Browsing by Author "Petrunin, Ivan"
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Item Open Access A review of Bayes filters with machine learning techniques and their applications(Elsevier, 2025-02-01) Kim, Sukkeun; Petrunin, Ivan; Shin, Hyo-SangA Bayes filter is a widely used estimation algorithm, but it has inherent limitations. Performance can degrade when the dynamics are highly nonlinear or when the probability distribution of the state is unknown. To mitigate these issues, machine learning (ML) techniques have been incorporated into many Bayes filters, due to their advantage of being able to map between the input and the output without explicit instructions. In this review, we reviewed 90 papers that proposed the use of ML techniques with Bayes filters to improve estimation performance. This review provides an overview of Bayes filters with ML techniques, categorised according to the role of ML, remaining challenges and research gaps. In the concluding section of this review, we point out directions for future research.Item Open Access AFJPDA: a multiclass multi-object tracking with appearance feature-aided joint probabilistic data association(AIAA, 2024-01-02) Kim, Sukkeun; Petrunin, Ivan; Shin, Hyo-SangThis study addresses a multiclass multi-object tracking problem in consideration of clutters in the environment. To alleviate issues with clutters, we propose the appearance feature-aided joint probabilistic data association filter. We also implemented simple adaptive gating logic for the computational efficiency and track maintenance logic, which can save the lost track for re-association after occlusion or missed detection. The performance of the proposed algorithm was evaluated against a state-of-the-art multi-object tracking algorithm using both multiclass multi-object simulation and real-world aerial images. The evaluation results indicate significant performance improvement of the proposed method against the benchmark state-of-the-art algorithm, especially in terms of reduction in identity switches and fragmentation.Item Open Access Analysis of synchronization in distributed avionics systems based on time-triggered ethernet(IEEE, 2021-11-15) Tariq, Nahman; Petrunin, Ivan; Al-Rubaye, SabaSignificant developments are made in unmanned aerial vehicles (UAVs) and in avionics, where messages sent in the network with critical Time play a vital role. Several studies in Time-Triggered Ethernet have been carried out, but these studies. Still, these improving QoS such as latency in end-to-end delays of an internally synchronized TTE network. However, no one monitors integrated modular avionics. We proposed a framework that enables TTE to be externally synchronized from a GNSS to overcome this problem. We have incorporated our proposed Algorithm in the TTE protocol based on specific parameters and multiple existing algorithms. The proposed Algorithm gives us the ability to control and synchronize the TTE network. Also, we have a developed scenario for analyzing the performance of externally synchronized end-to-end latency of TT messages in a TTE network. We simulated scenarios in our framework and analyzed QoS but, more specifically, the latency that affects the performance of time-triggered messages in externally synchronized TTE networks. The result shows that our proposed framework outperforms existing approaches.Item Open Access Analysis of wireless connectivity applications at airport surface(IEEE, 2020-04-30) Ayub, Shahid; Petrunin, Ivan; Tsourdos, Antonios; Xu, ZhengjiaThe main objective of the current work is to carry out the research to explore the potential wireless communication technologies that can be used during a flight operation at the airport surface for current and potential data applications in future. An important part of this work is the analysis of these services and applications from the perspective of understanding the stakeholders and communication means involved. Different communication services including both critical and non-critical ones are analyzed for aircrafts, airlines, and airport connectivity covering flight stages from landing at the airport to taking off from the airport. We are also proposing the ways of more effective use of communication means including the proposed measures for throughput improvement in order to better meet the needs of the airport stakeholdersItem Open Access Application of fibre optic sensing systems to measure rotor blade structural dynamics(Elsevier, 2021-03-09) Weber, Simone; Kissinger, Thomas; Chehura, Edmond; Staines, Stephen; Barrington, James; Mullaney, Kevin; Fragonara, Luca