Piston-driven laminar flow and heat transfer in a plane channel with a sudden expansion

 This paper presents a numerical study of piston-driven heat transfer and fluid flow in a plane channel containing a sudden expansion. The numerical method employed is based on a control-volume-based finite element method for incompressible flow with a staggered and moving grid and SIMPLER algorithm for pressure-velocity coupling. The numerical results show a good agreement with the experimental data reported in the literature. Results concerning time and space evolution of the thermal and flow fields are presented for different values of the expansion ratio, the initial clearance volume, and the piston velocity. Received: 20 April 2002 / Accepted: 23 January 2003     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Dynamics of localizable entanglement in a qutrit chain with Dzyaloshinskii–Moriya interaction

Dynamics of localizable entanglement in a qutrit chain, in the presence of the Dzyaloshinskii–Moriya (DM) interaction is studied. Three distinct initial states, namely, superposition of the ground and the first excited state (SGE), a GHZ state and a superposition of qutrit coherent states (SQCS) are considered in this investigation. While the ground and the first excited state exhibit the maximum of entanglement, the latter is diminished for any superposition of the states. In both SGE and GHZ cases, localizable entanglement (LE) oscillates and its period is a decreasing function of the ratio of the strength of DM interaction and the spin coupling constant (DS ratio), but its maximum value is independent of the latter. In SQCS case, LE also oscillates in time at small values of DS ratio; its average is reduced as the strength of the DM interaction increases and gains its maximum average and the highest peaks at a specific value of the coherence parameter.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

An adaptive genetic-based signature learning system for intrusion detection

Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for network intrusion detection using a supervised learning classifier system. The classifier is an online and incremental parallel production rule-based system.A signature extraction system is developed that adaptively extracts signatures to the knowledge base as they are discovered by the classifier. The signature extraction algorithm is augmented by introducing new generalisation operators that minimise overlap and conflict between signatures. Mechanisms are provided to adapt main algorithm parameters to deal with online noisy and imbalanced class data. Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt.The performance of the developed systems is evaluated with a publicly available intrusion detection dataset and results are presented that show the effectiveness of the proposed system.     

Neural-Based Learning Classifier Systems

UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.     

Modeling and Optimization of Nanofluid Flow in Flat Tubes Using a Combination of CFD and Response Surface Methodology

In this paper, modeling and optimization of Al₂O₃–water nanofluid flow in horizontal flat tubes is performed using a combination of computational fluid dynamics (CFD) and response surface methodology (RSM). At first, nanofluid flow is solved numerically in various flat tubes using CFD techniques and the heat transfer coefficient () and pressure drop () in tubes are calculated. The numerical simulations are performed using two phase mixture model by FORTRAN programming language. The flow regime and the wall boundary conditions are assumed to be laminar and constant heat flux respectively. In the second step, numerical data of the previous step will be used for a parametric study, modeling and optimization of nanofluid flow in flat tubes using the RSM technique.It is shown that the results include important design information on nanofluid parameters in flat tubes. The important design information about the relationship between design variables and responses will not be achieved without the simultaneous use of CFD and optimization approaches.     

Corrosion monitoring of hydrogen by HUP probe in the presence of sulphide and thiourea

The effect of sulphide and thiourea on hydrogen uptake during the corrosion of steel in deaerated pH3 has been studied using an electrochemical hydrogen probe incorporating a solid electrolyte (HUP) and lead dioxide (PbO₂) as a reference electrode. This probe has been used for the quantitative determination of the hydrogen activity generated during the corrosion of steel. The sensor voltage implies an average equivalent hydrogen pressure in uncharged steel of 2 × 10^?18 atmospheres. During the corrosion of steel, this value increases by very large amounts; thus in deaerated pH3, the equivalent pressure rose by 10^24.5 in the presence of 200 ppm sulphide and by 10² in the presence of 100 ppm thiourea. Weight loss and AC impedance methods were also performed under the same conditions as the hydrogen measurements. The hydrogen probe data were correlated with a.c impedance and weight loss results in order to understand the effect of S^2? and thiourea on hydrogen uptake.     

A practical approach for modelling and control of biomass pyrolysis pilot plant with heat recovery from combustion of pyrolysis products

A pilot plant of biomass pyrolysis using pyrolysis products as fuel has been tested and shown to improve energy balance of the process and to be environmentally friendly by avoiding rejection of pyrolysis pollutants fumes into the atmosphere. The high number of parameters involved in a pyrolysis process makes it difficult to specify an optimum procedure for charcoal yield and pyrolysis cycle durability. So the knowledge of the essential parameters which govern the kinetics mechanisms of the biomass thermal decomposition and the combustion of pyrolysis gases is very useful to understand the operating cycle of the plant. In the present study a thermochemical model is developed in order to simulate and control the operating cycle of the system. The effect of the inlet molar air flow rate on the temporal evolution of biomass mass loss rate and temperatures in the different active zones of the pilot plant as well as the determination of the critical inlet molar air flow rate for which accidental runaway of combustion reactions occurs are presented. To avoid this accidental phenomenon a Proportional-Integral-Derived (PID) anticipated regulation is used in order to control temperatures evolution in the different zones of the device and avoid the runaway of combustion reactions.     

An economical cognitive approach for bi-objective optimization using bliss points, visualization, and interaction

When an optimization problem encompasses multiple objectives, it is usually difficult to define a single optimal solution. The decision maker plays an important role when choosing the final single decision. Pareto-based evolutionary multiobjective optimization (EMO) methods are very informative for the decision making process since they provide the decision maker with a set of efficient solutions to choose from. Despite that the set of efficient solutions may not be the global efficient set, we show in this paper that the set can still be informative when used in an interactive session with the decision maker. We use a combination of EMO and single objective optimization methods to guide the decision maker in interactive sessions.