Data selection for system identification (DS4SID) from logged process records of continuously operated plants

Use of historical logged data can be considered for system identification if performing dedicated experiments is not possible. Continuously operated plants are examples of processes where experiments for system identification are typically restricted due to a possibly negative impact on production. However, process variables are logged for long periods of time which results in large databases that are a valuable source of information for model estimation. Automatic selection of informative data intervals can support system identification when use of logged process data is addressed. A new method is presented that differs in several aspects from current approaches. Firstly, interval bounding is performed using the gradient of a norm associated to the resulting information matrix which decreases interval misdetection. Secondly, process data do not need to be normalized for change detection. Thirdly, an instrumental variables identification method is used which offers robustness to autocorrelated noise. Lastly, the proposed selection technique can be applied to multivariate processes. The performance of the proposed method is demonstrated in a case study implemented in a lab-scale chemical plant.

Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.

Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.

Accurate autocorrelation modeling substantially improves fMRI reliability

Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. We employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. Though autocorrelation modeling in AFNI is not perfect, its performance is much higher than the performance of autocorrelation modeling in FSL and SPM. The residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. Our results show superior performance of SPM’s alternative pre-whitening: FAST, over SPM’s default. The reliability of task fMRI studies would increase with more accurate autocorrelation modeling. Furthermore, reliability could increase if the packages provided diagnostic plots. This way the investigator would be aware of pre-whitening problems.

Effect of Noise on the Decoherence of a Central Electron Spin Coupled to an Antiferromagnetic Spin Bath

We analyze the influence of a two-state autocorrelated noise on the decoherence and on the tunneling Landau-Zener (LZ) transitions during a two-level crossing of a central electron spin (CES) coupled to a one dimensional anisotropic-antiferomagnetic spin, driven by a time-dependent global external magnetic field. The energy splitting of the coupled spin system is found through an approach that computes the noise-averaged frequency. At low magnetic field intensity, the decoherence (or entangled state) of a coupled spin system is dominated by the noise intensity. The effects of the magnetic field pulse and the spin gap antiferromagnetic material used suggest to us that they may be used as tools for the direct observation of the tunneling splitting through the LZ transitions in the sudden limit. We found that the dynamical frequencies display basin-like behavior decay with time, with the birth of entanglement, while the LZ transition probability shows Gaussian shape.