Neural network model for designing automotive devices using SMD LED

The advantages offered by the electronic component LED (Light Emitting Diode) have resulted in a quick and extensive application of this device in the replacement of incandescent lights. In this combined application, however, the relationship between the design variables and the desired effect or result is very complex and renders it difficult to model using conventional techniques. This paper consists of the development of a technique using artificial neural networks that makes it possible to obtain the luminous intensity values of brake lights using SMD (Surface Mounted Device) LEDs from design data. This technique can be utilized to design any automotive device that uses groups of SMD LEDs. The results of industrial applications using SMD LED are presented to validate the proposed technique.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011

Artificial neural networks analysis of Sao Paulo subway tunnel settlement data

Artificial neural networks have been used to analyze a number of engineering problems, including settlement caused by different tunneling methods in various types of ground mass. This paper focuses on settlement over shotcrete- supported tunnels on Sao Paulo subway line 2 (West Extension) that were excavated in Tertiary sediments using the sequential excavation method. The adjusted network is a good tool for predicting settlement above new tunnels to be excavated in similar conditions. The influence of network training parameters on the quality of results is also discussed. (C) 2007 Elsevier Ltd. All rights reserved.

Friction loss correction in flowing well discharge tests

Artesian confined aquifers do not need pumping energy, and water from the aquifer flows naturally at the wellhead. This study proposes correcting the method for analyzing flowing well tests presented by Jacob and Lohman (1952) by considering the head losses due to friction in the well casing. The application of the proposed correction allowed the determination of a transmissivity (T = 411 m(2)/d) and storage coefficient (S = 3 x 10(-4)) which appear to be representative for the confined Guarani Aquifer in the study area. Ignoring the correction due to head losses in the well casing, the error in transmissivity evaluation is about 18%. For the storage coefficient the error is of 5 orders of magnitude, resulting in physically unacceptable value. The effect of the proposed correction on the calculated radius of the cone of depression and corresponding well interference is also discussed.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (C) 2009 Elsevier B.V. All rights reserved.

Transmission lines distance protection using artificial neural networks

This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays. (c) 2011 Elsevier Ltd. All rights reserved.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Combining Mager and Steinmetz: The Effect of Grain Size and Maximum Induction on Hysteresis Energy Loss

Twelve samples with different grain sizes were prepared by normal grain growth and by primary recrystallization, and the hysteresis dissipated energy was measured by a quasi-static method. Results showed a linear relation between hysteresis energy loss and the inverse of grain size, which is here called Mager`s law, for maximum inductions from 0.6 to 1.5 T, and a Steinmetz power law relation between hysteresis loss and maximum induction for all samples. The combined effect is better described by a Mager`s law where the coefficients follow Steinmetz law.

Hysteresis loss subdivision

Assuming that different energy dissipation mechanisms are at work along hysteresis, a hysteresis loss subdivision procedure has been proposed, using the induction at maximum permeability ( around 0.8 T, in electrical steels) as the boundary between the ""low-induction`` and the ""high-induction`` regions. This paper reviews the most important results obtained in 10 years of investigation of the effect of microstructure on these components of the hysteresis loss. As maximum induction increases, the ""low-induction loss`` increases linearly up to 1.2 T, while the ""high-induction loss`` is zero up to 0.7 T and then increases as a power law with n = 5. Low-induction loss behavior is linearly related to H(c) between 0.4 and 1.2 T. Grain size has a larger influence on low-induction losses than on high-induction losses. Texture has a much stronger influence on high loss than on low-induction loss, and it is related to the average magnetocrystalline energy. 6.5%Si steel shows smaler hysteresis loss at 1.5 T than 3.5%Si steel only because of its smaler high-induction component. The abrupt increase in hysteresis loss due to very small plastic deformation is strongly related to the high-induction loss component. These results are discussed in terms of energy dissipation mechanisms such as domain wall movement, irreversible rotation and domain wall energy dissipation at domain nucleation and annihilation. (C) 2008 Elsevier B.V. All rights reserved.

