Effect Of Model Order Research Articles

Name order effects in measuring adolescent social networks using rosters

Recent studies have found order effects in social network data collection, where later names on a roster receive fewer nominations. Some thus argue for randomizing name orders or sampling peer names for survey rosters. We model order effects as biases in nomination choices and demonstrate observational and experimental methods for assessing these biases and illuminating their mechanisms. Employing these lenses, we find little evidence of order effects on eight sociometric questions in four middle school cohorts over six waves. To inform future work, we investigate aspects of the survey situation that may amplify or attenuate order effects. Analyzing these moderating forces offers guidance for detecting, understanding, and mitigating order effects in future research.

Prediction of Power Output of Wind Turbines Using System Identification Techniques

In this paper, a generalized State Space Model for prediction of wind turbine output power has been developed via System Identification techniques. The techniques involved in this paper are the subspace methods, mainly, Numerical algorithms for Subspace State Space System Identification based on advanced linear algebra, and Prediction Error Method that based on iterative optimization. Predicting the power output of wind turbines in general is very useful in many applications, such as online monitoring of wind turbines, increasing the share of wind power from the wind farms to grids, operator training simulations, control system upgrades and so on. In order to attain sufficient accuracy for the model, many influencing inputs are taken into account, which are pitch angle, rotor speed with several other meteorological inputs as humidity, temperature, air pressure and wind speed have been considered in the system identification prediction; the result has shown that each input is sufficient and affects the model accuracy. Furthermore, the effect of model order on simulation accuracy has been investigated. In addition, a comparative study has been conducted between Prediction Error Method and Numerical algorithms for Subspace State Space System Identification methods in terms of complexity, accuracy, and speed of simulation. Simulations have demonstrated each methods’ ability to predict wind power with some slight differences in accuracy and computational requirements.

Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia

BackgroundComponent splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders. New methodThis study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-sample t-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140. ResultsResults show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks. Comparison with existing method(s)When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia. ConclusionsThese results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes.

Order and session size effects on treatment discrimination: Case study liking for Dulce de Leche

This study aimed to evaluate the implications of the number of samples per panelist in experimental precision and relative efficiency of treatment comparisons when modelling order effects for Dulce de Leche samples using a Sudoku design. Precision is defined as small variance and relative efficiency is defined as smaller variance than other alternative. Both are related to increasing discrimination power. A series of 8 Sudoku 16×16 squares being 4 randomized independently and 4 others in the reverse order, was designed to allow a linear model fitting to experiments of different sizes (orders 1 to 4, 1 to 8, 1 to 12 and 1 to 16). Data from 112 panelists were kept. Responses were recorded using a nine-point hedonic scale. Data was analyzed using a mixed linear model panelist, order and treatment (sample) effects. Analysis has shown a pronounced order effect, showing a first sample effect overestimation. Moreover, it was noted that order effect was rather evidenced in the first four orders, making for a relatively more precise experiment than the ones using remaining samples. Therefore, the use of Sudoku can be a convenient strategy to organize smaller testing sessions that could result in efficient experiments, with small variance of the estimated averages or effects and needing a lot less panelists to make a design as efficient as the large one used. Moreover, Sudoku can also inspire an efficient design that allows different test sessions.

Tensor factorisation for narrowband single channel source decomposition

In many subspace signal decomposition methods such as principal component analysis (PCA) or its extension, singular spectrum analysis (SSA), particularly meant for processing of single channel signals, there is need for a robust determination and validation of the number of sources. Here, we attempt to find a relation between the number of sources within single channel mixtures and the rank of a symmetric tensor constructed from such mixtures by adjusting the embedding dimension. This leads to a new approach for decomposition of single channel mixtures using tensor factorisation. Consequently, the effect of model order is analysed for simulated narrowband data. The inherent frequency diversity of the time series has also been effectively exploited in selection of the desired subspace. The proposed method has been applied to both simulated and real data. The results have been discussed and compared with those of a number of benchmark algorithms.

ICA model order selection of task co-activation networks

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis

People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain–computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.

Model order effects on the transmission zeros of flexible space structures

This paper proves that the transmission zeros of finite-dimensional models for a flexible structure exhibit precisely the same Rayleigh-Ritz convergence properties as do the poles of such models.

Structural Identification Using Order Overspecified Time-Series Models

An identification method that uses order overspecified time-series models and a truncated singular value decomposition (SVD) solution is studied. The overspecified model reduces the effects of noise during the identification process, but produces extraneous modes. A backwards approach coupled with a minimum norm approximation, using a truncated SVD solution, enables the system modes to be distinguished from the extraneous modes of the model. Experimental data from a large flexible truss is used to study the effects of varying the truncation of the SVD solution and an order recursive algorithm is used to study the effects of model order. Results show that the SVD may be ineffective in separating the data into signal and noise subspaces. However solutions for highly overspecified model orders exhibit solution properties similar to the minimum norm solution and system and computational modes can be discriminated without a truncated solution.