arXiv subjects

Fatima El Jamiy

Publications and source records attributed to Fatima El Jamiy.

3 recordsLinked to original sources

arXiv2019

A test bed for measuring UAV servo reliability

Extant literature suggests minimal research in the area of system reliability of components used in the design of these UAS (Unmanned Air Systems), thus, subjecting UAS to critical failures that may pose a safety hazard to flight operations. The purpose of the study was to critically assess the reliability of a laboratory designed UAS component test-bed operated using real-world data collected from a Boeing Scan Eagle UAS aileron servo unit via a flight data recorder. The study hypothesized that the test-bed unit's replicating a UAS aileron servo motor's reliability, in terms of a base-line measured encoder output of commanded servo positions, will not be significantly different after double and triple periods of time for continuous operating cycles. Study adds to paucity of extant research on UAS reliability and recommends further studies on commercial UAS components reliability and time to failure.

cs.RO↗

arXiv2017

Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration

This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.

cs.NE↗

arXiv2015

The potential and challenges of Big data - Recommendation systems next level application

The continuous increase of data generated provides enormous possibilities of both public and private companies. The management of this mass of data or big data will play a crucial role in the society of the future, as it finds applications in different fields. There are so much potential and extremely useful insights hidden in the huge volume of data. The advanced analysis techniques available including predictive analytics, text mining, semantic analysis are needed to enable organizations to create a competitive advantage through data analyzed with different levels of sophistication, speed and accuracy previously unavailable. Therefore, is it still possible to have that level of sophistication with the ubiquitous numeric ocean that accompanies use every day via connected devices that invade our lives? However, development of big data requires a good understanding of the issues associated with it. And this is the purpose of this paper, which focuses on giving a close-up view of big data analysis, opportunities and challenges.

cs.CY↗