Large gap magnetic suspension system

The design of a large gap magnetic suspension system is discussed. Some of the topics covered include: the system configuration, permanent magnet material, levitation magnet system, superconducting magnets, resistive magnets, superconducting levitation coils, resistive levitation coils, levitation magnet system, and the nitrogen cooled magnet system

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

Machine learning based anomaly detection in release testing of 5g mobile networks

Abstract. The need of high-quality phone and internet connections, high-speed streaming ability and reliable traffic with no interruptions has increased because of the advancements the wireless communication world witnessed since the start of 5G (fifth generation) networks. The amount of data generated, not just every day but also, every second made most of the traditional approaches or statistical methods used previously for data manipulation and modeling inefficient and unscalable. Machine learning (ML) and especially, the deep learning (DL)-based models achieve the state-of-art results because of their ability to recognize complex patterns that even human experts are not able to recognize. Machine learning-based anomaly detection is one of the current hot topics in both research and industry because of its practical applications in almost all domains. Anomaly detection is mainly used for two purposes. The first purpose is to understand why this anomalous behavior happens and as a result, try to prevent it from happening by solving the root cause of the problem. The other purpose is to, as well, understand why this anomalous behavior happens and try to be ready for dealing with this behavior as it would be predictable behavior in that case, such as the increased traffic through the weekends or some specific hours of the day. In this work, we apply anomaly detection on a univariate time series target, the block error rate (BLER). We experiment with different statistical approaches, classic supervised machine learning models, unsupervised machine learning models, and deep learning models and benchmark the final results. The main goal is to select the best model that achieves the balance of the best performance and less resources and apply it in a multivariate time series context where we are able to test the relationship between the different time series features and their influence on each other. Through the final phase, the model selected will be used, integrated, and deployed as part of an automatic system that detects and flags anomalies in real-time. The simple proposed deep learning model outperforms the other models in terms of the accuracy related metrics. We also emphasize the acceptable performance of the statistical approach that enters the competition of the best model due to its low training time and required computational resources

Searching Speech Keywords from Video

Video contains various types of data that can be extracted using various techniques and tools. The extracted data can be used in developing video retrieving and indexing systems. Video's speech is rich with information and can be used by students as a supporting tool in learning process. Video is divided into many parts depending on their content. Searching for the part of interest may require manual searching through the entire video which may be time consuming. Therefore, this study focuses on investigating the existing approaches of searching and retrieving videos and to develop a method to make video content more easily searchable. A web-application prototype was developed using Java and JSP for searching the video speech using keywords. Finally, the users' satisfaction of the developed prototype was measured using IBM CSUQ questionnaire

Electrochemistry of stress corrosion cracking of brass

The stress corrosion cracking (SCC) susceptibility of pure copper and two brass (copper-zinc alloy) compositions (80/20 and 60/39) was studied in several ammoniacal and nonammoniacal aqueous solutions at open circuit potential applying a constant load technique. The SCC tests, using tensile stress and loop specimens, showed pure copper to be immune in all solutions tested, the (alpha)(beta)\u27-brass (60/39) alloy to be most susceptible to SCC, and the (80/20) alloy to have intermediate SCC susceptibility. The electrochemical tests (corrosion potential and Tafel plots) have been utilized to prove the validity of the dissolution mechanism for the SCC propagation in solutions with intermediate corrosion rates ((TURN)0.1 18% nontarnishing solutions; *DOE Report IS-T-1188. This work was performed under contract No. W-7405-Eng-82 with the U.S. Department of Energy