Pienvesivoimalanmallinnus ja jakeluverkkovaikutusten tarkastelu

Tässä työssä simuloitiin Valkealassa sijaitsevaa Tirvan pienvesivoimalaitosta ja sen vaikutusta sähkönjakeluverkkoon. Pienvesivoimalaitos mallinnettiin PSCAD-ympäristöön verkko- ja voimalaitostietojen perusteella. Sähköverkko kuvattiin malliin koko 110 kV:n siirtävän verkon ja Tirvan pienjänniteverkon väliltä. Mallin avulla tehtiin sarja hajautettua sähköntuotantoa koskevia tarkasteluita. Tarkasteluissa huomattiin voimalaitoksen verkkoonliitynnän aiheuttavan standardien ylärajoilla olevan transienttisen muutoksen voimalaitoksen jännitteisiin. Työssä tehtiin yksityiskohtaisempi jännitehäviöiden mittaus voimalaitoksen pullonkaulana toimivalle pienitehoiselle jakelumuuntajalle. Tuotannon lisäämisen yhteydessä näkyvän yllättävän jännitteen laskun syyt paikannettiin muuntajalla tapahtuviin loistehon siirrosta johtuviin ylimääräisiin jännitehäviöihin. Voimalaitoksella ei ole merkittävää vaikutusta vikatilanteiden virtoihin.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Um novo modelo de transformadores trifásicos para análise harmônica trifásica em sistemas de distribuição

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo das variáveis do conforto térmico e luminoso em ambientes escolares

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetically altered AMPA-type glutamate receptor kinetics in interneurons disrupt long-range synchrony of gamma oscillation

Gamma oscillations synchronized between distant neuronal populations may be critical for binding together brain regions devoted to common processing tasks. Network modeling predicts that such synchrony depends in part on the fast time course of excitatory postsynaptic potentials (EPSPs) in interneurons, and that even moderate slowing of this time course will disrupt synchrony. We generated mice with slowed interneuron EPSPs by gene targeting, in which the gene encoding the 67-kDa form of glutamic acid decarboxylase (GAD67) was altered to drive expression of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptor subunit GluR-B. GluR-B is a determinant of the relatively slow EPSPs in excitatory neurons and is normally expressed at low levels in γ-aminobutyric acid (GABA)ergic interneurons, but at high levels in the GAD-GluR-B mice. In both wild-type and GAD-GluR-B mice, tetanic stimuli evoked gamma oscillations that were indistinguishable in local field potential recordings. Remarkably, however, oscillation synchrony between spatially separated sites was severely disrupted in the mutant, in association with changes in interneuron firing patterns. The congruence between mouse and model suggests that the rapid time course of AMPA receptor-mediated EPSPs in interneurons might serve to allow gamma oscillations to synchronize over distance.

Forming of Learning Set for Neural Networks in Problems of Losless Data Compression

questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.

An improved framework for dynamic origin-destination (O-D) matrix estimation

This dissertation aims to improve the performance of existing assignment-based dynamic origin-destination (O-D) matrix estimation models to successfully apply Intelligent Transportation Systems (ITS) strategies for the purposes of traffic congestion relief and dynamic traffic assignment (DTA) in transportation network modeling. The methodology framework has two advantages over the existing assignment-based dynamic O-D matrix estimation models. First, it combines an initial O-D estimation model into the estimation process to provide a high confidence level of initial input for the dynamic O-D estimation model, which has the potential to improve the final estimation results and reduce the associated computation time. Second, the proposed methodology framework can automatically convert traffic volume deviation to traffic density deviation in the objective function under congested traffic conditions. Traffic density is a better indicator for traffic demand than traffic volume under congested traffic condition, thus the conversion can contribute to improving the estimation performance. The proposed method indicates a better performance than a typical assignment-based estimation model (Zhou et al., 2003) in several case studies. In the case study for I-95 in Miami-Dade County, Florida, the proposed method produces a good result in seven iterations, with a root mean square percentage error (RMSPE) of 0.010 for traffic volume and a RMSPE of 0.283 for speed. In contrast, Zhou's model requires 50 iterations to obtain a RMSPE of 0.023 for volume and a RMSPE of 0.285 for speed. In the case study for Jacksonville, Florida, the proposed method reaches a convergent solution in 16 iterations with a RMSPE of 0.045 for volume and a RMSPE of 0.110 for speed, while Zhou's model needs 10 iterations to obtain the best solution, with a RMSPE of 0.168 for volume and a RMSPE of 0.179 for speed. The successful application of the proposed methodology framework to real road networks demonstrates its ability to provide results both with satisfactory accuracy and within a reasonable time, thus establishing its potential usefulness to support dynamic traffic assignment modeling, ITS systems, and other strategies.

Referência e contra referência em saúde bucal: regulação do acesso aos centros de especialidades odontológicas

Objective Based on the system of reference and counter-reference and comprehensiveness in oral health care, we aimed to examine ways of refering users to Specialized Dental Care Centers (SDCC) and the interface between them and Primary Care. Methods This is a cross-sectional study carried out with users and dentists of SDCC in a metropolitan region of Northeast of Brazil. Analyses were descriptive, and the association test was done with chi-square. Results Six forms of entry to specialized service were identified: free demand (13.8 %) and reference by the Primary Care dentist (63.2 %) were most frequent. Users referred by the basic health unit dentist had more interest in making a counter-reference than the others (p<0.001, PR=4.65, 95 % CI: 2.74 to 7.91), while individuals without this referral had 1.49 times more difficulty obtaining care (95 % CI: 1.02 to 2.17). Referral procedures are a decisive factor for counter-references. However, the high demand for primary care services and the short supply these services can offer in the face of needs make SDCC performance difficult. Conclusion The analysis of oral health practices from the perspective of network modeling points to the service's need to establish protocols for regulation in a bid to improve access to and the quality of care provided.

Referência e contra referência em saúde bucal: regulação do acesso aos centros de especialidades odontológicas

Objective Based on the system of reference and counter-reference and comprehensiveness in oral health care, we aimed to examine ways of refering users to Specialized Dental Care Centers (SDCC) and the interface between them and Primary Care. Methods This is a cross-sectional study carried out with users and dentists of SDCC in a metropolitan region of Northeast of Brazil. Analyses were descriptive, and the association test was done with chi-square. Results Six forms of entry to specialized service were identified: free demand (13.8 %) and reference by the Primary Care dentist (63.2 %) were most frequent. Users referred by the basic health unit dentist had more interest in making a counter-reference than the others (p<0.001, PR=4.65, 95 % CI: 2.74 to 7.91), while individuals without this referral had 1.49 times more difficulty obtaining care (95 % CI: 1.02 to 2.17). Referral procedures are a decisive factor for counter-references. However, the high demand for primary care services and the short supply these services can offer in the face of needs make SDCC performance difficult. Conclusion The analysis of oral health practices from the perspective of network modeling points to the service's need to establish protocols for regulation in a bid to improve access to and the quality of care provided.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

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.

Modeling of the lung impedance using a fractional order ladder network with constant phase elements

The self similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. The fractal geometry is typically characterized by a recurrent structure. This study investigates the identification of a model for the respiratory tree by means of its electrical equivalent based on intrinsic morphology. Measurements were obtained from seven volunteers, in terms of their respiratory impedance by means of its complex representation for frequencies below 5 Hz. A parametric modeling is then applied to the complex valued data points. Since at low-frequency range the inertance is negligible, each airway branch is modeled by using gamma cell resistance and capacitance, the latter having a fractional-order constant phase element (CPE), which is identified from measurements. In addition, the complex impedance is also approximated by means of a model consisting of a lumped series resistance and a lumped fractional-order capacitance. The results reveal that both models characterize the data well, whereas the averaged CPE values are supraunitary and subunitary for the ladder network and the lumped model, respectively.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.