental density, surface tension, electric field permittivity, and diffusion coefficient. The model was validated by comparison of statistical quantities with experimental observation. We found very good performance of the optimization procedure and good agreement of the model with experiment.

Application of support vector regression for optimization of vibration flow field of high-density polyethylene melts characterized by small angle light scattering

NASA Astrophysics Data System (ADS)

Xian, Guangming

2018-03-01

In this paper, the vibration flow field parameters of polymer melts in a visual slit die are optimized by using intelligent algorithm. Experimental small angle light scattering (SALS) patterns are shown to characterize the processing process. In order to capture the scattered light, a polarizer and an analyzer are placed before and after the polymer melts. The results reported in this study are obtained using high-density polyethylene (HDPE) with rotation speed at 28 rpm. In addition, support vector regression (SVR) analytical method is introduced for optimization the parameters of vibration flow field. This work establishes the general applicability of SVR for predicting the optimal parameters of vibration flow field.

Optimal correction and design parameter search by modern methods of rigorous global optimization

NASA Astrophysics Data System (ADS)

Makino, K.; Berz, M.

2011-07-01

Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle optics for the computation of aberrations allow the determination of particularly sharp underestimators for large regions. As a consequence, the subsequent progressive pruning of the allowed search space as part of the optimization progresses is carried out particularly effectively. The end result is the rigorous determination of the single or multiple optimal solutions of the parameter optimization, regardless of their location, their number, and the starting values of optimization. The methods are particularly powerful if executed in interplay with genetic optimizers generating their new populations within the currently active unpruned space. Their current best guess provides rigorous upper bounds of the minima, which can then beneficially be used for better pruning. Examples of the method and its performance will be presented, including the determination of all operating points of desired tunes or chromaticities, etc. in storage ring lattices.

Optimization of process parameters for a quasi-continuous tablet coating system using design of experiments.

PubMed

Cahyadi, Christine; Heng, Paul Wan Sia; Chan, Lai Wah

2011-03-01

The aim of this study was to identify and optimize the critical process parameters of the newly developed Supercell quasi-continuous coater for optimal tablet coat quality. Design of experiments, aided by multivariate analysis techniques, was used to quantify the effects of various coating process conditions and their interactions on the quality of film-coated tablets. The process parameters varied included batch size, inlet temperature, atomizing pressure, plenum pressure, spray rate and coating level. An initial screening stage was carried out using a 2(6-1(IV)) fractional factorial design. Following these preliminary experiments, optimization study was carried out using the Box-Behnken design. Main response variables measured included drug-loading efficiency, coat thickness variation, and the extent of tablet damage. Apparent optimum conditions were determined by using response surface plots. The process parameters exerted various effects on the different response variables. Hence, trade-offs between individual optima were necessary to obtain the best compromised set of conditions. The adequacy of the optimized process conditions in meeting the combined goals for all responses was indicated by the composite desirability value. By using response surface methodology and optimization, coating conditions which produced coated tablets of high drug-loading efficiency, low incidences of tablet damage and low coat thickness variation were defined. Optimal conditions were found to vary over a large spectrum when different responses were considered. Changes in processing parameters across the design space did not result in drastic changes to coat quality, thereby demonstrating robustness in the Supercell coating process. © 2010 American Association of Pharmaceutical Scientists

An Optimized Trajectory Planning for Welding Robot

NASA Astrophysics Data System (ADS)

Chen, Zhilong; Wang, Jun; Li, Shuting; Ren, Jun; Wang, Quan; Cheng, Qunchao; Li, Wentao

2018-03-01

In order to improve the welding efficiency and quality, this paper studies the combined planning between welding parameters and space trajectory for welding robot and proposes a trajectory planning method with high real-time performance, strong controllability and small welding error. By adding the virtual joint at the end-effector, the appropriate virtual joint model is established and the welding process parameters are represented by the virtual joint variables. The trajectory planning is carried out in the robot joint space, which makes the control of the welding process parameters more intuitive and convenient. By using the virtual joint model combined with the B-spline curve affine invariant, the welding process parameters are indirectly controlled by controlling the motion curve of the real joint. To solve the optimal time solution as the goal, the welding process parameters and joint space trajectory joint planning are optimized.

Mathematical modeling of a thermovoltaic cell

NASA Technical Reports Server (NTRS)

White, Ralph E.; Kawanami, Makoto

1992-01-01

A new type of battery named 'Vaporvolt' cell is in the early stage of its development. A mathematical model of a CuO/Cu 'Vaporvolt' cell is presented that can be used to predict the potential and the transport behavior of the cell during discharge. A sensitivity analysis of the various transport and electrokinetic parameters indicates which parameters have the most influence on the predicted energy and power density of the 'Vaporvolt' cell. This information can be used to decide which parameters should be optimized or determined more accurately through further modeling or experimental studies. The optimal thicknesses of electrodes and separator, the concentration of the electrolyte, and the current density are determined by maximizing the power density. These parameter sensitivities and optimal design parameter values will help in the development of a better CuO/Cu 'Vaporvolt' cell.

Optimization of trehalose production by a novel strain Brevibacterium sp. SY361.

PubMed

Wang, Lei; Huang, Rui; Gu, Guanbin; Fang, Hongying

2008-10-01

Trehalose production by a novel strain of Brevibacterium sp. SY361 was optimized in submerged fermentation. Different chemical and physical parameters such as carbon and nitrogen sources, inoculum level, initial pH, incubation temperature, aeration and time-course of fermentation, were studied in order to increase trehalose productivity. An optimal production medium containing 3% (w/v) glucose, 0.9% (v/v) corn steep liquor, 0.5% (w/v) KH(2)PO(4) and 0.4% (w/v) MgSO(4).7 H(2)O was found suitable for trehalose production. An optimal volume of medium in a 500 ml flask was 80 ml. The optimal levels of other parameters were 4.0% (v/v) of inoculum, initial pH of 6.0, incubation temperature of 28-32 degrees C and time-course of 60 h. Optimized parameters gave a maximum trehalose of 12.2 mg/ml with a conversion rate of 58.4%. (c) 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Numerical optimization of Ignition and Growth reactive flow modeling for PAX2A

NASA Astrophysics Data System (ADS)

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

1996-05-01

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

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

NASA Astrophysics Data System (ADS)

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.

Human-in-the-loop Bayesian optimization of wearable device parameters

PubMed Central

Malcolm, Philippe; Speeckaert, Jozefien; Siviy, Christoper J.; Walsh, Conor J.; Kuindersma, Scott

2017-01-01

The increasing capabilities of exoskeletons and powered prosthetics for walking assistance have paved the way for more sophisticated and individualized control strategies. In response to this opportunity, recent work on human-in-the-loop optimization has considered the problem of automatically tuning control parameters based on realtime physiological measurements. However, the common use of metabolic cost as a performance metric creates significant experimental challenges due to its long measurement times and low signal-to-noise ratio. We evaluate the use of Bayesian optimization—a family of sample-efficient, noise-tolerant, and global optimization methods—for quickly identifying near-optimal control parameters. To manage experimental complexity and provide comparisons against related work, we consider the task of minimizing metabolic cost by optimizing walking step frequencies in unaided human subjects. Compared to an existing approach based on gradient descent, Bayesian optimization identified a near-optimal step frequency with a faster time to convergence (12 minutes, p < 0.01), smaller inter-subject variability in convergence time (± 2 minutes, p < 0.01), and lower overall energy expenditure (p < 0.01). PMID:28926613

Optimal design and control of an electromechanical transfemoral prosthesis with energy regeneration.

PubMed

Rohani, Farbod; Richter, Hanz; van den Bogert, Antonie J

2017-01-01

In this paper, we present the design of an electromechanical above-knee active prosthesis with energy storage and regeneration. The system consists of geared knee and ankle motors, parallel springs for each motor, an ultracapacitor, and controllable four-quadrant power converters. The goal is to maximize the performance of the system by finding optimal controls and design parameters. A model of the system dynamics was developed, and used to solve a combined trajectory and design optimization problem. The objectives of the optimization were to minimize tracking error relative to human joint motions, as well as energy use. The optimization problem was solved by the method of direct collocation, based on joint torque and joint angle data from ten subjects walking at three speeds. After optimization of controls and design parameters, the simulated system could operate at zero energy cost while still closely emulating able-bodied gait. This was achieved by controlled energy transfer between knee and ankle, and by controlled storage and release of energy throughout the gait cycle. Optimal gear ratios and spring parameters were similar across subjects and walking speeds.

Application of controller partitioning optimization procedure to integrated flight/propulsion control design for a STOVL aircraft

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Schmidt, Phillip H.

1993-01-01

A parameter optimization framework has earlier been developed to solve the problem of partitioning a centralized controller into a decentralized, hierarchical structure suitable for integrated flight/propulsion control implementation. This paper presents results from the application of the controller partitioning optimization procedure to IFPC design for a Short Take-Off and Vertical Landing (STOVL) aircraft in transition flight. The controller partitioning problem and the parameter optimization algorithm are briefly described. Insight is provided into choosing various 'user' selected parameters in the optimization cost function such that the resulting optimized subcontrollers will meet the characteristics of the centralized controller that are crucial to achieving the desired closed-loop performance and robustness, while maintaining the desired subcontroller structure constraints that are crucial for IFPC implementation. The optimization procedure is shown to improve upon the initial partitioned subcontrollers and lead to performance comparable to that achieved with the centralized controller. This application also provides insight into the issues that should be addressed at the centralized control design level in order to obtain implementable partitioned subcontrollers.

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

PubMed

Xue, Dingyü; Li, Tingxue

2017-04-27

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

A thermal vacuum test optimization procedure

NASA Technical Reports Server (NTRS)

Kruger, R.; Norris, H. P.

1979-01-01

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

Grey Relational Analysis Coupled with Principal Component Analysis for Optimization of Stereolithography Process to Enhance Part Quality

NASA Astrophysics Data System (ADS)

Raju, B. S.; Sekhar, U. Chandra; Drakshayani, D. N.

2017-08-01

The paper investigates optimization of stereolithography process for SL5530 epoxy resin material to enhance part quality. The major characteristics indexed for performance selected to evaluate the processes are tensile strength, Flexural strength, Impact strength and Density analysis and corresponding process parameters are Layer thickness, Orientation and Hatch spacing. In this study, the process is intrinsically with multiple parameters tuning so that grey relational analysis which uses grey relational grade as performance index is specially adopted to determine the optimal combination of process parameters. Moreover, the principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively desired. The results of confirmation experiments reveal that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of process parameters. Hence, this confirm that the proposed approach in this study can be an useful tool to improve the process parameters in stereolithography process, which is very useful information for machine designers as well as RP machine users.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Development of mathematical models and optimization of the process parameters of laser surface hardened EN25 steel using elitist non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Vignesh, S.; Dinesh Babu, P.; Surya, G.; Dinesh, S.; Marimuthu, P.

2018-02-01

The ultimate goal of all production entities is to select the process parameters that would be of maximum strength, minimum wear and friction. The friction and wear are serious problems in most of the industries which are influenced by the working set of parameters, oxidation characteristics and mechanism involved in formation of wear. The experimental input parameters such as sliding distance, applied load, and temperature are utilized in finding out the optimized solution for achieving the desired output responses such as coefficient of friction, wear rate, and volume loss. The optimization is performed with the help of a novel method, Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based on an evolutionary algorithm. The regression equations obtained using Response Surface Methodology (RSM) are used in determining the optimum process parameters. Further, the results achieved through desirability approach in RSM are compared with that of the optimized solution obtained through NSGA-II. The results conclude that proposed evolutionary technique is much effective and faster than the desirability approach.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Multi-response optimization of process parameters for GTAW process in dissimilar welding of Incoloy 800HT and P91 steel by using grey relational analysis

NASA Astrophysics Data System (ADS)

vellaichamy, Lakshmanan; Paulraj, Sathiya

2018-02-01

The dissimilar welding of Incoloy 800HT and P91 steel using Gas Tungsten arc welding process (GTAW) This material is being used in the Nuclear Power Plant and Aerospace Industry based application because Incoloy 800HT possess good corrosion and oxidation resistance and P91 possess high temperature strength and creep resistance. This work discusses on multi-objective optimization using gray relational analysis (GRA) using 9CrMoV-N filler materials. The experiment conducted L9 orthogonal array. The input parameter are current, voltage, speed. The output response are Tensile strength, Hardness and Toughness. To optimize the input parameter and multiple output variable by using GRA. The optimal parameter is combination was determined as A2B1C1 so given input parameter welding current at 120 A, voltage at 16 V and welding speed at 0.94 mm/s. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics.

Study of optimal laser parameters for cutting QFN packages by Taguchi's matrix method

NASA Astrophysics Data System (ADS)

Li, Chen-Hao; Tsai, Ming-Jong; Yang, Ciann-Dong

2007-06-01

This paper reports the study of optimal laser parameters for cutting QFN (Quad Flat No-lead) packages by using a diode pumped solid-state laser system (DPSSL). The QFN cutting path includes two different materials, which are the encapsulated epoxy and a copper lead frame substrate. The Taguchi's experimental method with orthogonal array of L 9(3 4) is employed to obtain optimal combinatorial parameters. A quantified mechanism was proposed for examining the laser cutting quality of a QFN package. The influences of the various factors such as laser current, laser frequency, and cutting speed on the laser cutting quality is also examined. From the experimental results, the factors on the cutting quality in the order of decreasing significance are found to be (a) laser frequency, (b) cutting speed, and (c) laser driving current. The optimal parameters were obtained at the laser frequency of 2 kHz, the cutting speed of 2 mm/s, and the driving current of 29 A. Besides identifying this sequence of dominance, matrix experiment also determines the best level for each control factor. The verification experiment confirms that the application of laser cutting technology to QFN is very successfully by using the optimal laser parameters predicted from matrix experiments.

Optimization of the monitoring of landfill gas and leachate in closed methanogenic landfills.

PubMed

Jovanov, Dejan; Vujić, Bogdana; Vujić, Goran

2018-06-15

Monitoring of the gas and leachate parameters in a closed landfill is a long-term activity defined by national legislative worldwide. Serbian Waste Disposal Law defines the monitoring of a landfill at least 30 years after its closing, but the definition of the monitoring extent (number and type of parameters) is incomplete. In order to define and clear all the uncertainties, this research focuses on process of monitoring optimization, using the closed landfill in Zrenjanin, Serbia, as the experimental model. The aim of optimization was to find representative parameters which would define the physical, chemical and biological processes in the closed methanogenic landfill and to make this process less expensive. Research included development of the five monitoring models with different number of gas and leachate parameters and each model has been processed in open source software GeoGebra which is often used for solving optimization problems. The results of optimization process identified the most favorable monitoring model which fulfills all the defined criteria not only from the point of view of mathematical analyses, but also from the point of view of environment protection. The final outcome of this research - the minimal required parameters which should be included in the landfill monitoring are precisely defined. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-Objective Optimization of Moving-magnet Linear Oscillatory Motor Using Response Surface Methodology with Quantum-Behaved PSO Operator

NASA Astrophysics Data System (ADS)

Lei, Meizhen; Wang, Liqiang

2018-01-01

To reduce the difficulty of manufacturing and increase the magnetic thrust density, a moving-magnet linear oscillatory motor (MMLOM) without inner-stators was Proposed. To get the optimal design of maximum electromagnetic thrust with minimal permanent magnetic material, firstly, the 3D finite element analysis (FEA) model of the MMLOM was built and verified by comparison with prototype experiment result. Then the influence of design parameters of permanent magnet (PM) on the electromagnetic thrust was systematically analyzed by the 3D FEA to get the design parameters. Secondly, response surface methodology (RSM) was employed to build the response surface model of the new MMLOM, which can obtain an analytical model of the PM volume and thrust. Then a multi-objective optimization methods for design parameters of PM, using response surface methodology (RSM) with a quantum-behaved PSO (QPSO) operator, was proposed. Then the way to choose the best design parameters of PM among the multi-objective optimization solution sets was proposed. Then the 3D FEA of the optimal design candidates was compared. The comparison results showed that the proposed method can obtain the best combination of the geometric parameters of reducing the PM volume and increasing the thrust.

[Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm].

PubMed

Pan, Qing; Yao, Jialiang; Wang, Ruofan; Cao, Ping; Ning, Gangmin; Fang, Luping

2017-08-01

The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.

An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

PubMed Central

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. PMID:26880874

Optimal feedback scheme and universal time scaling for Hamiltonian parameter estimation.

PubMed

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.

Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

NASA Astrophysics Data System (ADS)

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.

Optimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Mariajayaprakash, Arokiasamy; Senthilvelan, Thiyagarajan; Vivekananthan, Krishnapillai Ponnambal

2013-07-01

The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and fixture height), washing process parameters (total alkalinity, temperature, pH value of rinsing water, and timing), and painting process parameters (flowability, coating thickness, pointage, and temperature). In this paper, the process parameters, namely, painting and washing process parameters, are optimized by Taguchi method. Though the defects are reasonably minimized by Taguchi method, in order to achieve zero defects during the processes, genetic algorithm technique is applied on the optimized parameters obtained by Taguchi method.

Flexible operation strategy for environment control system in abnormal supply power condition

NASA Astrophysics Data System (ADS)

Liping, Pang; Guoxiang, Li; Hongquan, Qu; Yufeng, Fang

2017-04-01

This paper establishes an optimization method that can be applied to the flexible operation of the environment control system in an abnormal supply power condition. A proposed conception of lifespan is used to evaluate the depletion time of the non-regenerative substance. The optimization objective function is to maximize the lifespans. The optimization variables are the allocated powers of subsystems. The improved Non-dominated Sorting Genetic Algorithm is adopted to obtain the pareto optimization frontier with the constraints of the cabin environmental parameters and the adjustable operating parameters of the subsystems. Based on the same importance of objective functions, the preferred power allocation of subsystems can be optimized. Then the corresponding running parameters of subsystems can be determined to ensure the maximum lifespans. A long-duration space station with three astronauts is used to show the implementation of the proposed optimization method. Three different CO2 partial pressure levels are taken into consideration in this study. The optimization results show that the proposed optimization method can obtain the preferred power allocation for the subsystems when the supply power is at a less-than-nominal value. The method can be applied to the autonomous control for the emergency response of the environment control system.

The Inverse Optimal Control Problem for a Three-Loop Missile Autopilot

NASA Astrophysics Data System (ADS)

Hwang, Donghyeok; Tahk, Min-Jea

2018-04-01

The performance characteristics of the autopilot must have a fast response to intercept a maneuvering target and reasonable robustness for system stability under the effect of un-modeled dynamics and noise. By the conventional approach, the three-loop autopilot design is handled by time constant, damping factor and open-loop crossover frequency to achieve the desired performance requirements. Note that the general optimal theory can be also used to obtain the same gain as obtained from the conventional approach. The key idea of using optimal control technique for feedback gain design revolves around appropriate selection and interpretation of the performance index for which the control is optimal. This paper derives an explicit expression, which relates the weight parameters appearing in the quadratic performance index to the design parameters such as open-loop crossover frequency, phase margin, damping factor, or time constant, etc. Since all set of selection of design parameters do not guarantee existence of optimal control law, explicit inequalities, which are named the optimality criteria for the three-loop autopilot (OC3L), are derived to find out all set of design parameters for which the control law is optimal. Finally, based on OC3L, an efficient gain selection procedure is developed, where time constant is set to design objective and open-loop crossover frequency and phase margin as design constraints. The effectiveness of the proposed technique is illustrated through numerical simulations.

High-precision method of binocular camera calibration with a distortion model.

PubMed

Li, Weimin; Shan, Siyu; Liu, Hui

2017-03-10

A high-precision camera calibration method for binocular stereo vision system based on a multi-view template and alternative bundle adjustment is presented in this paper. The proposed method could be achieved by taking several photos on a specially designed calibration template that has diverse encoded points in different orientations. In this paper, the method utilized the existing algorithm used for monocular camera calibration to obtain the initialization, which involves a camera model, including radial lens distortion and tangential distortion. We created a reference coordinate system based on the left camera coordinate to optimize the intrinsic parameters of left camera through alternative bundle adjustment to obtain optimal values. Then, optimal intrinsic parameters of the right camera can be obtained through alternative bundle adjustment when we create a reference coordinate system based on the right camera coordinate. We also used all intrinsic parameters that were acquired to optimize extrinsic parameters. Thus, the optimal lens distortion parameters and intrinsic and extrinsic parameters were obtained. Synthetic and real data were used to test the method. The simulation results demonstrate that the maximum mean absolute relative calibration errors are about 3.5e-6 and 1.2e-6 for the focal length and the principal point, respectively, under zero-mean Gaussian noise with 0.05 pixels standard deviation. The real result shows that the reprojection error of our model is about 0.045 pixels with the relative standard deviation of 1.0e-6 over the intrinsic parameters. The proposed method is convenient, cost-efficient, highly precise, and simple to carry out.

Ring rolling process simulation for microstructure optimization

NASA Astrophysics Data System (ADS)

Franchi, Rodolfo; Del Prete, Antonio; Donatiello, Iolanda; Calabrese, Maurizio

2017-10-01

Metal undergoes complicated microstructural evolution during Hot Ring Rolling (HRR), which determines the quality, mechanical properties and life of the ring formed. One of the principal microstructure properties which mostly influences the structural performances of forged components, is the value of the average grain size. In the present paper a ring rolling process has been studied and optimized in order to obtain anular components to be used in aerospace applications. In particular, the influence of process input parameters (feed rate of the mandrel and angular velocity of driver roll) on microstructural and on geometrical features of the final ring has been evaluated. For this purpose, a three-dimensional finite element model for HRR has been developed in SFTC DEFORM V11, taking into account also microstructural development of the material used (the nickel superalloy Waspalloy). The Finite Element (FE) model has been used to formulate a proper optimization problem. The optimization procedure has been developed in order to find the combination of process parameters which allows to minimize the average grain size. The Response Surface Methodology (RSM) has been used to find the relationship between input and output parameters, by using the exact values of output parameters in the control points of a design space explored through FEM simulation. Once this relationship is known, the values of the output parameters can be calculated for each combination of the input parameters. Then, an optimization procedure based on Genetic Algorithms has been applied. At the end, the minimum value of average grain size with respect to the input parameters has been found.

Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric

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

Song, Jung Heon; Wu, Kesheng; Simon, Horst D.

2014-03-01

VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010). The computation of VPIN requires the user to set up a handful of free parameters. The values of these parameters significantly affect the effectiveness of VPIN as measured by themore » false positive rate (FPR). An earlier publication reported that a brute-force search of simple parameter combinations yielded a number of parameter combinations with FPR of 7%. This work is a systematic attempt to find an optimal parameter set using an optimization package, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) by Audet, le digabel, and tribes (2009) and le digabel (2011). We have implemented a number of techniques to reduce the computation time with NOMAD. Tests show that we can reduce the FPR to only 2%. To better understand the parameter choices, we have conducted a series of sensitivity analysis via uncertainty quantification on the parameter spaces using UQTK (Uncertainty Quantification Toolkit). Results have shown dominance of 2 parameters in the computation of FPR. Using the outputs from NOMAD optimization and sensitivity analysis, We recommend A range of values for each of the free parameters that perform well on a large set of futures trading records.« less

Optimization of brain PET imaging for a multicentre trial: the French CATI experience.

PubMed

Habert, Marie-Odile; Marie, Sullivan; Bertin, Hugo; Reynal, Moana; Martini, Jean-Baptiste; Diallo, Mamadou; Kas, Aurélie; Trébossen, Régine

2016-12-01

CATI is a French initiative launched in 2010 to handle the neuroimaging of a large cohort of subjects recruited for an Alzheimer's research program called MEMENTO. This paper presents our test protocol and results obtained for the 22 PET centres (overall 13 different scanners) involved in the MEMENTO cohort. We determined acquisition parameters using phantom experiments prior to patient studies, with the aim of optimizing PET quantitative values to the highest possible per site, while reducing, if possible, variability across centres. Jaszczak's and 3D-Hoffman's phantom measurements were used to assess image spatial resolution (ISR), recovery coefficients (RC) in hot and cold spheres, and signal-to-noise ratio (SNR). For each centre, the optimal reconstruction parameters were chosen as those maximizing ISR and RC without a noticeable decrease in SNR. Point-spread-function (PSF) modelling reconstructions were discarded. The three figures of merit extracted from the images reconstructed with optimized parameters and routine schemes were compared, as were volumes of interest ratios extracted from Hoffman acquisitions. The net effect of the 3D-OSEM reconstruction parameter optimization was investigated on a subset of 18 scanners without PSF modelling reconstruction. Compared to the routine parameters of the 22 PET centres, average RC in the two smallest hot and cold spheres and average ISR remained stable or were improved with the optimized reconstruction, at the expense of slight SNR degradation, while the dispersion of values was reduced. For the subset of scanners without PSF modelling, the mean RC of the smallest hot sphere obtained with the optimized reconstruction was significantly higher than with routine reconstruction. The putamen and caudate-to-white matter ratios measured on 3D-Hoffman acquisitions of all centres were also significantly improved by the optimization, while the variance was reduced. This study provides guidelines for optimizing quantitative results for multicentric PET neuroimaging trials.

Study on feed forward neural network convex optimization for LiFePO4 battery parameters

NASA Astrophysics Data System (ADS)

Liu, Xuepeng; Zhao, Dongmei

2017-08-01

Based on the modern facility agriculture automatic walking equipment LiFePO4 Battery, the parameter identification of LiFePO4 Battery is analyzed. An improved method for the process model of li battery is proposed, and the on-line estimation algorithm is presented. The parameters of the battery are identified using feed forward network neural convex optimization algorithm.

Optimizing LED lighting for space plant growth unit: Joint effects of photon flux density, red to white ratios and intermittent light pulses

NASA Astrophysics Data System (ADS)

Avercheva, O. V.; Berkovich, Yu. A.; Konovalova, I. O.; Radchenko, S. G.; Lapach, S. N.; Bassarskaya, E. M.; Kochetova, G. V.; Zhigalova, T. V.; Yakovleva, O. S.; Tarakanov, I. G.

2016-11-01

The aim of this work were to choose a quantitative optimality criterion for estimating the quality of plant LED lighting regimes inside space greenhouses and to construct regression models of crop productivity and the optimality criterion depending on the level of photosynthetic photon flux density (PPFD), the proportion of the red component in the light spectrum and the duration of the duty cycle (Chinese cabbage Brassica chinensis L. as an example). The properties of the obtained models were described in the context of predicting crop dry weight and the optimality criterion behavior when varying plant lighting parameters. Results of the fractional 3-factor experiment demonstrated the share of the PPFD level participation in the crop dry weight accumulation was 84.4% at almost any combination of other lighting parameters, but when PPFD value increased up to 500 μmol m-2 s-1 the pulse light and supplemental light from red LEDs could additionally increase crop productivity. Analysis of the optimality criterion response to variation of lighting parameters showed that the maximum coordinates were the following: PPFD = 500 μmol m-2 s-1, about 70%-proportion of the red component of the light spectrum (PPFDLEDred/PPFDLEDwhite = 1.5) and the duty cycle with a period of 501 μs. Thus, LED crop lighting with these parameters was optimal for achieving high crop productivity and for efficient use of energy in the given range of lighting parameter values.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Robust design of configurations and parameters of adaptable products

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Parameter Optimization and Electrode Improvement of Rotary Stepper Micromotor

NASA Astrophysics Data System (ADS)

Sone, Junji; Mizuma, Toshinari; Mochizuki, Shunsuke; Sarajlic, Edin; Yamahata, Christophe; Fujita, Hiroyuki

We developed a three-phase electrostatic stepper micromotor and performed a numerical simulation to improve its performance for practical use and to optimize its design. We conducted its circuit simulation by simplifying its structure, and the effect of springback force generated by supported mechanism using flexures was considered. And we considered new improvement method for electrodes. This improvement and other parameter optimizations achieved the low voltage drive of micromotor.

Maximum Entropy/Optimal Projection (MEOP) control design synthesis: Optimal quantification of the major design tradeoffs

NASA Technical Reports Server (NTRS)

Hyland, D. C.; Bernstein, D. S.

1987-01-01

The underlying philosophy and motivation of the optimal projection/maximum entropy (OP/ME) stochastic modeling and reduced control design methodology for high order systems with parameter uncertainties are discussed. The OP/ME design equations for reduced-order dynamic compensation including the effect of parameter uncertainties are reviewed. The application of the methodology to several Large Space Structures (LSS) problems of representative complexity is illustrated.

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

NASA Astrophysics Data System (ADS)

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.

Designing Industrial Networks Using Ecological Food Web Metrics.

PubMed

Layton, Astrid; Bras, Bert; Weissburg, Marc

2016-10-18

Biologically Inspired Design (biomimicry) and Industrial Ecology both look to natural systems to enhance the sustainability and performance of engineered products, systems and industries. Bioinspired design (BID) traditionally has focused on a unit operation and single product level. In contrast, this paper describes how principles of network organization derived from analysis of ecosystem properties can be applied to industrial system networks. Specifically, this paper examines the applicability of particular food web matrix properties as design rules for economically and biologically sustainable industrial networks, using an optimization model developed for a carpet recycling network. Carpet recycling network designs based on traditional cost and emissions based optimization are compared to designs obtained using optimizations based solely on ecological food web metrics. The analysis suggests that networks optimized using food web metrics also were superior from a traditional cost and emissions perspective; correlations between optimization using ecological metrics and traditional optimization ranged generally from 0.70 to 0.96, with flow-based metrics being superior to structural parameters. Four structural food parameters provided correlations nearly the same as that obtained using all structural parameters, but individual structural parameters provided much less satisfactory correlations. The analysis indicates that bioinspired design principles from ecosystems can lead to both environmentally and economically sustainable industrial resource networks, and represent guidelines for designing sustainable industry networks.

Least-squares/parabolized Navier-Stokes procedure for optimizing hypersonic wind tunnel nozzles

NASA Technical Reports Server (NTRS)

Korte, John J.; Kumar, Ajay; Singh, D. J.; Grossman, B.

1991-01-01

A new procedure is demonstrated for optimizing hypersonic wind-tunnel-nozzle contours. The procedure couples a CFD computer code to an optimization algorithm, and is applied to both conical and contoured hypersonic nozzles for the purpose of determining an optimal set of parameters to describe the surface geometry. A design-objective function is specified based on the deviation from the desired test-section flow-field conditions. The objective function is minimized by optimizing the parameters used to describe the nozzle contour based on the solution to a nonlinear least-squares problem. The effect of the changes in the nozzle wall parameters are evaluated by computing the nozzle flow using the parabolized Navier-Stokes equations. The advantage of the new procedure is that it directly takes into account the displacement effect of the boundary layer on the wall contour. The new procedure provides a method for optimizing hypersonic nozzles of high Mach numbers which have been designed by classical procedures, but are shown to produce poor flow quality due to the large boundary layers present in the test section. The procedure is demonstrated by finding the optimum design parameters for a Mach 10 conical nozzle and a Mach 6 and a Mach 15 contoured nozzle.

Hearing AIDS and music.

PubMed

Chasin, Marshall; Russo, Frank A

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues-both compression ratio and knee-points-and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a "music program,'' unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions.

