Fast response static var generator for improving the power system performance of an electric arc furnace (EAF)

Cascaded multilevel inverters-based Static Var Generators (SVGs) are FACTS equipment introduced for active and reactive power flow control. They eliminate the need for zigzag transformers and give a fast response. However, with regard to their application for flicker reduction in using Electric Arc Furnace (EAF), the existing multilevel inverter-based SVGs suffer from the following disadvantages. (1) To control the reactive power, an off-line calculation of Modulation Index (MI) is required to adjust the SVG output voltage. This slows down the transient response to the changes of reactive power; and (2) Random active power exchange may cause unbalance to the voltage of the d.c. link (HBI) capacitor when the reactive power control is done by adjusting the power angle d alone. To resolve these problems, a mathematical model of 11-level cascaded SVG, was developed. A new control strategy involving both MI (modulation index) and power angle (d) is proposed. A selected harmonics elimination method (SHEM) is taken for switching pattern calculations. To shorten the response time and simplify the controls system, feed forward neural networks are used for on-line computation of the switching patterns instead of using look-up tables. The proposed controller updates the MI and switching patterns once each line-cycle according to the sampled reactive power Qs. Meanwhile, the remainder reactive power (compensated by the MI) and the reactive power variations during the line-cycle will be continuously compensated by adjusting the power angles, d. The scheme senses both variables MI and d, and takes action through the inverter switching angle, qi. As a result, the proposed SVG is expected to give a faster and more accurate response than present designs allow. In support of the proposal there is a mathematical model for reactive powered distribution and a sensitivity matrix for voltage regulation assessment, MATLAB simulation results are provided to validate the proposed schemes. The performance with non-linear time varying loads is analysed and refers to a general review of flicker, of methods for measuring flickers due to arc furnace and means for mitigation.

Minimum description length, regularisation and multi-modal data

Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.

Is the clustering of neurofibrillary tangles in Alzheimer's patients related to the cells of origin of specific cortico-cortical projections?

The spatial pattern of cellular neurofibrillary tangles (NFT) was studied in the supra- and infragranular layers of various cortical regions in cases of Alzheimer's disease (AD). The objective was to test the hypothesis that NFT formation was associated with the cells of origin of specific cortico-cortical projections. The novel feature of the study was that pattern analysis enabled the dimension and spacing of NFT clusters along the cortical ribbon to be estimated. In the majority of brain regions studied, NFT occurred in clusters of neurons which were regularly spaced along the cortical strip. This pattern is consistent with the predicted distribution of the cells of origin of specific cortico-cortico projections. Mean NFT cluster size varied from 250 to > 12800 microns in different cortical tissues suggesting either variation in the size of the cell clusters or a dynamic process in the development of NFT in relation to these cell clusters. The formation of NFT in cell clusters which may give rise to the feed-forward and feed-back cortico-cortical projections suggests a possible route of spread of NFT pathology in AD between cortical regions and from the cortex to subcortical areas.

Laminar distribution of ballooned neurons and tau positive neurons with inclusions in patients with corticobasal degeneration

The laminar distribution of ballooned neurons (BN) and tau positive neurons with inclusions (tau+ neurons) was studied in the frontal and temporal cortex in twelve patients with corticobasal degeneration (CBD). In the majority of brain areas, the density of BN and tau+ neurons was maximal in the lower and upper cortical laminae respectively. The densities of tau+ neurons in the upper and lower cortex were positively correlated. In the majority of brain areas, however, no correlations were observed between the densities of BN and tau+ neurons. The laminar distribution of the BN may reflect the degeneration of the feedback cortico-cortical and/or the efferent cortical pathways. By contrast, the distribution of the tau+ neurons may reflect the degeneration of the feed-forward cortico-cortical pathways. In addition, BN and tau+ neurons may arise as a result of distinct pathological processes.

Uncertainty and topographic visualisations

This thesis is a study of low-dimensional visualisation methods for data visualisation under certainty of the input data. It focuses on the two main feed-forward neural network algorithms which are NeuroScale and Generative Topographic Mapping (GTM) by trying to make both algorithms able to accommodate the uncertainty. The two models are shown not to work well under high levels of noise within the data and need to be modified. The modification of both models, NeuroScale and GTM, are verified by using synthetic data to show their ability to accommodate the noise. The thesis is interested in the controversy surrounding the non-uniqueness of predictive gene lists (PGL) of predicting prognosis outcome of breast cancer patients as available in DNA microarray experiments. Many of these studies have ignored the uncertainty issue resulting in random correlations of sparse model selection in high dimensional spaces. The visualisation techniques are used to confirm that the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of ‘unclassifiable’ should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

The visual cortex in Alzheimer disease:laminar distribution of the pathological changes in visual areas V1 and V2

Alzheimer’s disease (AD) is an important neurodegenerative disorder causing visual problems in the elderly population. The pathology of AD includes the deposition in the brain of abnormal aggregates of ?-amyloid (A?) in the form of senile plaques (SP) and abnormally phosphorylated tau in the form of neurofibrillary tangles (NFT). A variety of visual problems have been reported in patients with AD including loss of visual acuity (VA), colour vision and visual fields; changes in pupillary responses to mydriatics, defects in fixation and in smooth and saccadic eye movements; changes in contrast sensitivity and in visual evoked potentials (VEP); and disturbances in complex visual tasks such as reading, visuospatial function, and in the naming and identification of objects. In addition, pathological changes have been observed to affect the eye, visual pathway, and visual cortex in AD. To better understand degeneration of the visual cortex in AD, the laminar distribution of the SP and NFT was studied in visual areas V1 and V2 in 18 cases of AD which varied in disease onset and duration. In area V1, the mean density of SP and NFT reached a maximum in lamina III and in laminae II and III respectively. In V2, mean SP density was maximal in laminae III and IV and NFT density in laminae II and III. The densities of SP in laminae I of V1 and NFT in lamina IV of V2 were negatively correlated with patient age. No significant correlations were observed in any cortical lamina between the density of NFT and disease onset or duration. However, in area V2, the densities of SP in lamina II and lamina V were negatively correlated with disease duration and disease onset respectively. In addition, there were several positive correlations between the densities of SP and NFT in V1 with those in area V2. The data suggest: (1) NFT pathology is greater in area V2 than V1, (2) laminae II/III of V1 and V2 are most affected by the pathology, (3) the formation of SP and NFT in V1 and V2 are interconnected, and (4) the pathology may spread between visual areas via the feed-forward short cortico-cortical connections.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Storage capacity of ultrametric committee machines

The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inputs, and K units in the first hidden layer, is analyzed using techniques from statistical mechanics. We found that the storage capacity strongly depends on the network architecture αc ∼ (log K)1-1/2L and that the number of units K limits the number of possible hidden layers L through the relationship 2L - 1 < 2log K. © 2014 IOP Publishing Ltd.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Phase noise tolerance study in coherent optical circular QAM transmissions with Viterbi- Viterbi carrier phase estimation

We present a performance evaluation of a non-conventional approach to implement phase noise tolerant optical systems with multilevel modulation formats. The performance of normalized Viterbi-Viterbi carrier phase estimation (V-V CPE) is investigated in detail for circular m-level quadrature amplitude modulation (C-mQAM) signals. The intrinsic property of C-mQAM constellation points with a uniform phase separation allows a straightforward employment of V-V CPE without the need to adapt constellation. Compared with conventional feed-forward CPE for square QAM signals, the simulated results show an enhanced tolerance of linewidth symbol duration product (ΔvTs) at a low sensitivity penalty by using feed-forward CPE structure with C-mQAM. This scheme can be easily upgraded to higher order modulations without inducing considerable complexity.

Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip°s width, and chip°s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed. © ASM International.

The visual cortex in alzheimer's disease:laminar distribution of the pathological changes in visual areas V1 and V2

Alzheimer's disease (AD) is an important neurodegenerative disorder causing visual problems in the elderly population. The pathology of AD includes the deposition in the brain of abnormal aggregates of β-amyloid (Aβ) in the form of senile plaques (SP) and abnormally phosphorylated tau in the form of neurofibrillary tangles (NFT). A variety of visual problems have been reported in patients with AD including loss of visual acuity (VA), colour vision and visual fields; changes in pupillary responses to mydriatics, defects in fixation and in smooth and saccadic eye movements; changes in contrast sensitivity and in visual evoked potentials (VEP); and disturbances in complex visual tasks such as reading, visuospatial function, and in the naming and identification of objects. In addition, pathological changes have been observed to affect the eye, visual pathway, and visual cortex in AD. To better understand degeneration of the visual cortex in AD, the laminar distribution of the SP and NFT was studied in visual areas V1 and V2 in 18 cases of AD which varied in disease onset and duration. In area V1, the mean density of SP and NFT reached a maximum in lamina III and in laminae II and III respectively. In V2, mean SP density was maximal in laminae III and IV and NFT density in laminae II and III. The densities of SP in laminae I of V1 and NFT in lamina IV of V2 were negatively correlated with patient age. No significant correlations were observed in any cortical lamina between the density of NFT and disease onset or duration. However, in area V2, the densities of SP in lamina II and lamina V were negatively correlated with disease duration and disease onset respectively. In addition, there were several positive correlations between the densities of SP and NFT in V1 with those in area V2. The data suggest: (1) NFT pathology is greater in area V2 than V1, (2) laminae II/III of V1 and V2 are most affected by the pathology, (3) the formation of SP and NFT in V1 and V2 are interconnected, and (4) the pathology may spread between visual areas via the feed-forward short cortico-cortical connections. © 2012 by Nova Science Publishers, Inc. All rights reserved.

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.

Anisotropic opinion dynamics

We consider the process of opinion formation in a society of interacting agents, where there is a set B of socially accepted rules. In this scenario, we observed that agents, represented by simple feed-forward, adaptive neural networks, may have a conservative attitude (mostly in agreement with B) or liberal attitude (mostly in agreement with neighboring agents) depending on how much their opinions are influenced by their peers. The topology of the network representing the interaction of the society's members is determined by a graph, where the agents' properties are defined over the vertexes and the interagent interactions are defined over the bonds. The adaptability of the agents allows us to model the formation of opinions as an online learning process, where agents learn continuously as new information becomes available to the whole society (online learning). Through the application of statistical mechanics techniques we deduced a set of differential equations describing the dynamics of the system. We observed that by slowly varying the average peer influence in such a way that the agents attitude changes from conservative to liberal and back, the average social opinion develops a hysteresis cycle. Such hysteretic behavior disappears when the variance of the social influence distribution is large enough. In all the cases studied, the change from conservative to liberal behavior is characterized by the emergence of conservative clusters, i.e., a closed knitted set of society members that follow a leader who agrees with the social status quo when the rule B is challenged.

Sistema de conversão de energia eólica baseado no gerador de indução duplamente alimentado: análise e contribuição ao controle da máquina

The humanity reached a time of unprecedented technological development. Science has achieved and continues to achieve technologies that allowed increasingly to understand the universe and the laws which govern it, and also try to coexist without destroying the planet we live on. One of the main challenges of the XXI century is to seek and increase new sources of clean energy, renewable and able to sustain our growth and lifestyle. It is the duty of every researcher engage and contribute in this race of energy. In this context, wind power presents itself as one of the great promises for the future of electricity generation . Despite being a bit older than other sources of renewable energy, wind power still presents a wide field for improvement. The development of new techniques for control of the generator along with the development of research laboratories specializing in wind generation are one of the key points to improve the performance, efficiency and reliability of the system. Appropriate control of back-to-back converter scheme allows wind turbines based on the doubly-fed induction generator to operate in the variable-speed mode, whose benefits include maximum power extraction, reactive power injection and mechanical stress reduction. The generator-side converter provides control of active and reactive power injected into the grid, whereas the grid-side converter provides control of the DC link voltage and bi-directional power flow. The conventional control structure uses PI controllers with feed-forward compensation of cross-coupling dq terms. This control technique is sensitive to model uncertainties and the compensation of dynamic dq terms results on a competing control strategy. Therefore, to overcome these problems, it is proposed in this thesis a robust internal model based state-feedback control structure in order to eliminate the cross-coupling terms and thereby improve the generator drive as well as its dynamic behavior during sudden changes in wind speed. It is compared the conventional control approach with the proposed control technique for DFIG wind turbine control under both steady and gust wind conditions. Moreover, it is also proposed in this thesis an wind turbine emulator, which was developed to recreate in laboratory a realistic condition and to submit the generator to several wind speed conditions.