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25th International Conference on Human-Computer Interaction, HCII 2023

作者： Whitworth, Benjamin Grant, Rebecca Human Systems Integration Group Faculty of Engineering Environment and Computing Coventry University Coventry United Kingdom

ISBN: (纸本)9783031480379

Within some aircraft types, there are different variants that a pilot is permitted to fly on a single type rating. Previous research has investigated multi-variant flying on specific fleets, supporting the notion that differences should be proactively considered. Regulation describes the level of training pilots receive and this is based upon how different the variants are from one other. Recently, this training entered the spotlight, due to the tragic Boeing 737-Max accidents. This research further explored the human factors challenges and mitigations associated with multi-variant flying. Twenty-eight pilots including Captains and First Officers with recent multi-variant operational experience at multiple airlines, were interviewed using a semi-structured technique. A rich and detailed data set was subsequently analysed using a thematic method, with themes identified and examined. Whilst participants commented positively regarding multi-variant flying in the example used, the challenges of this were also highlighted and these differed from airline to airline, varying significantly as a result of fleet complexity, age, and commonality. Challenges were identified in terms of differences both within and between variants, information recall, navigation of variant information in manuals, and scheduling on multiple variants within the same day. How variant challenges were routinely managed, from an individual and organisational perspective were described, with documentation, briefings and training raised, but often variable and in some cases lacking in key areas. Differences were identified between those who perceived receiving adequate training, and those who had not, as well as those operating within simple or complex fleets. A simple approach to identifying and comprehending differences was identified. The results call for further research to explore the appropriateness of current regulation, considering fleet size, variability and fit, alongside industry recommendati

关键词： Aircraft accidents

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Agent-based heuristics model for measuring customer disruption impact on production and inventory replenishment

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International Journal of Industrial and Systems engineering 2022年第3期40卷 370-398页

作者： Al-Bazi, Ammar Adediran, Tunde V. Faculty of Engineering Environment and Computing Coventry University West Midlands CoventryCV1-5FB United Kingdom

Agent-based simulation approach in production and inventory environment is capable of responding and adapting to disruptions caused by customers’ changing requirements. The impacts of disruptions in production and inventory systems can be measured through learning and decision-making ability of system agents. In this paper, agent-based modelling integrated with heuristic optimisation approach is presented as embedded within a scheduling and rescheduling framework. The proposed approach is implemented in a disrupted OEMs parts manufacturing system. The integration of the framework modules in connection with inventory control helped production planners to manage disruptions by tracking order processing times and quantities and for performance measurement. The proposed approach is compared with the few existing related methods like the sequential method. The proposed approach not only revealed the impact of disruptions in terms of process times and order quantities but offered ‘available times’ which were applied for production support and inventory replenishment. This demonstrates a valuable and viable resolution strategy responding and adapting to disruptions caused by customers. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Decision making

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A FEA simulation study on impeller failures considering various materials

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Materials Today: Proceedings 2023年 80卷 54-61页

作者： Mckay, Innes Anbalagan, Arivazhagan Department of Mechanical Engineering Robert Gordon University Aberdeen AB10 7QB United Kingdom School of Mechanical Aerospace and Automotive Engineering Faculty of Engineering Environment & Computing Coventry University Priory St Coventry CV1 5FB

