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1.
Learning in multiple model adaptive control switch 总被引:1,自引:0,他引:1
Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers. 相似文献
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Edoardo Artioli Lourenco Beirão da Veiga Harri Hakula Carlo Lovadina 《Computational Mechanics》2009,44(1):45-60
We consider the free vibration problem of thin shells of revolution of constant type of geometry, focusing on the asymptotic
behaviour of the lowest eigenfrequency, as the thickness tends to zero. Numerical experiments are computed using two discretization
methods, collocation and finite elements, each corresponding to a different shell model. Our results are in agreement with
theoretical results obtained using interpolation theory and cited in literature. 相似文献
3.
A system is described whereby the integrated electrocardiogram is automatically subtracted from the total integrated electrical activity recorded in the esophagus. By this method, a measure of the electrical activity in the diaphragm is obtained, and the output from the respiratory center can be studied independently of mechanical alterations of chest wall and lungs. 相似文献
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Frequency response analysis was used to determine the dynamic response characteristics of cat diaphragm under isovolumetric conditions at functional residual capacity (FRC) and at lung volumes above and below FRC. In apneic cats, sinusoidally modulated pulse rate patterns were applied to both phrenic nerves. Modulation frequencies over the range of 0.05-4 Hz were used. Amplitude ratio vs. frequency plots obtained at FRC for intratracheal, intraesophageal, and intragastric pressures were essentially flat at low frequencies but decreased at higher frequencies. Intratracheal and intraesophageal pressure responses were altered by changes in resting lung volume while intragastric pressure was not. The amplitude ratio was decreased at lung volumes above FRC but increased at volumes below FRC. Thus, lung volume significantly affected the input-output relations between phrenic nerve input and diaphragm muscle output. In all preparations studied, significant phase lags were present throughout the entire modulation frequency range. However, in contrast to the effect of lung volume on amplitude ratio, phase lag was not dependent on changes in lung volume. 相似文献
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H.R. Oliveira L.F. Brito D.A.L. Lourenco F.F. Silva J. Jamrozik L.R. Schaeffer F.S. Schenkel 《Journal of dairy science》2019,102(9):7664-7683
An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits. 相似文献
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S. Andonov D.A.L. Lourenco B.O. Fragomeni Y. Masuda I. Pocrnic S. Tsuruta I. Misztal 《Journal of dairy science》2017,100(1):395-401
Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population (PL) with >20 generations was simulated, and a small subpopulation (PS) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for PS; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from PL as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for PL; and (S4) single nucleotide polymorphism (SNP) effects calculated based on PL data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from PL did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for PS and PL was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from PL were used to predict GEBV for young genotyped animals from PS, accuracy was greatest (0.56). With 6,000 genotyped animal in PS, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population. 相似文献
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The objective of this study was to clarify how bias in genomic predictions is created by investigating a relationship among selection intensity, a change in heritability (Δh2), and assortative mating (ASM). A change in heritability, resulting from selection, reflects the impact that the Bulmer effect has on the reduction in between-family variation, whereas assortative mating impacts the within-family variance or Mendelian sampling variation. A partial data set up to 2014, including 841K genotyped animals, was used to calculate genomic predictions with a single-step genomic model for 18 linear type traits in US Holsteins. A full data set up to 2018, including 2.3 million genotyped animals, was used to calculate benchmark genomic predictions. Inbreeding and unknown parent groups for missing parents of animals were included in the model. Genomic evaluation was performed using 2 different genetic parameters: those estimated 14 yr ago, which have been used in the national genetic evaluation for linear type traits in the United States, and those newly estimated with recent records from 2015 to 2018 and those corresponding pedigrees. Genetic trends for 18 type traits were estimated for bulls with daughters and cows with phenotypes in 2018. Based on selection intensity and mating decisions, these traits can be categorized into 3 groups: (a) high directional selection, (b) moderate selection, and (c) intermediate optimum selection. The first 2 categories can be explained by positive assortative mating, and the last can be explained by negative assortative or disassortative mating. Genetic progress was defined by genetic gain per year based on average standardized genomic predictions for cows from 2000 to 2014. Traits with more genetic progress tended to have more “inflated” genomic predictions (i.e., “inflation” means here that genomic predictions are larger in absolute values than expected, whereas “deflation” means smaller than expected). Heritability estimates for 14 out of 18 traits declined in the last 16 yr, and Δh2 ranged from ?0.09 to 0.04. Traits with a greater decline in heritability tended to have more deflated genomic predictions. Biases (inflation or deflation) in genomic predictions were not improved by using the latest genetic parameters, implying that bias in genomic predictions due to preselection was not substantial for a large-scale genomic evaluation. Moreover, the strong selection intensity was not fully responsible for bias in genomic predictions. The directional selection can decrease heritability; however, positive assortative mating, which was strongly associated with large genetic gains, could minimize the decline in heritability for a trait under strong selection and could affect bias in genomic predictions. 相似文献