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Return: group design project for pin-point landing demonstrator using drone technologies
Get PDFIn a rapidly changing world, engineers must identify societal needs, solve problems with ingenuity, and create new knowledge. While conventional taught-based courses provide a solid foundation of knowledge, they often fall short in stimulating creative thinking, translating theoretical knowledge into real-world solutions, and fostering leadership and teamwork. This paper proposes a project-based learning course designed to solidify and amplify the technical knowledge gained in preceding taught-based modules while developing essential soft skills. The course covers various stages of designing a reusable launcher-like demonstrator, aiming to simulate take-of and pinpoint landing operations using drone technology. This involves identifying system requirements and designing both software and hardware components. Throughout the course, students enhance their ability to critically formulate, solve, and evaluate engineering problems. They gain and apply technical knowledge in all aspects of drone technology, including fight dynamics, control, navigation, guidance, situational awareness, and communication. Additionally, students explore key aspects of systems engineering practices, risk management, and time management. This paper details the problem design, course timeline, outcomes and key lessons learned from the course.2nd IFAC Workshop on Aerospace Control Education - WACE 2024IFAC-PapersOnLin
Securing industry 4.0: Assessing cybersecurity challenges and proposing strategies for manufacturing management
Get PDFIndustry 4.0 represents the foundation of the fourth industrial revolution, characterised by the integration of innovative technology into the manufacturing process. This integration enhances automation, diagnostics, data analysis, and autonomous decision-making through the networking of equipment and machinery. However, the increased reliance on technology raises concerns about the implementation and maintenance of cybersecurity. This paper aims to address cybersecurity challenges in the manufacturing industry and suggest strategies to reduce risks. In particular, it examines the level of awareness and understanding of cybersecurity issues among manufacturing employees, establishes accountability for cyberattacks, and evaluates the effectiveness of existing industry practices. The current cybersecurity landscape in the manufacturing industry was thoroughly analysed. Data were gathered through surveys, interviews, and case studies to measure awareness, identify knowledge gaps, and assess existing practices. The research findings indicate a significant knowledge gap regarding cybersecurity among manufacturing employees. This vulnerability can be attributed to the lack of funding and training, especially compared to the resources provided to information technology departments and corporate employees. The study emphasises the importance of redirecting cybersecurity resources and protocols towards the manufacturing industry. This paper puts forward a series of recommendations to mitigate risks and safeguard the manufacturing industry.Cyber Security and Application
Application of Spatial Offset Raman Spectroscopy (SORS) and machine learning for sugar syrup adulteration detection in UK Honey
Get PDFHoney authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.Food Standards Agency, Science and Technology Facilities CouncilThis research was funded by the Science and Technology Facilities Council (STFC) Food Network+ (SFN+) Grant No.: ST/T002921/1 and the Food Standards Agency (FSA) Project No.: FS900185Food
Cybersecurity of embedded systems a novel approach for detecting cyberattacks based on anomalous patterns of resource utilisation
Get PDFZolotas, Argyrios - Associate SupervisorAn embedded system (ES) is a processing unit that has been embedded into a larger
cyber-physical system (CPS) to steer its functions. The ES has played an essential role in
modern life, where it has been used widely in sensing, controlling and computing for
countless applications in different domains, such as the internet of things (IoT), smart
cities, healthcare, transportation, communication, military, transportation, gas
distribution, avionics and national infrastructures. Due to its widespread application in
different domains and its evolution in conjunction with many key technologies, it is
crucial that these systems are secured against cyberattacks as the ES has the same generic
security goals – confidentiality, integrity and availability – as conventional computer
systems.
Although the ES is exposed to the numerous and unpredicted security threats that are
experienced by conventional computer systems, it is significantly limited in its ability to
manage the advanced security solutions that are implemented on conventional computer
systems. The limitations in resources of the ES, due to its identity or characteristics,
impose tight constraints on both its communication and computing capacity, thereby
hindering the implementation of advanced security solutions. Thus, the cybersecurity of
an ES is limited by constraints on its resources rather than by the absence of advanced
security solutions. There is an urgent need, therefore, to develop security solutions that
are compatible with the capabilities of the ES.
