Dr Bugra Alkan holds a PhD in Engineering from Warwick Manufacturing Group (WMG), University of Warwick, where he specialised in cyber-physical industrial automation. He is currently a Senior Lecturer in Artificial Intelligence and Robotics at the School of Computer Science and Digital Technologies, within the College of Technology and Environment at London South Bank University (LSBU). Prior to this, he was a Postdoctoral Research Associate in Computer Science at the University of Bristol, contributing to interdisciplinary projects at the intersection of systems engineering and smart autonomous systems.
Dr Alkan’s academic background spans mechanical engineering, manufacturing systems, and computational intelligence. His research focuses on the convergence of smart manufacturing, industrial artificial intelligence, industrial robotics, and human–robot collaboration. He has significant experience in data-driven decision-making, cyber-physical systems, and digital twins, and is actively engaged in developing intelligent solutions for real-world manufacturing challenges. He has led and collaborated on numerous research initiatives funded by major organisations including the Engineering and Physical Sciences Research Council (EPSRC), Innovate UK, the Royal Society, EU Horizon, and various industry partners, securing an institutional share of over £670k as Principal Investigator (PI) and Co-Investigator (Co-I). Dr. Alkan has authored more than 40 peer-reviewed publications (h-index: 16, i10-index: 21) in leading international journals and conferences, and regularly serves as a reviewer for high-impact venues. His work has been recognised with several Best Paper Awards, including one from the prestigious CIRP CATS in Gothenburg (2016).
At LSBU, Dr. Alkan teaches at both undergraduate and postgraduate levels, where he leads and coordinates modules on artificial intelligence and industrial cyber-physical systems. He is a Fellow of the Higher Education Academy (FHEA). Beyond academia, he maintains close collaborations with industry, aiming to bridge the gap between research innovation and practical application. He currently serves as an Associate Editor for two SCI-indexed journals: Heliyon (Mechanical Engineering section) and Industrial Robot: The International Journal of Robotics Research and Application.
Human-centric Manufacturing and Collaborative Robotics
Industrial Automation and Industrial Cyber-Physical Automation Systems
Artificial Intelligence in Manufacturing Systems and Applied Machine/Deep Learning
Sustainable Smart Manufacturing and Net-Zero Manufacturing
Convolutional Neural Networks and Machine Vision Applications in Manufacturing
Multi-objective and Multi-Disciplinary Optimisation Applications, and Meta-heuristics Algorithms
Manual Assembly Systems, Perceived Complexity and Industrial Ergonomics
New! 08/08/2024 A new journal paper has been accepted. Webb, L, Mohammad O T, and Alkan B. “State of the art and future directions of digital twin-enabled smart assembly automation in discrete manufacturing industries.” International Journal of Computer Integrated Manufacturing
New! 05/06/2024 A book chapter has been published. Webb, L, Mohammad O T, and Alkan B. ”KPI-Driven Metric Acquisition Methodology with a Energy- Centric Robotic Performance Case Study.” In Producing Artificial Intelligent Systems: The Roles of Benchmarking, Standardisation and Certification, pp. 105-117 Link
New! 27/05/2024 Project funding has been secured! Twinning IZTECH in Robotics Manufacturing Systems (TWIN-IT-ROMANS) has been accepted to be funded by the EU Commission. The project total cost is €1,760,212, and the consortium includes IZTECH, RWTH Aachen, MCI Management Center Innsbruck, HKTM, Politecnico di Torino, University of Bath, and LSBU. The project will last for three years.
23/10/2023 Dr Alkan has been appointed as Associated Editor for Industrial Robot: An International Journal Q2, IF:1.9 indexed by ProQuest, Scopus, Web of Science™ and Science Citation Index Expanded™ (SCIE)
25/07/2023 Dr Alkan has been appointed as Associated Editor for Heliyon (Engineering Section) Q1, IF:4 indexed by PubMed, Scopus, Web of Science™ and Science Citation Index Expanded™ (SCIE)
This section provides an overview of our intelligent manufacturing projects. If you wish to participate in any of the mentioned projects, please send me an email along with your curriculum vitae.
