Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

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

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.

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.

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.

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.

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.

In silico analysis of candidate genes involved in light sensing and signal transduction pathways in soybean

Several aspects of photoperception and light signal transduction have been elucidated by studies with model plants. However, the information available for economically important crops, such as Fabaceae species, is scarce. In order to incorporate the existing genomic tools into a strategy to advance soybean research, we have investigated publicly available expressed sequence tag ( EST) sequence databases in order to identify Glycine max sequences related to genes involved in light-regulated developmental control in model plants. Approximately 38,000 sequences from open-access databases were investigated, and all bona fide and putative photoreceptor gene families were found in soybean sequence databases. We have identified G. max orthologs for several families of transcriptional regulators and cytoplasmic proteins mediating photoreceptor-induced responses, although some important Arabidopsis phytochrome-signaling components are absent. Moreover, soybean and Arabidopsis gene-family homologs appear to have undergone a distinct expansion process in some cases. We propose a working model of light perception, signal transduction and response-eliciting in G. max, based on the identified key components from Arabidopsis. These results demonstrate the power of comparative genomics between model systems and crop species to elucidate several aspects of plant physiology and metabolism.

NMR imaging of fluid pathways during drainage of softwood in a pressure membrane chamber

An experiment was implemented to study fluid flow in a pressure media. This procedure successfully combines nuclear magnetic resonance imaging with a pressure membrane chamber in order to visualize the non-wetting and wetting fluid flows with controlled boundary conditions. A specially designed pressure membrane chamber, made of non-magnetic materials and able to withstand 4 MPa, was designed and built for this purpose. These two techniques were applied to the drainage of Douglas fir sapwood. In the study of the longitudinal flow, narrow drainage fingers are formed in the latewood zones. They follow the longitudinal direction of wood and spread throughout the sample length. These fingers then enlarge in the cross-section plane and coalesce until drainage reaches the whole latewood part. At the end of the experiments, when the drainage of liquid water in latewood is completed, just a few sites of percolation appear in earlywood zones. This difference is a result of the wood anatomical structure, where pits, the apertures that allow the sap to flow between wood cells, are more easily aspirated in earlywood than in latewood. (C) 2007 Elsevier Ltd. All rights reserved.

Persistent and remodeling hepatic preneoplastic lesions present differences in cell proliferation and apoptosis, as well as in p53, BcI-2 and NF-kappa B pathways

During rat hepatocarcinogenesis preneoplastic lesions (PNL) emerge which may persist (pPNL) and be sites of progress to cancer or suffer remodeling (rPNL) tending to disappear. Cellular and molecular mechanisms involved in both phenotypes are not sufficiently elucidated. pPNL and rPNL cellular proliferation and apoptosis were evaluated in rats submitted to the resistant hepatocyte (RH) model, and an adjusted growth index (AGI) was established. p53, Bcl-2, and NF-kappa B p65 subunit expression was evaluated by immunohistochemistry in pPNL and rPNL. p65 expression and NF-kappa B activation was evaluated by Western blot assays in whole livers. A lower number of BrdU-stained hepatocyte nuclei/mm(2) and higher number of apoptotic bodies (AB) per mm(2) were observed in remodeling compared to pPNL. Cytoplasmic p53 accumulation is related to increased hepatocarcinoma malignancy. We observed that 71.3% pPNL and 25.4% rPNL (P < 0.05) presented p53 staining in the cytoplasm. Similarly, 67.7% pPNL and 23.1 % rPNL (P < 0.05) presented increased Bcl-2 staining. Thirty-two percent pPNL and 15.6% rPNL (P < 0.05) presented p65 staining. Compared to normal rats, increase (P < 0.05) of hepatic p65 expression and NF-kappa B activation in rats submitted to the RH model was observed. in agreement to previous studies hepatic pPNL and rPNL differ regarding cell proliferation and apoptosis. Moreover, persistence and remodeling involve differences in p53, Bcl-2, and NF-kappa B pathways. These data point to molecular pathways that may direct preneoplastic lesions to spontaneously regress or to progress to cancer.

Serum amyloid A induces CCL20 secretion in mononuclear cells through MAPK (p38 and ERK1/2) signaling pathways

Although the serum levels of SAA had been reported to be upregulated during inflammatory/infectious process, the role of this acute-phase protein has not been completely elucidated. In previous studies, we demonstrated that SAA stimulated the production of TNF-alpha, IL-1 beta, IL-8, NO, and ROS by neutrophils and/or mononuclear cells. Herein we demonstrate that SAA induces the expression and release of CCL20 from Cultured human blood mononuclear cells. We also focus on the signaling pathways triggered by SAA. in THP-1 cells SAA promotes phosphorylation of p38 and ERK1/2. Furthermore, the addition of SB203580 (p38 inhibitor) and PD98059 (ERK 1/2 inhibitor) inhibits the expression and release of CCL20 in mononuclear cells treated with SAA. Our results point to SAA as an important link of innate to adaptive immunity, once it might act on the recruitment of mononuclear cells. (C) 2008 Elsevier B.V. All rights reserved.

Excitability changes in human forearm corticospinal projections and spinal reflex pathways during rhythmic voluntary movement of the opposite limb

Rhythmic movements brought about by the contraction of muscles on one side of the body give rise to phase-locked changes in the excitability of the homologous motor pathways of the opposite limb. Such crossed facilitation should favour patterns of bimanual coordination in which homologous muscles are engaged simultaneously, and disrupt those in which the muscles are activated in an alternating fashion. In order to examine these issues, we obtained responses to transcranial magnetic stimulation (TMS), to stimulation of the cervicomedullary junction (cervicomedullary-evoked potentials, CMEPs), to peripheral nerve stimulation (H-reflexes and f-waves), and elicited stretch reflexes in the relaxed right flexor carpi radialis (FCR) muscle during rhythmic (2 Hz) flexion and extension movements of the opposite (left) wrist. The potentials evoked by TMS in right FCR were potentiated during the phases of movement in which the left FCR was most strongly engaged. In contrast, CMEPs were unaffected by the movements of the opposite limb. These results suggest that there was systematic variation of the excitability of the motor cortex ipsilateral to the moving limb. H-reflexes and stretch reflexes recorded in right FCR were modulated in phase with the activation of left FCR. As the f-waves did not vary in corresponding fashion, it appears that the phasic modulation of the H-reflex was mediated by presynaptic inhibition of Ia afferents. The observation that both H-reflexes and f-waves were depressed markedly during movements of the opposite indicates that there may also have been postsynaptic inhibition or disfacilitation of the largest motor units. Our findings indicate that the patterned modulation of excitability in motor pathways that occurs during rhythmic movements of the opposite limb is mediated primarily by interhemispheric interactions between cortical motor areas.