This work focuses on various FEA simulations conducted to study the behaviour of six impeller materials in order to predict their failures. It is through analysing the various stresses, strains and safety factor during its operation. Two static structural simulations were performed to (i) benchmark common impeller materials namely FV520B, 316 L and 17-4 precipitation hardened stainless steels, and (ii) to investigate three proposed alternative materials namely duplex stainless-steel, nickel aluminium bronze (NAB) and cast iron. After this, explicit dynamic simulation was undertaken to study the same benchmarked impeller materials. Failures and large stress concentrations occurring at the blade-hub leading and trailing edge interface were predicted which has been found to be consistent with some cases outlined in relevant literature. Similarly, trend has also been predicted at the trailing edge of the discharge side of the impeller. Specifically, FV520B was found to withstand the application of the combined rotational and aerodynamic load, for both static and dynamic simulations. Of the three alternative materials investigated, duplex stainless-steel was the only one to satisfy the failure criteria. From static and dynamic simulation of each material, the operation aerodynamic load was found to have little effect on the resultant stress and strain responses. This is consistent with many FEA simulations of impellers seen in literature, wherein the aerodynamic load is often simplified or neglected completely. In both the simulations of benchmark materials, the material data available to the solver greatly impacted the magnitude and behaviour of the material's stress/strain response. © 2022

关键词： Finite element method

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Institutional Data Analysis and Machine Learning Prediction of Student Performance 25

Institutional Data Analysis and Machine Learning Prediction ...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Suleiman, Rahanatu Anane, Rachid Coventry University Faculty of Engineering and Computing United Kingdom

ISBN: (纸本)9781665405270

Concern over student progression and retention is driving the systematic analysis of the vast amount of student data recorded in institutional data sets and in learning management systems (LMS) data sets. This is motivated by the belief that student analytics will help uncover patterns of behaviour and predict student performance, and thus facilitate the deployment of supportive educational interventions. This paper is concerned with the investigation of the institutional data of a Nigerian university, and the predictive impact of its attributes on student performance, measured in terms of cumulative grade point average (CGPA). The statistical analysis of the data set reveals that age and marital status have no significant relationship with final CGPA, whereas gender and pre-entry score have a weak relationship but are not good predictors. The application of four representative machine learning methods, namely linear regression, support vector regression, decision tree and random forests indicate that the third year CGPA is a good predictor of final year CGPA. A higher accuracy is achieved by using the aggregate value of the CGPAs of three previous years. Support vector regression has the best performance in predicting the final CGPA, whereas decision tree is the least performing model. © 2022 IEEE.

关键词： Students

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An Effective Machine Learning Technique for Rice Leaf Disease Detection 4

An Effective Machine Learning Technique for Rice Leaf Diseas...

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4th IEEE International Conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chikati, Divya Manyam, Rajasekhar Reddy Kancharla, Ramkumar Amrita School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati India Environment and Computing Coventry University Faculty of Engineering Coventry United Kingdom

ISBN: (纸本)9798350306118

Plant diseases, especially those that attack rice leaves, cause serious problems for farmers since they make it more challenging to provide enough food to satisfy the world's growing population. Farmers' livelihoods that rely on agriculture have been threatened by the economic losses brought on by rice illnesses. As a tragic outcome of the hardships they encounter, several farmers have taken their own lives. It is crucial to detect and identify disease-infected plants in order to execute efficient disease management strategies. The Inception V3 model is utilized for disease classification, and the Convolutional Neural Network (CNN) autoencoder is employed to reduce noise in images of rice leaves. The dataset is then expanded through data augmentation approaches. The primary objective of the initial stage is to gather photographs of rice leaves from the UCI Machine Learning Repository. Once a collection of photographs has been gathered, the images are subjected to processing using a Convolutional Neural Network (CNN) autoencoder model. This process aims to enhance the quality of the images and eliminate any unwantednoise present in them. Subsequently, data augmentation techniques are employed to increase the volume of the dataset and standardize the dimensions of the images, both of which play a role in training a robust and adaptable model. Diseases are categorized using the popular inception v3 model, which can process vast and diverse picture datasets. This method allows for the precise categorization and detection of a wide variety of rice leaf diseases with the accuracy of 98.8%. © 2023 IEEE.

