Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H₂O₂, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization.     

Removal of As(V) and Cr(VI) from aqueous solutions using solid waste from leather industry

This study evaluated the feasibility of using a solid waste from the leather industry as an adsorbent for removal of Cr(VI) and As(V) from aqueous media. The adsorbent material was characterized by chemical analyses, infrared spectroscopy, and scanning electronic microscopy (SEM) prior to reaction in order to assess its surface properties. Langmuir and Freundlich equations were used for analyzing the experimental data, which showed a better fit to the Langmuir model, thus suggesting a monolayer adsorption process in the surface of the adsorbent. The high amounts of Cr(VI)-133 mg g(-1) and As(V)-26 mg g(-1) adsorbed demonstrates the great potential for using this solid waste from the leather industry as a low-cost alternative to the traditionally used adsorbent materials.     

Improving the clinical potential of ultra-high field fMRI using a model-free analysis method based on response consistency

Objective

To develop an analysis method that is sensitive to non-model-conform responses often encountered in ultra-high field presurgical planning fMRI. Using the consistency of time courses over a number of experiment repetitions, it should exclude low quality runs and generate activation maps that reflect the reliability of responses.

Materials and methods

7 T fMRI data were acquired from six healthy volunteers: three performing purely motor tasks and three a visuomotor task. These were analysed with the proposed approach (UNBIASED) and the GLM.

Results

UNBIASED results were generally less affected by false positive results than the GLM. Runs that were identified as being of low quality were confirmed to contain little or no activation. In two cases, regions were identified as activated in UNBIASED but not GLM results. Signal changes in these areas were time-locked to the task, but were delayed or transient.

Conclusion

UNBIASED is shown to be a reliable means of identifying consistent task-related signal changes regardless of response timing. In presurgical planning, UNBIASED could be used to rapidly generate reliable maps of the consistency with which eloquent brain regions are activated without recourse to task timing and despite modified hemodynamics.

     

Application of Park's power components to the differential protection of three-phase transformers

This paper presents a new scheme for power transformers differential protection, in which the concept of the Park's instantaneous differential powers is introduced. The proposed method is able to detect winding insulation failures and to distinguish them from magnetizing inrush current transients. Experimental and simulation results are presented and discussed.     

Modeling and Evaluation of a Policy Provisioning Architecture for Mobile Ad-Hoc Networks

The ability to dynamically configure mobile ad-hoc network (MANET) devices is critical for supporting complex services such as quality of service (QoS), security and access control in these networks. In our work, we address the problem of policy distribution and provisioning in MANETs. Previously, we have proposed a policy provisioning architecture that enables the operator, e.g., a military command and control center, to maintain a logically centralized control of the network as a whole, while allowing a physically decentralized and self-managing implementation essential for a MANET environment. In this paper, we present an analytical model of the availability of the policy distribution service in a MANET and analyze the performance using stochastic Petri nets (SPNs). We develop the model at two levels of complexity: as a simple Markovian model with Poisson assumptions and as a more accurate non-Markovian model with general distributions obtained using statistical parameterization. We compare and cross-validate the analytical results with simulation and experimental results. Finally, we illustrate the effectiveness of the architecture for managing QoS for soft real-time applications, using an emulated mobile ad-hoc network testbed.     

Modeling the leadership – project performance relation: radial basis function,Gaussian and Kriging methods as alternatives to linear regression

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.     

Reconstruction of 3D Object Meshes from Silhouette Images

In this work we propose a method for computing mesh representations of 3D objects reconstructed from a set of silhouette images. Our method is based on the polygonization of volumetric reconstructions by using a modified version of the dual contouring method. In order to apply dual contouring on volumetric reconstruction from silhouettes we devised a method that is able to determine the discrete topology of the surface in relation to the octree cells. We also developed a new scheme for computing hermitian data representing the intersections of conic volumes with the octree cells and their corresponding normals with subpixel accuracy. Due to the discrete and extremely noisy nature of the data used in the reconstruction we had to devise a different criterion for mesh simplification that applies topological consistency tests only when the geometric error measure is beyond a given tolerance. We present results of the application of the proposed method in the extraction of a mesh corresponding to the surface of objects of a real scene.