Neural network-based H(infinity) control for fully actuated and underactuated cooperative manipulators

This paper develops H(infinity) control designs based on neural networks for fully actuated and underactuated cooperative manipulators. The neural networks proposed in this paper only adapt the uncertain dynamics of the robot manipulators. They work as a complement of the nominal model. The H(infinity) performance index includes the position errors as well the squeeze force errors between the manipulator end-effectors and the object, which represents a complete disturbance rejection scenario. For the underactuated case, the squeeze force control problem is more difficult to solve due to the loss of some degrees of manipulator actuation. Results obtained from an actual cooperative manipulator, which is able to work as a fully actuated and an underactuated manipulator, are presented. (C) 2008 Elsevier Ltd. All rights reserved.

MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

Treatment of Aqueous Effluents Containing Phenol by the O(3), O(3)-UV, and O(3)-H(2)O(2) Processes: Experimental Study and Neural Network Modeling

In this work, the oxidation of the model pollutant phenol has been studied by means of the O(3), O(3)-UV, and O(3)-H(2)O(2) processes. Experiments were carried out in a fed-batch system to investigate the effects of initial dissolved organic carbon concentration, initial, ozone concentration in the gas phase, the presence or absence of UVC radiation, and initial hydrogen peroxide concentration. Experimental results were used in the modeling of the degradation processes by neural networks in order to simulate DOC-time profiles and evaluate the relative importance of process variables.

Title Model for Computer Networks Optimization

The TCP/IP architecture was consolidated as a standard to the distributed systems. However, there are several researches and discussions about alternatives to the evolution of this architecture and, in this study area, this work presents the Title Model to contribute with the application needs support by the cross layer ontology use and the horizontal addressing, in a next generation Internet. For a practical viewpoint, is showed the network cost reduction for the distributed programming example, in networks with layer 2 connectivity. To prove the title model enhancement, it is presented the network analysis performed for the message passing interface, sending a vector of integers and returning its sum. By this analysis, it is confirmed that the current proposal allows, in this environment, a reduction of 15,23% over the total network traffic, in bytes.

Who should wear mask against airborne infections? Altering the contact network for controlling the spread of contagious diseases

There are several ways of controlling the propagation of a contagious disease. For instance, to reduce the spreading of an airborne infection, individuals can be encouraged to remain in their homes and/or to wear face masks outside their domiciles. However, when a limited amount of masks is available, who should use them: the susceptible subjects, the infective persons or both populations? Here we employ susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations and probabilistic cellular automata in order to investigate how the deletion of links in the random complex network representing the social contacts among individuals affects the dynamics of a contagious disease. The inspiration for this study comes from recent discussions about the impact of measures usually recommended by health public organizations for preventing the propagation of the swine influenza A (H1N1) virus. Our answer to this question can be valid for other eco-epidemiological systems. (C) 2010 Elsevier BM. All rights reserved.

Optimized network structure for full-synchronization

A network of Kuramoto oscillators with different natural frequencies is optimized for enhanced synchronizability. All node inputs are normalized by the node connectivity and some important properties of the network Structure are determined in this case: (i) optimized networks present a strong anti-correlation between natural frequencies of adjacent nodes: (ii) this anti-correlation should be as high as possible since the average path length between nodes is maintained as small as in random networks: and (iii) high anti-correlation is obtained without any relation between nodes natural frequencies and the degree of connectivity. We also propose a network construction model with which it is shown that high anti-correlation and small average paths may be achieved by randomly rewiring a fraction of the links of a totally anti-correlated network, and that these networks present optimal synchronization properties. (C) 2008 Elsevier B.V. All rights reserved.

On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata

We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called R(0)) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) R(0) cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations. (C) 2009 Elsevier B.V. All rights reserved.

Synaptic compensation on Hopfield network: implications for memory rehabilitation

The discrete-time neural network proposed by Hopfield can be used for storing and recognizing binary patterns. Here, we investigate how the performance of this network on pattern recognition task is altered when neurons are removed and the weights of the synapses corresponding to these deleted neurons are divided among the remaining synapses. Five distinct ways of distributing such weights are evaluated. We speculate how this numerical work about synaptic compensation may help to guide experimental studies on memory rehabilitation interventions.

A hybrid commodity price-forecasting model applied to the sugar-alcohol sector

Accurate price forecasting for agricultural commodities can have significant decision-making implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar-alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non-observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman filter, which is able to account for non-observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.

A modified epidemiological model for computer viruses

Since the computer viruses pose a serious problem to individual and corporative computer systems, a lot of effort has been dedicated to study how to avoid their deleterious actions, trying to create anti-virus programs acting as vaccines in personal computers or in strategic network nodes. Another way to combat viruses propagation is to establish preventive policies based on the whole operation of a system that can be modeled with population models, similar to those that are used in epidemiological studies. Here, a modified version of the SIR (Susceptible-Infected-Removed) model is presented and how its parameters are related to network characteristics is explained. Then, disease-free and endemic equilibrium points are calculated, stability and bifurcation conditions are derived and some numerical simulations are shown. The relations among the model parameters in the several bifurcation conditions allow a network design minimizing viruses risks. (C) 2009 Elsevier Inc. All rights reserved.