This study tried to bridge the gap by addressing both theoretical and empirical aspects of
ES cybersecurity. The study can be divided into three main blocks. The first block
identifies the key factors, involved parties or entities, and creates the cybersecurity
landscape for embedded systems (CSES), while considering the conflict between the
requirements for cybersecurity and the computing capabilities of an ESs. Additionally,
twelve factors influencing CSES have been extracted and identified based on the direction
of the research. These factors have been used to shape a multiple layers feedback
framework of embedded system cybersecurity (MuLFESC), with nine layers of
protection. It has been developed in line with an expanded model of risk assessment
metrics, which will enable cybersecurity practitioners to evaluate the security
countermeasures of their systems and assist in the development of more comprehensive
solutions for CSES.
A novel security approach, called anomalous resource consumption detection (ARCD),
was developed in the second block of this study. This involved the design of a testbed to
provide a realistic hardware-software environment to analyse an example application of
an ES. A Smart PiCar was run repeatedly under different operational conditions – typical
conditions and under attack. The data of seven designated parameters based on seven
statistical criteria was analysed to measure the range, pattern of performance and resource
utilisation. The results from this statistical analysis demonstrated the potential for
defining a standard pattern for the resource utilisation and performance of the embedded
system due to a significant similarity with the values of the parameters at normal states.
In contrast, the results from the attacked cases showed a definite and detectable impact
on the consumption and performance of the resources of the ES, which presented
anomalous patterns. The ARCD method can be implemented as an additional layer of
protection to detect cyber-attacks in an ES, where a septenary tuple model, consisting of
seven parameters, is the core of the detection mechanism.
In the final block, the ARCD approach has been placed within an architectural
framework, which may pave the way for software engineers to build secure operating
systems in line with the capabilities of the ES. The architectural framework was
developed after the efficiency of the approach was computationally validated by machine
learning. This involved the design of a classifier and predictor model to find the predictive
accuracy percentage in terms of separating patterns of anomalous performance and
resource utilisation from the typical pattern. Based on the confusion matrix, the prediction
accuracy for classifying anomalous patterns compared with default patterns revealed
promising results, thus proving the effectiveness of the ARCD approach. The results
confirmed very high prediction accuracies as regards distinguishing anomalous patterns
from the typical patterns.PhD in Aerospac
Future flight safety monitoring: comparison of different computational methods for predicting pilot performance under time series during descent by flight data and eye-tracking data
Get PDFIntroduction. Effective and real-time analysis of pilot performance is important for improving flight safety and enabling remote flight safety control. The use of flight data and pilot physiological data to analyse and predict pilot performance is an effective means of achieving this monitoring. Research question. This research aims to compare two forecasting methods (XGBoost and Transformer) in evaluating and predicting pilot performance using flight data and eye tracking data. Method. Twenty participants were invited to fly an approach using Instrument Landing System (ILS) guidance in the Future Systems Simulator (FSS) while wearing Pupil-Lab eye tracker. The deviation to the desired route, the pupil diameter and the gaze positions were selected for forecasting the flight performance indicator: the difference between the aircraft altitude and the reference altitude corresponding to the ideal 3-degree glide path. Utilize XGBoost and the Transformer forecasting technique to develop a forecasting model using the data from this research, and conduct a comparative analysis of the accuracy and convenience of both models. Results & Discussion. The result demonstrates that using XGBoost regression model had a higher prediction accuracy, (RMSEXGBoots = 42.29, RMSETransformer = 102.10) and its easier to achieve a high prediction accuracy than Transformer as Transformer forecasting method placed a high demand on debugging model and computing equipment. The deviation to desired route and the pupil diameter were more important in the XGBoost model. Conclusion. The use of machine learning and deep learning methods enables the monitoring and prediction of flight performance using flight data and pilot physiological data. The comparison of the two methods shows that it is not necessarily the newer and more complex technology that can build more accurate and faster prediction models, but building the right model based on the data is important for real-time flight data monitoring and prediction in the future.21st International Conference, EPCE 2024, Held as Part of the 26th HCI International Conference, HCII 202
Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method
Get PDFDetecting wheel slip is important in railway operations, to prevent damage to wheels and tracks, reduce maintenance costs, improve safety and enhance passenger comfort. Slip activity is characterised by reduced adhesion between the wheel and the rail and limits effective braking or acceleration, causing also operational risks. It is influenced by environmental conditions, vehicle load, track and axle quality, contaminants, inclines, rail oxidation, and braking forces. This paper introduces an innovative method for wheel slip detection in operational trains, utilizing wavelet analysis combined with Long-Short Term Memory (LSTM) modelling. This method analyzes operational data to effectively identify wheel slip, showing promising results when compared to traditional classification-based machine learning methods such as decision trees, forests, logistic regression, naïve Bayes, and support vector machines. This novel approach addresses the complexities of wheel slip detection and is capable of identifying the conditions leading to slip several seconds prior to the commencement of the slip event, offering a practical solution for real-world railway systems.The authors wish to thank Unipart Rail, Instrumentel for providing the industrial data and domain-specific expertise and the Engineering and Physical Sciences Research Council (EPSRC) for the financial support through grant 2203091Engineering Applications of Artificial Intelligenc
From HR model to contextual system: An exploration of how the HR function is organised and the factors that influence the organising of HR
Get PDFThis research examines the organisation of the Human Resources (HR) function. The narrative from
the profession has been dominated by a single model, the ‘Ulrich model’ (Ulrich et al., 2008) that has
influenced the way that HR functions organise; however, there is limited empirical research which has
examined the organisation of HR in practice. Research in this area of HR has focused upon examining
individual elements such as shared services, outsourcing etc. and there has been a lack of holistic
examination. For this reason, little is understood about what influences the organisation of HR and
there is an absence of theoretical explanation. At a time when the HR function has increased its
standing in the organisation following COVID-19 and the aftermath, and as organisations go through a
period of rapid change requiring the support of HR, it is important that HR is capable of continuing to
be an integral part and meet the needs of the organisation.PhD in Leadership and Managemen
Developing engineering systems with stakeholder engagement
Get PDFWilliams, Leon - Associate SupervisorThe activities of the early stages of new product development (NPD) projects
have a significant impact on its results. This is evident by the failures of outputs
to meet expected levels of adoption being traced back to inadequate elicitation of
requirements and validation of the need for the output itself. To address this, the
development of consumer goods, software and large-scale construction projects
have been shown to benefit from the early involvement of stakeholders in these
outputs. However, few studies focus on how this early involvement of
stakeholders might advance the development of engineering systems. Although
studied in the context of complex products and systems (CoPS), which includes
large-scale constructions, engineering systems differ significantly to these and
other NPD project outputs. This is due to the absence of end-users, or the general
public, being involved as such systems are often automated and hidden within
other systems or infrastructure. Adhering to development frameworks prioritising
technical maturation furthers this difference, although, like consumer products, a
market needs to be addressed. To explore this phenomenon, a single case study
of the beginning of a wastewater system NPD project is conducted and supported
by studies that identify, engage and evaluate the extent stakeholders can affect
its activities. The results support the need for a dedicated early stage for
stakeholder engagement. This will help address the implications of findings
evidencing problems engineering systems need to address so to achieve
expected levels of adoption. Successfully incorporating this new stage whilst
prototyping the engineering system itself requires further research. The
contributions of this thesis challenge prioritising technical interests during early
stages of development, demonstrate the extensive value of analysing qualitative
data and support iterative and collaborative NPD approaches. Incorporating
these findings will contribute to ensuring highly innovative engineering systems
effectively address the needs of their salient stakeholders.PhD in Water, including Desig
Swarm decoys deployment for missile deceive using multi-agent reinforcement learning
No full textThe development of novel radar seeker technologies has improved the hit-to-kill capability of missiles. This is particularly worrying in safety and security domains that need the design of appropriate countermeasures against adversarial missiles to ensure protection of naval facilities. This paper aims to contribute in these domains by developing an artificial intelligence (AI) based decoy deployment system capable of deceiving the missile threat. Here, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is developed to maximise the distance between the target and the missile by learning the optimal/near optimal route planning of the six decoys to reach the global mission. As case study, the deployment of six decoys from the top deck of the main platform is assumed. The decoys are launched from the platform at the initial phase of the mission, and they establish a leader-follower formation that enhances the signal strength of the swarm decoys. The reward function is designed to guarantee a triangular formation configuration for swarm decoys. The reported results show that the proposed approach is capable to deceive the missile threat and has the potential to be integrated in current naval platforms.Engineering and Physical Sciences Research Council (EPSRC)2024 International Conference on Unmanned Aircraft Systems (ICUAS
Dataset for - Sapper - MSc Thesis - Survey Data
No full textUsing a mixed-methods approach, this research explores how the PPP model has influenced the sustainability of RWS delivery in Rwanda. Survey was collected from 318 respondents, 12 semi-structured interviews, two focus group discussions, and 78 site visits of assets across two PPP managed systems in Rwanda and supplemented by secondary data from institutions