Neuro-fuzzy networks for voltage security monitoring based onsynchronized phasor measurements

The ability to rapidly acquire synchronized phasor measurements from around a power network opens up new possibilities for power system operation and control. A novel neuro-fuzzy network, the fuzzy hyperrectangular composite neural network, is proposed for voltage security monitoring (VSM) using synchronized phasor measurements as input patterns. This paper demonstrates how neuro-fuzzy networks can be constructed offline and then utilized online for monitoring voltage security. The neuro-fuzzy network is tested on 3000 simulated data from randomly generated operating conditions on the IEEE 30-bus system to indicate its high classification rate for voltage security monitoring     

Lattice and Surface Engineering of Ruthenium Nanostructures for Enhanced Hydrogen Oxidation Catalysis

Ru has recently been considered as a promising alternative of Pt toward hydrogen oxidation reaction (HOR) due to its lower price and similar hydrogen binding energy (HBE) in comparison to Pt. Nevertheless, the catalytic performance of Ru toward HOR is far from the satisfaction of practical application. Herein, it is demonstrated that the modification of Ru multi-layered nanosheet (MLNS) with Ni can significantly promote the HOR performance. In particular, the HOR performance is strongly related to the Ni location on the surface or in the lattice of Ru MLNS. Experimental and theoretical investigations suggest that Ni in the lattice of Ru MLNS (lattice engineering) optimizes the HBE, while Ni species on the surface (surface engineering) decrease the free energy of water formation, as a result of the significantly enhanced HOR performance. The optimal catalyst, where Ni is located both on the surface and in the lattice, displays superior alkaline HOR performance to commercial Pt/C and Ru/C. The present study not only systematically reveals the significance of Ni modification on Ru toward HOR, but also promotes the fundamental researches on catalyst design for fuel cell reactions and beyond.     

Exact, closed-form representations for the time-domain surface impedances of a homogeneous, lossy half-space

In this paper, we derive exact, closed-form representations for the transverse electric and transverse magnetic time-domain, surface impedances associated with a plane wave that is obliquely incident on a homogeneous, lossy half-space. The time-domain surface impedances are expressed in terms of modified Bessel functions and incomplete Lipschitz-Hankel integrals. The limitations on the validity of a previously published approximate expression are also discussed.     

Triple-channel InP-based HEMT with highly nonlinear transconductance for nonlinear circuit applications

The authors report the development of a triple-channel HEMT with two undoped In/sub 0.53/Ga/sub 0.47/As layers and a spike-doped In/sub 0.52/Al/sub 0.48/As layer on InP, designed to have a strongly nonlinear transconductance. The effective electron velocity and the electron density in the InGaAs layers are higher than in the thick InAlAs layer resulting in a highly nonlinear transconductance device with the potential for improved performance in nonlinear microwave circuit applications, as compared with conventional MESFETs and HEMTs.<>     

Orientation of graphitic planes during annealing of “dip deposited” amorphous carbon film: A carbon K-edge X-ray absorption near-edge study

Annealing effect of amorphous carbon thin films on Si(1 0 0) substrates is studied by normal incidence and angle dependent carbon K-edge X-ray absorption near-edge structure (XANES) spectroscopy. The angle dependence of the XANES signal shows that the graphitic basal planes are oriented perpendicular to the surface when the film is annealed at 1000 °C. Micro-Raman spectroscopy reveals two well-separated bands the D band at 1355 cm⁻¹ and G band at ∼1600 cm⁻¹, and their I_D/I_G intensity ratio indicates the formation of more graphitic film at higher annealing temperatures. X-ray diffraction pattern of 1000 °C temperature annealed film confirms the formation of graphite structure.     

Effects of Drying Methods and Tea Preparation Temperature on the Amount of Vitamin C in Indian Gooseberry Tea

Indian gooseberry is a rich source of vitamin C. The fruit can be consumed either fresh or after processing into different products including Indian gooseberry tea. Both drying and tea preparation steps affect quality of the gooseberry tea drink made using dried gooseberry. This study investigated the effects of different drying methods, i.e., hot air drying, vacuum drying, and low-pressure superheated steam drying, on the retention and degradation of vitamin C (in terms of the total ascorbic acid content, TAA) in dried gooseberry flakes. In addition, the effect of temperature of water used to prepare the tea on the release of TAA and on its later degradation was also investigated.     

等温马氏体转变动力学模型的修正

自促发形核机制对于等温马氏体转变的解释是正确的.但是现有几种动力学模型尚存在一些不妥之处.文中给出一种物理意义更为明确的修正模型.     

A neural-net based short term load forecasting using moving window procedure

An improved neural-net approach based on a combined unsupervised/supervised learning concept is proposed. A ‘moving window’ procedure is applied to the most recent load and weather information for creating training set data base. A forecasting lead time that varies from 16 hours to 88 hours is introduced to produce the short term electric load forecasting that meets requirements of real electric utility operating practice. The unsupervised learning (UL) is used to identify days with similar daily load patterns. A feed forward three-layer neural net is designed to predict 24-hour loads within the supervised learning (SL) phase. The effectiveness of proposed methods is demonstrated by comparison of forecasted hourly loads in every single day during 1991 with data realized in the same period in the Electric Power Utility of Serbia (EPS). A better choice of input features and more appropriate training set selection procedure allow significant improvement in forecasting results comparing with our previous UL/SL concept characterized by a fixed neural-net structure and absence of re-training procedure. The improvement is illustrated by reduction of average error in daily energy forecasting for 0.83% and reduction of 90th percentile of 2.04%.