Modeling the fluid-flow-induced stress and collapse in a dendritic network

An analytical approach to the stress development in the coherent dendritic network during solidification is proposed. Under the assumption that stresses are developed in the network as a result of the friction resisting shrinkage-induced interdendritic fluid flow, the model predicts the stresses in the solid. The calculations reflect the expected effects of postponed dendrite coherency, slower solidification conditions, and variations of eutectic volume fraction and shrinkage. Comparing the calculated stresses to the measured shear strength of equiaxed mushy zones shows that it is possible for the stresses to exceed the strength, thereby resulting in reorientation or collapse of the dendritic network.

A transgenic mouse model that recapitulates the clinical features of both neonatal and adult forms of the skin disease epidermolytic hyperkeratosis

Keratins are the major structural proteins of keratinocytes, which are the most abundant cell type in the mammalian epidermis. Mutations in epidermal keratin genes have been shown to cause severe blistering skin abnormalities. One such disease, epidermolytic hyperkeratosis (EHK), also known as bullous congenital ichthyosiform erythroderma, occurs as a result of mutations in highly conserved regions of keratins K1 and K10. Patients with EHK first exhibit erythroderma with severe blistering, which later is replaced by thick patches of scaly skin. To assess the effect of a mutated K1 gene on skin biology and to produce an animal model for EHK, we removed 60 residues from the 2B segment of HK1 and observed the effects of its expression in the epidermis of transgenic mice. Phenotypes of the resultant mice closely resembled those observed in the human disease, first with epidermal blisters, then later with hyperkeratotic lesions. In neonatal mice homozygous for the transgene, the skin was thicker, with an increased labeling index, and the spinous cells showed a collapse of the keratin filament network around the nuclei, suggesting that a critical concentration of the mutant HK1, over the endogenous MK1, was required to disrupt the structural integrity of the spinous cells. Additionally, footpad epithelium, which is devoid of hair follicles, showed blistering in the spinous layer, suggesting that hair follicles can stabilize or protect the epidermis from trauma. Blisters were not evident in adult mice, but instead they showed a thick, scaly hyperkeratotic skin with increased mitosis, resulting in an increased number of corneocytes and granular cells. Irregularly shaped keratohyalin granules were also observed. To date, this is the only transgenic model to show the typical morphology found in the adult form of EHK.

Experimental tuberculosis: Designing a better model to test vaccines against tuberculosis

Experimental models of infection are good tools for establishing immunological parameters that have an effect on the host-pathogen relationship and also for designing new vaccines and immune therapies. In this work, we evaluated the evolution of experimental tuberculosis in mice infected with increasing bacterial doses or via distinct routes. We showed that mice infected with low bacterial doses by the intratracheal route were able to develop a progressive infection that was proportional to the inoculum size. In the initial phase of disease, mice developed a specific Th1-driven immune response independent of inoculum concentration. However, in the late phase, mice infected with higher concentrations exhibited a mixed Th1/Th2 response, while mice infected with lower concentrations sustained the Th1 pattern. Significant IL-10 concentrations and a more preeminent T regulatory cell recruitment were also detected at 70 days post-infection with high bacterial doses. These results suggest that mice infected with higher concentrations of bacilli developed an immune response similar to the pattern described for human tuberculosis wherein patients with progressive tuberculosis exhibit a down modulation of IFN-gamma production accompanied by increased levels of IL-4. Thus, these data indicate that the experimental model is important in evaluating the protective efficacy of new vaccines and therapies against tuberculosis. (C) 2010 Elsevier Ltd. All rights reserved.

Impact of a program for the prevention of traffic accidents in a Southern Brazilian city: a model for implementation in a developing country

Background: Traffic accidents constitute the main cause of death in the first decades of life. Traumatic brain injury is the event most responsible for the severity of these accidents. The SBN started an educational program for the prevention of traffic accidents, adapted from the American model ""Think First"" to the Brazilian environment, since 1995, with special effort devoted to the prevention of TBI by using seat belts and motorcycle helmets. The objective of the present study was to set up a traffic accident prevention program based on the adapted Think First and to evaluate its impact by comparing epidemiological variables before and after the beginning of the program. Methods: The program was executed in Maringa city, from September 2004 to August 2005, with educational actions targeting the entire population, especially teenagers and young adults. The program was implemented by building a network of information facilitators and multipliers inside the organized civil society, with widespread population dissemination. To measure the impact of the program, a specific software was developed for the storage and processing of the epidemiological variables. Results: The results showed a reduction of trauma severity due to traffic accidents after the execution of the program, mainly TBI. Conclusions: The adapted Think First was systematically implemented and its impact measured for the first time in Brazil, revealing the usefulness of the program for reducing trauma and TBI severity in traffic accidents through public education and representing a standardized model of implementation in a developing country. (C) 2009 Elsevier Inc. All rights reserved.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.