A simplified model based supercritical power plant controller

We present a simplified state-space model of a once-through supercritical boiler turbine power plant. This phenomenological model has been developed from a greatly simplified application of the first principles of physical laws. When we fit our model to a far more complex and physically accurate simulation model commissioned by EPRI for operator training, we find that the input-output responses are surprisingly close. Encouraged by this initial success, we describe some initial steps toward a design method for supercritical boiler control suggested by the geometric structure arising from the simplified model. Preliminary simulation results suggest that this approach may offer a closed loop response considerably improved relative to that achieved by the linear controllers presently in place in typical industrial settings

Intelligent control of a boiler-turbine plant based on switching control scheme

This paper reports on our present achievement toward the intelligent control of a boiler-turbine power-plant based on switching control scheme, recently revived by some active reports. To overcome strong nonlinearity emerging in load following operations of boiler-turbine power plants, which is not efficiently compensated by the conventional PI-based gain scheduling control, a neural-based nonlinear feed-forward switching control scheme is employed. Owing to its 2-degree freedom type installment in the control system and proper switching of nonlinear feed-forward control by monitoring contribution of inverse dynamics error to control error, effective suppression of nonlinearity is achieved

Relativistic Particle Acceleration in a Folded Current Sheet

Two-dimensional particle simulations of a relativistic Harris current sheet of pair plasmashave demonstrated that the system is unstable to the relativistic drift kink instability (RDKI) and that a new kind of acceleration process takes place in the deformed current sheet. This process contributes to the generation of non-thermal particles and contributes to the fast magnetic dissipation in the current sheet structure. The acceleration mechanism and a brief comparison with relativistic magnetic reconnection are presented.Comment: 11 preprint pages, including 3 .eps figure

Particle Acceleration and Magnetic Dissipation in Relativistic Current Sheet of Pair Plasmas

We study linear and nonlinear development of relativistic and ultrarelativistic current sheets of pair plasmas with antiparallel magnetic fields. Two types of two-dimensional problems are investigated by particle-in-cell simulations. First, we present the development of relativistic magnetic reconnection, whose outflow speed is an order of the light speed c. It is demonstrated that particles are strongly accelerated in and around the reconnection region, and that most of magnetic energy is converted into "nonthermal" part of plasma kinetic energy. Second, we present another two-dimensional problem of a current sheet in a cross-field plane. In this case, the relativistic drift kink instability (RDKI) occurs. Particle acceleration also takes place, but the RDKI fast dissipates the magnetic energy into plasma heat. We discuss the mechanism of particle acceleration and the theory of the RDKI in detail. It is important that properties of these two processes are similar in the relativistic regime of T > mc^2, as long as we consider the kinetics. Comparison of the two processes indicates that magnetic dissipation by the RDKI is more favorable process in the relativistic current sheet. Therefore the striped pulsar wind scenario should be reconsidered by the RDKI.Comment: To appear in ApJ vol. 670; 60 pages, 27 figures; References and typos are fixe

A BROAD SYMMETRY CRITERION FOR NONPARAMETRIC VALIDITY OF PARAMETRICALLY-BASED TESTS IN RANDOMIZED TRIALS

Summary. Pilot phases of a randomized clinical trial often suggest that a parametric model may be an accurate description of the trial\u27s longitudinal trajectories. However, parametric models are often not used for fear that they may invalidate tests of null hypotheses of equality between the experimental groups. Existing work has shown that when, for some types of data, certain parametric models are used, the validity for testing the null is preserved even if the parametric models are incorrect. Here, we provide a broader and easier to check characterization of parametric models that can be used to (a) preserve nonparametric validity of testing the null hypothesis, i.e., even when the models are incorrect, and (b) increase power compared to the non- or semiparametric bounds when the models are close to correct. We demonstrate our results in a clinical trial of depression in Alzheimer\u27s patients

Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles

Glueball mass from quantized knot solitons and gauge-invariant gluon mass

We propose an approach which enables one to obtain simultaneously the glueball mass and the gluon mass in the gauge-invariant way to shed new light on the mass gap problem in Yang-Mills theory. First, we point out that the Faddeev (Skyrme--Faddeev-Niemi) model can be induced through the gauge-invariant vacuum condensate of mass dimension two from SU(2) Yang-Mills theory. Second, we obtain the glueball mass spectrum by performing the collective coordinate quantization of the topological knot soliton in the Faddeev model. Third, we demonstrate that a relationship between the glueball mass and the gluon mass is obtained, since the gauge-invariant gluon mass is also induced from the relevant vacuum condensate. Finally, we determine physical values of two parameters in the Faddeev model and give an estimate of the relevant vacuum condensation in Yang-Mills theory. Our results indicate that the Faddeev model can play the role of a low-energy effective theory of the quantum SU(2) Yang-Mills theory.Comment: 17 pages, 2 figures, 3 tables; a version accepted for publication in J. Phys. A: Math. Gen.; Sect. 2 and sect. 5 (old sect. 4) are modified. Sect. 4, Tables 1 and Table 3 are adde

Estimating effects by combining instrumental variables with case-control designs: the role of principal stratification

The instrumental variable framework is commonly used in the estimation of causal effects from cohort samples. In the case of more efficient designs such as the case-control study, however, the combination of the instrumental variable and complex sampling designs requires new methodological consideration. As the prevalence of Mendelian randomization studies is increasing and the cost of genotyping and expression data can be high, the analysis of data gathered from more cost-effective sampling designs is of prime interest. We show that the standard instrumental variable analysis is not applicable to the case-control design and can lead to erroneous estimation and inference. We also propose a method based on principal stratification for the analysis of data arising from the combination of case-control sampling and instrumental variable design and illustrate it with a study in oncology

Faster Family-wise Error Control for Neuroimaging with a Parametric Bootstrap

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted \emph{p}-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow