A Multi Agent System Design for Power Distribution Restoration Using Neural Networks

The state of the art of power distribution systems is to demand a more accurate response. It also provides more reliability for fault location and restoration respectively. A multi-agent system design for power distribution has been developed using the change of current methodology to detect and locate any type of faults. Employing the artificial intelligence for restoration process is the most important contribution to this study. Since feed-forward neural networks are weight training based back propagation concept, radial basis neural networks showed more efficiency by using the minimum error method to optimize the decision. A Probabilistic radial basis Neural Network (PNN) is designated at each feeder agent to implement the reconfiguration by analyzing the impedance and current values for each zone. The appropriate decision for the optimal reconfiguration case is a vector of activation signals associated with each switch to restore the power to the un-faulted zones of distribution feeder.;This study examines the role of Universal Asynchronous Receiver Transmitter (UART) buffer circuits in the laboratory experiment demonstration of the multi-agent system design. The main approach of a self-healing concept is the protection system. A recloser has been developed and improved for more sensitivity and faster response to detecting a fault where ever it occurs and lead the process of isolating and re-configuration. An electronic buffer circuit using digital microcontroller has been associated with the recloser and agents switches in order to offer a satisfying feedback for the proposed approach. Simulation studies, using MATLAB SimPowerSystems and, Neural Network toolboxes, for the proposed power distribution system showed improved results for fault location and restoration using Radbas neural networks. Hardware implementation with high accurate software data scoping of results has been employed to show the difference in time response using Universal Asynchronous Receiver Transmitter buffers at each switching relay in the design

Extended Delta-Bar-Delta Algorithm Application to Evaluate Transmission Lines Overvoltages

In this paper an intelligent approach is introduced to study switching overvolatges during transmission lines energization. In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. An artificial neural network (ANN) has been used to evaluate the overvoltages due to transmission lines energization. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters; therefore developed ANNs have proper generalization capability. The simulated results for 39-bus New England test system, show that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy and EDBD algorithm presents best performance

Signal processing and image restoration techniques for two-dimensional eddy current nondestructive evaluation

This dissertation presents a comprehensive study on the forward modeling methods, signal processing techniques, and image restoration techniques for two-dimensional eddy current nondestructive evaluation. The basic physical forward method adopted in this study is the volume integral method. We have applied this model to the eddy current modeling problem for half space geometry and thin plate geometry. To reduce the computational complexity of the volume integral method, we have developed a wavelet expansion method which utilizes the multiresolution compression capability of the wavelet basis to greatly reduce the amount of computation with small loss in accuracy. To further improve the speed of forward modeling, we have developed a fast eddy current model based on a radial basis function neural network. This dissertation also contains investigations on signal processing techniques to enhance flaw signals in two-dimensional eddy current inspection data. The processing procedures developed in this study include a set of preprocessing techniques, a background removal technique based on principal component analysis, and grayscale morphological operations to detect flaw signals. Another important part of the dissertation concerns image restoration techniques which can remove the blurring in impedance change images due to the diffusive nature of the eddy current testing. We have developed two approximate linear image restoration methods--the Wiener filtering method and the maximum entropy method. Both linear restoration methods are based on an approximate linear forward model formulated by using the Born approximation. To improve the quality of restoration, we have also developed nonlinear image restoration methods based on simulated annealing and a genetic algorithm. Those nonlinear methods are based on the neural network forward model which is more accurate than the approximate linear forward model

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Structural Neuroimaging of Anorexia Nervosa: Future Directions in the Quest for Mechanisms Underlying Dynamic Alterations.

Anorexia nervosa (AN) is a serious eating disorder characterized by self-starvation and extreme weight loss. Pseudoatrophic brain changes are often readily visible in individual brain scans, and AN may be a valuable model disorder to study structural neuroplasticity. Structural magnetic resonance imaging studies have found reduced gray matter volume and cortical thinning in acutely underweight patients to normalize following successful treatment. However, some well-controlled studies have found regionally greater gray matter and persistence of structural alterations following long-term recovery. Findings from diffusion tensor imaging studies of white matter integrity and connectivity are also inconsistent. Furthermore, despite the severity of AN, the number of existing structural neuroimaging studies is still relatively low, and our knowledge of the underlying cellular and molecular mechanisms for macrostructural brain changes is rudimentary. We critically review the current state of structural neuroimaging in AN and discuss the potential neurobiological basis of structural brain alterations in the disorder, highlighting impediments to progress, recent developments, and promising future directions. In particular, we argue for the utility of more standardized data collection, adopting a connectomics approach to understanding brain network architecture, employing advanced magnetic resonance imaging methods that quantify biomarkers of brain tissue microstructure, integrating data from multiple imaging modalities, strategic longitudinal observation during weight restoration, and large-scale data pooling. Our overarching objective is to motivate carefully controlled research of brain structure in eating disorders, which will ultimately help predict therapeutic response and improve treatment

Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility