Nickel Oxide Based Supported Catalysts for the In-situ Reactions of Methanation and Desulfurization in the Removal of Sour Gases from Simulated Natural Gas

Supported nickel oxide based catalysts were prepared by wetness impregnation method for the in-situ reactions of H₂S desulfurization and CO₂ methanation from ambient temperature up to 300 °C. Fe/Co/Ni (10:30:60)–Al₂O₃ and Pr/Co/Ni (5:35:60)–Al₂O₃ catalysts were revealed as the most potential catalysts, which yielded 2.9% and 6.1% of CH₄ at reaction temperature of 300 °C, respectively. From XPS, Ni₂O₃ and Fe₃O₄ were suggested as the surface active components on the Fe/Co/Ni (10:30:60)–Al₂O₃ catalyst, while Ni₂O₃ and Co₃O₄ on the Pr/Co/Ni (5:35:60)–Al₂O₃ catalyst.     

Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

We present an evaluation of incremental learning algorithms for the estimation of hidden Markov model (HMM) parameters. The main goal is to investigate incremental learning algorithms that can provide as good performances as traditional batch learning techniques, but incorporating the advantages of incremental learning for designing complex pattern recognition systems. Experiments on handwritten characters have shown that a proposed variant of the ensemble training algorithm, employing ensembles of HMMs, can lead to very promising performances. Furthermore, the use of a validation dataset demonstrated that it is possible to reach better performances than the ones presented by batch learning.     

A graphical approach for confidence limits of optimal preventive maintenance cycles

Facilities management (FM) is the management of infrastructure resources and services to support and sustain the operational strategy of an organization over time. Maintenance is often the business process that has not been optimized and is considered as a liability of business operations. Therefore, extensive studies have been done to determine the optimal replacement interval for irreparable parts of repairable systems where typically the time between failures is characterized by lifetime distribution in which the parameters are estimated from failure data. As a result, the optimal preventive maintenance (PM) interval computed is exposed to sampling risk as the repair cost and failure data used for estimation are typically highly censored due to issues related to data collection and unobserved failures. In this paper, we present a graphical approach to obtain the confidence interval for the optimal PM interval that resulted from sampling variations parameter estimates. The proposed methodology is applied in the context of FM as a strategy for opportunistic replacement and for the purpose of validating the cost components in maintenance. Copyright © 2008 John Wiley & Sons, Ltd.     

A Bipolar Host Material Containing Triphenylamine and Diphenylphosphoryl‐Substituted Fluorene Units for Highly Efficient Blue Electrophosphorescence

Highly efficient blue electrophosphorescent organic light‐emitting diodes incorporating a bipolar host, 2,7‐bis(diphenylphosphoryl)‐9‐[4‐(N,N‐diphenylamino)phenyl]‐9‐phenylfluorene (POAPF), doped with a conventional blue triplet emitter, iridium(III) bis[(4,6‐difluoro‐phenyl)pyridinato‐N,C^2´]picolinate (FIrpic) are fabricated. The molecular architecture of POAPF features an electron‐donating (p‐type) triphenylamine group and an electron‐accepting (n‐type) 2,7‐bis(diphenyl‐phosphoryl)fluorene segment linked through the sp³‐hybridized C9 position of the fluorene unit. The lack of conjugation between these p‐ and n‐type groups endows POAPF with a triplet energy gap (E_T) of 2.75 eV, which is sufficiently high to confine the triplet excitons on the blue‐emitting guest. In addition, the built‐in bipolar functionality facilitates both electron and hole injection. As a result, a POAPF‐based device doped with 7 wt% FIrpic exhibits a very low turn‐on voltage (2.5 V) and high electroluminescence efficiencies (20.6% and 36.7 lm W^?1). Even at the practical brightnesses of 100 and 1000 cd m^?2, the efficiencies remain high (20.2%/33.8 lm W^?1 and 18.8%/24.3 lm W^?1, respectively), making POAPF a promising material for use in low‐power‐consumption devices for next‐generation flat‐panel displays and light sources.     

A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process

Supplier selection negotiation is a sophisticated and challenged job due to the diversity of intellectual backgrounds of the negotiating parties, the many variables involved in supply–demand relationship, the complex interactions and the inadequate negotiation knowledge of project participants. To do the job well, it is necessary to develop an intelligent system for negotiation support in supplier selection process. Therefore, an artificial neural network-based predictive model with application for forecasting the supplier’s bid prices in supplier selection negotiation process (SSNP) is developed in this paper. By means of the model, demander can foresee the relationship between its alternative bids and corresponding supplier’s next bid prices in advance. The purpose of this paper is applying the model’s forecast ability to provide negotiation supports or recommendations for demander in deciding the better current bid price to decrease meaningless negotiation times, reduce procurement cost, improve negotiation efficiency or shorten supplier selection lead-time in SSNP.     

Root Locus for Random Reference Tracking in Systems With Saturating Actuators

This paper develops a root locus technique for random reference tracking in systems with saturating actuators. This is accomplished by introducing the notion of S-poles, which are the poles of the quasilinear system obtained by applying the method of stochastic linearization to the original system with saturation. The path traced by the S-poles on the complex plane when the gain of the controller changes from 0 to infin is the S-root locus. We show that the S-root locus is a subset of the standard root locus, which may terminate prematurely in the so-called termination points. A method for calculating these points is presented and a number of other, more subtle, differences between the usual and the S-root loci are described. In addition, the issue of amplitude truncation in terms of the S-root locus is investigated. Finally, an application of the S-root locus to hard disk drive controller design is presented.     

Numerical algorithms for dynamic traffic demand estimation between zones in a network

This paper presents numerical methods for dynamic traffic demand estimation between N zones in a network, where the zones are disjoint subsets of nodes of the network. Traffic is assumed to be generated or absorbed only in the zones and nowhere else in the network. Traffic volumes between zones over a fixed period of time are modeled as independent random variables with unknown means which it is desired to estimate. For each zone, the volume of all incoming and outgoing traffic is counted on a regular basis but no information about the origin or destination of the observed traffic is used. Procedures are suggested for a regular update of estimates of the N(N - 1) mean traffic demands between the zones on the basis of an incoming stream of the 2N traffic counts. The procedures are based on an exponential smoothing scheme and are reminiscent of the expectation maximization (EM) algorithm if smoothing is removed. Fast and reliable numerical algorithms, based on the conjugate gradient method, are presented for normal as well as for Poisson traffic demands. The Poisson case is linked with entropy maximization. Computational tests based on simulated data demonstrate both the numerical and statistical efficiency of the procedures.