Analysis of small signal stability margins using genetic optimization

Power system small signal stability analysis aims to explore different small signal stability conditions and controls, namely: (1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load flow feasibility, saddle node and Hopf bifurcation ones; (2) finding the maximum and minimum damping conditions; and (3) determining control actions to provide and increase small signal stability. These problems are presented in this paper as different modifications of a general optimization to a minimum/maximum, depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are difficulties with finding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In this paper, we propose a new black-box genetic optimization technique for comprehensive small signal stability analysis, which can effectively cope with highly nonlinear objective functions with multiple minima and maxima, and derivatives that can not be expressed analytically. The optimization result can then be used to provide such important information such as system optimal control decision making, assessment of the maximum network's transmission capacity, etc. (C) 1998 Elsevier Science S.A. All rights reserved.

Seven retinal specializations in the tubular eye of the deep-sea pearleye, Scopelarchus michaelsarsi: A case study in visual optimization

The deep-sea pearleye, Scopelarchus michaelsarsi (Scopelarchidae) is a mesopelagic teleost with asymmetric or tubular eyes. The main retina subtends a large dorsal binocular field, while the accessory retina subtends a restricted monocular field of lateral visual space. Ocular specializations to increase the lateral visual field include an oblique pupil and a corneal lens pad. A detailed morphological and topographic study of the photoreceptors and retinal ganglion cells reveals seven specializations: a centronasal region of the main retina with ungrouped rod-like photoreceptors overlying a retinal tapetum; a region of high ganglion cell density (area centralis of 56.1x10(3) cells per mm(2)) in the centrolateral region of the main retina; a centrotemporal region of the main retina with grouped rod-like photoreceptors; a region (area giganto cellularis) of large (32.2+/-5.6 mu m(2)), alpha-like ganglion cells arranged in a regular array (nearest neighbour distance 53.5+/-9.3 mu m with a conformity ratio of 5.8) in the temporal main retina; an accessory retina with grouped rod-like photoreceptors; a nasotemporal band of a mixture of rod-and cone-like photoreceptors restricted to the ventral accessory retina; and a retinal diverticulum comprised of a ventral region of differentiated accessory retina located medial to the optic nerve head. Retrograde labelling from the optic nerve with DiI shows that approximately 14% of the cells in the ganglion cell layer of the main retina are displaced amacrine cells at 1.5 mm eccentricity. Cryosectioning of the tubular eye confirms Matthiessen's ratio (2.59), and calculations of the spatial resolving power suggests that the function of the area centralis (7.4 cycles per degree/8.1 minutes of are) and the cohort of temporal alpha-like ganglion cells (0.85 cycles per degree/70.6 minutes of are) in the main retina may be different. Low summation ratios in these various retinal zones suggests that each zone may mediate distinct visual tasks in a certain region of the visual field by optimizing sensitivity and/or resolving power.

Simulation and optimization of heated, inviscid flows in scramjet ducts

A space-marching code for the simulation and optimization of inviscid supersonic flow in three dimensions is described. The now in a scramjet module with a relatively complex three-dimensional geometry is examined and wall-pressure estimates are compared with experimental data. Given that viscous effects are not presently included, the comparison is reasonable. The thermodynamic compromise of adding heat in a diverging combustor is also examined. The code is then used to optimize the shape of a thrust surface for a simpler (box-section) scramjet module in the presence of uniform and nonuniform heat distributions. The optimum two-dimensional profiles for the thrust surface are obtained via a perturbation procedure that requires about 30-50 now solutions. It is found that the final shapes are fairly insensitive to the details of the heat distribution.

Design analysis for refolding monomeric protein

Renaturation of protein expressed as inclusion bodies within Escherichia coli is a key step in many bioprocesses. Operating conditions for the refolding step dramatically affect the amount of protein product recovered, and hence profoundly influence the process economics. The first systematic comparison of refolding conducted in batch, fed-batch and continuous stirred-tank reactors is provided Refolding is modeled as kinetic competition between first-order refolding (equilibrium reaction) and irreversible aggregation (second-order). Simulations presented allow direct comparison between different flowsheets and refolding schemes using a dimensionless economic objective. As expected from examination of the reaction kinetics, batch operation is the most inefficient merle. For the base process considered, the overall cost of fed-batch and continuous refolding is virtually identical (less than half that of the batch process). Reactor selection and optimization of refolding using overall economics are demonstrated to be vitally important.

Structure optimization combining soft-core interaction functions, the diffusion equation method, and molecular dynamics

Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.

Anisotropy-based performance analysis of linear discrete time invariant control systems

The anisotropic norm of a linear discrete-time-invariant system measures system output sensitivity to stationary Gaussian input disturbances of bounded mean anisotropy. Mean anisotropy characterizes the degree of predictability (or colouredness) and spatial non-roundness of the noise. The anisotropic norm falls between the H-2 and H-infinity norms and accommodates their loss of performance when the probability structure of input disturbances is not exactly known. This paper develops a method for numerical computation of the anisotropic norm which involves linked Riccati and Lyapunov equations and an associated special type equation.

A retrospective analysis of mycophenolic acid and cyclosporin concentrations with acute rejection in renal transplant recipients

Objectives: Although monitoring of cyclosporin (CsA) is standard clinical practice postrenal transplantation. mycophenolic acid (MPA) concentrations are not routinely measured. There is evidence that a relationship exists between MPA area under the concentration-time curve (AUC) and rejection. In this study, a retrospective analysis was undertaken of 27 adult renal transplant recipients. Methods: Patients received CsA and MPA therapy and had a four-point MPA AUC investigation. The relationship between MPA AUC performed in the first week after transplantation, as well as median trough cyclosporin concentrations, and clinical outcomes in the first month posttransplant were evaluated. Results: A total of 12 patients experienced biopsy proven rejection (44.4%) and 4 patients had gastrointestinal adverse events (14.8%). A statistically significant relationship was observed between the incidence of biopsy proven rejection and both MPA AUC (p = 0.02) and median trough CsA concentration (p = 0.008). No relationship between trough MPA concentration and rejection was observed (p = 0.21). Only 3 of 11 (27%) patients with an MPA AUC > 30 mg.h/L and a median trough CsA > 175 mug/L experienced acute rejection, compared with a 56% incidence of rejection for the remaining 16 patients. Patients who experienced adverse gastrointestinal events had significantly lower MPA AUC (p = 0.04), but median trough CsA concentrations were not significantly different (p = 0.24). Further, 3 of these 4 patients had rejection episodes. Conclusions: in addition to standard CsA monitoring, we propose further investigation of the use of a 4-point sampling strategy to predict MPA AUC in the first week posttransplant, which may facilitate optimization of mycophenolate mofetil dose at a rime when patients are most vulnerable to acute rejection. (C) 2001 The Canadian Society of Clinical Chemists. All rights reserved.

Analysis of flexural waves excited by rectangular contact type transducers

In this paper, an attempt was made to investigate a fundamental problem related to the flexural waves excited by rectangular transducers. Due to the disadvantages of the Green's function approach for solving this problem, a direct and effective method is proposed using a multiple integral transform method and contour integration technique. The explicit frequency domain solutions obtained from this newly developed method are convenient for understanding transducer behavior and theoretical optimization and experimental calibration of rectangular transducers. The time domain solutions can then be easily obtained by using the fast Fourier transform technique. (C) 2001 Elsevier Science B.V. All rights reserved.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Strength analysis of steel-concrete composite beams in combined bending and shear

Despite experimental evidences, the contributions of the concrete slab and composite action to the vertical shear strength of simply supported steel-concrete composite beams are not considered in current design codes, which lead to conservative designs. In this paper, the finite element method is used to investigate the flexural and shear strengths of simply supported composite beams under combined bending and shear. A three-dimensional finite element model has been developed to account for geometric and material nonlinear behavior of composite beams, and verified by experimental results. The verified finite element model is than employed to quantify the contributions of the concrete slab and composite action to the moment and shear capacities of composite beams. The effect of the degree of shear connection on the vertical shear strength of deep composite beams loaded in shear is studied. Design models for vertical shear strength including contributions from the concrete slab and composite action and for the ultimate moment-shear interaction ate proposed for the design of simply supported composite beams in combined bending and shear. The proposed design models provide a consistent and economical design procedure for simply supported composite beams.

Optimization of integrated chemical-biological degradation of a reactive azo dye using response surface methodology

The integrated chemical-biological degradation combining advanced oxidation by UV/H2O2 followed by aerobic biodegradation was used to degrade C.I. Reactive Azo Red 195A, commonly used in the textile industry in Australia. An experimental design based on the response surface method was applied to evaluate the interactive effects of influencing factors (UV irradiation time, initial hydrogen peroxide dosage and recirculation ratio of the system) on decolourisation efficiency and optimizing the operating conditions of the treatment process. The effects were determined by the measurement of dye concentration and soluble chemical oxygen demand (S-COD). The results showed that the dye and S-COD removal were affected by all factors individually and interactively. Maximal colour degradation performance was predicted, and experimentally validated, with no recirculation, 30 min UV irradiation and 500 mg H2O2/L. The model predictions for colour removal, based on a three-factor/five-level Box-Wilson central composite design and the response surface method analysis, were found to be very close to additional experimental results obtained under near optimal conditions. This demonstrates the benefits of this approach in achieving good predictions while minimising the number of experiments required. (c) 2006 Elsevier B.V. All rights reserved.

The cross-entropy method for continuous multi-extremal optimization

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints.