Development of a Novel Magnetic Resonance Imaging Acquisition and Analysis Workflow for the Quantification of Shock Wave Lithotripsy-Induced Renal Hemorrhagic Injury

Introduction The current accepted standard for quantifying shock wave lithotripsy (SWL)-induced tissue damage is based on morphometric detection of renal hemorrhage in serial tissue sections from fixed kidneys. This methodology is time and labor intensive and is tissue destructive. We have developed a non-destructive magnetic resonance imaging (MRI) method that permits rapid assessment of SWL-induced hemorrhagic lesion volumes in post-mortem kidneys using native tissue contrast to reduce cycle time. Methods Kidneys of anesthetized pigs were targeted with shock waves using the Dornier Compact S lithotripter. Harvested kidneys were then prepared for tissue injury quantification. T1 weighted (T1W) and T2 weighted (T2W) images were acquired on a Siemens 3T Tim Trio MRI scanner. Images were co-registered, normalized, difference (T1W–T2W) images generated, and volumes classified and segmented using a Multi-Spectral Neural Network (MSNN) classifier. Kidneys were then subjected to standard morphometric analysis for measurement of lesion volumes. Results Classifications of T1W, T2W and difference image volumes were correlated with morphometric measurements of whole kidney and parenchymal lesion volumes. From these relationships, a mathematical model was developed that allowed predictions of the morphological parenchymal lesion volume from MRI whole kidney lesion volumes. Predictions and morphology were highly correlated (R=0.9691, n=20) and described by the relationship y=0.84x+0.09, and highly accurate with a sum of squares difference error of 0.79%. Conclusions MRI and the MSNN classifier provide a semi-automated segmentation approach, which provide a rapid and reliable means to quantify renal injury lesion volumes due to SWL

Non Deterministic Processing in Neural Networks : An Introduction to Multi-Threaded Neural Networks

Since McCullough and Pitts first published their work on the Binary Decision Neuron much research has been accumulated in the area of neural networks. This work has for the most part centred on network topologies and learning algorithms. The neural networks that have found their way into devices such as handheld PC’s are the fruit of NN research that has spanned 57 years. There is a simplistic beauty in the way that artificial neural networks model the biological foundations of the human thought process, but one piece of the jigsaw puzzle is still missing. We have so far been unable to match the massive parallelness of the human brain. This paper attempts to explain how multithreaded neural networks can be used as a basis for building parallel networks. By studying simple concurrent networks is hoped that significant inroads can be made into a better understanding of how neural network processing can be spread across multiple processors. The paper outlines some biological foundations and introduces some approaches that may be used to recreate software implementations of concurrent artificial neural networks

Review of rational (total) nonlinear dynamic system modelling, identification, and control

© 2013 Taylor & Francis. This paper is a summary of the research development in the rational (total) nonlinear dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined as those where the model parameters and input (controller outputs) are subject to nonlinear to the output. Previously, this class of models has been known as rational models, which is a model that can be considered to belong to the nonlinear autoregressive moving average with exogenous input (NARMAX) model subset and is an extension of the well-known polynomial NARMAX model. The justification for using the rational model is that it provides a very concise and parsimonious representation for highly complex nonlinear dynamic systems and has excellent interpolatory and extrapolatory properties. However, model identification and controller design are much more challenging compared to the polynomial models. This has been a new and fascinating research trend in the area of mathematical modelling, control, and applications, but still within a limited research community. This paper brings several representative algorithms together, developed by the authors and their colleagues, to form an easily referenced archive for promotion of the awareness, tutorial, applications, and even further research expansion

Artificial Neural Network Application In Environmental Engineering.

The objective of this thesis research is to apply two artificial neural network (ANN) methods, back-propagation neural network (BPN) and radial basis function generalized regression neural network (RBFGRNN) in two environmental engineering case studies to explore their ability to modeling the complex environmental engineering systems. The traditional environmental engineering systems modeling are frequently using the physical-based modeling methods

Parallel Learning by Multitasking Neural Networks

A modern challenge of Artificial Intelligence is learning multiple patterns at once (i.e.parallel learning). While this can not be accomplished by standard Hebbian associative neural networks, in this paper we show how the Multitasking Hebbian Network (a variation on theme of the Hopfield model working on sparse data-sets) is naturally able to perform this complex task. We focus on systems processing in parallel a finite (up to logarithmic growth in the size of the network) amount of patterns, mirroring the low-storage level of standard associative neural networks at work with pattern recognition. For mild dilution in the patterns, the network handles them hierarchically, distributing the amplitudes of their signals as power-laws w.r.t. their information content (hierarchical regime), while, for strong dilution, all the signals pertaining to all the patterns are raised with the same strength (parallel regime). Further, confined to the low-storage setting (i.e., far from the spin glass limit), the presence of a teacher neither alters the multitasking performances nor changes the thresholds for learning: the latter are the same whatever the training protocol is supervised or unsupervised. Results obtained through statistical mechanics, signal-to-noise technique and Monte Carlo simulations are overall in perfect agreement and carry interesting insights on multiple learning at once: for instance, whenever the cost-function of the model is minimized in parallel on several patterns (in its description via Statistical Mechanics), the same happens to the standard sum-squared error Loss function (typically used in Machine Learning)

Wind farm power output prediction based on machine learning recurrent neural networks

Scientists, investors and policy makers have become aware of the importance of providing near accurate prediction of renewable energy. This is why current studies show improvements in methodologies to provide more precise energy predictions. Wind energy is tied to variabilities of weather patterns, especially wind speeds, which are irregular in climates with erratic weather conditions. To predict wind power output, model technologies like autoregressive integrated moving average (ARIMA), variants of ARIMA, hybrid models involving ARIMA and artificial neural networks (ANN), Kalman filters and support vector regressions (SVR) have been applied for wind speed involving short, ultra-short, medium and long terms kind of predictions. ARIMA ensemble with ANN has shown better performance for short and ultra-short terms of two to three hours ahead. On the other hand, SVR, Kalman filters and ensemble of both has recorded good performance for medium-term kinds of wind speed predictions. Recently, neural networks in particular recurrent neural networks (RNN) have reported immense achievement in time series predictions particularly for medium and long-term. This is largely due to its retentive memory-mapping capabilities in fitting sequence in series. These capabilities are short-lived; when the sequence grows over time, the RNN tend to lose correlated information on back-propagation operations. This can lead to errors in the predicted potentials. Therefore, RNNs are exploited for enhanced wind-farm power output prediction. The main contribution of this research is the study of a model involving a combination of RNN regularisation methods using dropout and long short-term memory (LSTM) for wind-power output predictions. In this research, the regularisation method modifies and adapts to the stochastic nature of the wind and is optimised for the wind-farm power output (WFPO) prediction for up to 12-hours ahead – 72-timesteps. This algorithm implements a dropout method to suit the non-deterministic wind speed by applying LSTM to prevent RNN from overfitting. A demonstration for accuracy using the proposed method is performed on a 14-turbine wind farm with up to ten thousand wind samples for model training and five hundred for model validation and testing. The model out performs the ARIMA model with up to 90% accuracy and is expected to be applied to erratic weather condition, especially those observed in an off-shore wind farms

Improved sequential and batch learning in neural networks using the tangent plane algorithm

The principal aim of this research is to investigate and develop improved sequential and batch learning algorithms based upon the tangent plane algorithm for artificial neural networks. A secondary aim is to apply the newly developed algorithms to multi-category cancer classification problems in the bio-informatics area, which involves the study of dna or protein sequences, macro-molecular structures, and gene expressions

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique