A self‐tuning iterative learning controller for time variant systems

We consider the iterative learning control problem from an adaptive control viewpoint. The self‐tuning iterative learning control systems (STILCS) problem is formulated in a general case, where the underlying linear system is time‐variant and its parameters are all unknown and where its initial conditions are not constant and not determinable in various iterations. A procedure for solving this problem will be presented. The Lyapunov technique is employed to ensure the convergence of the presented STILCS. Computer simulation results are included to illustrate the effectiveness of the proposed STILCS. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

The hyperspherical acceleration effect particle swarm optimizer

This paper proposes Hyperspherical Acceleration Effect Particle Swarm Optimization (HAEPSO) for optimizing complex, multi-modal functions. The HAEPSO algorithm finds the particles that are trapped in deep local minima and accelerates them in the direction of global optima. This novel technique improves the efficiency by manipulating PSO parameters in hyperspherical coordinate system. Performance comparisons of HAEPSO are provided against different PSO variants on standard benchmark functions. Results indicate that the proposed algorithm gives robust results with good quality solution and faster convergence. The proposed algorithm is an effective technique for solving complex, higher dimensional multi-modal functions.     

Validation of caregiver interviews to diagnose common causes of severe neonatal illness

The objective of this study was to validate retrospective caregiver interviews for diagnosing major causes of severe neonatal illness and death. A convenience sample of 149 infants aged < 28 days with one or more suspected diagnoses of interest (low birthweight/severe malnutrition, preterm birth, birth asphyxia, birth trauma, neonatal tetanus, pneumonia, meningitis, septicaemia, diarrhoea, congenital malformation or injury) was taken from patients admitted to two hospitals in Dhaka, Bangladesh. Study paediatricians performed a standardised history and physical examination and ordered laboratory and radiographic tests according to study criteria. With a median interval of 64.5 days after death or hospital discharge, caregivers of 118 (79%) infants were interviewed about their child's illness. Using reference diagnoses based on predefined clinical and laboratory criteria, the sensitivity and specificity of particular combinations of signs (algorithms) reported by the caregivers were ascertained. Sufficient numbers of children with five reference standard diagnoses were studied to validate caregiver reports. Algorithms with sensitivity and specificity > 80% were identified for neonatal tetanus, low birthweight/severe malnutrition and preterm delivery. Algorithms with specificities > 80% for birth asphyxia and pneumonia had sensitivities < 70%, or alternatively had high sensitivity with lower specificity. In settings with limited access to medical care, retrospective caregiver interviews provide a valid means of diagnosing several of the most common causes of severe neonatal illness and death.     

Effects of Pressure on the Performance of CNTs-Supported Catalyst in a Fischer-Tropsch Reaction

Fischer-Tropsch （FT） reaction involves conversion of syngas （a mixture of carbon monoxide and hydrogen） into higher hydrocarbons in the presence of an active catalyst. The syngas can be derived from non-petroleum feedstocks such as coal, biomass and natural gas, thus the FT reaction provides an alternative route for production of clean fuels. The FT process has received growing interest in recent years due to uncertainty in the Middle East, fast depletion of fossil fuel and environmental concern. This paper reports the synthesis, physicochemical properties and catalytic performance of cobalt-based catalyst in the FT reaction. The catalysts comprised metal nanoparticles supported on carbon nanotubes （CNTs） which were synthesized via a wet impregnation method. The catalysts were characterized using transmission electron microscopy （TEM）, temperature-programmed reduction/desorption （TPR/TPD） and X-ray photoelectron spectroscopy （XPS）. The performance of the cobalt-based catalyts in a FT reaction was evaluated in a fixed bed microreactor equipped with an on-line gas chromatograph for analyses of hydrocarbon products. The catalysts investigated in this work were Co/CNTs, 70Co30Mn/CNTs, 0.06%K/70Co30Fe/CNTs and 0.04%Nb/70Co30Fe/CNTs. TEM analyses revealed that the average sizes of the metal nanoparticles were 4-5 nm. Based on TPD analyses, the dispersion of these nanoparticles on CNTs were greater than 90%. The presence of both Co2＋ and Co3＋ ions were confirmed by XPS analysis. The 0.04% Nb/70Co30Fe/CNTs catalyst performed better than other catalysts in the FT reaction where it resulted in CO conversion of 35% and 16% C5＋ selectivity at pressure of 1 bar, 220 ~C and H2：CO of 2：1. Using the same catalyst, the CO conversion and C5＋ selectivity increased to 60% and 57%, respectively when the pressure was increased to 20 bar.     

Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.     

An alternative measure of the ICT-Opportunity Index

We developed an alternative approach for measuring information and communication technology (ICT), applying Data Envelopment Analysis (DEA) using data from the International Telecommunications Union as a sample of 183 economies. We compared the ICT-Opportunity Index (ICT-OI) with our DEA-Opportunity Index (DEA-OI) and found a high correlation between the two. Our findings suggest that both indices are consistent in their measurement of digital opportunity, though differences still exist in different regions. Our new DEA-OI offers much more than the ICT-OI. Using our model, the target and peer groups for each country can be identified.     

Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning

We propose a set of kinematic features that are derived from the optical flow for human action recognition in videos. The set of kinematic features includes divergence, vorticity, symmetric and antisymmetric flow fields, second and third principal invariants of flow gradient and rate of strain tensor, and third principal invariant of rate of rotation tensor. Each kinematic feature, when computed from the optical flow of a sequence of images, gives rise to a spatiotemporal pattern. It is then assumed that the representative dynamics of the optical flow are captured by these spatiotemporal patterns in the form of dominant kinematic trends or kinematic modes. These kinematic modes are computed by performing Principal Component Analysis (PCA) on the spatiotemporal volumes of the kinematic features. For classification, we propose the use of multiple instance learning (MIL) in which each action video is represented by a bag of kinematic modes. Each video is then embedded into a kinematic-mode-based feature space and the coordinates of the video in that space are used for classification using the nearest neighbor algorithm. The qualitative and quantitative results are reported on the benchmark data sets.     

Simultaneous Determination of Metronidazole Benzoate Methylparaben, and Propylparaben by High-Performance Liquid Chromatography

The simultaneous determination of metronidazole benzoate (MB), methylparaben (MP), and propylparaben (PP) in an oral suspension formulation was developed using high-performance liquid chromatography (HPLC). The method was developed using a Novapak C₁₈ (3.9 × 150 mm, 4 μm) column, methanol-water (50:50, v/v) as the mobile phase and an ultraviolet (UV) detector at 254 nm. The peak area response versus concentration was linear in a concentration range from 40 to 400 μg/ml of MB, 0.8 to 8.0 μg/ml of MP, and 0.2 to 2.0 μg of PP. The correlation coefficients were 0.9997 for MB, 0.9987 for MP, and 0.9983 for PP, with relative standard errors of 1.12%, 1.28%, and 1.67%, respectively.