Zanotti; Petrunin, Ivan; James, Stephen; Lone, Mudassir; Tatam, Ralph P.This paper compares two fibre optic sensing techniques for vibration characterisation: (a) optical fibre Bragg grating (FBG) strain gauges and (b) a novel direct fibre optic shape sensing (DFOSS) approach based on differential interferometric strain measurements between multiple fibres within the same fibre arrangement. Operational mode shapes and frequency measurements of an Airbus Helicopters H135 bearingless main rotor blade (5.1 m radius) were acquired during a series of ground vibration tests undertaken in a controlled laboratory environment. Data recorded by the fibre optic instrumentation systems were validated using commercially available accelerometers and compared against a baseline finite element model. Both fibre optic sensing systems proved capable of identifying the natural frequencies of the blade in the frequency range of interest (0–100 Hz). The data from the FBG sensors exhibited a dependency on their position relative to the neutral axes of the blade, which meant that full characterisation of the flapping and lagging modes required careful consideration of sensor location in the chordwise direction. The DFOSS system was able to identify all structural dynamics, despite being located on the neutral axis in the lagging direction, due to its sensitivity to angle changes, rather than strain, and its biaxial measurement capability. The DFOSS system also allowed the operational mode shapes of the blade to be determined directly, without the requirement for strain transfer from the blade to the sensor and without the requirement for a model of the underlying structure. The accuracy of obtained natural frequencies and operational mode shapes is assessed, demonstrating the potential of the use of both fibre optic sensing systems for determining blade structural dynamics.Item Open Access Application of fibre optic sensing systems to measure rotor blade structural dynamics - underlying data(Cranfield University, 2021-03-12 09:17) James, Stephen; Kissinger, Thomas; Tatam, Ralph; Barrington, James; Chehura, Edmon; Weber, Simone; Mullaney, Kevin; Zanotti Fragonara, Luca; Petrunin, Ivan; Staines, StephenRefer to the paper for full details. Fig9a.csv: Comparison of the Power Spectral Density (PSD) of data recorded by the direct optical fibre shape sensing system, an optical fibre Bragg grating strain sensor and a 1D accelerometer with finite element modeling predictions, measured on the top surface of an Airbus Helicopters H135 bearingless main rotor blade on the quarter chord line at approximately 40% rotor radius. Fig9b.csv: Comparison of the Power Spectral Density (PSD) of data recorded by the direct optical fibre shape sensing system, an optical fibre Bragg grating strain sensor and a 1D accelerometer with finite element modeling predictions, measured on the top surface of an Airbus Helicopters H135 bearingless main rotor blade on the quarter chord line at approximately 60% rotor radius. Fig10_FBG_top.csv: Power Spectral Density (PSD) of the 7th fibre Bragg grating strain (FBG) sensor (FBG7) in the three FBG arrays bonded to the top surface of the Airbus Helicopters H135 bearingless main rotor blade, located at approximately 60% rotor radius. Fig10_FBG_bottom.csv: Power Spectral Density (PSD) of the 7th fibre Bragg grating strain sensor (FBG7) in the three FBG arrays bonded to the bottom surface of the Airbus Helicopters H135 bearingless main rotor blade, located at approximately 60% rotor radius. Fig11.csv: Time series of raw data of 3F frequency input collected at approximately 60% rotor radius for the accelerometer, fibre Bragg grating strain sensor and direct optical fibre shape sensor (vertical direction). Fig12.csv: Comparison of Power Spectral Density (PSD) for the 3F mode measured at approximately 60% rotor radius by the accelerometer, fibre Bragg grating strain sensor and direct optical fibre shape sensor (vertical direction). Fig14.csv: Mode shapes measured using the direct optical fibre shape sensor Fig15.cvs: Comparison of normalised displacement mode shapes measured using a 1D accelerometer, the direct optical fibre shape sensor with the finite element model prediction Fig16.csv: Normalised angle measurements performed by the direct optical fibre shape sensor with the ouput from the FE model for Mode 5F Fig17.csv:Comparison of normalised strain mode shapes determined by the FBG strain sensors and the output from the FE model.Item Open Access Assuring safe and efficient operation of UAV using explainable machine learning(MDPI, 2023-05-19) Alharbi, Abdulrahman; Petrunin, Ivan; Panagiotakopoulos, DimitriosThe accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements.Item Open Access Autonomous inspection and repair of aircraft composite structures(Elsevier, 2018-11-23) Kostopoulos, Vassilis; Psarras, Spyridon; Loutas, Theodoros; Sotiriadis, George; Gray, Iain; Padiyar M, Janardhan; Petrunin, Ivan; Raposo Gaudencio Campos, Joao; Zanotti Fragonara, Luca; Tzitzilonis, Vasileios; Dassios, Konstantinos; Exarchos, Dimitrios; Andrikopoulos, George; Nikolakopoulos, GeorgeThis paper deals with the development of an innovative approach for inspection and repair of damage in aeronautical composites that took place in the first two years of the H2020 CompInnova project which. The aim is a newly designed robotic platform for autonomous inspection using combined infrared thermography (IRT) and phased array (PA) non-destructive investigation for damage detection and characterization, while integrated with laser repair capabilities. This will affect the increasing societal need for safer aircraft in the lowest possible cost, while new and effective techniques of inspection are needed because of the rapidly expanding use of composites in the aerospace industry.Item Unknown Autonomous navigation with taxiway crossings identification using camera vision and airport map(AIAA, 2024-01-04) Delezenne, Quentin; Petrunin, Ivan; Xu, Zhengjia; Neptune, Jonathan; Bleakley, TimothyWith increasing demands of unmanned aerial vehicle (UAV) operations envisioned for the future of aviation, the number of pilots will be much lower than the number of drones, necessitating an increased level of autonomy in drones to alleviate workload. Autonomous UAV taxiing enables autonomy to move on the ground, specifically from the gate to the runway and vice versa without human intervention. This study presents a lightweight vision-based autonomous taxiway navigation system, exploring the fusion of camera vision feed under the nose and airport map data to offer guidance and navigation. A sliding window mechanism is applied in centreline identification to detect line divergence. Centreline representations including divergence, direction and heading are cross-referenced with the airport database for localisation and generating navigation solutions. A simple proportional integral derivative (PID) controller is developed over aircraft dynamic models aligned with Eagle Dynamic’s Digital Combat Simulator to demonstrate the centreline following function. The overall system performance is assessed through simulations, encompassing individual functionality performance tests including centreline extraction test, line matching test, line-to-follow test, generalisation capability test, and computational complexity test. The performance evaluations indicate the promising potential of camera visions in enabling autonomous UAV taxiing with a 71% successful rate of detecting correct lines to follow and the remaining 29% as background. The proposed system also suggests a high generalization capability of more than a 67% success rate when testing over other paths. The source code of this proposition is open-sourced at https://github.com/DelQuentin/TaxiEye.Item Unknown Bladesense – a novel approach for measuring dynamic helicopter rotor blade deformation(European Rotorcraft Forum, 2018-12-31) Weber, Simone; Southgate, Dominic; Mullaney, Kevin; James, Stephen; Rutherford, Robert; Sharma, Anuj; Lone, Mudassir; Kissinger, Thomas; Chehura, Edmond; Staines, Stephen; Pekmezci, Huseyin; Fragonara, Luca Zanotti; Petrunin, Ivan; Williams, Dan; Moulitsas, Irene; Cooke, Alastair; Rosales, Waldo; Tatam, Ralph P.; Morrish, Peter; Fairhurst, Mark; Atack, Richard; Bailey, Gordon; Morley, StuartTechnologies that allow accurate measurement of rotorblade dynamics can impact almost all areas of the rotorcraft sector; ranging from maintenance all the way to blade design. The BladeSense project initiated in 2016 aims to take a step in developing and demonstrating such a capability using novel fibre optic sensors that allow direct shape measurement. In this article the authors summarise key project activities in modelling and simulation, instrumentation development and ground testing. The engineering approach and associated challenges and achievements in each of these disciplines are discussed albeit briefly. This ranges from the use of computational aerodynamics and structural modelling to predict blade dynamics to the development of direct fibre optic shape sensing that allows measurements above 1kHz over numerous positions on the blade. Moreover, the development of the prototype onboard system that overcomes the challenge of transferring data between the