Online and Offline Rotary Regression Analysis of Torque Estimator for Switched Reluctance Motor Drives

A new torque estimator for switched reluctance motor (SRM) drives based on 2-D rotary regression analysis is presented in this paper. The proposed torque estimator is composed of a bicubic regressive polynomial as a function of rotor position and input current. The regressive coefficients can be computed offline or online from the torque characteristics acquired either experimentally or from numerical computation. Furthermore, a torque estimation method by taking mutual coupling into consideration is proposed. It can be seen that the estimated and experimentally obtained self-coupling and mutual-coupling torque characteristics are in good agreement with each other. In addition, the dynamic torque waveforms with and without the mutual coupling, estimated by the proposed estimator, are found to be virtually the same as those obtained from the bicubic spline interpolation for SRM drives with single-pulse voltage, hysteresis current chopping, as well as with voltage pulse width modulation control. The success of all the case studies being reported is a good validation of the usefulness and accuracy of the proposed real-time torque estimator that, as described in this paper, can be used to quickly estimate the instantaneous output torque of SRM drives.     

3D velocity from 3D Doppler radial velocity

We present local least squares and regularization frameworks for computing 3D velocity (3D optical flow) from 3D radial velocity measured by a Doppler radar. We demonstrate the performance of our algorithms quantitatively on synthetic radial velocity data and qualitatively on real radial velocity data, obtained from the Doppler radar at Kurnell Radar station, Botany Bay, New South Wales, Australia. Radial velocity can be used to predict the future positions of storms in sequences of Doppler radar datasets.© 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 189–198, 2005     

Effect of surfactants and liquid hydrocarbons on gas hydrate formation rate and storage capacity

Hydrate formation rate plays an important role in making hydrates for the storage and transport of natural gas. Micellar surfactant solutions were found to increase gas hydrate formation rate and storage capacity. With the presence of surfactant, hydrate could form quickly in a quiescent system and the energy costs of hydrate formation reduced. Surfactants (an anionic surfactant, a non‐ionic surfactant and their mixtures) and liquid hydrocarbons (cyclopentane and methylcyclohexane) were used to improve hydrate formation. The experiments of hydrate formation were carried out in the pressure range 3.69–6.82 MPa and the temperature range 274.05–277.55 K. The experimental pressures were kept constant during hydrate formation in each experimental run. The effect of anionic surfactant (sodium dodecyl sulphate (SDS)) on natural gas storage in hydrates is more pronounced compared to a non‐ionic surfactant (dodecyl polysaccharide glycoside (DPG)). The induction time of hydrate formation was reduced with the presence of cyclopentane (CP). Cyclopentane and methylcyclohexane (MCH) could increase hydrate formation rate, but reduced hydrate storage capacity The higher methylcyclohexane concentration, the lower the hydrate storage capacity. Copyright © 2003 John Wiley & Sons, Ltd.     

Ennoblement of stainless steel in natural seawater – A new explanation

The ennoblement of corrosion potential (E_corr) of passive metal immersed in seawater was investigated with electrochemical technology and epifluorescence microscopy. The in situ observation showed that the bacteria number increased on the metal surface according to an exponential law which was in the same way with the ennoblement of E_corr. At the same time, the anodic polarization current of high‐Mo stainless steel decreased in the initial days. According to the mix‐potential theory and the characteristics of polarization curves of high‐Mo steel in natural seawater, the ennoblement of corrosion potential may be induced by the decrease of the passive current density.     

Novel tiO2 nanocatalysts for wastewater purification: tapping energy from the sun.

Water treatment using TiO2 semiconductor as a durable heterogeneous photocatalyst has been the focus of environmentalists in recent years. Currently, we developed an inexpensive and highly efficient approach for synthesizing nitrogen-doped TiO2 with lower band-gap energy that can respond to visible light. Doping on the molecular scale led to an enhanced nitrogen concentration of up to 21.8%. Reflectance measurements showed the synthesized N-doped TiO2 nanoparticles are catalytically active with the absorbance that extends into the visible region up to 600 nm. The water purification potential of this new class of compound was evaluated by studying the photodegradation of Acid Orange 7 (AO7) and E. coli. Experiments were conducted to compare the photocatalytic activities of N-doped TiO2 nanocatalysts and commercially available Degussa P25 power under identical solar light exposure. N-doped TiO2 demonstrated superior photocatalytic activities in both chemical compound degradation and bactericidal reactions. The result of this study shows the potential of applying new generations of catalyst for wastewater purification and disinfection.     

Entropy of multidimensional cellular automata

Since the topological entropy of a vast class of two-dimensional cellular automata (CA) is infinite, of interest is the possibility to renormalize it so that to obtain a positive finite value. We find the asymptotics of the information function of a multidimensional CA and, accordingly, introduce the renormalized topological entropy as a coefficient of this asymptotics. We describe some properties of the introduced quantity, in particular, its positivity for CA of the type of “The Game of Life.” Also, we give an example of an explicit evaluation of this parameter for a particular cellular automaton.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.