Magnetic loss, permeability dispersion, and role of eddy currents in Mn-Zn sintered ferrites

Magnetic energy losses and permeability have been investigated in laboratory prepared and commercial Mn-Zn sintered ferrites from quasi-static conditions up to 10 MHz. The mechanisms leading to energy dissipation, either due to domain wall displacements or magnetization rotations, have been quantitatively assessed and their respective roles have been clarified. Domain wall processes dissipate energy by pure relaxation effects, while rotations display resonant absorption of energy over a broad range of frequencies. Their specific contributions to the permeability and its frequency dispersion are thus identified and separately evaluated. It is shown that eddy currents are always too weak to appreciably contribute to the losses over the whole investigated frequency range and that rotations are the dominant magnetization and loss producing mechanisms on approaching the MHz range, as predicted by the Landau-Lifshitz-Gilbert equation with distributed anisotropy fields. (C) 2008 Elsevier B.V. All rights reserved.

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.

THE EFFECT OF REFRIGERATED STORAGE ON SENSORY PROFILE AND PHYSICAL-CHEMICAL CHARACTERISTICS OF MINIMALLY PASTEURIZED ORANGE JUICE

Minimal pasteurization of orange juice (OJ) consists of using minimum holding time and temperature to ensure partial inactivation of pectin methylesterase (PME). This process produces juice with preserved sensory attributes and has a better acceptance by consumers when compared with commercially pasteurized OJ. Sensory profile and physical-chemical characteristics of minimally processed OJ was determined, during refrigerated storage, for two OJ blends with different pH values and the same level of PME thermal inactivation. A selected and trained sensorial panel (n = 16) performed sensory analysis, based on a quantitative descriptive analysis, twice a week for 30 days, evaluating the attributes of appearance (suspended particles and color intensity), odor (natural orange and fermented orange) and flavor (orange characteristic, fermented orange, acid and bitter taste). Storage presented great effect on OJ sensory profile; however, it was not noticeable on physical-chemical characteristics.

Network of phase-locking oscillators and a possible model for neural synchronization

In order to model the synchronization of brain signals, a three-node fully-connected network is presented. The nodes are considered to be voltage control oscillator neurons (VCON) allowing to conjecture about how the whole process depends on synaptic gains, free-running frequencies and delays. The VCON, represented by phase-locked loops (PLL), are fully-connected and, as a consequence, an asymptotically stable synchronous state appears. Here, an expression for the synchronous state frequency is derived and the parameter dependence of its stability is discussed. Numerical simulations are performed providing conditions for the use of the derived formulae. Model differential equations are hard to be analytically treated, but some simplifying assumptions combined with simulations provide an alternative formulation for the long-term behavior of the fully-connected VCON network. Regarding this kind of network as models for brain frequency signal processing, with each PLL representing a neuron (VCON), conditions for their synchronization are proposed, considering the different bands of brain activity signals and relating them to synaptic gains, delays and free-running frequencies. For the delta waves, the synchronous state depends strongly on the delays. However, for alpha, beta and theta waves, the free-running individual frequencies determine the synchronous state. (C) 2011 Elsevier B.V. All rights reserved.

Sensory Stability of Ultra-High Temperature Milk in Polyethylene Bottle

The objective of this study was to evaluate the sensory stability of ultra-high temperature (UHT) milk subjected to different heat treatments and stored at room temperature in white high density polyethylene bottles (HDPE) pigmented with titanium dioxide. Two lots of 300 units each were processed, respectively, at 135 and 141 degrees C/10 s using indirect heating and subsequently aseptically filled in an ISO class 7 clean room. These experimental lots were evaluated for appearance, aroma, flavor, and overall appreciation and compared to samples of commercial UHT milk purchased from local commercial stores. The time-temperature combinations investigated did not affect either the acceptability or the shelf life of the milk. Despite the limited light barrier properties of HDPE bottles, the product contained in the package tested exhibited good stability, with a shelf life ranging from 4 to 11 wk. Within this time period, the acceptability of the experimental lots was similar to that of the commercial products. The results achieved in this study contribute to turn the low-cost UHT system investigated into a technically viable option for small-size dairy processing plants.