Optimal time points sampling in pathway modelling.

PubMed

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

PubMed

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

Rapid optimization of MRM-MS instrument parameters by subtle alteration of precursor and product m/z targets.

PubMed

Sherwood, Carly A; Eastham, Ashley; Lee, Lik Wee; Risler, Jenni; Mirzaei, Hamid; Falkner, Jayson A; Martin, Daniel B

2009-07-01

Multiple reaction monitoring (MRM) is a highly sensitive method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides based on the screening of specified precursor peptide-to-fragment ion transitions. MRM-MS sensitivity depends critically on the tuning of instrument parameters, such as collision energy and cone voltage, for the generation of maximal product ion signal. Although generalized equations and values exist for such instrument parameters, there is no clear indication that optimal signal can be reliably produced for all types of MRM transitions using such an algorithmic approach. To address this issue, we have devised a workflow functional on both Waters Quattro Premier and ABI 4000 QTRAP triple quadrupole instruments that allows rapid determination of the optimal value of any programmable instrument parameter for each MRM transition. Here, we demonstrate the strategy for the optimizations of collision energy and cone voltage, but the method could be applied to other instrument parameters, such as declustering potential, as well. The workflow makes use of the incremental adjustment of the precursor and product m/z values at the hundredth decimal place to create a series of MRM targets at different collision energies that can be cycled through in rapid succession within a single run, avoiding any run-to-run variability in execution or comparison. Results are easily visualized and quantified using the MRM software package Mr. M to determine the optimal instrument parameters for each transition.

Rapid Optimization of MRM-MS Instrument Parameters by Subtle Alteration of Precursor and Product m/z Targets

PubMed Central

Sherwood, Carly A.; Eastham, Ashley; Lee, Lik Wee; Risler, Jenni; Mirzaei, Hamid; Falkner, Jayson A.; Martin, Daniel B.

2009-01-01

Multiple reaction monitoring (MRM) is a highly sensitive method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides based on the screening of specified precursor peptide-to-fragment ion transitions. MRM-MS sensitivity depends critically on the tuning of instrument parameters, such as collision energy and cone voltage, for the generation of maximal product ion signal. Although generalized equations and values exist for such instrument parameters, there is no clear indication that optimal signal can be reliably produced for all types of MRM transitions using such an algorithmic approach. To address this issue, we have devised a workflow functional on both Waters Quattro Premier and ABI 4000 QTRAP triple quadrupole instruments that allows rapid determination of the optimal value of any programmable instrument parameter for each MRM transition. Here, we demonstrate the strategy for the optimizations of collision energy and cone voltage, but the method could be applied to other instrument parameters, such as declustering potential, as well. The workflow makes use of the incremental adjustment of the precursor and product m/z values at the hundredth decimal place to create a series of MRM targets at different collision energies that can be cycled through in rapid succession within a single run, avoiding any run-to-run variability in execution or comparison. Results are easily visualized and quantified using the MRM software package Mr. M to determine the optimal instrument parameters for each transition. PMID:19405522

Design of multi-energy Helds coupling testing system of vertical axis wind power system

NASA Astrophysics Data System (ADS)

Chen, Q.; Yang, Z. X.; Li, G. S.; Song, L.; Ma, C.

2016-08-01

The conversion efficiency of wind energy is the focus of researches and concerns as one of the renewable energy. The present methods of enhancing the conversion efficiency are mostly improving the wind rotor structure, optimizing the generator parameters and energy storage controller and so on. Because the conversion process involves in energy conversion of multi-energy fields such as wind energy, mechanical energy and electrical energy, the coupling effect between them will influence the overall conversion efficiency. In this paper, using system integration analysis technology, a testing system based on multi-energy field coupling (MEFC) of vertical axis wind power system is proposed. When the maximum efficiency of wind rotor is satisfied, it can match to the generator function parameters according to the output performance of wind rotor. The voltage controller can transform the unstable electric power to the battery on the basis of optimizing the parameters such as charging times, charging voltage. Through the communication connection and regulation of the upper computer system (UCS), it can make the coupling parameters configure to an optimal state, and it improves the overall conversion efficiency. This method can test the whole wind turbine (WT) performance systematically and evaluate the design parameters effectively. It not only provides a testing method for system structure design and parameter optimization of wind rotor, generator and voltage controller, but also provides a new testing method for the whole performance optimization of vertical axis wind energy conversion system (WECS).

Generally astigmatic Gaussian beam representation and optimization using skew rays

NASA Astrophysics Data System (ADS)

Colbourne, Paul D.

2014-12-01

Methods are presented of using skew rays to optimize a generally astigmatic optical system to obtain the desired Gaussian beam focus and minimize aberrations, and to calculate the propagating generally astigmatic Gaussian beam parameters at any point. The optimization method requires very little computation beyond that of a conventional ray optimization, and requires no explicit calculation of the properties of the propagating Gaussian beam. Unlike previous methods, the calculation of beam parameters does not require matrix calculations or the introduction of non-physical concepts such as imaginary rays.

Shape optimization of the modular press body

NASA Astrophysics Data System (ADS)

Pabiszczak, Stanisław

2016-12-01

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

Structural damage identification using an enhanced thermal exchange optimization algorithm

NASA Astrophysics Data System (ADS)

Kaveh, A.; Dadras, A.

2018-03-01

The recently developed optimization algorithm-the so-called thermal exchange optimization (TEO) algorithm-is enhanced and applied to a damage detection problem. An offline parameter tuning approach is utilized to set the internal parameters of the TEO, resulting in the enhanced heat transfer optimization (ETEO) algorithm. The damage detection problem is defined as an inverse problem, and ETEO is applied to a wide range of structures. Several scenarios with noise and noise-free modal data are tested and the locations and extents of damages are identified with good accuracy.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model.

PubMed

An, Yan; Zou, Zhihong; Zhao, Yanfei

2015-03-01

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting. Copyright © 2015. Published by Elsevier B.V.

SPOTting model parameters using a ready-made Python package

NASA Astrophysics Data System (ADS)

Houska, Tobias; Kraft, Philipp; Breuer, Lutz

2015-04-01

The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for optimization methods. Here we see simple algorithms like the MCMC struggling to find the global optimum of the function, while algorithms like SCE-UA and DE-MCZ show their strengths. Thirdly, we apply an uncertainty analysis of a one-dimensional physically based hydrological model build with the Catchment Modelling Framework (CMF). The model is driven by meteorological and groundwater data from a Free Air Carbon Enrichment (FACE) experiment in Linden (Hesse, Germany). Simulation results are evaluated with measured soil moisture data. We search for optimal parameter sets of the van Genuchten-Mualem function and find different equally optimal solutions with some of the algorithms. The case studies reveal that the implemented SPOT methods work sufficiently well. They further show the benefit of having one tool at hand that includes a number of parameter search methods, likelihood functions and a priori parameter distributions within one platform independent package.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Estimating parameters with pre-specified accuracies in distributed parameter systems using optimal experiment design

NASA Astrophysics Data System (ADS)

Potters, M. G.; Bombois, X.; Mansoori, M.; Hof, Paul M. J. Van den

2016-08-01

Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the optimal input signal, and optimal sensor and actuator locations. Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments. We find good correspondence between numerical and theoretical results.

Aerodynamic optimization by simultaneously updating flow variables and design parameters

NASA Technical Reports Server (NTRS)

Rizk, M. H.

1990-01-01

The application of conventional optimization schemes to aerodynamic design problems leads to inner-outer iterative procedures that are very costly. An alternative approach is presented based on the idea of updating the flow variable iterative solutions and the design parameter iterative solutions simultaneously. Two schemes based on this idea are applied to problems of correcting wind tunnel wall interference and optimizing advanced propeller designs. The first of these schemes is applicable to a limited class of two-design-parameter problems with an equality constraint. It requires the computation of a single flow solution. The second scheme is suitable for application to general aerodynamic problems. It requires the computation of several flow solutions in parallel. In both schemes, the design parameters are updated as the iterative flow solutions evolve. Computations are performed to test the schemes' efficiency, accuracy, and sensitivity to variations in the computational parameters.

Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

PubMed Central

Düking, Peter; Hotho, Andreas; Holmberg, Hans-Christer; Fuss, Franz Konstantin; Sperlich, Billy

2016-01-01

Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health. PMID:27014077

Optimization of biological sulfide removal in a CSTR bioreactor.

PubMed

Roosta, Aliakbar; Jahanmiri, Abdolhossein; Mowla, Dariush; Niazi, Ali; Sotoodeh, Hamidreza

2012-08-01

In this study, biological sulfide removal from natural gas in a continuous bioreactor is investigated for estimation of the optimal operational parameters. According to the carried out reactions, sulfide can be converted to elemental sulfur, sulfate, thiosulfate, and polysulfide, of which elemental sulfur is the desired product. A mathematical model is developed and was used for investigation of the effect of various parameters on elemental sulfur selectivity. The results of the simulation show that elemental sulfur selectivity is a function of dissolved oxygen, sulfide load, pH, and concentration of bacteria. Optimal parameter values are calculated for maximum elemental sulfur selectivity by using genetic algorithm as an adaptive heuristic search. In the optimal conditions, 87.76% of sulfide loaded to the bioreactor is converted to elemental sulfur.

Optimal line drop compensation parameters under multi-operating conditions

NASA Astrophysics Data System (ADS)

Wan, Yuan; Li, Hang; Wang, Kai; He, Zhe

2017-01-01

Line Drop Compensation (LDC) is a main function of Reactive Current Compensation (RCC) which is developed to improve voltage stability. While LDC has benefit to voltage, it may deteriorate the small-disturbance rotor angle stability of power system. In present paper, an intelligent algorithm which is combined by Genetic Algorithm (GA) and Backpropagation Neural Network (BPNN) is proposed to optimize parameters of LDC. The objective function proposed in present paper takes consideration of voltage deviation and power system oscillation minimal damping ratio under multi-operating conditions. A simulation based on middle area of Jiangxi province power system is used to demonstrate the intelligent algorithm. The optimization result shows that coordinate optimized parameters can meet the multioperating conditions requirement and improve voltage stability as much as possible while guaranteeing enough damping ratio.

Modified harmony search

NASA Astrophysics Data System (ADS)

Mohamed, Najihah; Lutfi Amri Ramli, Ahmad; Majid, Ahmad Abd; Piah, Abd Rahni Mt

2017-09-01

A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in many areas. HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. Propose in this paper Modified Harmony Search for solving optimization problems, which employs a concept from genetic algorithm method and particle swarm optimization for generating new solution vectors that enhances the performance of HS algorithm. The performances of MHS and HS are investigated on ten benchmark optimization problems in order to make a comparison to reflect the efficiency of the MHS in terms of final accuracy, convergence speed and robustness.

Development of a multiple-parameter nonlinear perturbation procedure for transonic turbomachinery flows: Preliminary application to design/optimization problems

NASA Technical Reports Server (NTRS)

Stahara, S. S.; Elliott, J. P.; Spreiter, J. R.

1983-01-01

An investigation was conducted to continue the development of perturbation procedures and associated computational codes for rapidly determining approximations to nonlinear flow solutions, with the purpose of establishing a method for minimizing computational requirements associated with parametric design studies of transonic flows in turbomachines. The results reported here concern the extension of the previously developed successful method for single parameter perturbations to simultaneous multiple-parameter perturbations, and the preliminary application of the multiple-parameter procedure in combination with an optimization method to blade design/optimization problem. In order to provide as severe a test as possible of the method, attention is focused in particular on transonic flows which are highly supercritical. Flows past both isolated blades and compressor cascades, involving simultaneous changes in both flow and geometric parameters, are considered. Comparisons with the corresponding exact nonlinear solutions display remarkable accuracy and range of validity, in direct correspondence with previous results for single-parameter perturbations.

Automated parameter tuning applied to sea ice in a global climate model

NASA Astrophysics Data System (ADS)

Roach, Lettie A.; Tett, Simon F. B.; Mineter, Michael J.; Yamazaki, Kuniko; Rae, Cameron D.

2018-01-01

This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters result in a simulated sea-ice time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence from observed Antarctic trends and mean regional sea ice distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology. This simple approach provides evidence for the contribution of parameter uncertainty to spread in sea ice extent trends and could be customized to investigate uncertainties in other climate variables.

Optimization of process parameters in welding of dissimilar steels using robot TIG welding

NASA Astrophysics Data System (ADS)

Navaneeswar Reddy, G.; VenkataRamana, M.

2018-03-01

Robot TIG welding is a modern technique used for joining two work pieces with high precision. Design of Experiments is used to conduct experiments by varying weld parameters like current, wire feed and travelling speed. The welding parameters play important role in joining of dissimilar stainless steel SS 304L and SS430. In this work, influences of welding parameter on Robot TIG Welded specimens are investigated using Response Surface Methodology. The Micro Vickers hardness tests of the weldments are measured. The process parameters are optimized to maximize the hardness of the weldments.

Optimal solutions for a bio mathematical model for the evolution of smoking habit

NASA Astrophysics Data System (ADS)

Sikander, Waseem; Khan, Umar; Ahmed, Naveed; Mohyud-Din, Syed Tauseef

In this study, we apply Variation of Parameter Method (VPM) coupled with an auxiliary parameter to obtain the approximate solutions for the epidemic model for the evolution of smoking habit in a constant population. Convergence of the developed algorithm, namely VPM with an auxiliary parameter is studied. Furthermore, a simple way is considered for obtaining an optimal value of auxiliary parameter via minimizing the total residual error over the domain of problem. Comparison of the obtained results with standard VPM shows that an auxiliary parameter is very feasible and reliable in controlling the convergence of approximate solutions.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Warpage investigation on side arms using response surface methodology (RSM) and glow-worm swarm optimizations (GSO)

NASA Astrophysics Data System (ADS)

Sow, C. K.; Fathullah, M.; Nasir, S. M.; Shayfull, Z.; Shazzuan, S.

2017-09-01

This paper discusses on an analysis run via injection moulding process in determination of the optimum processing parameters used for manufacturing side arms of catheters in minimizing the warpage issues. The optimization method used was RSM. Moreover, in this research tries to find the most significant factor affecting the warpage. From the previous literature review,4 most significant parameters on warpage defect was selected. Those parameters were melt temperature, packing time, packing pressure, mould temperature and cooling time. At the beginning, side arm was drawn using software of CATIA V5. Then, software Mouldflow and Design Expert were employed to analyses on the popular warpage issues. After that, GSO artificial intelligence was apply using the mathematical model from Design Expert for more optimization on RSM result. Recommended parameter settings from the simulation work were then compared with the optimization work of RSM and GSO. The result show that the warpage on the side arm was improved by 3.27 %

Influence of Thrust Level on the Architecture and Optimal Working Process Parameters of a Small-scale Turbojet for UAV

NASA Astrophysics Data System (ADS)

Kuz`michev, V. S.; Filinov, E. P.; Ostapyuk, Ya A.

2018-01-01

This article describes how the thrust level influences the turbojet architecture (types of turbomachines that provide the maximum efficiency) and its working process parameters (turbine inlet temperature (TIT) and overall pressure ratio (OPR)). Functional gasdynamic and strength constraints were included, total mass of fuel and the engine required for mission and the specific fuel consumption (SFC) were considered optimization criteria. Radial and axial turbines and compressors were considered. The results show that as the engine thrust decreases, optimal values of working process parameters decrease too, and the regions of compromise shrink. Optimal engine architecture and values of working process parameters are suggested for turbojets with thrust varying from 100N to 100kN. The results show that for the thrust below 25kN the engine scale factor should be taken into the account, as the low flow rates begin to influence the efficiency of engine elements substantially.