Data-driven Configuration Optimisation of CPPS towards Resilient Flexible Manufacturing
In today’s manufacturing settings, a sudden increase in customer demand may force manufacturers to alter their manufacturing systems, either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research project aims to fill this gap by proposing novel decision support systems that build upon technologies such as digital twins and artificial intelligence. Our approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations is identified using knowledge mapping between product, process, resource, and resource attribute domains. Workstation design data for selected configurations is streamlined into a common data model that is accessed at the system level, where DES software and a multi-objective optimisation algorithm are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in demand for electric vehicle powertrains.
Status: On-going (MSc/PhD candidates will be considered.)
The Proposed Data-Driven Scale-up Model (DDSM) for Disturbance Handling
Multi-objective System Configuration Optimisation for Throughput and Changeover Cost
AI-Enabled Digital Life-cycle Management towards Smart Sustainable CPPS in Industry 5.0
Energy-intensive industries can be classified into those that process metal, glass, ceramics, paper, cement, and bulk chemicals. They are associated with significantly high proportions of carbon emissions, consume a lot of energy and raw materials, and cause energy wastage as a result of heat escaping from furnaces, reheating of products, and rejection of parts. In alignment with UN sustainable development goals of industry, innovation, infrastructure, and responsible consumption and production, it is important to ensure that the energy consumption of EIIs is monitored and reduced so that their energy efficiency can be improved. To achieve this aim, it is possible to employ the concepts of digitalisation and smart manufacturing to identify the critical areas for improvement and establish enablers that can help improve energy efficiency. The aim of this study is to review the current state of digitalisation in energy-intensive industries and propose a framework to support the realisation of sustainable smart manufacturing (EIIs). The work's key objectives are (i) investigating process mining and simulation modelling to support sustainability; (ii) embedding intelligence in EIIs to improve energy and material efficiency; and (iii) proposing a framework to enable the digital transformation of EIIs. In the demonstrated case study, the process management layer uses Disco software for process mining, the simulation layer uses Matlab SimEvent for discrete-event simulation, the artificial intelligence layer uses Matlab for energy prediction, and the visualisation layer uses Grafana to dashboard the e-KPIs.
Status: On-going (MSc/PhD candidates will be considered.)
The Proposed Digital Life-cycle Management Framework for Energy Intensive Industries
Grafana-based Energy Dashboard designed for the Shop Floor e-KPI Monitoring
Deep Learning-based Inline Quality Inspection and Monitoring towards Zero-Defect Manufacturing
Modern manufacturing must prioritise the sustainability of its processes and systems. Zero Defect Manufacturing (ZDM) focuses on minimising waste of any kind using data-driven technology, thereby enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time and thus adapt the system to disturbances. This work addresses the need for automatic detection of product defects with a two-fold procedure that accurately localises and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localising defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). The experimental results demonstrate that our method meets the robustness and accuracy requirements for surface defect detection. Meanwhile, it can be expanded to other detection applications.
Status: On-going (MSc/PhD candidates will be considered.)
Automated Surface Defect Detection and Severity Grading using Deep Learning Models
CNN Model Results with Confusion Matrix and Convergence Curves
Smart Augmented Reality towards Human-centric Collaborative Assembly 5.0
Robots are becoming more adaptive and aware of their surroundings. This has opened up the research area of tight human-robot collaboration, where humans and robots work directly interconnected rather than in separate cells. The manufacturing industry is in constant need of developing new products. This means that operators are in constant need of learning new ways of manufacturing. Virtualizing operator instructions and robot-operator interactions may make them more modifiable and available to operators. Augmented reality has previously been shown to be effective in giving operators instructions in assembly, but there are still knowledge gaps regarding evaluation and general design guidelines. This project aims to design and implement an augmented reality (AR) platform to aid operators in a hybrid, human-robot collaborative industrial environment. The system aims to provide production and process-related information, as well as enhance the operators’ immersion in the safety mechanisms dictated by the collaborative workspace using artificial intelligence and ontology models.
Status: On-going (MSc/PhD candidates will be considered.)
