185 research outputs found
Principal component analysis for second-order stationary vector time series
We extend the principal component analysis (PCA) to second-order stationary
vector time series in the sense that we seek for a contemporaneous linear
transformation for a -variate time series such that the transformed series
is segmented into several lower-dimensional subseries, and those subseries are
uncorrelated with each other both contemporaneously and serially. Therefore
those lower-dimensional series can be analysed separately as far as the linear
dynamic structure is concerned. Technically it boils down to an eigenanalysis
for a positive definite matrix. When is large, an additional step is
required to perform a permutation in terms of either maximum cross-correlations
or FDR based on multiple tests. The asymptotic theory is established for both
fixed and diverging when the sample size tends to infinity.
Numerical experiments with both simulated and real data sets indicate that the
proposed method is an effective initial step in analysing multiple time series
data, which leads to substantial dimension reduction in modelling and
forecasting high-dimensional linear dynamical structures. Unlike PCA for
independent data, there is no guarantee that the required linear transformation
exists. When it does not, the proposed method provides an approximate
segmentation which leads to the advantages in, for example, forecasting for
future values. The method can also be adapted to segment multiple volatility
processes.Comment: The original title dated back to October 2014 is "Segmenting Multiple
Time Series by Contemporaneous Linear Transformation: PCA for Time Series
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
We consider a multivariate time series model which represents a high
dimensional vector process as a sum of three terms: a linear regression of some
observed regressors, a linear combination of some latent and serially
correlated factors, and a vector white noise. We investigate the inference
without imposing stationary conditions on the target multivariate time series,
the regressors and the underlying factors. Furthermore we deal with the
endogeneity that there exist correlations between the observed regressors and
the unobserved factors. We also consider the model with nonlinear regression
term which can be approximated by a linear regression function with a large
number of regressors. The convergence rates for the estimators of regression
coefficients, the number of factors, factor loading space and factors are
established under the settings when the dimension of time series and the number
of regressors may both tend to infinity together with the sample size. The
proposed method is illustrated with both simulated and real data examples
Analysis of the forming characteristics for Cu/Al bimetal tubes produced by the spinning process
Tube spinning technology represents a process with high forming precision and good flexibility and is increasingly being used in the manufacture of bimetal composite tubular structures. In the present study, a forming analysis of clad tube and base tube in spinning process was conducted through numerical simulations and experiments. There was an equivalent stress transition on the interface since the stress transmission was retarded from clad tube to base tube. The yield strength became a main consideration during a design bimetal composite tube. Meanwhile, the strain distributions in axial direction, tangential direction, and radial direction were also investigated to determine the deformation characteristics of each component. As the press amount increased, the strain of clad tube changed more than base tube. As the feed rate increased, the strain decreased in axial direction and tangential direction but almost unchanged in radial direction. Simultaneously, a method for controlling the wall thickness of the clad tube and the base tube is proposed. These results to guide the design of bimetal tube composite spinning process have the certain meanings
High-dimensional and banded vector autoregressions
We consider a class of vector autoregressive models with banded coefficient matrices. The setting represents a type of sparse structure for high-dimensional time series, though the implied autocovariance matrices are not banded. The structure is also practically meaningful when the order of component time series is arranged appropriately. The convergence rates for the estimated banded autoregressive coefficient matrices are established. We also propose a Bayesian information criterion for determining the width of the bands in the coefficient matrices, which is proved to be consistent. By exploring some approximate banded structure for the autocovariance functions of banded vector autoregressive processes, consistent estimators for the auto-covariance matrices are constructed
Relationship of Family Environment, Psychological Resilience, Campus Bullying with Tobacco Use among Preadolescents