Optimization of laser butt welding parameters with multiple performance characteristics

NASA Astrophysics Data System (ADS)

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.

[Feedforward control strategy and its application in quality improvement of ethanol precipitation process of danhong injection].

PubMed

Yan, Bin-Jun; Guo, Zheng-Tai; Qu, Hai-Bin; Zhao, Bu-Chang; Zhao, Tao

2013-06-01

In this work, a feedforward control strategy basing on the concept of quality by design was established for the manufacturing process of traditional Chinese medicine to reduce the impact of the quality variation of raw materials on drug. In the research, the ethanol precipitation process of Danhong injection was taken as an application case of the method established. Box-Behnken design of experiments was conducted. Mathematical models relating the attributes of the concentrate, the process parameters and the quality of the supernatants produced were established. Then an optimization model for calculating the best process parameters basing on the attributes of the concentrate was built. The quality of the supernatants produced by ethanol precipitation with optimized and non-optimized process parameters were compared. The results showed that using the feedforward control strategy for process parameters optimization can control the quality of the supernatants effectively. The feedforward control strategy proposed can enhance the batch-to-batch consistency of the supernatants produced by ethanol precipitation.

Calibration of a biome-biogeochemical cycles model for modeling the net primary production of teak forests through inverse modeling of remotely sensed data

NASA Astrophysics Data System (ADS)

Imvitthaya, Chomchid; Honda, Kiyoshi; Lertlum, Surat; Tangtham, Nipon

2011-01-01

In this paper, we present the results of a net primary production (NPP) modeling of teak (Tectona grandis Lin F.), an important species in tropical deciduous forests. The biome-biogeochemical cycles or Biome-BGC model was calibrated to estimate net NPP through the inverse modeling approach. A genetic algorithm (GA) was linked with Biome-BGC to determine the optimal ecophysiological model parameters. The Biome-BGC was calibrated by adjusting the ecophysiological model parameters to fit the simulated LAI to the satellite LAI (SPOT-Vegetation), and the best fitness confirmed the high accuracy of generated ecophysioligical parameter from GA. The modeled NPP, using optimized parameters from GA as input data, was evaluated using daily NPP derived by the MODIS satellite and the annual field data in northern Thailand. The results showed that NPP obtained using the optimized ecophysiological parameters were more accurate than those obtained using default literature parameterization. This improvement occurred mainly because the model's optimized parameters reduced the bias by reducing systematic underestimation in the model. These Biome-BGC results can be effectively applied in teak forests in tropical areas. The study proposes a more effective method of using GA to determine ecophysiological parameters at the site level and represents a first step toward the analysis of the carbon budget of teak plantations at the regional scale.

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

PubMed Central

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

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

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 parameters.

Application and optimization of input parameter spaces in mass flow modelling: a case study with r.randomwalk and r.ranger

NASA Astrophysics Data System (ADS)

Krenn, Julia; Zangerl, Christian; Mergili, Martin

2017-04-01

r.randomwalk is a GIS-based, multi-functional, conceptual open source model application for forward and backward analyses of the propagation of mass flows. It relies on a set of empirically derived, uncertain input parameters. In contrast to many other tools, r.randomwalk accepts input parameter ranges (or, in case of two or more parameters, spaces) in order to directly account for these uncertainties. Parameter spaces represent a possibility to withdraw from discrete input values which in most cases are likely to be off target. r.randomwalk automatically performs multiple calculations with various parameter combinations in a given parameter space, resulting in the impact indicator index (III) which denotes the fraction of parameter value combinations predicting an impact on a given pixel. Still, there is a need to constrain the parameter space used for a certain process type or magnitude prior to performing forward calculations. This can be done by optimizing the parameter space in terms of bringing the model results in line with well-documented past events. As most existing parameter optimization algorithms are designed for discrete values rather than for ranges or spaces, the necessity for a new and innovative technique arises. The present study aims at developing such a technique and at applying it to derive guiding parameter spaces for the forward calculation of rock avalanches through back-calculation of multiple events. In order to automatize the work flow we have designed r.ranger, an optimization and sensitivity analysis tool for parameter spaces which can be directly coupled to r.randomwalk. With r.ranger we apply a nested approach where the total value range of each parameter is divided into various levels of subranges. All possible combinations of subranges of all parameters are tested for the performance of the associated pattern of III. Performance indicators are the area under the ROC curve (AUROC) and the factor of conservativeness (FoC). This strategy is best demonstrated for two input parameters, but can be extended arbitrarily. We use a set of small rock avalanches from western Austria, and some larger ones from Canada and New Zealand, to optimize the basal friction coefficient and the mass-to-drag ratio of the two-parameter friction model implemented with r.randomwalk. Thereby we repeat the optimization procedure with conservative and non-conservative assumptions of a set of complementary parameters and with different raster cell sizes. Our preliminary results indicate that the model performance in terms of AUROC achieved with broad parameter spaces is hardly surpassed by the performance achieved with narrow parameter spaces. However, broad spaces may result in very conservative or very non-conservative predictions. Therefore, guiding parameter spaces have to be (i) broad enough to avoid the risk of being off target; and (ii) narrow enough to ensure a reasonable level of conservativeness of the results. The next steps will consist in (i) extending the study to other types of mass flow processes in order to support forward calculations using r.randomwalk; and (ii) in applying the same strategy to the more complex, dynamic model r.avaflow.

Polishing parameter optimization for end-surface of chalcogenide glass fiber connector

NASA Astrophysics Data System (ADS)

Guo, Fangxia; Dai, Shixun; Tang, Junzhou; Wang, Xunsi; Li, Xing; Xu, Yinsheng; Wu, Yuehao; Liu, Zijun

2017-11-01

We have investigated the optimization parameters for polishing end-surface of chalcogenide glass fiber connector in the paper. Six SiC abrasive particles of different sizes were used to polish the fiber in order of size from large to small. We analyzed the effects of polishing parameters such as particle sizes, grinding speeds and polishing durations on the quality of the fiber end surface and determined the optimized polishing parameters. We found that, high-quality fiber end surface can be achieved using only three different SiC abrasives. The surface roughness of the final ChG fiber end surface is about 48 nm without any scratches, spots and cracks. Such polishing processes could reduce the average insertion loss of the connector to about 3.4 dB.

Optimal experimental design for parameter estimation of a cell signaling model.

PubMed

Bandara, Samuel; Schlöder, Johannes P; Eils, Roland; Bock, Hans Georg; Meyer, Tobias

2009-11-01

Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP(3) lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay.

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Multi-objective optimization of composite structures. A review

NASA Astrophysics Data System (ADS)

Teters, G. A.; Kregers, A. F.

1996-05-01

Studies performed on the optimization of composite structures by coworkers of the Institute of Polymers Mechanics of the Latvian Academy of Sciences in recent years are reviewed. The possibility of controlling the geometry and anisotropy of laminar composite structures will make it possible to design articles that best satisfy the requirements established for them. Conflicting requirements such as maximum bearing capacity, minimum weight and/or cost, prescribed thermal conductivity and thermal expansion, etc. usually exist for optimal design. This results in the multi-objective compromise optimization of structures. Numerical methods have been developed for solution of problems of multi-objective optimization of composite structures; parameters of the structure of the reinforcement and the geometry of the design are assigned as controlling parameters. Programs designed to run on personal computers have been compiled for multi-objective optimization of the properties of composite materials, plates, and shells. Solutions are obtained for both linear and nonlinear models. The programs make it possible to establish the Pareto compromise region and special multicriterial solutions. The problem of the multi-objective optimization of the elastic moduli of a spatially reinforced fiberglass with stochastic stiffness parameters has been solved. The region of permissible solutions and the Pareto region have been found for the elastic moduli. The dimensions of the scatter ellipse have been determined for a multidimensional Gaussian probability distribution where correlation between the composite's properties being optimized are accounted for. Two types of problems involving the optimization of a laminar rectangular composite plate are considered: the plate is considered elastic and anisotropic in the first case, and viscoelastic properties are accounted for in the second. The angle of reinforcement and the relative amount of fibers in the longitudinal direction are controlling parameters. The optimized properties are the critical stresses, thermal conductivity, and thermal expansion. The properties of a plate are determined by the properties of the components in the composite, eight of which are stochastic. The region of multi-objective compromise solutions is presented, and the parameters of the scatter ellipses of the properties are given.

Cast Off expansion plan by rapid improvement through Optimization tool design, Tool Parameters and using Six Sigma’s ECRS Technique

NASA Astrophysics Data System (ADS)

Gopalakrishnan, T.; Saravanan, R.

2017-03-01

Powerful management concepts step-up the quality of the product, time saving in producing the product thereby increase the production rate, improves tools and techniques, work culture, work place and employee motivation and morale. In this paper discussed about the case study of optimizing the tool design, tool parameters to cast off expansion plan according ECRS technique. The proposed designs and optimal tool parameters yielded best results and meet the customer demand without expansion plan. Hence the work yielded huge savings of money (direct and indirect cost), time and improved the motivation and more of employees significantly.

Warpage minimization on wheel caster by optimizing process parameters using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, it is important to keep the productivity increase constantly with least of waste produced such as warpage defect. Thus, this study is concerning on minimizing warpage defect on wheel caster part. Apart from eliminating product wastes, this project also giving out best optimization techniques using response surface methodology. This research studied on five parameters A-packing pressure, B-packing time, C-mold temperature, D-melting temperature and E-cooling time. The optimization showed that packing pressure is the most significant parameter. Warpage have been improved 42.64% from 0.6524 mm to 0.3742mm.

Error reduction and parameter optimization of the TAPIR method for fast T1 mapping.

PubMed

Zaitsev, M; Steinhoff, S; Shah, N J

2003-06-01

A methodology is presented for the reduction of both systematic and random errors in T(1) determination using TAPIR, a Look-Locker-based fast T(1) mapping technique. The relations between various sequence parameters were carefully investigated in order to develop recipes for choosing optimal sequence parameters. Theoretical predictions for the optimal flip angle were verified experimentally. Inversion pulse imperfections were identified as the main source of systematic errors in T(1) determination with TAPIR. An effective remedy is demonstrated which includes extension of the measurement protocol to include a special sequence for mapping the inversion efficiency itself. Copyright 2003 Wiley-Liss, Inc.

Least squares QR-based decomposition provides an efficient way of computing optimal regularization parameter in photoacoustic tomography.

PubMed

Shaw, Calvin B; Prakash, Jaya; Pramanik, Manojit; Yalavarthy, Phaneendra K

2013-08-01

A computationally efficient approach that computes the optimal regularization parameter for the Tikhonov-minimization scheme is developed for photoacoustic imaging. This approach is based on the least squares-QR decomposition which is a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution enabled via finding an optimal regularization parameter. The computational efficiency and performance of the proposed method are shown using a test case of numerical blood vessel phantom, where the initial pressure is exactly known for quantitative comparison.

Optimization of GATE and PHITS Monte Carlo code parameters for spot scanning proton beam based on simulation with FLUKA general-purpose code

NASA Astrophysics Data System (ADS)

Kurosu, Keita; Das, Indra J.; Moskvin, Vadim P.

2016-01-01

Spot scanning, owing to its superior dose-shaping capability, provides unsurpassed dose conformity, in particular for complex targets. However, the robustness of the delivered dose distribution and prescription has to be verified. Monte Carlo (MC) simulation has the potential to generate significant advantages for high-precise particle therapy, especially for medium containing inhomogeneities. However, the inherent choice of computational parameters in MC simulation codes of GATE, PHITS and FLUKA that is observed for uniform scanning proton beam needs to be evaluated. This means that the relationship between the effect of input parameters and the calculation results should be carefully scrutinized. The objective of this study was, therefore, to determine the optimal parameters for the spot scanning proton beam for both GATE and PHITS codes by using data from FLUKA simulation as a reference. The proton beam scanning system of the Indiana University Health Proton Therapy Center was modeled in FLUKA, and the geometry was subsequently and identically transferred to GATE and PHITS. Although the beam transport is managed by spot scanning system, the spot location is always set at the center of a water phantom of 600 × 600 × 300 mm3, which is placed after the treatment nozzle. The percentage depth dose (PDD) is computed along the central axis using 0.5 × 0.5 × 0.5 mm3 voxels in the water phantom. The PDDs and the proton ranges obtained with several computational parameters are then compared to those of FLUKA, and optimal parameters are determined from the accuracy of the proton range, suppressed dose deviation, and computational time minimization. Our results indicate that the optimized parameters are different from those for uniform scanning, suggesting that the gold standard for setting computational parameters for any proton therapy application cannot be determined consistently since the impact of setting parameters depends on the proton irradiation technique. We therefore conclude that customization parameters must be set with reference to the optimized parameters of the corresponding irradiation technique in order to render them useful for achieving artifact-free MC simulation for use in computational experiments and clinical treatments.

Parameterization of Shape and Compactness in Object-based Image Classification Using Quickbird-2 Imagery

NASA Astrophysics Data System (ADS)

Tonbul, H.; Kavzoglu, T.

2016-12-01

In recent years, object based image analysis (OBIA) has spread out and become a widely accepted technique for the analysis of remotely sensed data. OBIA deals with grouping pixels into homogenous objects based on spectral, spatial and textural features of contiguous pixels in an image. The first stage of OBIA, named as image segmentation, is the most prominent part of object recognition. In this study, multiresolution segmentation, which is a region-based approach, was employed to construct image objects. In the application of multi-resolution, three parameters, namely shape, compactness and scale must be set by the analyst. Segmentation quality remarkably influences the fidelity of the thematic maps and accordingly the classification accuracy. Therefore, it is of great importance to search and set optimal values for the segmentation parameters. In the literature, main focus has been on the definition of scale parameter, assuming that the effect of shape and compactness parameters is limited in terms of achieved classification accuracy. The aim of this study is to deeply analyze the influence of shape/compactness parameters by varying their values while using the optimal scale parameter determined by the use of Estimation of Scale Parameter (ESP-2) approach. A pansharpened Qickbird-2 image covering Trabzon, Turkey was employed to investigate the objectives of the study. For this purpose, six different combinations of shape/compactness were utilized to make deductions on the behavior of shape and compactness parameters and optimal setting for all parameters as a whole. Objects were assigned to classes using nearest neighbor classifier in all segmentation observations and equal number of pixels was randomly selected to calculate accuracy metrics. The highest overall accuracy (92.3%) was achieved by setting the shape/compactness criteria to 0.3/0.3. The results of this study indicate that shape/compactness parameters can have significant effect on classification accuracy with 4% change in overall accuracy. Also, statistical significance of differences in accuracy was tested using the McNemar's test and found that the difference between poor and optimal setting of shape/compactness parameters was statistically significant, suggesting a search for optimal parameterization instead of default setting.

Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

2016-05-01

One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.

Parametric study of a canard-configured transport using conceptual design optimization

NASA Technical Reports Server (NTRS)

Arbuckle, P. D.; Sliwa, S. M.

1985-01-01

Constrained-parameter optimization is used to perform optimal conceptual design of both canard and conventional configurations of a medium-range transport. A number of design constants and design constraints are systematically varied to compare the sensitivities of canard and conventional configurations to a variety of technology assumptions. Main-landing-gear location and canard surface high-lift performance are identified as critical design parameters for a statically stable, subsonic, canard-configured transport.

Evaluation of laser cutting process with auxiliary gas pressure by soft computing approach

NASA Astrophysics Data System (ADS)

Lazov, Lyubomir; Nikolić, Vlastimir; Jovic, Srdjan; Milovančević, Miloš; Deneva, Heristina; Teirumenieka, Erika; Arsic, Nebojsa

2018-06-01

Evaluation of the optimal laser cutting parameters is very important for the high cut quality. This is highly nonlinear process with different parameters which is the main challenge in the optimization process. Data mining methodology is one of most versatile method which can be used laser cutting process optimization. Support vector regression (SVR) procedure is implemented since it is a versatile and robust technique for very nonlinear data regression. The goal in this study was to determine the optimal laser cutting parameters to ensure robust condition for minimization of average surface roughness. Three cutting parameters, the cutting speed, the laser power, and the assist gas pressure, were used in the investigation. As a laser type TruLaser 1030 technological system was used. Nitrogen as an assisted gas was used in the laser cutting process. As the data mining method, support vector regression procedure was used. Data mining prediction accuracy was very high according the coefficient (R2) of determination and root mean square error (RMSE): R2 = 0.9975 and RMSE = 0.0337. Therefore the data mining approach could be used effectively for determination of the optimal conditions of the laser cutting process.

Statistical optimization of bioprocess parameters for enhanced gallic acid production from coffee pulp tannins by Penicillium verrucosum.

PubMed

Bhoite, Roopali N; Navya, P N; Murthy, Pushpa S

2013-01-01

Gallic acid (3,4,5-trihydroxybenzoic acid) was produced by microbial biotransformation of coffee pulp tannins by Penicillium verrucosum. Gallic acid production was optimized using response surface methodology (RSM) based on central composite rotatable design. Process parameters such as pH, moisture, and fermentation period were considered for optimization. Among the various fungi isolated from coffee by-products, Penicillium verrucosum produced 35.23 µg/g of gallic acid on coffee pulp as sole carbon source in solid-state fermentation. The optimum values of the parameters obtained from the RSM were pH 3.32, moisture 58.40%, and fermentation period of 96 hr. Gallic acid production with an increase of 4.6-fold was achieved upon optimization of the process parameters. The results optimized could be translated to 1-kg tray fermentation. High-performance liquid chromatography (HPLC) analysis and spectral studies such as mass spectroscopy (MS) and (1)H-nuclear magnetic resonance (NMR) confirmed that the bioactive compound isolated was gallic acid. Thus, coffee pulp, which is available in enormous quantity, could be used for the production of value-added products that can find avenues in food, pharmaceutical, and chemical industries.

A new real-time guidance strategy for aerodynamic ascent flight

NASA Astrophysics Data System (ADS)

Yamamoto, Takayuki; Kawaguchi, Jun'ichiro

2007-12-01

Reusable launch vehicles are conceived to constitute the future space transportation system. If these vehicles use air-breathing propulsion and lift taking-off horizontally, the optimal steering for these vehicles exhibits completely different behavior from that in conventional rockets flight. In this paper, the new guidance strategy is proposed. This method derives from the optimality condition as for steering and an analysis concludes that the steering function takes the form comprised of Linear and Logarithmic terms, which include only four parameters. The parameter optimization of this method shows the acquired terminal horizontal velocity is almost same with that obtained by the direct numerical optimization. This supports the parameterized Liner Logarithmic steering law. And here is shown that there exists a simple linear relation between the terminal states and the parameters to be corrected. The relation easily makes the parameters determined to satisfy the terminal boundary conditions in real-time. The paper presents the guidance results for the practical application cases. The results show the guidance is well performed and satisfies the terminal boundary conditions specified. The strategy built and presented here does guarantee the robust solution in real-time excluding any optimization process, and it is found quite practical.

[Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

PubMed

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.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Ring rolling process simulation for geometry optimization

NASA Astrophysics Data System (ADS)

Franchi, Rodolfo; Del Prete, Antonio; Donatiello, Iolanda; Calabrese, Maurizio

2017-10-01

Ring Rolling is a complex hot forming process where different rolls are involved in the production of seamless rings. Since each roll must be independently controlled, different speed laws must be set; usually, in the industrial environment, a milling curve is introduced to monitor the shape of the workpiece during the deformation in order to ensure the correct ring production. In the present paper a ring rolling process has been studied and optimized in order to obtain anular components to be used in aerospace applications. In particular, the influence of process input parameters (feed rate of the mandrel and angular speed of main roll) on geometrical features of the final ring has been evaluated. For this purpose, a three-dimensional finite element model for HRR (Hot Ring Rolling) has been implemented in SFTC DEFORM V11. The FEM model has been used to formulate a proper optimization problem. The optimization procedure has been implemented in the commercial software DS ISight in order to find the combination of process parameters which allows to minimize the percentage error of each obtained dimension with respect to its nominal value. The software allows to find the relationship between input and output parameters applying Response Surface Methodology (RSM), by using the exact values of output parameters in the control points of the design space explored through FEM simulation. Once this relationship is known, the values of the output parameters can be calculated for each combination of the input parameters. After the calculation of the response surfaces for the selected output parameters, an optimization procedure based on Genetic Algorithms has been applied. At the end, the error between each obtained dimension and its nominal value has been minimized. The constraints imposed were the maximum values of standard deviations of the dimensions obtained for the final ring.

[Optimization of end-tool parameters based on robot hand-eye calibration].

PubMed

Zhang, Lilong; Cao, Tong; Liu, Da

2017-04-01

A new one-time registration method was developed in this research for hand-eye calibration of a surgical robot to simplify the operation process and reduce the preparation time. And a new and practical method is introduced in this research to optimize the end-tool parameters of the surgical robot based on analysis of the error sources in this registration method. In the process with one-time registration method, firstly a marker on the end-tool of the robot was recognized by a fixed binocular camera, and then the orientation and position of the marker were calculated based on the joint parameters of the robot. Secondly the relationship between the camera coordinate system and the robot base coordinate system could be established to complete the hand-eye calibration. Because of manufacturing and assembly errors of robot end-tool, an error equation was established with the transformation matrix between the robot end coordinate system and the robot end-tool coordinate system as the variable. Numerical optimization was employed to optimize end-tool parameters of the robot. The experimental results showed that the one-time registration method could significantly improve the efficiency of the robot hand-eye calibration compared with the existing methods. The parameter optimization method could significantly improve the absolute positioning accuracy of the one-time registration method. The absolute positioning accuracy of the one-time registration method can meet the requirements of the clinical surgery.

Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach.

PubMed

Stroet, Martin; Koziara, Katarzyna B; Malde, Alpeshkumar K; Mark, Alan E

2017-12-12

A general method for parametrizing atomic interaction functions is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters (parameter space mapping). The consideration of surfaces in parameter space as opposed to local values or gradients leads to a better understanding of the relationships between the parameters being optimized and a given set of target data. This in turn enables for a range of target data from multiple molecules to be combined in a robust manner and for the optimal region of parameter space to be trivially identified. The effectiveness of the approach is illustrated by using the method to refine the chlorine 6-12 Lennard-Jones parameters against experimental solvation free enthalpies in water and hexane as well as the density and heat of vaporization of the liquid at atmospheric pressure for a set of 10 aromatic-chloro compounds simultaneously. Single-step perturbation is used to efficiently calculate solvation free enthalpies for a wide range of parameter combinations. The capacity of this approach to parametrize accurate and transferrable force fields is discussed.

SPOTting Model Parameters Using a Ready-Made Python Package

NASA Astrophysics Data System (ADS)

Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

2017-04-01

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

SPOTting Model Parameters Using a Ready-Made Python Package.

PubMed

Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

2015-01-01

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

SPOTting Model Parameters Using a Ready-Made Python Package

PubMed Central

Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

2015-01-01

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function. PMID:26680783

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

PubMed

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

2018-06-12

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

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

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

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

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

Zarepisheh, M; Li, R; Xing, L

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) andmore » 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 quality of resultant treatment plans as compared with conventional VMAT or IMRT treatments.« less

Parameter Optimization for Turbulent Reacting Flows Using Adjoints

NASA Astrophysics Data System (ADS)

Lapointe, Caelan; Hamlington, Peter E.

2017-11-01

The formulation of a new adjoint solver for topology optimization of turbulent reacting flows is presented. This solver provides novel configurations (e.g., geometries and operating conditions) based on desired system outcomes (i.e., objective functions) for complex reacting flow problems of practical interest. For many such problems, it would be desirable to know optimal values of design parameters (e.g., physical dimensions, fuel-oxidizer ratios, and inflow-outflow conditions) prior to real-world manufacture and testing, which can be expensive, time-consuming, and dangerous. However, computational optimization of these problems is made difficult by the complexity of most reacting flows, necessitating the use of gradient-based optimization techniques in order to explore a wide design space at manageable computational cost. The adjoint method is an attractive way to obtain the required gradients, because the cost of the method is determined by the dimension of the objective function rather than the size of the design space. Here, the formulation of a novel solver is outlined that enables gradient-based parameter optimization of turbulent reacting flows using the discrete adjoint method. Initial results and an outlook for future research directions are provided.

The solution of private problems for optimization heat exchangers parameters

NASA Astrophysics Data System (ADS)

Melekhin, A.

2017-11-01

The relevance of the topic due to the decision of problems of the economy of resources in heating systems of buildings. To solve this problem we have developed an integrated method of research which allows solving tasks on optimization of parameters of heat exchangers. This method decides multicriteria optimization problem with the program nonlinear optimization on the basis of software with the introduction of an array of temperatures obtained using thermography. The author have developed a mathematical model of process of heat exchange in heat exchange surfaces of apparatuses with the solution of multicriteria optimization problem and check its adequacy to the experimental stand in the visualization of thermal fields, an optimal range of managed parameters influencing the process of heat exchange with minimal metal consumption and the maximum heat output fin heat exchanger, the regularities of heat exchange process with getting generalizing dependencies distribution of temperature on the heat-release surface of the heat exchanger vehicles, defined convergence of the results of research in the calculation on the basis of theoretical dependencies and solving mathematical model.

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

NASA Astrophysics Data System (ADS)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

Characterization and Optimization Design of the Polymer-Based Capacitive Micro-Arrayed Ultrasonic Transducer

NASA Astrophysics Data System (ADS)

Chiou, De-Yi; Chen, Mu-Yueh; Chang, Ming-Wei; Deng, Hsu-Cheng

2007-11-01

This study constructs an electromechanical finite element model of the polymer-based capacitive micro-arrayed ultrasonic transducer (P-CMUT). The electrostatic-structural coupled-field simulations are performed to investigate the operational characteristics, such as collapse voltage and resonant frequency. The numerical results are found to be in good agreement with experimental observations. The study of influence of each defined parameter on the collapse voltage and resonant frequency are also presented. To solve some conflict problems in diversely physical fields, an integrated design method is developed to optimize the geometric parameters of the P-CMUT. The optimization search routine conducted using the genetic algorithm (GA) is connected with the commercial FEM software ANSYS to obtain the best design variable using multi-objective functions. The results show that the optimal parameter values satisfy the conflicting objectives, namely to minimize the collapse voltage while simultaneously maintaining a customized frequency. Overall, the present result indicates that the combined FEM/GA optimization scheme provides an efficient and versatile approach of optimization design of the P-CMUT.

Iterative optimization method for design of quantitative magnetization transfer imaging experiments.

PubMed

Levesque, Ives R; Sled, John G; Pike, G Bruce

2011-09-01

Quantitative magnetization transfer imaging (QMTI) using spoiled gradient echo sequences with pulsed off-resonance saturation can be a time-consuming technique. A method is presented for selection of an optimum experimental design for quantitative magnetization transfer imaging based on the iterative reduction of a discrete sampling of the Z-spectrum. The applicability of the technique is demonstrated for human brain white matter imaging at 1.5 T and 3 T, and optimal designs are produced to target specific model parameters. The optimal number of measurements and the signal-to-noise ratio required for stable parameter estimation are also investigated. In vivo imaging results demonstrate that this optimal design approach substantially improves parameter map quality. The iterative method presented here provides an advantage over free form optimal design methods, in that pragmatic design constraints are readily incorporated. In particular, the presented method avoids clustering and repeated measures in the final experimental design, an attractive feature for the purpose of magnetization transfer model validation. The iterative optimal design technique is general and can be applied to any method of quantitative magnetization transfer imaging. Copyright © 2011 Wiley-Liss, Inc.

Development and application of computer assisted optimal method for treatment of femoral neck fracture.

PubMed

Wang, Monan; Zhang, Kai; Yang, Ning

2018-04-09

To help doctors decide their treatment from the aspect of mechanical analysis, the work built a computer assisted optimal system for treatment of femoral neck fracture oriented to clinical application. The whole system encompassed the following three parts: Preprocessing module, finite element mechanical analysis module, post processing module. Preprocessing module included parametric modeling of bone, parametric modeling of fracture face, parametric modeling of fixed screw and fixed position and input and transmission of model parameters. Finite element mechanical analysis module included grid division, element type setting, material property setting, contact setting, constraint and load setting, analysis method setting and batch processing operation. Post processing module included extraction and display of batch processing operation results, image generation of batch processing operation, optimal program operation and optimal result display. The system implemented the whole operations from input of fracture parameters to output of the optimal fixed plan according to specific patient real fracture parameter and optimal rules, which demonstrated the effectiveness of the system. Meanwhile, the system had a friendly interface, simple operation and could improve the system function quickly through modifying single module.

Parameter Estimation and Sensitivity Analysis of an Urban Surface Energy Balance Parameterization at a Tropical Suburban Site

NASA Astrophysics Data System (ADS)

Harshan, S.; Roth, M.; Velasco, E.

2014-12-01

Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model validation studies to identify inherent deficiencies in model physics.

Time-saving design of experiment protocol for optimization of LC-MS data processing in metabolomic approaches.

PubMed

Zheng, Hong; Clausen, Morten Rahr; Dalsgaard, Trine Kastrup; Mortensen, Grith; Bertram, Hanne Christine

2013-08-06

We describe a time-saving protocol for the processing of LC-MS-based metabolomics data by optimizing parameter settings in XCMS and threshold settings for removing noisy and low-intensity peaks using design of experiment (DoE) approaches including Plackett-Burman design (PBD) for screening and central composite design (CCD) for optimization. A reliability index, which is based on evaluation of the linear response to a dilution series, was used as a parameter for the assessment of data quality. After identifying the significant parameters in the XCMS software by PBD, CCD was applied to determine their values by maximizing the reliability and group indexes. Optimal settings by DoE resulted in improvements of 19.4% and 54.7% in the reliability index for a standard mixture and human urine, respectively, as compared with the default setting, and a total of 38 h was required to complete the optimization. Moreover, threshold settings were optimized by using CCD for further improvement. The approach combining optimal parameter setting and the threshold method improved the reliability index about 9.5 times for a standards mixture and 14.5 times for human urine data, which required a total of 41 h. Validation results also showed improvements in the reliability index of about 5-7 times even for urine samples from different subjects. It is concluded that the proposed methodology can be used as a time-saving approach for improving the processing of LC-MS-based metabolomics data.

Optimal experimental designs for the estimation of thermal properties of composite materials

NASA Technical Reports Server (NTRS)

Scott, Elaine P.; Moncman, Deborah A.

1994-01-01

Reliable estimation of thermal properties is extremely important in the utilization of new advanced materials, such as composite materials. The accuracy of these estimates can be increased if the experiments are designed carefully. The objectives of this study are to design optimal experiments to be used in the prediction of these thermal properties and to then utilize these designs in the development of an estimation procedure to determine the effective thermal properties (thermal conductivity and volumetric heat capacity). The experiments were optimized by choosing experimental parameters that maximize the temperature derivatives with respect to all of the unknown thermal properties. This procedure has the effect of minimizing the confidence intervals of the resulting thermal property estimates. Both one-dimensional and two-dimensional experimental designs were optimized. A heat flux boundary condition is required in both analyses for the simultaneous estimation of the thermal properties. For the one-dimensional experiment, the parameters optimized were the heating time of the applied heat flux, the temperature sensor location, and the experimental time. In addition to these parameters, the optimal location of the heat flux was also determined for the two-dimensional experiments. Utilizing the optimal one-dimensional experiment, the effective thermal conductivity perpendicular to the fibers and the effective volumetric heat capacity were then estimated for an IM7-Bismaleimide composite material. The estimation procedure used is based on the minimization of a least squares function which incorporates both calculated and measured temperatures and allows for the parameters to be estimated simultaneously.

