Modelling of Shre Drag Tilt Velocimeter (DTV) with Curvilinear, Gompertz and Artificial Neural Network Method

Different method of modelling presented in this paper on Shre Drag Tilt Velocimeter non-linear data. The idea of different non-linear modelling method is to know which makes more possible to describe more accurate on interacting effects between velocities and tilt angle when compared among modellers. The models, which were used are static analytic approximation model, curvilinear bivariate regression model, Gompertz the classical growth model and Artificial Neural Network (ANN) model. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), bias and R Square. The datasets gathered from an experiment of Shre DTV at flume were divided into training data and testing data for the purpose of developing and validating all type of models. The difference between the model and the observed value become the forecasting error measurements. For the training data, the lowest MSE, RMSE and better R Square were noted for the Gompertz model. But ANN generalized better on testing data by obtaining lowest MSE, RMSE and higher R Square among others. ANN generalization result is 88.60%, Gompertz is 54.89%, curvilinear is 69.28% and static analytic is -1.29%. Lower bias was also for the neural network test data. As demonstrated by the bias values, only curvilinear model presenting overestimation model while other models produce little or no overestimation of the observed tilt response. Interpretations of the parameters estimation on Gompertz model have been attempted previously. However, focusing on the ability of Shre DTV to predict responses may be more practical than the relevance of parameter estimates

Analysis of Machine Learning Algorithms for Time Series Prediction

Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This research investigates and evaluates machine learning algorithms that can be used for time series prediction. Experiments were carried out using the Artificial Neural Network (ANN), Support Vector Regressor (SVR) and Long Short-Term Memory (LSTM) algorithms on eight datasets. An empirical analysis was carried out by applying each machine learning algorithm to the selected datasets. A critical comparison of the algorithm performance was carried out using the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE) and the Mean Absolute Scaled Error (MASE). The second experiment focused on evaluating the stability and robustness of the optimal models identified in the first experiment. The key dataset characteristics identified; were the dataset size, stationarity, trend and seasonality. It was found that the LSTM performed the best for majority of the datasets, due to the algorithm's ability to deal with sequential dependency. The performance of the ANN and SVR were similar for datasets with trend and seasonality, while the LSTM overall proved superior to the aforementioned algorithms. The LSTM outperformed the ANN and SVR due to its ability to handle temporal dependency. However, due to its stochastic nature, the LSTM and ANN algorithms can have poor stability and robustness. In this regard, the LSTM was found to be a more robust algorithm than the ANN and SVR

A generalised feedforward neural network architecture and its applications to classification and regression

Shunting inhibition is a powerful computational mechanism that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks where the basic synaptic computations are based on shunting inhibition. SIANNs were designed to solve difficult machine learning problems by exploiting the inherent non-linearity mediated by shunting inhibition. The aim was to develop powerful, trainable networks, with non-linear decision surfaces, for classification and non-linear regression tasks. This work enhances and extends the original SIANN architecture to a more general form called the Generalised Feedforward Neural Network (GFNN) architecture, which contains as subsets both SIANN and the conventional Multilayer Perceptron (MLP) architectures. The original SIANN structure has the number of shunting neurons in the hidden layers equal to the number of inputs, due to the neuron model that is used having a single direct excitatory input. This was found to be too restrictive, often resulting in inadequately small or inordinately large network structures

Channel prediction in wireless communications

Knowledge of the channel over which signals are sent is of prime importance in modern wireless communications. Inaccurate or incomplete channel information leads to high error rates and wasted bandwidth and energy. Although active channel measurement is commonly used to gain channel knowledge, it can only accurately represent the channel at the time the measurement was taken, makes energy and bandwidth demands, and adds significant complexity to the radio system. Due to the highly time variant nature of wireless channels, active measurements become invalid almost as soon as they are taken, making alternative approaches to predicting future behaviour highly attractive. Such systems would allow maximum advantage to be taken of the limited bandwidth available and make significant power savings. This thesis investigates a number of complementary technologies, leading towards a channel prediction scheme suitable for mobile devices. As a first step towards channel prediction, anomaly detection is investigated within periodic wireless signals to establish when radical changes in the channel occur. In pre- vious experiments, long monotonic sequences had been observed to coincide with certain anomalies but not others when using Kullback-Leibler Divergence (KLD) analysis, possibly allowing the characterisation of anomaly types. An investigation is described to explain the origin of these features in a rigorous mathematical sense. A proof is given for the causes of the monotonic sequences, followed by a discussion of the types of signal anomaly which would underly such a feature and the value of this information. The second part describes a novel channel characterisation method which uses a class of Recurrent Neural Network (RNN) called an Echo State Network (ESN). Using this tool, a channel characterisation system can be constructed without an explicit statistical or mathematical model of the wireless environment, relying instead on observed data. This approach is much more convenient than existing models which require detailed information about the wireless system's parameters and also allows for new channel classifications to be added easily. It is able to achieve double the correct classification rate of a conventional statistical classifier, and is computationally simple to implement, making it ideal for inclusion on low-power mobile devices. Following their successful use in characterisation, ESNs are used in the final part in an investigation into channel prediction in a number of different scenarios. They were however found to be unable to produce useful predictions for all but the most trivial channel models. An alternative method is described for indoor environments using an approach inspired by ray tracing. It is simple and computationally lightweight to implement, again making it suitable for mobile devices. Simulation results show that it can outperform pilot-assisted methods by a significant margin, while not wasting bandwidth on channel measurement

Time series forecasting methodologies for electricity supply systems

Forecasting is an essential function in the electricity supply industry. Electricity demand forecasting is performed on number of different time-scales depending on the function for which they are required. In the short term (hourly) forecasts of electricity demand are required for the safe and efficient operation of the power system. Medium term forecasts (weekly) are needed for economic planning and long term (yearly) forecasts are required for deciding on system generation and transmission expansion plans. In recent years the electricity supply industry in some countries has undergone significant changes mainly due to a levelling off in the growth of electricity demand and also due to technological advances. There has been a move toward the existence of a number of smaller generating companies and the emergence of a competitors market has resulted. These changes in the structure of the industry have led to new requirements in the area of forecasting, where forecasts are now required on a small time-scale over a longer forecasting horizon, for example, the production of hourly forecasts over a period of a month. The thesis presents a novel approach to the solution of the production of short term forecasts over a relatively long term forecast horizon. The mathematical formulation of the technique is presented and an application procedure is developed. Two applications of the technique are given and the issues involved in the implementation investigated. In addition, the production of weekly electricity demand forecasts using the optimal form of the available weather variables is investigated. The value of using such a variable in cases where it is not a dominant influencing factor in the system is assessed. The application of neural networks to the problem of weekly electricity demand forecasting is examined. Neural networks are also applied to the problem of the production of both aggregate and disaggregate electricity sales forecasts for up to five years in advance. Conclusions regarding the methodologies presented in the thesis are drawn and directions for future works are considered

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Attitudes towards old age and age of retirement across the world: findings from the future of retirement survey

The 21st century has been described as the first era in human history when the world will no longer be young and there will be drastic changes in many aspects of our lives including socio-demographics, financial and attitudes towards the old age and retirement. This talk will introduce briefly about the Global Ageing Survey (GLAS) 2004 and 2005 which is also popularly known as “The Future of Retirement”. These surveys provide us a unique data source collected in 21 countries and territories that allow researchers for better understanding the individual as well as societal changes as we age with regard to savings, retirement and healthcare. In 2004, approximately 10,000 people aged 18+ were surveyed in nine counties and one territory (Brazil, Canada, China, France, Hong Kong, India, Japan, Mexico, UK and USA). In 2005, the number was increased to twenty-one by adding Egypt, Germany, Indonesia, Malaysia, Poland, Russia, Saudi Arabia, Singapore, Sweden, Turkey and South Korea). Moreover, an additional 6320 private sector employers was surveyed in 2005, some 300 in each country with a view to elucidating the attitudes of employers to issues relating to older workers. The paper aims to examine the attitudes towards the old age and retirement across the world and will indicate some policy implications

Attitudes towards old age and age of retirement across the world: findings from the future of retirement survey

The 21st century has been described as the first era in human history when the world will no longer be young and there will be drastic changes in many aspects of our lives including socio-demographics, financial and attitudes towards the old age and retirement. This talk will introduce briefly about the Global Ageing Survey (GLAS) 2004 and 2005 which is also popularly known as “The Future of Retirement”. These surveys provide us a unique data source collected in 21 countries and territories that allow researchers for better understanding the individual as well as societal changes as we age with regard to savings, retirement and healthcare. In 2004, approximately 10,000 people aged 18+ were surveyed in nine counties and one territory (Brazil, Canada, China, France, Hong Kong, India, Japan, Mexico, UK and USA). In 2005, the number was increased to twenty-one by adding Egypt, Germany, Indonesia, Malaysia, Poland, Russia, Saudi Arabia, Singapore, Sweden, Turkey and South Korea). Moreover, an additional 6320 private sector employers was surveyed in 2005, some 300 in each country with a view to elucidating the attitudes of employers to issues relating to older workers. The paper aims to examine the attitudes towards the old age and retirement across the world and will indicate some policy implications

Fiscal Year 2014

This report summarizes NOAA's budget request for the 2014 fiscal year