On-line dead-time compensation method using disturbance observer

A new on-line dead-time compensation method for a permanent magnet (PM) synchronous motor drive is proposed. Using a simple disturbance observer without any additional circuit and off-line experimental measurement, disturbance voltages in the rotor reference dq frame caused by the dead time and nonideal switching characteristics of power devices are estimated in an on-line manner and fed to voltage references in order to compensate the dead-time effects. The proposed method is applied to a PM synchronous motor drive system and implemented by using software of a digital signal processor (DSP) TMS320C31. Simulations and experiments are carried out for this system and the results well demonstrate the effectiveness of the proposed method.     

Extraction of penicillin G from simulated media by an emulsion liquid membrane process

The extraction of penicillin G from simulated media was performed by water/oil/water (w/o/w) emulsion liquid membranes (ELMs) and studied under various operational conditions in a batch system. The degree of extraction achieved was between 80% and 95% under specific conditions. A concentration of greater than nine times the initial concentration of penicillin G in the external phase was obtained in the internal phase. The pH of the internal aqueous solution, containing a basic salt, was theoretically calculated on the basis of the amount of penicillin G transported into the internal phase. The calculated results agreed with the experimental data well and were used to select a suitable type and concentration of a basic salt in the internal phase to give a pH within the range 5 to 8 where penicillin G was stable after the termination of extraction. The extraction of penicillin G was successfully performed by the ELM process with sodium carbonate in the internal phase.     

Deactivation of copper-ion-exchanged hydrogen-mordenite-type zeolite catalyst by SO2 for no reduction by NH3

Deactivation of copper-ion-exchanged hydrogen-mordenite-type zeolite catalyst by SO₂ for NO reduction by NH₃ was examined in a fixed-bed flow reactor. The deactivation of the catalyst was strongly dependent on reaction temperature. At high reaction temperatures over 300°C, the catalyst did not lose its initial activity up to 50 h of operation, regardless of SO₂ feed concentration from 500 to 20,000 ppm. However, at low reaction temperatures near 250°C, apparent deactivation did occur. Changes in the physicochemical properties such as surface area and sulfur content of deactivated catalyst well correlated with catalyst activity, depending upon reaction temperatures. The deactivation was due to pore blocking and/or filling by deactivating agents, which plugged and/or filled the pores of catalyst. The deactivating agents deposited on the catalyst surface were presumed to be (NH₄)₂SO₄ and/or (NH₄)HSO₄ from the results of TGA and ion-chromatography measurement.     

Shortest path algorithms: A computational study with the C programming language

The main purpose of this study is to evaluate the computational efficiency of algorithms for calculating shortest paths when they are correctly coded by using the C programming language. The eight algorithms that we selected for this experiment are the most efficient, either measured in terms of worst-case bounds or marked as such from previous computational studies; they include the redistributive heap algorithm. We suggest computer implementations that use the full power of C. In particular, the network representation and the various data structures used to keep the scan eligible list may be managed by using only additions and no multiplications, while it is not possible with FORTRAN. These capabilities, unique to C, yield several interesting conclusions: one may expect to speed up a shortest path algorithm by a factor of 20%; in some cases, this factor may reach 30%. Interestingly, the level of programming difficulty required to achieve these benefits is not greater than that required by implementations using arrays.     

Parathion degradation and toxicity reduction in solar photocatalysis and photolysis.

The solar photocatalytic degradation of methyl parathion was investigated using a circulating TiO2/solar light reactor. Under solar photocatalysis condition, parathion was more effectively degraded than solar photolysis and TiO2-only conditions. With solar photocatalysis, 20 mg/L of parathion was completely degraded within 60 min with a TOC decrease of 63% after 150 min. The main ionic byproducts during photocatalysis recovered from parathion degradation were mainly as NO3-, NO2- and NH4+, 80% of the sulphur as SO4(2-), and 5% of phosphorus as PO4(3-). The organic intermediates 4-nitrophenol and methyl paraoxon were also identified, and these were further degraded in solar photocatalytic condition. Two different bioassays (Vibrio fischeri and Daphnia magna) were used to test the acute toxicity of solutions treated by solar photocatalysis and photolysis. The Microtox test using V. fischeri showed that the toxicity expressed as EC50 (%) value increased from 5.5% to >82% in solar photocatalysis, indicating that the treated solution is non-toxic, but only increased from 4.9 to 20.5% after 150 min in solar photolysis. The acute toxicity test using D. magna showed that EC50 (%) increased from 0.05 to 1.08% under solar photocatalysis, but only increased to 0.12% after 150 min with solar photolysis, indicating the solution is still toxic. The pattern of toxicity reduction parallels the decrease in TOC and the parathion concentrations.     

Hierarchical classifier design in high-dimensional numerous classcases

In applying pattern recognition methods in remote sensing problems, an inherent limitation is that there is almost always only a small number of training samples with which to design the classifier. A hybrid decision tree classifier design procedure that produces efficient and accurate classifiers for this situation is proposed. In doing so, several key questions are addressed, among them the question of the feature extraction techniques to be used and the mathematical relationship between sample size, dimensionality, and risk value. Empirical tests comparing the hybrid design classifier with a conventional single layered one are presented. They suggest that the hybrid design produces higher accuracy with fewer features. The need for fewer features is an important advantage, because it reflects favorably on both the size of the training set needed and the amount of computation time that will be needed in analysis     

Improved Poisson intensity estimation: denoising application using Poisson data

Recently, Timmermann and Nowak developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data.     

Traffic grooming on WDM rings using optical burst transport

A sublambda traffic-grooming scheme on wavelength-division-multiplexed (WDM) rings, which is called optical burst transport (OBT), is proposed. The network protocol and architecture allow increased flexibility to tailor the transport network behavior for efficient delivery of bursty data traffic. Using different network parameters, its performance is analyzed via simulation, and the implementation issues including the media-access-control (MAC) protocol, tunable-filter controller, and burst-mode receiver are addressed. Finally, the feasibility of the OBT with an experimental testbed built by the authors is demonstrated and a streaming-video application is used to present its overall functionality.     

Efficient particle filter-based processing algorithm for clutter suppression and IR point targets enhancement

A novel soft decision-based median subtraction filter is presented for clutter suppression and infrared (IR) point targets enhancement. The decision is made based on a jump Markov model and its state and parameter estimation using a particle filter. The scheme is compared with other conventional clutter background removal techniques and good results are obtained.