Objective. To explore the relationship between family environment, psychological resilience, campus bullying and tobacco use in early adolescence. Methods. According to the principle of cluster sampling, 4,792 students from grade 4 to grade 6 in five primary schools in Baise City and county were selected from February to November 2018, including 2,522 males (52.63%), 2,236 females (46.66%)and 34 missing genders (0.71%); the average age was (11.8 ± 0.5) years; 2,721 students in urban areas (56.78%) and 2,071 students in county towns (43.22%); 4,313 Zhuang (90.00%), 365 Han (7.62%), 98 other ethnic groups (Yao, Miao, Yi, etc.) (2.05%). The General Family Environment Questionnaire, Adolescent Mental Resilience Scale, School Bullying Questionnaire, and Tobacco Use Questionnaire were used for evaluation, and logistic regression was used to analyze the effect relationship between the study variables. Results. 467 people tried to smoke, and the total detection rate was 9.75%. The number of smokers was 334, and the total detection rate was 6.97%. Boys’ tobacco attempt and smoking behavior were higher than girls (χ2 were 57.230 and 56.013, P < 0. 001). Multivariate logistic regression analysis showed that the risk of tobacco attempt of boys was 2.37 times thanthat of girls (OR = 0.468, 95% CI 0.377 ~ 0.582), the risk of smoking in boys is 2.5 times that in girls 32 times (OR = 0.422, 95% CI 0.324 ~ 0.551); older adolescents had more tobacco attempts (OR = 1.609, 95% CI 1.446 ~ 1.791)and smoking behavior (OR = 2.026, 95% CI 1.776 ~ 2.310); campus bullying increased the risk of smoking behavior among adolescents (OR = 1.106, 95% CI 1.073 ~ 1.140). Psychological resilience (personal strength), family intimacy and family rules can effectively reduce the risk of adolescent tobacco attempts (personal strength, OR = 0.964, 95% CI = 0.951 ~ 0.976; family intimacy, OR = 0.946, 95% CI 0.892 ~ 0.984; family rules, OR = 0.949, 95% CI 0.930 ~ 0.965) and smoking behavior (personal strength, OR = 0.962, 95% CI 0.947 ~ 0.977; family intimacy, OR = 0.937, 95% CI 0.885 ~ 0.992; family rules, OR = 0.952, 95% CI 0.932 ~ 0.973). Conclusion. Campus bullying increases the risk of smoking behavior among adolescents. Psychological resilience (personal strength), family intimacy and family rules can effectively reduce teenagers’ tobacco attempts and smoking behavior
Label-free photoacoustic tomography of whole mouse brain structures ex vivo
Capitalizing on endogenous hemoglobin contrast, photoacoustic-computed tomography (PACT), a deep-tissue high-resolution imaging modality, has drawn increasing interest in neuroimaging. However, most existing studies are limited to functional imaging on the cortical surface and the deep brain structural imaging capability of PACT has never been demonstrated. Here, we explicitly studied the limiting factors of deep brain PACT imaging. We found that the skull distorted the acoustic signal and blood suppressed the structural contrast from other chromophores. When the two effects are mitigated, PACT can potentially provide high-resolution label-free imaging of structures in the entire mouse brain. With 100-μm in-plane resolution, we can clearly identify major structures of the brain, which complements magnetic resonance microscopy for imaging small-animal brain structures. Spectral PACT studies indicate that structural contrasts mainly originate from cytochrome distribution and that the presence of lipid sharpens the image contrast; brain histology results provide further validation. The feasibility of imaging the structure of the brain in vivo is also discussed. Our results demonstrate that PACT is a promising modality for both structural and functional brain imaging
A Simple and Low-Cost Strategy to Improve Conidial Yield and Stress Resistance of Trichoderma guizhouense through Optimizing Illumination Conditions
Light is perceived by photoreceptors in fungi and further integrated into the stress-activated MAPK HOG pathway, and thereby potentially activates the expression of genes for stress responses. This indicates that the precise control of light conditions can likely improve the conidial yield and stress resistance to guarantee the low cost and long shelf life of Trichoderma-based biocontrol agents and biofertilizers. In this study, effects of wavelengths and intensities of light on conidial yield and stress tolerance to osmotic, oxidative and pH stresses in Trichoderma guizhouense were investigated. We found that 2 μmol photons/(m × s) of blue light increased the conidial yield more than 1000 folds as compared to dark condition and simultaneously enhanced conidial stress resistance. The enhanced conidial stress resistance is probably due to the upregulated stress-related genes in blue light, which is under the control of the blue light receptor BLR1 and the MAP kinase HOG1
A novel weighting factor determined method of model predictive torque control for permanent magnet synchronous motor
In the cost function of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), torque and flux linkage have different dimensions, and the weighting factor needs to be properly adjusted to optimise the control performance. Therefore, based on the discrete mathematical model of PMSM, the torque predictive error and flux predictive error expressions are mapped to the two-phase static coordinate system, which is equivalent to the distance expressions from the endpoint of the voltage vector to the straight line and to the circle respectively. Because they have the same dimension, they can be directly superimposed to form the cost function proposed, which eliminates the selection process of the weighting factor. Then, the candidate voltage vector is substituted into the cost function in turn, and the optimal voltage vector with the minimum cost function is selected as the voltage input of the next control cycle. Finally, the simulation model and an experimental platform are established to compare the steady-state and dynamic performance of the geometric solution-based weighting factor determined method proposed with other MPTC methods
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