SmartShift Human-Robot Collaboration Platform Architecture /w AR
Augmented Reality Interface with CNN-based Inspection Assistance
Complexity-Inclusive Verification and Optimisation of CPPS using Static and Dynamic Considerations
Highly diverse factors, including technological advancements, uncertain global markets, and mass personalisation, are believed to be the main causes of the ever-growing complexity of manufacturing systems. Although complex systems may be required to meet global manufacturing requirements, complexity affects various factors, such as system development effort and cost, ease of re-configuration, and the level of skill required across the system life-cycle (e.g., design, operate, and maintain). This project aims to develop a scientifically valid and industrially applicable complexity assessment approach to support the early life-cycle phases of CPPS against the unwanted implications of system complexity. The presented approach defines a CPPS as a constellation of basic components that can be represented in various design domains, such as mechanical, electrical, pneumatic, control, etc. Accordingly, system complexity is expressed as the combination of both the inherent complexity of system entities and the topological complexity resulting from the integration of elements of such constellations in a multi-layered network. The proposed approach is used to specify and implement a complexity assessment module that can be integrated into a series of virtual system design software solutions in order to add complexity assessment as part of the design support and validation tools used by manufacturing engineers. The proposed approach is demonstrated on a variety of CPPSs with differing degrees of system complexity.
Status: On-going (MSc/PhD candidates will be considered.)
Festo MPS Test Rig was used in the Experiments
Our Approach for Verification of System Complexity
Automatic Detection and Classification of Physiological Workload in Manual Assembly Systems using Deep Learning
Human operators perform the majority of manufacturing operations in the UK's automotive industry. However, the growing complexity of assembly tasks due to the wide range of products is causing mental strain on operators during manual manufacturing and assembly operations. As a result, cognitive stress is causing a decrease in production quality, an increase in production defects, and an elevated risk of work-related hazards and illnesses. The existing body of research in the relevant literature has primarily concentrated on physical human factors, while cognitive ergonomics has been significantly overlooked. The objective of this study is to analyse the underlying factors that contribute to cognitive stress in manual assembly operations using techniques derived from artificial intelligence and cognitive neuroscience. In pursuit of this goal, a manual assembly test bed was created, in which human participants engaged in a sequence of product assembly tasks of varying levels of complexity. Throughout these experiments, a mobile electroencephalogram was utilised to measure real-time data, encompassing brain activity, heart rate, breathing, and body movements. The collected data were analysed using artificial intelligence techniques to predict the cognitive stress encountered during each assembly operation. The results were then correlated with multiple factors, including the complexity of the assembly tasks and product designs, the design attributes of the assembly operation, visibility challenges during assembly, and characteristics of the parts, among others.
Status: On-going (MSc/PhD candidates will be considered.)
High-level overview of the workspace setup
Detailed view of the SAR Workspace components
Digital Twin-enabled Reactive Automation Process Planning in Smart Factories with AMR-based Material Transportation
This project aims at developing a reactive manufacturing scheduling pipeline that manages incident responses on the factory floor. This pipeline generates optimal schedules using meta-modelling, optimisation algorithms, and digital models, which enhance overall efficiency by aiming to reduce total processing times of tasks and lower the average power consumption of material handling autonomous mobile robots (AMRs). The reactive scheduling system is designed to quickly respond to incidents, minimising costly disruptions and ensuring continuous production flow. By embedding this scheduling system within a digital twin, we aim to provide predictive scheduling and rapid reactive scheduling capabilities, allowing the system to pre-emptively adjust to potential issues and react swiftly to unforeseen events. The lightweight design of the system ensures seamless operation and interoperability with both physical environments and high-fidelity simulations. Our research is highly system-focused, with plans for a real-world case study to validate the practical applicability of our proposed solution. Additionally, we proposed a comprehensive framework and architecture to support this integration, effectively addressing the identified gaps in current DT-enabled manufacturing systems and enhancing overall operational efficiency, energy usage, and incident response times.
Status: On-going (MSc/PhD candidates will be considered.)
