Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).     

Electrospinning hollow and core/sheath nanofibers using hydrodynamic fluid focusing

Hollow poly (vinylpyrrolidone) (PVP) + TiO₂ and polypyrrole (core)/PVP (sheath) nanofibers were successfully electrospun using hydrodynamic fluid focusing. Utilizing a two-dimensional fluid focusing technique previously applied to aqueous solutions, intersecting microchannels cast in (poly)dimethylsiloxane were utilized to dynamically center core fluids in immiscible sheath fluids prior to electrospinning at the channel outlet. Advantages of using microfluidic channel networks for the electrospinning of composite nanofibers include spatiotemporal control over input reagents, ease of fabrication and the ability to focus the core stream into sheath layer without the need of complex co-annular nozzles.     

Reconstruction-based recognition of scenes with translationallyrepeated quadrics

This paper addresses the problem of invariant-based recognition of quadric configurations from a single image. These configurations consist of a pair of rigidly connected translationally repeated quadric surfaces. This problem is approached via a reconstruction framework. A new mathematical framework, using relative affine structure, on the lines of Luong and Vieville (1996), has been proposed. Using this mathematical framework, translationally repeated objects have been projectively reconstructed, from a single image, with four image point correspondences of the distinguished points on the object and its translate. This has been used to obtain a reconstruction of a pair of translationally repeated quadrics. We have proposed joint projective invariants of a pair of proper quadrics. For the purpose of recognition of quadric configurations, we compute these invariants for the pair of reconstructed quadrics. Experimental results on synthetic and real images, establish the discriminatory power and stability of the proposed invariant-based recognition strategy. As a specific example, we have applied this technique for discriminating images of monuments which are characterized by translationally repeated domes modeled as quadrics     

One-dimensional and multi-dimensional substring selectivity estimation

With the increasing importance of XML, LDAP directories, and text-based information sources on the Internet, there is an ever-greater need to evaluate queries involving (sub)string matching. In many cases, matches need to be on multiple attributes/dimensions, with correlations between the multiple dimensions. Effective query optimization in this context requires good selectivity estimates. In this paper, we use pruned count-suffix trees (PSTs) as the basic data structure for substring selectivity estimation. For the 1-D problem, we present a novel technique called MO (Maximal Overlap). We then develop and analyze two 1-D estimation algorithms, MOC and MOLC, based on MO and a constraint-based characterization of all possible completions of a given PST. For the k-D problem, we first generalize PSTs to multiple dimensions and develop a space- and time-efficient probabilistic algorithm to construct k-D PSTs directly. We then show how to extend MO to multiple dimensions. Finally, we demonstrate, both analytically and experimentally, that MO is both practical and substantially superior to competing algorithms. Received April 28, 2000 / Accepted July 11, 2000     

On the mechanism of pairing in thebcs theory

An explanation has been offered for the mechanism of cooper pairing in thebcs theory of superconductivity on the basis of the relativistic Darwin interaction. The theory leads to an expression for critical temperature which depends on a few atomic parameters. Calculated values ofT _c for elements and alloys are in good agreement with experiment.     

Modeling mineral phase change chemistry of groundwater in a rural-urban fringe

This research paper aims to determine the genetic origin of the chemical elements in groundwater. It deals with the results of physicochemical parameters, to evaluate the hydro-geochemistry of groundwater in rural-urban fringe of district Bareilly, India. Pre- and post-monsoon sampling has been carried out, which reveals inter-seasonal variability effect on the hydro-geochemical processes. Geochemical modeling especially computation of saturation index was undertaken using the WATEQ4F model. Majority of samples fall in the category of undersaturation, which further suggests that groundwater still has potential to dissolve more minerals. Chemical categorizations of groundwater samples were performed with the help of the Aquachem model. Grouping of groundwater on the Piper diagram reveals a common composition and origin. In most of the area, water facies is of Ca(2+)-HCO(3)(-) type in both the seasons. It also indicates that in pre-monsoon, ion exchange is the dominant process, whereas in post-monsoon, both ion exchanges as well as reverse ion exchanges are reported in the groundwater of the study area.     