关键词： Diseases

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Industrial IoT-based submetering solution for real-time energy monitoring

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Discover Internet of Things 2025年第1期5卷 1-16页

作者： Khasawneh, Hussam J. Al Asbahi, Raghad Alzariqi, Abdelrahman W. Al Qada, Danah R. Bujuk, Ayman Nawfal, Muhammad A. Tareen, Maria Department of Electrical Engineering Al Hussein Technical University King Hussein Business Park Amman11118 Jordan Department of Mechatronics Engineering The University of Jordan Amman11942 Jordan InJo4.0 Innovation Center SAM Engineering Amman11185 Jordan Engineering Department RAM Pharmaceuticals Amman11652 Jordan Faculty of Engineering Environment and Computing Coventry University CoventryCV1 5FB United Kingdom

The Sustainable Monitoring & Advanced Real-Time Energy Efficiency (SMARTEE) project addresses the critical challenge of optimizing energy management in pharmaceutical manufacturing through an Industrial Internet of Things (IIoT)-based submetering solution. This study presents the design and deployment of a tailored IoT system, integrating IoT-enabled submeters, a Mini-PC gateway, and cloud-based analytics for continuous, real-time energy consumption monitoring across critical equipment such as chillers, boilers, and HVAC systems. Following a systematic methodology, the project implemented advanced data acquisition and communication protocols, including Modbus over RS485, Node-RED for data processing, and MQTT for transmitting energy metrics to a cloud-based visualization platform. Results demonstrated the system’s efficacy in delivering actionable insights into energy patterns, significantly improving operational efficiency and waste reduction. The IoT-based solution is highly scalable, adaptable to various industrial contexts, and provides a secure platform for real-time monitoring. This work highlights the potential of IoT in transforming industrial energy management, with future research focusing on AI-driven predictive analytics and seamless integration with renewable energy systems to enhance sustainability and resilience further. © The Author(s) 2025.

关键词： Gateways (computer networks)

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R-WhONet: recalibrated wheel odometry neural network for vehicular positioning using transfer learning

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Neural computing and Applications 2025年第9期37卷 6547-6565页

作者： Onyekpe, Uche Szkolnik, Alicja Palade, Vasile Kanarachos, Stratis Fitzpatrick, Michael E. Research Centre for Computational Science and Mathematical Modelling Coventry University Priory Road CoventryCV1 5FB United Kingdom African Institute for Artificial Intelligence Accra North Legon Ghana University of Birmingham BirminghamB15 2TT United Kingdom Faculty of Engineering and Computing Coventry University Priory Road CoventryCV1 5FB United Kingdom

This paper proposes a transfer learning approach to recalibrate our previously developed Wheel Odometry Neural Network (WhONet) for vehicle positioning in environments where Global Navigation Satellite Systems (GNSS) are unavailable. The WhONet has been shown to possess the capability to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning of vehicles. These uncertainties may be manifested as tyre pressure changes from driving on muddy and uneven terrains or wheel slips. However, a common cause for concern for data-driven approaches, such as the WhONet model, is usually the inability to generalise the models to a new vehicle. In scenarios where machine learning models are trained in a specific domain but deployed in another domain, the model’s performance degrades. In real-life scenarios, several factors are influential to this degradation, from changes to the dynamics of the vehicle to new pattern distributions of the sensor’s noise, and bias will make the test sensor data vary from training data. Therefore, the challenge is to explore techniques that allow the trained machine learning models to spontaneously adjust to new vehicle domains. As such, we propose the Recalibrated-Wheel Odometry neural Network, based on transfer learning, that adapts the WhONet model from its source domain (a vehicle and environment on which the model is initially trained) to the target domain (a new vehicle on which the trained model is to be deployed). Through a performance evaluation on several GNSS outage scenarios—short-term complex driving scenarios such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts), and on longer-term GNSS outage scenarios of 30s, 60s, 120s and 180s duration—we demonstrate that a model trained in the source domain does not generalise well to a new vehicle in the target domain. However, we show that our new proposed framework improves the generalisation of the WhONet model to new vehicles in

关键词： Inertial navigation systems

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It Was This Wing Wasn’t It? Identifying the Importance of Verbal Communication in Aviation Maintenance