The digital twin model was created to accurately replicate various what-if scenarios
System architecture for adaptive AMR Fleet Manager
Book Chapters
2024: Webb, Louie, Mohammad Osman Tokhi, and Bugra Alkan. ”KPI-Driven Metric Acquisition Methodology with a Energy- Centric Robotic Performance Case Study.” In Producing Artificial Intelligent Systems: The Roles of Benchmarking, Standardisation and Certification, pp. 105-117. Cham: Springer Nature Switzerland, Chapter
2020: Alkan, Bugra, B. Seth, Kevin Galvin, and Angus Johnson. ”A design process framework to deal with non-functional requirements in conceptual system designs.” Complex Systems Design and Management, Cham: Springer Nature Switzerland, Chapter
Peer-reviewed Journal Papers
2024: L. Webb, M. Tokhi, B. Alkan. “State of the art and future directions of digital twin-enabled smart assembly automation in discrete manufacturing industries.” International Journal of Computer Integrated Manufacturing, DOI: 10.1080/0951192X.2024.2387775
2023: Chinnathai, Malarvizhi Kaniappan, and Bugra Alkan. “A digital life-cycle management framework for sustainable smart manufacturing in energy intensive industries.” Journal of Cleaner Production (IF:11.1, Cite Score: 18.5) 419 (2023): 138259, Journal, Open Access
2022: S. Kahveci, B. Alkan, M. Ahmad, B. Ahmad, and R. Harrison. “An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Assembly System for Electric Vehicles.”, Journal of Manufacturing Systems (IF:12.1, Cite Score: 16), 63 (2022): 214-223, Journal, Open Access
2022: Koushiki, KC and B. Alkan. “A Hybrid Extreme Learning Machine Model with Harris Hawks Optimization Algorithm: An Optimized Model for Product Demand Forecasting Applications.”, Applied Intelligence, IF:5.3, 2022 Jan 26:1-7, Journal, Open Access
2021: Alkan, Bugra, and Malarvizhi Kaniappan Chinnathai. “Performance comparison of recent population-based metaheuristic optimisation algorithms in mechanical design problems of machinery components.”, Machines, IF:2.6, 9, no. 12 (2021): 341, Journal, Open Access
2021: Alkan, Bugra, and Seth Bullock. “Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series.” Journal of the Operational Research Society, (IF:3.6, Cite Score: 5.5), 72, no. 10 (2021): 2241-2255, Journal
2021: Alkan, Bugra, Seth Bullock, and Kevin Galvin. “Identifying optimal granularity level of modular assembly supply chains based on complexity-modularity trade-off.” IEEE Access (IF:3.9, Cite Score: 9) 9 (2021): 57907-57921, Journal, Open Access
2021: Chinnathai, Malarvizhi Kaniappan, Bugra Alkan, and Robert Harrison. “A novel data-driven approach to support decision-making during production scale-up of assembly systems.” Journal of Manufacturing Systems (IF:12.1, Cite Score: 16), 59 (2021): 577-595, Journal
2020: Yao, Fengjia, Bugra Alkan, Bilal Ahmad, and Robert Harrison. “Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation.” Sensors (IF:3.9, Cite Score: 6.8) 20, no. 21 (2020): 6333, Journal, Open Access
2019: Alkan, Bugra, and Robert Harrison. “A virtual engineering based approach to verify structural complexity of component-based automation systems in early design phase.” Journal of Manufacturing Systems (IF:12.1, Cite Score: 16), 53 (2019): 18-31, Journal
2019: Alkan, Bugra. “An experimental investigation on the relationship between perceived assembly complexity and product design complexity.” International Journal on Interactive Design and Manufacturing (IJIDeM) 13, no. 3 (2019): 1145-1157, Journal, Open Access
2018: Alkan, Bugra, Daniel Vera, Bilal Ahmad, and Robert Harrison. “A method to assess assembly complexity of industrial products in early design phase.” IEEE Access (IF:3.9, Cite Score: 9) 6 (2017): 989-999, Journal, Open Access