rotating main rotor to the fixed fuselage frames is also discussed.Item Unknown Classification of RF transmitters in the presence of multipath effects using CNN-LSTM(IEEE, 2024-06-09) Patil, Pradnya; Wei, Zhuangkun; Petrunin, Ivan; Guo, WeisiRadio frequency (RF) communication systems are the backbone of many intelligent transport and aerospace operations, ensuring safety, connectivity, and efficiency. Accurate classification of RF transmitters is vital to achieve safe and reliable functioning in various operational contexts. One challenge in RF classification lies in data drifting, which is particularly prevalent due to atmospheric and multipath effects. This paper provides a convolutional neural network based long short-term memory (CNN-LSTM) framework to classify the RF emitters in drift environments. We first simulate popular-used RF transmitters and capture the RF signatures, while considering both power amplifier dynamic imperfections and the multipath effects through wireless channel models for data drifting. To mitigate data drift, we extract the scattering coefficient and approximate entropy, and incorpo-rate them with the in-phase quadrature (I/Q) signals as the input to the CNN-LSTM classifier. This adaptive approach enables the model to adjust to environmental variations, ensuring sustained accuracy. Simulation results show the accuracy performance of the proposed CNN-LSTM classifier, which achieves an overall 91.11% in the presence of different multipath effects, bolstering the resilience and precision of realistic classification systems over state of the art ensemble voting approaches.Item Unknown Cognitive communication scheme for unmanned aerial vehicle operation(IEEE, 2020-02-17) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios; Sabyasachi, Mondal; Williamson, AlexAn intelligent and agile wireless communication scheme is a key factor in provision of efficient air-to-ground (A2G) communication for unmanned aerial vehicles (UAVs) operations. For this purpose we review and propose an architecture for aeronautical cognitive communication system (ACCS) that will be providing command, control and communication (C3) link between ground control stations (GCSs) and multiple UAVs utilizing cognitive radio (CR) concept. The factors reviewed and accounted for in the design process are the topology of cognitive detectors, connectivity between cognitive detector and control agency, connection with unmanned traffic management (UTM) system, data link requirements imposed by cognitive scheme, failure notification and recovery, etc. The proposed ACCS is suitable for supporting UAV operations and features a distributed non-communication architecture consisting of GCS network in the ground zone, hybrid data link with the static uplink and the flexible downlink, demonstrating a dynamic nature overall with the frequency handoff scheme generated periodically in accordance with current spectrum environment.Item Unknown Combination and selection of machine learning algorithms in GNSS architecture design for concurrent executions with HIL testing(IEEE, 2023-11-10) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios; Grech, Raphael; Peltola, Pekka; Tiwari, SmitaAs machine learning (ML) continuing to gain popularity, ML-assisted Global Navigation Satellite System (GNSS) receivers facilitate the performance of Autonomous Systems (AS) navigation solutions. However, selections of ML is often a trade-off in practice where empirical knowledge is taken to alleviate complexities. Therefore, this paper explores decision-making solutions for maximising determined hardware performance under quantitative and qualitative considerations. This work proposes Algorithm Selection and Matching with Fuzzy Analytic Hierarchy Process (ASM-FAHP) that maps multiple trade-off concerns into a Multi-Criteria Decision-Making (MCDM) problem. The ASM-FAHP firstly searches all the possible alternatives to find possible combinations with hardware resource limitations taken into account. Afterwards, ASM-FAHP prioritizes the most significant candidate by constructing a hierarchical structure with several attributes and scoring with fuzzy numbers. Hereby, the most suitable ML combinations are determined by calculating synthesised fuzzy weights per each alternative. The performance of the ML combination is evaluated by concurrently executing it on resource-constrained hardware, specifically the Jetson Nano board. The ML models are trained and tested using high-fidelity synthetic datasets produced from Spirent GSS7000 simulator and SimGen while connected to hardware-in-the-loop (HIL). It has been discovered that