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The International Journal of Aviation Psychology 2023年第2期33卷 139-152页

作者： Michael Newman Steve Scott Faculty of Engineering Environment and Computing Coventry University Coventry UK

ABSTRACTABSTRACTTo place communication in context against other maintenance error promoting contributory factors and identify whether communication issues were written or verbal, a semi-structured interview schedule was used to ask 40 aircraft maintenance personnel what goes wrong when maintaining aircraft. Participants described 746 examples of contributory factors and undesired outcomes which were categorized using Dupont’s “dirty dozen.” 147 of the 746 descriptions were communication factors, of which 99 were verbal, 34 were written and 14 were general. Of 34 verbal and 19 written task miscommunication examples, 22 verbal and 10 written were accompanied by descriptions of undesired outcomes. The results suggest that commercial aircraft maintainers consider verbal and written communication as more important than other contributory factors.

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An overview of soil–water characteristic curves of stabilised soils and their influential factors

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Journal of King Saud university - engineering Sciences 2022年第1期34卷 31-45页

作者： Eyo, E.U. Ng'ambi, S. Abbey, S.J. School of Energy Construction and Environment Faculty of Engineering Environment and Computing Coventry University Coventry United Kingdom Faculty of Environment and Technology Department of Geography and Environmental Management Civil Engineering Cluster University of the West of England United Kingdom

Since unsaturated soil conditions are normally experienced above the groundwater table, most treated or stabilised soils for roadworks, earth dams’ embankments, landfill sites, hydraulic barriers etc. could be regarded as existing in this region. The soil–water characteristic (or retention) curve (SWCC) is a useful conceptual tool by which an evaluation of unsaturated soil's property functions and corresponding macro-scale behaviour (strength, volume change, hydraulic conductivity, fluid flow, diffusivity, etc.) can be carried out. Hence, an examination of some of the various factors that could affect the hydraulic or water retention property of the stabilised soil is very vital both for laboratory studies and field practice. However, a thorough assessment of the water retention behaviour of stabilised soils can be understandably limited sometimes. This could be partly due to some of the perculiar conditions associated with soil preparation methods, soil type, soil-stabiliser mix proportion used, curing conditions, method of compaction, durability assessment modalities and other logistical issues surrounding either laboratory instrumentation or in-situ application. This article presents a critical and comprehensive review of these factors on the stabilised soil's water retention behaviour and also provides a systematic understanding of the mechanisms of stabilisation occurring at the micro- and macro-mechanical levels. Recommendations are also made to stimulate further discussions on the synthesis of SWCC of stabilised soils vis-à-vis factors influencing them with possible interpreted engineering behaviours such as shear strength and soil consolidation. © 2020 The Authors

关键词： Lime

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Cyberbullying Detection: An Advanced Machine Learning and GANs Approach 9

Cyberbullying Detection: An Advanced Machine Learning and GA...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Haidar, Batoul Rida Ezzeddine, Mariam Coventry University - Egypt Branch Hosted at The Knowledge Hub Universities School of Computing Cairo Egypt University of Saint Joseph Faculty of Engineering Beirut Lebanon

ISBN: (纸本)9798350385403

The increase of cyberbullying in the digital era creates enormous issues, demanding novel techniques for identification and prevention. This study explores a cyberbullying dataset from Kaggle including various types of online hostility for the goal of cyberbullying identification using advanced methods of machine learning such as Support Vector Machine (SVM) and fine-tuned BERT. The work handles an unbalanced dataset with thorough NLP preprocessing steps enhancing the model's accuracy to 90% for SVM and 97% for fine-tuned BERT. To achieve this enhancement, a generative adversarial network (GAN) is used in the experiment to produce 10,000 additional bullying texts, diversifying the dataset and increasing model performance. These developments highlight the importance of machine learning in cyberbullying detection and open the way for future research in Arabic language cyberbullying detection. © 2024 IEEE.

关键词： Adversarial machine learning

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