Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Feature Papers of Forecasting 2021

Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND

Feature Papers of Forecasting 2021

This book focuses on fundamental and applied research on forecasting methods and analyses on how forecasting can affect a great number of fields, spanning from Computer Science, Engineering, and Economics and Business to natural sciences. Forecasting applications are increasingly important because they allow for improving decision-making processes by providing useful insights about the future. Scientific research is giving unprecedented attention to forecasting applications, with a continuously growing number of articles about novel forecast approaches being publishe

Forecasting time series with artificial neural networks

Having accurate time series forecasts helps to be prepared for upcoming events. As many real world time series have nonlinear and irregular behavior, traditional approaches may be lacking performance. A suitable alternative method is artificial neural network models, that can achieve high accuracy in various difficult tasks. The objective of given thesis is to give theoretical and practical guidelines for applying neural networks in time series forecasting with packages h2o and neuralnet for statistical programming language R, and library Keras for programming language Python. An empirical study was conducted on five different datasets to compare multilayer perceptron model performance with long short-term memory model, and iterative, direct and multi-neural network modeling strategies with each other. The performance of neural network models were compared with liner baseline models to expose whether the results have any practical gain. When comparing the network structures, the results indicate the superiority of long short-term memory models. Furthermore, long short-term memory models offered improvement over linear baseline model almost in case of all datasets. Based on these results, neural networks proved to have great performance for time series forecasting, and should be considered as an alternative to linear models

LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data

In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requires considerable effort from domain experts. An alternative approach is to use deep learning algorithms. Research has shown that modern deep neural networks are very effective in automated extraction of abstract features from raw data in classification tasks. Long short-term memory networks, or LSTMs in short, are a special kind of recurrent neural networks that are capable of learning long-term dependencies. These networks have proved to be especially effective in the classification of raw time-series data in various domains. This dissertation systematically investigates the effectiveness of the LSTM model for anomaly detection and classification in raw time-series sensor data. As a proof of concept, this work used time-series data of sensors that measure blood glucose levels. A large number of time-series sequences was created based on a genuine medical diabetes dataset. Anomalous series were constructed by six methods that interspersed patterns of common anomaly types in the data. An LSTM network model was trained with k-fold cross-validation on both anomalous and valid series to classify raw time-series sequences into one of seven classes: non-anomalous, and classes corresponding to each of the six anomaly types. As a control, the accuracy of detection and classification of the LSTM was compared to that of four traditional machine learning classifiers: support vector machines, Random Forests, naive Bayes, and shallow neural networks. The performance of all the classifiers was evaluated based on nine metrics: precision, recall, and the F1-score, each measured in micro, macro and weighted perspective. While the traditional models were trained on vectors of features, derived from the raw data, that were based on knowledge of common sources of anomaly, the LSTM was trained on raw time-series data. Experimental results indicate that the performance of the LSTM was comparable to the best traditional classifiers by achieving 99% accuracy in all 9 metrics. The model requires no labor-intensive feature engineering, and the fine-tuning of its architecture and hyper-parameters can be made in a fully automated way. This study, therefore, finds LSTM networks an effective solution to anomaly detection and classification in sensor data