when approaching hardware limits, the selected combination of machine learning algorithms makes full use of memory resources but sacrifices processing speed.Item Unknown A composite learning approach for multiple fault diagnosis in gears(SAGE, 2022-11-05) Inyang, Udeme Ibanga; Petrunin, Ivan; Jennions, IanA major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.Item Unknown Deep autoencoders for unsupervised anomaly detection in wildfire prediction(American Geophysical Union (AGU), 2024-11-28) Üstek, İrem; Arana‐Catania, Miguel; Farr, Alexander; Petrunin, IvanWildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a unique approach, differentiating from classical supervised learning, and addressed the gap in unsupervised wildfire prediction using autoencoders and clustering techniques for anomaly detection. Historical weather and normalized difference vegetation index data sets of Australia for 2005–2021 were utilized. Two main unsupervised approaches were analyzed. The first used a deep autoencoder to obtain latent features, which were then fed into clustering models, isolation forest, local outlier factor and one‐class support vector machines for anomaly detection. The second approach used a deep autoencoder to reconstruct the input data and use reconstruction errors to identify anomalies. Long Short‐Term Memory autoencoders and fully connected (FC) autoencoders were employed in this part, both in an unsupervised way learning only from nominal data. The FC autoencoder outperformed its counterparts, achieving an accuracy of 0.71, an F1‐score of 0.74, and an MCC of 0.42. These findings highlight the practicality of this method, as it effectively predicts wildfires in the absence of ground truth, utilizing an unsupervised learning technique.Item Unknown Deep learning architecture for UAV traffic-density prediction(MDPI, 2023-01-22) Alharbi, Abdulrahman; Petrunin, Ivan; Panagiotakopoulos, DimitriosThe research community has paid great attention to the prediction of air traffic flows. Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft traffic management (UTM) is relatively sparse at present. Thus, this paper proposes a one-dimensional convolutional neural network and encoder-decoder LSTM framework to integrate air traffic flow prediction with the intrinsic complexity metric. This adapted complexity metric takes into account the important differences between ATM and UTM operations, such as dynamic flow structures and airspace density. Additionally, the proposed methodology has been evaluated and verified in a simulation scenario environment, in which a drone delivery system that is considered essential in the delivery of COVID-19 sample tests, package delivery services from multiple post offices, an inspection of the railway infrastructure and fire-surveillance tasks. Moreover, the prediction model also considers the impacts of other significant factors, including emergency UTM operations, static no-fly zones (NFZs), and variations in weather conditions. The results show that the proposed model achieves the smallest RMSE value in all scenarios compared to other approaches. Specifically, the prediction error of the proposed model is 8.34% lower than the shallow neural network (on average) and 19.87% lower than the regression model on average.Item Unknown Design and hardware-in-the-loop evaluation of a time dissemination framework for drone operations in urban environments(IEEE, 2023-11-10) Negru, Sorin Andrei; Petrunin, Ivan; Guo, Weisi; Tsourdos, AntoniosWith the advent of UAVs (Unmanned Aerial Vehicles) several companies started to offer services, in urban, semi-urban, and rural regions. Although GNSS (Global Navigation Satellite System) can disseminate time information to different platforms, external factors may degrade the signal quality and lead to erroneous time synchronization. The paper is presenting a resilient time dissemination framework, using a wireless 802.11ax protocol and NTP (Network Time Protocol) for the synchronization aspect. A time server, formed by a rubidium clock, a GNSS receiver, and time information provided by NPL (UK’s National Physical Laboratory) traceable to UTC, dictates the time to all the users within the WLAN (Wireless Local Area Network). To evaluate the proposed framework, a lab-based HIL (Hardware in the Loop) simulation is performed using two Jetson Nanos as CC (Companion Computer) and a Pixhawk 2.4 as FCU (Flight Control Unit) representing the end-users in the dissemination framework. In this way, all the communication links are tested and evaluated. Results showed that the two platforms can be synchronized to the time server