Mining Aircraft Telemetry Data With Evolutionary Algorithms

The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS) operations developed by the University of North Dakota. GPAR-RMS detected proximate aircraft with various sensor systems, including a 2D radar and an Automatic Dependent Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then displayed to UAS operators via visualization software developed by the University of North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However, accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR in Class E airspace were needed before the RM subsystem could be implemented. In this dissertation the author presents the results of data mining an aircraft telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000 devices onboard every Cessna 172 in the University of North Dakota\u27s training fleet. Data from aircraft which were potentially within the controlled airspace surrounding controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E airspace were assumed to be flying under VFR, which is usually a valid assumption. Complex subpaths were discovered from the aircraft telemetry data set using a novel application of an ant colony algorithm. Then, probabilistic models were data mined from those subpaths using extensions of the Genetic K-Means (GKA) and Expectation- Maximization (EM) algorithms. The results obtained from the subpath discovery and data mining suggest a pilot flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of the GA aircraft. However, since only aircraft telemetry data from the University of North Dakota\u27s training fleet were data mined, these results are not likely to be applicable to GA aircraft operating in a non-training environment

Game analytics - maximizing the value of player data

During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.peer-reviewe

Telemetry Data Mining For Unmanned Aircraft Systems

With ever more data becoming available to the US Air Force, it is vital to develop effective methods to leverage this strategic asset. Machine learning (ML) techniques present a means of meeting this challenge, as these tools have demonstrated successful use in commercial applications. For this research, three ML methods were applied to a unmanned aircraft system (UAS) telemetry dataset with the aim of extracting useful insight related to phases of flight. It was shown that ML provides an advantage in exploratory data analysis and as well as classification of phases. Neural network models demonstrated the best performance with over 90% accuracy in classifying of UAS phases of flight. Categorical and Regression Trees (CART) also performed well, whereas C5.0 is less suited for this task. In addition, several interesting patterns were uncovered within the dataset, which can aid UAS operators in identifying mission anomalies and atypical system operation

High resilience wireless mesh networking characteristics and safety applications within underground mines

The work presented in this thesis has investigated the feasibility, characteristics and potential applications of low power wireless networking technology, particularly aimed at improving underground mine safety. Following an initial review, wireless technology was identified as having many desirable attributes as a modern underground data transmission medium. Wireless systems are mobile, flexible, and easily scalable. Installation time can be reduced and there is scope for rapid deployment of wireless sensor networks following an emergency incident such as a mine explosion or roof rock fall. Low power mesh technology, relating to the Zigbee and IEEE 802.15.4 LR-WPAN (low-rate wireless personal area network) standards, has been of particular interest within this research project. The new breed of LR-WPAN technology is specifically designed for low power, low data rate wireless sensor applications. The mesh networking characteristics of the technology significantly increase network robustness and resilience. The self-healing, self-organising, multiple pathway redundancy, and highly scalable attributes of mesh networks are particularly advantageous for underground, or confined space, high-integrity safety and emergency applications. The study and potential use of this type of technology in an underground mine is a novel aspect of this thesis. The initial feasibility and review examined the current and future trends of modern underground data transmission systems, with particular focus on mine safety. The findings following the review determined the ideal requirements of an underground data transmission in terms of robustness, integrity, interoperability, survivability and flexibility; with wireless mesh networking meeting many of these requirements. This research has investigated underground wireless propagation characteristics at UHF and microwave frequencies in tunnels. This has involved examining electromagnetic (EM) waveguide theory, in particular the lossy dielectric tunnel waveguide model e.g. (Emslie et al., 1975 and Delogne, 1982). Extensive tests have been carried out in three different underground locations (railway tunnel, hard rock mine, coal mine test facility) using continuous wave (CW), or ‘pure’ transmission at 2.3GHz and 5.8GHz, along with a range of throughput performance tests using various wireless technologies: IEEE 802.11b, 802.11g, SuperG, SuperG (plus BeamFlex antennas), 802.11pre-n. 802.11draft-n, and Bluetooth. The results of these practical tests have been compared with the lossy dielectric tunnel waveguide model showing good agreement that tunnels will in fact enhance the EM propagation through the waveguide effect. Building on previous research during the last 30 years into high frequency underground radio transmission, this work presents a novel investigation into the performance of modern underground wireless technologies operating in underground mines and tunnels. 4 The feasibility and performance of low power wireless mesh networking technology, relating to Zigbee/IEEE 802.15.4, operating in various underground and confined space environments has been investigated through a series of practical tests in different locations including: a hard rock test mine, a coal mine and a fire training centre (confined space built infrastructure). The results of these tests are presented discussing the significant benefits in employing ‘mesh’ topologies in mines and tunnels. Following this, key applications were identified for potential development. Distributed smart sensor network e.g. environmental monitoring, machine diagnostics or remote telemetry, applications were developed to a proof-of-concept stage. A remote 3D surveying telemetry application was also developed in conjunction with the ‘RSV’ (remote surveying vehicle) project at CSM. Vital signs monitoring of personnel has also been examined, with tests carried out in conjunction with the London Fire Service. ‘Zonal location information’ was another key application identified using underground mesh wireless networks to provide active tracking of personnel and vehicles as a lower cost alternative to RFID. Careful consideration has also been given to potential future work, ranging from ‘mine friendly’ antennas, to a ‘hybrid Zigbee’, such as, optimised routing algorithms, and improved physical RF performance, specifically for high-integrity underground safety and emergency applications. Both the tests carried out and key safety applications investigated have been a novel contribution of this thesis. In summary, this thesis has contributed to furthering the knowledge within the field of subsurface electromagnetic wave propagation at UHF and microwave frequencies. Key characteristics and requirements of an underground critical safety data transmission system have been identified. Novel aspects of this work involved investigating the application of new wireless mesh technology for underground environments, and investigating the performance of modern wireless technologies in tunnels through practical tests and theoretical analysis. Finally, this thesis has proved that robust and survivable underground data transmission, along with associated mine safety applications, can feasibly be achieved using the low power wireless mesh networking technology. Robust underground wireless networking also has potential benefits for other industrial and public sectors including tunnelling, emergency services and transport

Knowledge discovery from trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAs a newly proliferating study area, knowledge discovery from trajectories has attracted more and more researchers from different background. However, there is, until now, no theoretical framework for researchers gaining a systematic view of the researches going on. The complexity of spatial and temporal information along with their combination is producing numerous spatio-temporal patterns. In addition, it is very probable that a pattern may have different definition and mining methodology for researchers from different background, such as Geographic Information Science, Data Mining, Database, and Computational Geometry. How to systematically define these patterns, so that the whole community can make better use of previous research? This paper is trying to tackle with this challenge by three steps. First, the input trajectory data is classified; second, taxonomy of spatio-temporal patterns is developed from data mining point of view; lastly, the spatio-temporal patterns appeared on the previous publications are discussed and put into the theoretical framework. In this way, researchers can easily find needed methodology to mining specific pattern in this framework; also the algorithms needing to be developed can be identified for further research. Under the guidance of this framework, an application to a real data set from Starkey Project is performed. Two questions are answers by applying data mining algorithms. First is where the elks would like to stay in the whole range, and the second is whether there are corridors among these regions of interest