Open-loop and feedback-controlled mouse cursor movements in linear paths

Path length (A), path width (W) and movement direction (θ) are identified as the main factors affecting visually-controlled movement times in linear paths. Effects of A and W are well described by Drury's ( 1971 . Movements with lateral constraint. Ergonomics, l4 (2), 293-305.) model in which movement time is linearly related to the ratio of A/W. At low A/W values, departure from linearity has been identified but not investigated in detail. Data are presented for both open-loop and feedback-controlled movements in linear paths at 0, 60 and 150° movement directions. Movement amplitude and path width were varied over a wide range to determine the effects of A and (A/W) on movement time. Movements were found to be made ballistically or in open-loop mode when the ratio (A/W) was less than about 8 to 10 and the movement times were linearly related to √A for all angles of movement. Feedback-controlled movements followed Drury's law; ballistic movements had movement speed linear with √A. PRACTITIONER SUMMARY: Many tasks require manoeuvring equipment or devices through a path of limited width. These movements can be made with or without feedback control, depending on the path constraints. The conditions for the two forms of movement are determined in this research.     

Fuzzy parametric programming model for multi-objective integrated solid waste management under uncertainty

Solid waste management is increasingly becoming a challenging task for the municipal authorities due to increasing waste quantities, changing waste composition, decreasing land availability for waste disposal sites and increasing awareness about the environmental risk associated with the waste management facilities. The present study focuses on the optimum selection of the treatment and disposal facilities, their capacity planning and waste allocation under uncertainty associated with the long-term planning for solid waste management. The fuzzy parametric programming model is based on a multi-objective, multi-period system for integrated planning for solid waste management. The model dynamically locates the facilities and allocates the waste considering fuzzy waste quantity and capacity of waste management facility. The model addresses uncertainty in waste quantity as well as uncertainties in the operating capacities of waste management facilities simultaneously. It was observed that uncertainty in waste quantity is likely to affect the planning for waste treatment/disposal facilities more as compared with the uncertainty in the capacities of the waste management facilities. The relationship between increase in waste quantity and increase in the total cost/risk involved in waste management is found to be nonlinear. Therefore, it is possible that a marginal change in waste quantity could increase the total cost/risk substantially. The information obtained from the analysis of modeling results can be effectively used for understanding the effect of changing the priorities and objectives of planning decisions on facility selections and waste diversions.     

Data reduction algorithm based on principle of distributional equivalence for fault diagnosis

Historical data based fault diagnosis methods exploit two key strengths of multivariate statistical approaches, viz.: (i) data compression ability, and (ii) discriminatory ability. It has been shown that correspondence analysis (CA) is superior to principal components analysis (PCA) on both these counts (Detroja, Gudi, Patwardhan, & Roy, 2006a), and hence is more suited for the task of fault detection and isolation (FDI). In this paper, we propose a CA based methodology for fault diagnosis that can facilitate significant data reduction as well as better discrimination. The proposed methodology is based on the principle of distributional equivalence (PDE). The PDE is a property unique to the CA algorithm and can be very useful in analyzing large datasets. The principle, when applied to historical data sets for FDI, can significantly reduce the data matrix size without significantly affecting the discriminatory ability of the CA algorithm. This can significantly reduce computational load during statistical model building. The data reduction ability of the proposed methodology is demonstrated using a simulation case study involving benchmark quadruple tank laboratory process. The proposed methodology when applied to experimental data obtained from the quadruple tank process also demonstrated data reduction capabilities of the principle of distributional equivalence. The above aspect has also been validated for large-scale data sets using the benchmark Tennessee Eastman process simulation case study.     

Nano and micro structural studies of thin films of ZnO

Zinc oxide thin films grown by sol–gel and RF sputtering methods have been characterized. The characterization techniques used involve ellipsometry, optical absorption, scanning tunneling microscopy, scanning and transmission electron microscopy. The films grown by sol–gel spin method which followed zinc acetate route exhibited a smoother texture than the films, which were deposited by using zinc nitrate route. The later type of films showed a dendritic character. Nano-structured fine grains of size ranging from 20 to 60 nm were observed with zinc nitrate precursor film. Individual grains show a sharp contrast with different facets and boundaries. Crystal planes and lattice parameters calculated by electron diffraction and X-ray diffraction are quite close and in agreement with the reported values in literature. Scanning tunneling microscopy has been used for measuring the average roughness of the surface and estimating the lattice constants. The STM studies of RF sputtered films, although showing a ZnO structure, exhibited a disturbed lattice. This was presumably due to the fact that after deposition the films were not annealed. Nanographs of 2D and 3D view of atomic positions of ZnO have been presented by using scanning tunneling microscopy.