as an alternative time source, achieving an average RTT (Round Trip Delay) of 8 ms from the Research and Innovation timing node to the FCU, and an average time offset of -0.2 ms.Item Unknown Detect and avoid considerations for safe sUAS operations in urban environments(IEEE, 2021-11-15) Celdran Martinez, Victor; Ince, Bilkan; Kumar Selvam, Praveen; Petrunin, Ivan; Seo, Min-Guk; Anastassacos, Edward; Royall, Paul G.; Cole, Adrian; Tsourdos, Antonios; Knorr, SebastianOperations involving small Unmanned Aerial Systems (sUAS) in urban environments are occurring ever more frequently as recognized applications gain acceptance, and new use cases emerge, such as urban air mobility, medical deliveries, and support of emergency services. Higher demands in these operations and the requirement to access urban airspace present new challenges in sUAS operational safety. The presence of Detect and Avoid (DAA) capability of sUAS is one of the major requirements to its safe operation in urban environments according to the current legislation, such as the CAP 722 in the United Kingdom (UK). The platform or its operator proves a full awareness of all potential obstacles within the mission, maintains a safe distance from other airspace users, and, ultimately, performs Collision Avoidance (CA) maneuvers to avoid imminent impacts. Different missions for the defined scenarios are designed and performed within the simulation model in Software Tool Kit (STK) software environment, covering a wide range of practical cases. The acquired data supports assessment of feasibility and requirements to real-time processing. Analysis of the findings and simulation results leads to a holistic approach to implementation of sUAS operations in urban environments, focusing on extracting critical DAA capability for safe mission completion. The proposed approach forms a valuable asset for safe operations validation, enabling better evaluation of risk mitigation for sUAS urban operations and safety-focused design of the sensor payload and algorithms.Item Unknown Developing drone experimentation facility: progress, challenges and cUAS consideration(IEEE, 2021-07-02) Panagiotakopoulos, Dimitrios; Williamson, Alex; Petrunin, Ivan; Harman, Stephen; Quilter, Tim; Williams-Wynn, Ian; Goudie, Gavin; Watson, Neil; Vernall, Phil; Reid, Jonathan; Puscius, Eimantas; Cole, Adrian; Tsourdos, AntoniosThe operation of Unmanned Aerial Systems (UAS) is widely recognised to be limited globally by challenges associated with gaining regulatory approval for flight Beyond Visual Line of Sight (BVLOS) from the UAS Remote Pilot. This challenge extends from unmanned aircraft flights having to follow the same ‘see and avoid’ regulatory principles with respect to collision avoidance as for manned aircraft. Due to the technical challenges of UAS and Remote Pilots being adequately informed of potential traffic threats, this requirement effectively prohibits BVLOS UAS flight in uncontrolled airspace, unless a specific UAS operational airspace is segregated from manned aviation traffic, often achieved by use of a Temporary Danger Area (TDA) or other spatial arrangements. The UK Civilian Aviation Authority (CAA) has defined a Detect and Avoid (DAA) framework for operators of UAS to follow in order to demonstrate effective collision avoidance capability, and hence the ability to satisfy the ‘see and avoid’ requirement. The National BVLOS Experimentation Corridor (NBEC) is an initiative to create a drone experimentation facility that incorporates a range of surveillance and navigation information sources, including radars, data fusion, and operational procedures in order to demonstrate a capable DAA System. The NBEC is part located within an active Airodrome Traffic Zone (ATZ) at Cranfield Airport, which further creates the opportunity to develop and test systems and procedures together with an operational Air Traffic Control (ATC) unit. This allows for manned and unmanned traffic to be integrated from both systems and procedural perspectives inside segregated airspace in a first stage, and then subsequently transiting to/from non-segregated airspace. The NBEC provides the environment in which a number of challenges can be addressed. This paper discusses the lack of target performance parameters, the methodology for gaining regulatory approval for non-segregated BVLOS flights and for defining peformance parameters for counter UAS (cUAS).Item Unknown Diagnosis of multiple faults in rotating machinery using ensemble learning(MDPI, 2023-01-15) Inyang, Udeme Ibanga; Petrunin, Ivan; Jennions, IanFault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.