Results of an integrated structure-control law design sensitivity analysis

NASA Technical Reports Server (NTRS)

Gilbert, Michael G.

1988-01-01

Next generation air and space vehicle designs are driven by increased performance requirements, demanding a high level of design integration between traditionally separate design disciplines. Interdisciplinary analysis capabilities have been developed, for aeroservoelastic aircraft and large flexible spacecraft control for instance, but the requisite integrated design methods are only beginning to be developed. One integrated design method which has received attention is based on hierarchal problem decompositions, optimization, and design sensitivity analyses. This paper highlights a design sensitivity analysis method for Linear Quadratic Cost, Gaussian (LQG) optimal control laws, which predicts change in the optimal control law due to changes in fixed problem parameters using analytical sensitivity equations. Numerical results of a design sensitivity analysis for a realistic aeroservoelastic aircraft example are presented. In this example, the sensitivity of the optimally controlled aircraft's response to various problem formulation and physical aircraft parameters is determined. These results are used to predict the aircraft's new optimally controlled response if the parameter was to have some other nominal value during the control law design process. The sensitivity results are validated by recomputing the optimal control law for discrete variations in parameters, computing the new actual aircraft response, and comparing with the predicted response. These results show an improvement in sensitivity accuracy for integrated design purposes over methods which do not include changess in the optimal control law. Use of the analytical LQG sensitivity expressions is also shown to be more efficient that finite difference methods for the computation of the equivalent sensitivity information.

SPECT System Optimization Against A Discrete Parameter Space

PubMed Central

Meng, L. J.; Li, N.

2013-01-01

In this paper, we present an analytical approach for optimizing the design of a static SPECT system or optimizing the sampling strategy with a variable/adaptive SPECT imaging hardware against an arbitrarily given set of system parameters. This approach has three key aspects. First, it is designed to operate over a discretized system parameter space. Second, we have introduced an artificial concept of virtual detector as the basic building block of an imaging system. With a SPECT system described as a collection of the virtual detectors, one can convert the task of system optimization into a process of finding the optimum imaging time distribution (ITD) across all virtual detectors. Thirdly, the optimization problem (finding the optimum ITD) could be solved with a block-iterative approach or other non-linear optimization algorithms. In essence, the resultant optimum ITD could provide a quantitative measure of the relative importance (or effectiveness) of the virtual detectors and help to identify the system configuration or sampling strategy that leads to an optimum imaging performance. Although we are using SPECT imaging as a platform to demonstrate the system optimization strategy, this development also provides a useful framework for system optimization problems in other modalities, such as positron emission tomography (PET) and X-ray computed tomography (CT) [1, 2]. PMID:23587609

Petermann I and II spot size: Accurate semi analytical description involving Nelder-Mead method of nonlinear unconstrained optimization and three parameter fundamental modal field

NASA Astrophysics Data System (ADS)

Roy Choudhury, Raja; Roy Choudhury, Arundhati; Kanti Ghose, Mrinal

2013-01-01

A semi-analytical model with three optimizing parameters and a novel non-Gaussian function as the fundamental modal field solution has been proposed to arrive at an accurate solution to predict various propagation parameters of graded-index fibers with less computational burden than numerical methods. In our semi analytical formulation the optimization of core parameter U which is usually uncertain, noisy or even discontinuous, is being calculated by Nelder-Mead method of nonlinear unconstrained minimizations as it is an efficient and compact direct search method and does not need any derivative information. Three optimizing parameters are included in the formulation of fundamental modal field of an optical fiber to make it more flexible and accurate than other available approximations. Employing variational technique, Petermann I and II spot sizes have been evaluated for triangular and trapezoidal-index fibers with the proposed fundamental modal field. It has been demonstrated that, the results of the proposed solution identically match with the numerical results over a wide range of normalized frequencies. This approximation can also be used in the study of doped and nonlinear fiber amplifier.

Computation of physiological human vocal fold parameters by mathematical optimization of a biomechanical model

PubMed Central

Yang, Anxiong; Stingl, Michael; Berry, David A.; Lohscheller, Jörg; Voigt, Daniel; Eysholdt, Ulrich; Döllinger, Michael

2011-01-01

With the use of an endoscopic, high-speed camera, vocal fold dynamics may be observed clinically during phonation. However, observation and subjective judgment alone may be insufficient for clinical diagnosis and documentation of improved vocal function, especially when the laryngeal disease lacks any clear morphological presentation. In this study, biomechanical parameters of the vocal folds are computed by adjusting the corresponding parameters of a three-dimensional model until the dynamics of both systems are similar. First, a mathematical optimization method is presented. Next, model parameters (such as pressure, tension and masses) are adjusted to reproduce vocal fold dynamics, and the deduced parameters are physiologically interpreted. Various combinations of global and local optimization techniques are attempted. Evaluation of the optimization procedure is performed using 50 synthetically generated data sets. The results show sufficient reliability, including 0.07 normalized error, 96% correlation, and 91% accuracy. The technique is also demonstrated on data from human hemilarynx experiments, in which a low normalized error (0.16) and high correlation (84%) values were achieved. In the future, this technique may be applied to clinical high-speed images, yielding objective measures with which to document improved vocal function of patients with voice disorders. PMID:21877808

Measurement of damping and temperature: Precision bounds in Gaussian dissipative channels

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

Monras, Alex; Illuminati, Fabrizio

2011-01-15

We present a comprehensive analysis of the performance of different classes of Gaussian states in the estimation of Gaussian phase-insensitive dissipative channels. In particular, we investigate the optimal estimation of the damping constant and reservoir temperature. We show that, for two-mode squeezed vacuum probe states, the quantum-limited accuracy of both parameters can be achieved simultaneously. Moreover, we show that for both parameters two-mode squeezed vacuum states are more efficient than coherent, thermal, or single-mode squeezed states. This suggests that at high-energy regimes, two-mode squeezed vacuum states are optimal within the Gaussian setup. This optimality result indicates a stronger form ofmore » compatibility for the estimation of the two parameters. Indeed, not only the minimum variance can be achieved at fixed probe states, but also the optimal state is common to both parameters. Additionally, we explore numerically the performance of non-Gaussian states for particular parameter values to find that maximally entangled states within d-dimensional cutoff subspaces (d{<=}6) perform better than any randomly sampled states with similar energy. However, we also find that states with very similar performance and energy exist with much less entanglement than the maximally entangled ones.« less

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J. D.

2017-09-01

Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes.

Optimizing LED lighting for space plant growth unit: Joint effects of photon flux density, red to white ratios and intermittent light pulses.

PubMed

Avercheva, O V; Berkovich, Yu A; Konovalova, I O; Radchenko, S G; Lapach, S N; Bassarskaya, E M; Kochetova, G V; Zhigalova, T V; Yakovleva, O S; Tarakanov, I G

2016-11-01

The aim of this work were to choose a quantitative optimality criterion for estimating the quality of plant LED lighting regimes inside space greenhouses and to construct regression models of crop productivity and the optimality criterion depending on the level of photosynthetic photon flux density (PPFD), the proportion of the red component in the light spectrum and the duration of the duty cycle (Chinese cabbage Brassica сhinensis L. as an example). The properties of the obtained models were described in the context of predicting crop dry weight and the optimality criterion behavior when varying plant lighting parameters. Results of the fractional 3-factor experiment demonstrated the share of the PPFD level participation in the crop dry weight accumulation was 84.4% at almost any combination of other lighting parameters, but when PPFD value increased up to 500µmol m -2 s -1 the pulse light and supplemental light from red LEDs could additionally increase crop productivity. Analysis of the optimality criterion response to variation of lighting parameters showed that the maximum coordinates were the following: PPFD = 500µmol m -2 s -1 , about 70%-proportion of the red component of the light spectrum (PPFD LEDred /PPFD LEDwhite = 1.5) and the duty cycle with a period of 501µs. Thus, LED crop lighting with these parameters was optimal for achieving high crop productivity and for efficient use of energy in the given range of lighting parameter values. Copyright © 2016 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

[Impurity removal technology of Tongan injection in liquid preparation process].

PubMed

Yang, Xu-fang; Wang, Xiu-hai; Bai, Wei-rong; Kang, Xiao-dong; Liu, Jun-chao; Wu, Yun; Xiao, Wei

2015-08-01

In order to effectively remove the invalid impurities in Tongan injection, optimize the optimal parameters of the impurity removal technology of liquid mixing process, in this paper, taking Tongan injection as the research object, with the contents of celandine alkali, and sinomenine, solids reduction efficiency, and related substances inspection as the evaluation indexes, the removal of impurities and related substances by the combined process of refrigeration, coction and activated carbon adsorption were investigated, the feasibility of the impurity removal method was definited and the process parameters were optimized. The optimized process parameters were as follows: refrigerated for 36 h, boiled for 15 min, activated carbon dosage of 0.3%, temperature 100 degrees C, adsorption time 10 min. It can effectively remove the tannin, and other impurities, thus ensure the quality and safety of products.

Optimization of subcritical water extraction parameters of antioxidant polyphenols from sea buckthorn (Hippophaë rhamnoides L.) seed residue.

PubMed

Gong, Ying; Zhang, Xiaofei; He, Li; Yan, Qiuli; Yuan, Fang; Gao, Yanxiang

2015-03-01

Polyphenols was extracted with subcritical water from the sea buckthorn seed residue (after oil recovery), and the extraction parameters were optimized using response surface methodology (RSM). The independent processing variables were extraction temperature, extraction time and the ratio of water to solid. The optimal extraction parameters for the extracts with highest ABTS radical scavenging activity were 120 °C, 36 min and the water to solid ratio of 20, and the maximize antioxidant capacity value was 32.42 mmol Trolox equivalent (TE)/100 g. Under the optimal conditions, the yield of total phenolics, total flavonoids and proanthocyanidins was 36.62 mg gallic acid equivalents (GAE)/g, 19.98 mg rutin equivalent (RE)/g and 10.76 mg catechin equivalents (CE)/g, respectively.

Optimization of single photon detection model based on GM-APD

NASA Astrophysics Data System (ADS)

Chen, Yu; Yang, Yi; Hao, Peiyu

2017-11-01

One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Image quality, threshold contrast and mean glandular dose in CR mammography

NASA Astrophysics Data System (ADS)

Jakubiak, R. R.; Gamba, H. R.; Neves, E. B.; Peixoto, J. E.

2013-09-01

In many countries, computed radiography (CR) systems represent the majority of equipment used in digital mammography. This study presents a method for optimizing image quality and dose in CR mammography of patients with breast thicknesses between 45 and 75 mm. Initially, clinical images of 67 patients (group 1) were analyzed by three experienced radiologists, reporting about anatomical structures, noise and contrast in low and high pixel value areas, and image sharpness and contrast. Exposure parameters (kV, mAs and target/filter combination) used in the examinations of these patients were reproduced to determine the contrast-to-noise ratio (CNR) and mean glandular dose (MGD). The parameters were also used to radiograph a CDMAM (version 3.4) phantom (Artinis Medical Systems, The Netherlands) for image threshold contrast evaluation. After that, different breast thicknesses were simulated with polymethylmethacrylate layers and various sets of exposure parameters were used in order to determine optimal radiographic parameters. For each simulated breast thickness, optimal beam quality was defined as giving a target CNR to reach the threshold contrast of CDMAM images for acceptable MGD. These results were used for adjustments in the automatic exposure control (AEC) by the maintenance team. Using optimized exposure parameters, clinical images of 63 patients (group 2) were evaluated as described above. Threshold contrast, CNR and MGD for such exposure parameters were also determined. Results showed that the proposed optimization method was effective for all breast thicknesses studied in phantoms. The best result was found for breasts of 75 mm. While in group 1 there was no detection of the 0.1 mm critical diameter detail with threshold contrast below 23%, after the optimization, detection occurred in 47.6% of the images. There was also an average MGD reduction of 7.5%. The clinical image quality criteria were attended in 91.7% for all breast thicknesses evaluated in both patient groups. Finally, this study also concluded that the use of the AEC of the x-ray unit based on the constant dose to the detector may bring some difficulties to CR systems to operate under optimal conditions. More studies must be performed, so that the compatibility between systems and optimization methodologies can be evaluated, as well as this optimization method. Most methods are developed for phantoms, so comparative studies including clinical images must be developed.

Optimization of electro-optical parameters of LCD for advertising systems

NASA Astrophysics Data System (ADS)

Olifierczuk, Marek; Zielinski, Jerzy; Klosowicz, Stanislaw J.

1998-02-01

The analysis of the optimization of negative image twisted nematic LCD is presented. Theoretical considerations are confirmed by experimental results. The effect of material parameters and technology on the contrast ratio and display dynamics is given. The effect in TN display with black dye is presented.

Real-time monitoring of peanut drying parameters in semitrailers

USDA-ARS?s Scientific Manuscript database

Knowledge of peanut drying parameters such as temperature and relative humidity of the ambient air, temperature and relative humidity of the air being blown into the peanuts and kernel moisture content is essential in managing the dryer for optimal drying rate. The optimal drying rate is required to...

Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range

USDA-ARS?s Scientific Manuscript database

The primary advantage of Dynamically Dimensioned Search algorithm (DDS) is that it outperforms many other optimization techniques in both convergence speed and the ability in searching for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation f...

Majorization as a Tool for Optimizing a Class of Matrix Functions.

ERIC Educational Resources Information Center

Kiers, Henk A.

1990-01-01

General algorithms are presented that can be used for optimizing matrix trace functions subject to certain constraints on the parameters. The parameter set that minimizes the majorizing function also decreases the matrix trace function, providing a monotonically convergent algorithm for minimizing the matrix trace function iteratively. (SLD)

40 CFR 141.87 - Monitoring requirements for water quality parameters.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... (c) Monitoring after installation of corrosion control. Any large system which installs optimal corrosion control treatment pursuant to § 141.81(d)(4) shall measure the water quality parameters at the...)(i). Any small or medium-size system which installs optimal corrosion control treatment shall conduct...

40 CFR 141.87 - Monitoring requirements for water quality parameters.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... (c) Monitoring after installation of corrosion control. Any large system which installs optimal corrosion control treatment pursuant to § 141.81(d)(4) shall measure the water quality parameters at the...)(i). Any small or medium-size system which installs optimal corrosion control treatment shall conduct...

40 CFR 141.87 - Monitoring requirements for water quality parameters.

Code of Federal Regulations, 2012 CFR

2012-07-01

.... (c) Monitoring after installation of corrosion control. Any large system which installs optimal corrosion control treatment pursuant to § 141.81(d)(4) shall measure the water quality parameters at the...)(i). Any small or medium-size system which installs optimal corrosion control treatment shall conduct...

40 CFR 141.87 - Monitoring requirements for water quality parameters.

Code of Federal Regulations, 2014 CFR

2014-07-01

.... (c) Monitoring after installation of corrosion control. Any large system which installs optimal corrosion control treatment pursuant to § 141.81(d)(4) shall measure the water quality parameters at the...)(i). Any small or medium-size system which installs optimal corrosion control treatment shall conduct...

40 CFR 141.87 - Monitoring requirements for water quality parameters.

Code of Federal Regulations, 2013 CFR

2013-07-01

.... (c) Monitoring after installation of corrosion control. Any large system which installs optimal corrosion control treatment pursuant to § 141.81(d)(4) shall measure the water quality parameters at the...)(i). Any small or medium-size system which installs optimal corrosion control treatment shall conduct...

Extremal Optimization: Methods Derived from Co-Evolution

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

Boettcher, S.; Percus, A.G.

1999-07-13

We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance provesmore » competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.« less

Hierarchical optimization for neutron scattering problems

DOE PAGES

Bao, Feng; Archibald, Rick; Bansal, Dipanshu; ...

2016-03-14

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

Hierarchical optimization for neutron scattering problems

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

Bao, Feng; Archibald, Rick; Bansal, Dipanshu

In this study, we present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.

Automated parameterization of intermolecular pair potentials using global optimization techniques

NASA Astrophysics Data System (ADS)

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.

Process Integration and Optimization of ICME Carbon Fiber Composites for Vehicle Lightweighting: A Preliminary Development

DOE PAGES

Xu, Hongyi; Li, Yang; Zeng, Danielle

2017-01-02

Process integration and optimization is the key enabler of the Integrated Computational Materials Engineering (ICME) of carbon fiber composites. In this paper, automated workflows are developed for two types of composites: Sheet Molding Compounds (SMC) short fiber composites, and multi-layer unidirectional (UD) composites. For SMC, the proposed workflow integrates material processing simulation, microstructure representation volume element (RVE) models, material property prediction and structure preformation simulation to enable multiscale, multidisciplinary analysis and design. Processing parameters, microstructure parameters and vehicle subframe geometry parameters are defined as the design variables; the stiffness and weight of the structure are defined as the responses. Formore » multi-layer UD structure, this work focuses on the discussion of different design representation methods and their impacts on the optimization performance. Challenges in ICME process integration and optimization are also summarized and highlighted. Two case studies are conducted to demonstrate the integrated process and its application in optimization.« less

Photoinjector optimization using a derivative-free, model-based trust-region algorithm for the Argonne Wakefield Accelerator

NASA Astrophysics Data System (ADS)

Neveu, N.; Larson, J.; Power, J. G.; Spentzouris, L.

2017-07-01

Model-based, derivative-free, trust-region algorithms are increasingly popular for optimizing computationally expensive numerical simulations. A strength of such methods is their efficient use of function evaluations. In this paper, we use one such algorithm to optimize the beam dynamics in two cases of interest at the Argonne Wakefield Accelerator (AWA) facility. First, we minimize the emittance of a 1 nC electron bunch produced by the AWA rf photocathode gun by adjusting three parameters: rf gun phase, solenoid strength, and laser radius. The algorithm converges to a set of parameters that yield an emittance of 1.08 μm. Second, we expand the number of optimization parameters to model the complete AWA rf photoinjector (the gun and six accelerating cavities) at 40 nC. The optimization algorithm is used in a Pareto study that compares the trade-off between emittance and bunch length for the AWA 70MeV photoinjector.

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

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

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

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

Valence electronic structure of cobalt phthalocyanine from an optimally tuned range-separated hybrid functional.

PubMed

Brumboiu, Iulia Emilia; Prokopiou, Georgia; Kronik, Leeor; Brena, Barbara

2017-07-28

We analyse the valence electronic structure of cobalt phthalocyanine (CoPc) by means of optimally tuning a range-separated hybrid functional. The tuning is performed by modifying both the amount of short-range exact exchange (α) included in the hybrid functional and the range-separation parameter (γ), with two strategies employed for finding the optimal γ for each α. The influence of these two parameters on the structural, electronic, and magnetic properties of CoPc is thoroughly investigated. The electronic structure is found to be very sensitive to the amount and range in which the exact exchange is included. The electronic structure obtained using the optimal parameters is compared to gas-phase photo-electron data and GW calculations, with the unoccupied states additionally compared with inverse photo-electron spectroscopy measurements. The calculated spectrum with tuned γ, determined for the optimal value of α = 0.1, yields a very good agreement with both experimental results and with GW calculations that well-reproduce the experimental data.

Nonlinear programming extensions to rational function approximation methods for unsteady aerodynamic forces

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Adams, William M., Jr.

1988-01-01

The approximation of unsteady generalized aerodynamic forces in the equations of motion of a flexible aircraft are discussed. Two methods of formulating these approximations are extended to include the same flexibility in constraining the approximations and the same methodology in optimizing nonlinear parameters as another currently used extended least-squares method. Optimal selection of nonlinear parameters is made in each of the three methods by use of the same nonlinear, nongradient optimizer. The objective of the nonlinear optimization is to obtain rational approximations to the unsteady aerodynamics whose state-space realization is lower order than that required when no optimization of the nonlinear terms is performed. The free linear parameters are determined using the least-squares matrix techniques of a Lagrange multiplier formulation of an objective function which incorporates selected linear equality constraints. State-space mathematical models resulting from different approaches are described and results are presented that show comparative evaluations from application of each of the extended methods to a numerical example.

What is the Optimal Strategy for Adaptive Servo-Ventilation Therapy?

PubMed

Imamura, Teruhiko; Kinugawa, Koichiro

2018-05-23

Clinical advantages in the adaptive servo-ventilation (ASV) therapy have been reported in selected heart failure patients with/without sleep-disorder breathing, whereas multicenter randomized control trials could not demonstrate such advantages. Considering this discrepancy, optimal patient selection and device setting may be a key for the successful ASV therapy. Hemodynamic and echocardiographic parameters indicating pulmonary congestion such as elevated pulmonary capillary wedge pressure were reported as predictors of good response to ASV therapy. Recently, parameters indicating right ventricular dysfunction also have been reported as good predictors. Optimal device setting with appropriate pressure setting during appropriate time may also be a key. Large-scale prospective trial with optimal patient selection and optimal device setting is warranted.

Pricing policy for declining demand using item preservation technology.

PubMed

Khedlekar, Uttam Kumar; Shukla, Diwakar; Namdeo, Anubhav

2016-01-01

We have designed an inventory model for seasonal products in which deterioration can be controlled by item preservation technology investment. Demand for the product is considered price sensitive and decreases linearly. This study has shown that the profit is a concave function of optimal selling price, replenishment time and preservation cost parameter. We simultaneously determined the optimal selling price of the product, the replenishment cycle and the cost of item preservation technology. Additionally, this study has shown that there exists an optimal selling price and optimal preservation investment to maximize the profit for every business set-up. Finally, the model is illustrated by numerical examples and sensitive analysis of the optimal solution with respect to major parameters.

Parameter estimation of a pulp digester model with derivative-free optimization strategies

NASA Astrophysics Data System (ADS)

Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.

2017-07-01

The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.

Optimal parameters uncoupling vibration modes of oscillators

NASA Astrophysics Data System (ADS)

Le, K. C.; Pieper, A.

2017-07-01

This paper proposes a novel optimization concept for an oscillator with two degrees of freedom. By using specially defined motion ratios, we control the action of springs to each degree of freedom of the oscillator. We aim at showing that, if the potential action of the springs in one period of vibration, used as the payoff function for the conservative oscillator, is maximized among all admissible parameters and motions satisfying Lagrange's equations, then the optimal motion ratios uncouple vibration modes. A similar result holds true for the dissipative oscillator having dampers. The application to optimal design of vehicle suspension is discussed.

Finite burn maneuver modeling for a generalized spacecraft trajectory design and optimization system.

PubMed

Ocampo, Cesar

2004-05-01

The modeling, design, and optimization of finite burn maneuvers for a generalized trajectory design and optimization system is presented. A generalized trajectory design and optimization system is a system that uses a single unified framework that facilitates the modeling and optimization of complex spacecraft trajectories that may operate in complex gravitational force fields, use multiple propulsion systems, and involve multiple spacecraft. The modeling and optimization issues associated with the use of controlled engine burn maneuvers of finite thrust magnitude and duration are presented in the context of designing and optimizing a wide class of finite thrust trajectories. Optimal control theory is used examine the optimization of these maneuvers in arbitrary force fields that are generally position, velocity, mass, and are time dependent. The associated numerical methods used to obtain these solutions involve either, the solution to a system of nonlinear equations, an explicit parameter optimization method, or a hybrid parameter optimization that combines certain aspects of both. The theoretical and numerical methods presented here have been implemented in copernicus, a prototype trajectory design and optimization system under development at the University of Texas at Austin.

A Computational approach in optimizing process parameters of GTAW for SA 106 Grade B steel pipes using Response surface methodology

NASA Astrophysics Data System (ADS)

Sumesh, A.; Sai Ramnadh, L. V.; Manish, P.; Harnath, V.; Lakshman, V.

2016-09-01

Welding is one of the most common metal joining techniques used in industry for decades. As in the global manufacturing scenario the products should be more cost effective. Therefore the selection of right process with optimal parameters will help the industry in minimizing their cost of production. SA 106 Grade B steel has a wide application in Automobile chassis structure, Boiler tubes and pressure vessels industries. Employing central composite design the process parameters for Gas Tungsten Arc Welding was optimized. The input parameters chosen were weld current, peak current and frequency. The joint tensile strength was the response considered in this study. Analysis of variance was performed to determine the statistical significance of the parameters and a Regression analysis was performed to determine the effect of input parameters over the response. From the experiment the maximum tensile strength obtained was 95 KN reported for a weld current of 95 Amp, frequency of 50 Hz and peak current of 100 Amp. With an aim of maximizing the joint strength using Response optimizer a target value of 100 KN is selected and regression models were optimized. The output results are achievable with a Weld current of 62.6148 Amp, Frequency of 23.1821 Hz, and Peak current of 65.9104 Amp. Using Die penetration test the weld joints were also classified in to 2 categories as good weld and weld with defect. This will also help in getting a defect free joint when welding is performed using GTAW process.

The dimensional reduction method for identification of parameters that trade-off due to similar model roles.

PubMed

Davidson, Shaun M; Docherty, Paul D; Murray, Rua

2017-03-01

Parameter identification is an important and widely used process across the field of biomedical engineering. However, it is susceptible to a number of potential difficulties, such as parameter trade-off, causing premature convergence at non-optimal parameter values. The proposed Dimensional Reduction Method (DRM) addresses this issue by iteratively reducing the dimension of hyperplanes where trade off occurs, and running subsequent identification processes within these hyperplanes. The DRM was validated using clinical data to optimize 4 parameters of the widely used Bergman Minimal Model of glucose and insulin kinetics, as well as in-silico data to optimize 5 parameters of the Pulmonary Recruitment (PR) Model. Results were compared with the popular Levenberg-Marquardt (LMQ) Algorithm using a Monte-Carlo methodology, with both methods afforded equivalent computational resources. The DRM converged to a lower or equal residual value in all tests run using the Bergman Minimal Model and actual patient data. For the PR model, the DRM attained significantly lower overall median parameter error values and lower residuals in the vast majority of tests. This shows the DRM has potential to provide better resolution of optimum parameter values for the variety of biomedical models in which significant levels of parameter trade-off occur. Copyright © 2017 Elsevier Inc. All rights reserved.

Taguchi Method Applied in Optimization of Shipley SJR 5740 Positive Resist Deposition

NASA Technical Reports Server (NTRS)

Hui, A.; Blosiu, J. O.; Wiberg, D. V.

1998-01-01

Taguchi Methods of Robust Design presents a way to optimize output process performance through an organized set of experiments by using orthogonal arrays. Analysis of variance and signal-to-noise ratio is used to evaluate the contribution of each of the process controllable parameters in the realization of the process optimization. In the photoresist deposition process, there are numerous controllable parameters that can affect the surface quality and thickness of the final photoresist layer.

Hearing Aids and Music

PubMed Central

Chasin, Marshall; Russo, Frank A.

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues—both compression ratio and knee-points—and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a “music program,” unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions. PMID:15497032

[Optimization of application parameters of soil seed bank in vegetation recovery via response surface methodology].

PubMed

He, Meng-Xuan; Li, Hong-Yuan; Mo, Xun-Qiang; Meng, Wei-Qing; Yang, Jia-Nan

2014-08-01

The thickness of surface soil, the covering thickness and the number of adding arbor seeds are all important factors to be considered in the application of soil seed bank (SSB) for vegetation recovery. To determine the optimal conditions, the Box-Behnken central composite design with three parameters and three levels was conducted and Design-Expert was used for response surface optimization. Finally, the optimal model and optimal level of each parameter were selected. The quadratic model was more suitable for response surface optimization (P < 0.0001), indicating the model had good statistical significance which could express ideal relations between all the independent variable and dependent variable. For the optimum condition, the thickness of surface soil was 4.3 cm, the covering thickness was 2 cm, and the number of adding arbor seeds was 224 ind x m(-2), under which the number of germinated seedlings could be reached up to 6222 plants x m(-2). During the process of seed germination, significant interactions between the thickness of surface soil and the covering thickness, as well as the thickness of surface soil and the number of adding arbor seeds were found, but the relationship between the covering thickness and the number of adding arbor seeds was relatively unremarkable. Among all the parameters, the thickness of surface soil was the most important one, which had the steepest curve and the largest standardized coefficient.

Optimized Kernel Entropy Components.

PubMed

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Simultaneous beam sampling and aperture shape optimization for SPORT.

PubMed

Zarepisheh, Masoud; Li, Ruijiang; Ye, Yinyu; Xing, Lei

2015-02-01

Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

Simultaneous beam sampling and aperture shape optimization for SPORT

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

Zarepisheh, Masoud; Li, Ruijiang; Xing, Lei, E-mail: Lei@stanford.edu

Purpose: Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: The authors build a mathematical model with the fundamental station point parameters as the decisionmore » variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. Results: A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. Conclusions: The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.« less

Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model

NASA Astrophysics Data System (ADS)

Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr

2017-10-01

Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations, gradient based and nature inspired optimization algorithms and experimental data, the latter of which take the form of a load-extension curve obtained from the evaluation of uniaxial tensile test results. The aim of this research was to obtain material model parameters corresponding to the quasi-static tensile loading which may be further used for the research involving dynamic and high-speed tensile loading. Based on the obtained results it can be concluded that the set goal has been reached.

Development of a parameter optimization technique for the design of automatic control systems

NASA Technical Reports Server (NTRS)

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.

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

OPTIMAL EXPERIMENT DESIGN FOR MAGNETIC RESONANCE FINGERPRINTING

PubMed Central

Zhao, Bo; Haldar, Justin P.; Setsompop, Kawin; Wald, Lawrence L.

2017-01-01

Magnetic resonance (MR) fingerprinting is an emerging quantitative MR imaging technique that simultaneously acquires multiple tissue parameters in an efficient experiment. In this work, we present an estimation-theoretic framework to evaluate and design MR fingerprinting experiments. More specifically, we derive the Cramér-Rao bound (CRB), a lower bound on the covariance of any unbiased estimator, to characterize parameter estimation for MR fingerprinting. We then formulate an optimal experiment design problem based on the CRB to choose a set of acquisition parameters (e.g., flip angles and/or repetition times) that maximizes the signal-to-noise ratio efficiency of the resulting experiment. The utility of the proposed approach is validated by numerical studies. Representative results demonstrate that the optimized experiments allow for substantial reduction in the length of an MR fingerprinting acquisition, and substantial improvement in parameter estimation performance. PMID:28268369

Optimal experiment design for magnetic resonance fingerprinting.

PubMed

Bo Zhao; Haldar, Justin P; Setsompop, Kawin; Wald, Lawrence L

2016-08-01

Magnetic resonance (MR) fingerprinting is an emerging quantitative MR imaging technique that simultaneously acquires multiple tissue parameters in an efficient experiment. In this work, we present an estimation-theoretic framework to evaluate and design MR fingerprinting experiments. More specifically, we derive the Cramér-Rao bound (CRB), a lower bound on the covariance of any unbiased estimator, to characterize parameter estimation for MR fingerprinting. We then formulate an optimal experiment design problem based on the CRB to choose a set of acquisition parameters (e.g., flip angles and/or repetition times) that maximizes the signal-to-noise ratio efficiency of the resulting experiment. The utility of the proposed approach is validated by numerical studies. Representative results demonstrate that the optimized experiments allow for substantial reduction in the length of an MR fingerprinting acquisition, and substantial improvement in parameter estimation performance.

Slot Optimization Design of Induction Motor for Electric Vehicle

NASA Astrophysics Data System (ADS)

Shen, Yiming; Zhu, Changqing; Wang, Xiuhe

2018-01-01

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

Optimal observables for multiparameter seismic tomography

NASA Astrophysics Data System (ADS)

Bernauer, Moritz; Fichtner, Andreas; Igel, Heiner

2014-08-01

We propose a method for the design of seismic observables with maximum sensitivity to a target model parameter class, and minimum sensitivity to all remaining parameter classes. The resulting optimal observables thereby minimize interparameter trade-offs in multiparameter inverse problems. Our method is based on the linear combination of fundamental observables that can be any scalar measurement extracted from seismic waveforms. Optimal weights of the fundamental observables are determined with an efficient global search algorithm. While most optimal design methods assume variable source and/or receiver positions, our method has the flexibility to operate with a fixed source-receiver geometry, making it particularly attractive in studies where the mobility of sources and receivers is limited. In a series of examples we illustrate the construction of optimal observables, and assess the potentials and limitations of the method. The combination of Rayleigh-wave traveltimes in four frequency bands yields an observable with strongly enhanced sensitivity to 3-D density structure. Simultaneously, sensitivity to S velocity is reduced, and sensitivity to P velocity is eliminated. The original three-parameter problem thereby collapses into a simpler two-parameter problem with one dominant parameter. By defining parameter classes to equal earth model properties within specific regions, our approach mimics the Backus-Gilbert method where data are combined to focus sensitivity in a target region. This concept is illustrated using rotational ground motion measurements as fundamental observables. Forcing dominant sensitivity in the near-receiver region produces an observable that is insensitive to the Earth structure at more than a few wavelengths' distance from the receiver. This observable may be used for local tomography with teleseismic data. While our test examples use a small number of well-understood fundamental observables, few parameter classes and a radially symmetric earth model, the method itself does not impose such restrictions. It can easily be applied to large numbers of fundamental observables and parameters classes, as well as to 3-D heterogeneous earth models.

User-customized brain computer interfaces using Bayesian optimization

NASA Astrophysics Data System (ADS)

Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

2016-04-01

Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Parameter optimization for reproducible cardiac 1 H-MR spectroscopy at 3 Tesla.

PubMed

de Heer, Paul; Bizino, Maurice B; Lamb, Hildo J; Webb, Andrew G

2016-11-01

To optimize data acquisition parameters in cardiac proton MR spectroscopy, and to evaluate the intra- and intersession variability in myocardial triglyceride content. Data acquisition parameters at 3 Tesla (T) were optimized and reproducibility measured using, in total, 49 healthy subjects. The signal-to-noise-ratio (SNR) and the variance in metabolite amplitude between averages were measured for: (i) global versus local power optimization; (ii) static magnetic field (B 0 ) shimming performed during free-breathing or within breathholds; (iii) post R-wave peak measurement times between 50 and 900 ms; (iv) without respiratory compensation, with breathholds and with navigator triggering; and (v) frequency selective excitation, Chemical Shift Selective (CHESS) and Multiply Optimized Insensitive Suppression Train (MOIST) water suppression techniques. Using the optimized parameters intra- and intersession myocardial triglyceride content reproducibility was measured. Two cardiac proton spectra were acquired with the same parameters and compared (intrasession reproducibility) after which the subject was removed from the scanner and placed back in the scanner and a third spectrum was acquired which was compared with the first measurement (intersession reproducibility). Local power optimization increased SNR on average by 22% compared with global power optimization (P = 0.0002). The average linewidth was not significantly different for pencil beam B 0 shimming using free-breathing or breathholds (19.1 Hz versus 17.5 Hz; P = 0.15). The highest signal stability occurred at a cardiac trigger delay around 240 ms. The mean amplitude variation was significantly lower for breathholds versus free-breathing (P = 0.03) and for navigator triggering versus free-breathing (P = 0.03) as well as for navigator triggering versus breathhold (P = 0.02). The mean residual water signal using CHESS (1.1%, P = 0.01) or MOIST (0.7%, P = 0.01) water suppression was significantly lower than using frequency selective excitation water suppression (7.0%). Using the optimized parameters an intrasession limits of agreement of the myocardial triglyceride content of -0.11% to +0.04%, and an intersession of -0.15% to +0.9%, were achieved. The coefficient of variation was 5% for the intrasession reproducibility and 6.5% for the intersession reproducibility. Using approaches designed to optimize SNR and minimize the variation in inter-average signal intensities and frequencies/phases, a protocol was developed to perform cardiac MR spectroscopy on a clinical 3T system with high reproducibility. J. Magn. Reson. Imaging 2016;44:1151-1158. © 2016 International Society for Magnetic Resonance in Medicine.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

PubMed

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

PubMed Central

2011-01-01

Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology. PMID:21989196

Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

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

Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach

NASA Astrophysics Data System (ADS)

Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto

2017-12-01

In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \

Stochastic resonance algorithm applied to quantitative analysis for weak chromatographic signals of alkyl halides and alkyl benzenes in water samples.

PubMed

Xiang, Suyun; Wang, Wei; Xia, Jia; Xiang, Bingren; Ouyang, Pingkai

2009-09-01

The stochastic resonance algorithm is applied to the trace analysis of alkyl halides and alkyl benzenes in water samples. Compared to encountering a single signal when applying the algorithm, the optimization of system parameters for a multicomponent is more complex. In this article, the resolution of adjacent chromatographic peaks is first involved in the optimization of parameters. With the optimized parameters, the algorithm gave an ideal output with good resolution as well as enhanced signal-to-noise ratio. Applying the enhanced signals, the method extended the limit of detection and exhibited good linearity, which ensures accurate determination of the multicomponent.

A Minimum Delta V Orbit Maintenance Strategy for Low-Altitude Missions Using Burn Parameter Optimization

NASA Technical Reports Server (NTRS)

Brown, Aaron J.

2011-01-01

Orbit maintenance is the series of burns performed during a mission to ensure the orbit satisfies mission constraints. Low-altitude missions often require non-trivial orbit maintenance Delta V due to sizable orbital perturbations and minimum altitude thresholds. A strategy is presented for minimizing this Delta V using impulsive burn parameter optimization. An initial estimate for the burn parameters is generated by considering a feasible solution to the orbit maintenance problem. An low-lunar orbit example demonstrates the Delta V savings from the feasible solution to the optimal solution. The strategy s extensibility to more complex missions is discussed, as well as the limitations of its use.

Optimizing the parameters of the Lyman-Kutcher-Burman, Källman, and Logit+EUD models for the rectum - a comparison between normal tissue complication probability and clinical data

NASA Astrophysics Data System (ADS)

Trojková, Darina; Judas, Libor; Trojek, Tomáš

2014-11-01

Minimizing the late rectal toxicity of prostate cancer patients is a very important and widely-discussed topic. Normal tissue complication probability (NTCP) models can be used to evaluate competing treatment plans. In our work, the parameters of the Lyman-Kutcher-Burman (LKB), Källman, and Logit+EUD models are optimized by minimizing the Brier score for a group of 302 prostate cancer patients. The NTCP values are calculated and are compared with the values obtained using previously published values for the parameters. χ2 Statistics were calculated as a check of goodness of optimization.

Recent experience in simultaneous control-structure optimization

NASA Technical Reports Server (NTRS)

Salama, M.; Ramaker, R.; Milman, M.

1989-01-01

To show the feasibility of simultaneous optimization as design procedure, low order problems were used in conjunction with simple control formulations. The numerical results indicate that simultaneous optimization is not only feasible, but also advantageous. Such advantages come at the expense of introducing complexities beyond those encountered in structure optimization alone, or control optimization alone. Examples include: larger design parameter space, optimization may combine continuous and combinatoric variables, and the combined objective function may be nonconvex. Future extensions to include large order problems, more complex objective functions and constraints, and more sophisticated control formulations will require further research to ensure that the additional complexities do not outweigh the advantages of simultaneous optimization. Some areas requiring more efficient tools than currently available include: multiobjective criteria and nonconvex optimization. Efficient techniques to deal with optimization over combinatoric and continuous variables, and with truncation issues for structure and control parameters of both the model space as well as the design space need to be developed.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

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

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less

[Induction of free radicals after dietary exposure by mercury contaminated rice and protective effect of coexisting selenium].

PubMed

Ji, Xiu-ling; Cheng, Jin-ping; Wang, Wen-hua; Qu, Li-ya; Zhao, Xiao-xiang; Zhuang, Hui-sheng

2006-10-01

Sprague-Dawley rats were reared by environmental mercury contaminated rice to survey the potential health risk of Wanshan mercury mining area. Electron spin resonance (ESR) was introduced to detect the species and the intensities of free radicals, using spin trap 5,5-dimethyl-1-pyrroline-N-oxide (DMPO). The results showed that the mercury-contaminated rice significantly increased the levels of free radicals and MDA in rat brain at 7 days (p < 0.05). ESR spectrums showed that the principal spin adducts resulted from the trapping of alkyl free radical (alphaH = 22.7 x 10(-4)T +/- 1.6 x 10(-4)T, alphaN = 15.5 x 10(-4)T +/- 0.5 x 10(-4)T), and hydroxyl radical. Levels of free radicals and MDA increased slowly until after 90-day exposure period (83%, 100%). Element correlation analysis showed high correlations of mercury and selenium in the brain of rat fed with Wanshan rice, suggesting that the coexisting selenium in rice exhibited antagonistic effects on both mercury accumulation and toxicity. The slight increases of free radicals in rat brain at 7, 20 and 30-day exposure periods should be related with the scavenger effect of Se.

Respiratory chain components involved in the glycerophosphate dehydrogenase-dependent ROS production by brown adipose tissue mitochondria.

PubMed

Vrbacký, Marek; Drahota, Zdenek; Mrácek, Tomás; Vojtísková, Alena; Jesina, Pavel; Stopka, Pavel; Houstek, Josef

2007-07-01

Involvement of mammalian mitochondrial glycerophosphate dehydrogenase (mGPDH, EC 1.1.99.5) in reactive oxygen species (ROS) generation was studied in brown adipose tissue mitochondria by different spectroscopic techniques. Spectrofluorometry using ROS-sensitive probes CM-H2DCFDA and Amplex Red was used to determine the glycerophosphate- or succinate-dependent ROS production in mitochondria supplemented with respiratory chain inhibitors antimycin A and myxothiazol. In case of glycerophosphate oxidation, most of the ROS originated directly from mGPDH and coenzyme Q while complex III was a typical site of ROS production in succinate oxidation. Glycerophosphate-dependent ROS production monitored by KCN-insensitive oxygen consumption was highly activated by one-electron acceptor ferricyanide, whereas succinate-dependent ROS production was unaffected. In addition, superoxide anion radical was detected as a mGPDH-related primary ROS species by fluorescent probe dihydroethidium, as well as by electron paramagnetic resonance (EPR) spectroscopy with DMPO spin trap. Altogether, the data obtained demonstrate pronounced differences in the mechanism of ROS production originating from oxidation of glycerophosphate and succinate indicating that electron transfer from mGPDH to coenzyme Q is highly prone to electron leak and superoxide generation.

A testbed to explore the optimal electrical stimulation parameters for suppressing inter-ictal spikes in human hippocampal slices.

PubMed

Min-Chi Hsiao; Pen-Ning Yu; Dong Song; Liu, Charles Y; Heck, Christi N; Millett, David; Berger, Theodore W

2014-01-01

New interventions using neuromodulatory devices such as vagus nerve stimulation, deep brain stimulation and responsive neurostimulation are available or under study for the treatment of refractory epilepsy. Since the actual mechanisms of the onset and termination of the seizure are still unclear, most researchers or clinicians determine the optimal stimulation parameters through trial-and-error procedures. It is necessary to further explore what types of electrical stimulation parameters (these may include stimulation frequency, amplitude, duration, interval pattern, and location) constitute a set of optimal stimulation paradigms to suppress seizures. In a previous study, we developed an in vitro epilepsy model using hippocampal slices from patients suffering from mesial temporal lobe epilepsy. Using a planar multi-electrode array system, inter-ictal activity from human hippocampal slices was consistently recorded. In this study, we have further transferred this in vitro seizure model to a testbed for exploring the possible neurostimulation paradigms to inhibit inter-ictal spikes. The methodology used to collect the electrophysiological data, the approach to apply different electrical stimulation parameters to the slices are provided in this paper. The results show that this experimental testbed will provide a platform for testing the optimal stimulation parameters of seizure cessation. We expect this testbed will expedite the process for identifying the most effective parameters, and may ultimately be used to guide programming of new stimulating paradigms for neuromodulatory devices.

Optimization of Process Parameters of Pulsed Electro Deposition Technique for Nanocrystalline Nickel Coating Using Gray Relational Analysis (GRA)

NASA Astrophysics Data System (ADS)

Venkatesh, C.; Sundara Moorthy, N.; Venkatesan, R.; Aswinprasad, V.

The moving parts of any mechanism and machine parts are always subjected to a significant wear due to the development of friction. It is an utmost important aspect to address the wear problems in present environment. But the complexity goes on increasing to replace the worn out parts if they are very precise. Technology advancement in surface engineering ensures the minimum surface wear with the introduction of polycrystalline nano nickel coating. The enhanced tribological property of the nano nickel coating was achieved by the development of grain size and hardness of the surface. In this study, it has been decided to focus on the optimized parameters of the pulsed electro deposition to develop such a coating. Taguchi’s method coupled gray relational analysis was employed by considering the pulse frequency, average current density and duty cycle as the chief process parameters. The grain size and hardness were considered as responses. Totally, nine experiments were conducted as per L9 design of experiment. Additionally, response graph method has been applied to determine the most significant parameter to influence both the responses. In order to improve the degree of validation, confirmation test and predicted gray grade were carried out with the optimized parameters. It has been observed that there was significant improvement in gray grade for the optimal parameters.

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

NASA Astrophysics Data System (ADS)

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.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Optimization of Surface Roughness Parameters of Al-6351 Alloy in EDC Process: A Taguchi Coupled Fuzzy Logic Approach

NASA Astrophysics Data System (ADS)

Kar, Siddhartha; Chakraborty, Sujoy; Dey, Vidyut; Ghosh, Subrata Kumar

2017-10-01

This paper investigates the application of Taguchi method with fuzzy logic for multi objective optimization of roughness parameters in electro discharge coating process of Al-6351 alloy with powder metallurgical compacted SiC/Cu tool. A Taguchi L16 orthogonal array was employed to investigate the roughness parameters by varying tool parameters like composition and compaction load and electro discharge machining parameters like pulse-on time and peak current. Crucial roughness parameters like Centre line average roughness, Average maximum height of the profile and Mean spacing of local peaks of the profile were measured on the coated specimen. The signal to noise ratios were fuzzified to optimize the roughness parameters through a single comprehensive output measure (COM). Best COM obtained with lower values of compaction load, pulse-on time and current and 30:70 (SiC:Cu) composition of tool. Analysis of variance is carried out and a significant COM model is observed with peak current yielding highest contribution followed by pulse-on time, compaction load and composition. The deposited layer is characterised by X-Ray Diffraction analysis which confirmed the presence of tool materials on the work piece surface.

Determining optimal parameters in magnetic spacecraft stabilization via attitude feedback

NASA Astrophysics Data System (ADS)

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.