2018: Alkan, Bugra, Daniel A. Vera, Mussawar Ahmad, Bilal Ahmad, and Robert Harrison. “Complexity in manufacturing systems and its measures: a literature review.” European Journal of Industrial Engineering (IF:1.7, Cite Score: 2.5) 12, no. 1 (2018): 116-150, Journal
2017: Alkan, Bugra, Daniel Vera, Malarvizhi Kaniappan Chinnathai, and Robert Harrison. “Assessing complexity of component- based control architectures used in modular automation systems.” International Journal of Computer and Electrical Engineering, 9, no. 1 (2017), Open Access
2016: Schmidt, K.W., Alkan, B., Schmidt, E.G., Karani, D.C. and Karakaya, U., 2016. Controller area network with priority queues and FIFO queues: improved schedulability analysis and message set extension. International Journal of Vehicle Design, 71(1-4), pp.335-357
Peer-reviewed Conference Proceedings
2024: Webb, L., Tokhi, M.O., Alkan, B. (2024). A Solution Architecture for Energy Monitoring and Visualisation in Smart Factories with Robotic Automation. In: Youssef, E.S.E., Tokhi, M.O., Silva, M.F., Rincon, L.M. (eds) Synergetic Cooperation between Robots and Humans. Lecture Notes in Networks and Systems, vol 811. Springer, Cham, Chapter
2023: Hasan, M., L. Webb, M.K. Chinnathai, M. Hossain, E.C. Ozkat, M. Tokhi, and B. Alkan. “Positional Health Assessment of Collaborative Robots based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network.” In: Youssef, E.S.E., Tokhi, M.O., Silva, M.F., Rincon, L.M. (eds) Synergetic Cooperation between Robots and Humans. CLAWAR 2023. Lecture Notes in Networks and Systems, vol 811. Springer, Cham, Chapter
2023: Saleem, K., B. Alkan, and S. Dudley-Mcevoy. “Data Driven Machine Learning Model for Condition Monitoring and Anomaly Detection in Power Grids.” In 2023 IEEE Power and Energy Society General Meeting. Institute of Electrical and Electronics Engineers (IEEE), 2023, Journal
2023: Ruoyu Jin, Nai-wen Chi, Clara Cheung, Bugra Alkan, Jacob Lin and Zulfikar Adamu. “Critical review of Digitalization Adoptions in Construction Safety Education ” In Construction Digitalisation for Sustainable Development (CDSD) 2023 Conference. Honoi, Vietnam, 2023.
2020: Alkan, Bugra, B. Seth, Kevin Galvin, and Angus Johnson. “A design process framework to deal with non-functional requirements in conceptual system designs.” 11th Complex Systems Design and Management Conference, Paris, France.(2020), Preprint
2019: Chinnathai, Malarvizhi Kaniappan, Zeinab Al-Mowafy, Bugra Alkan, Daniel Vera, and Robert Harrison. “A framework for pilot line scale-up using digital manufacturing.” Procedia CIRP 81 (2019): 962-967, Journal, Open Access
2019: Assad, Fadi, Bugra Alkan, M. K. Chinnathai, M. H. Ahmad, E. J. Rushforth, and Robert Harrison. “A framework to predict energy related key performance indicators of manufacturing systems at early design phase.” Procedia CIRP 81 (2019): 145-150, Journal, Open Access
2018: Alkan, B. “Proposing a holistic framework for the assessment and management of manufacturing complexity through data-centric and human-centric approaches.” In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2018). 2018, Preprint
2018: Chinnathai, Malarvizhi Kaniappan, Bugra Alkan, Daniel Vera, and Robert Harrison. “Pilot to full-scale production: A battery module assembly case study.” Procedia CIRP 72 (2018): 796-801, Journal, Open Access
2017: Chinnathai, Malarvizhi Kaniappan, Bugra Alkan, and Robert Harrison. “Convertibility evaluation of automated assembly system designs for high variety production.” Procedia CIRP 60 (2017): 74-79, Journal, Open Access
2016: Ahmad, Mussawar, Bilal Ahmad, Robert Harrison, Bugra Alkan, Daniel Vera, James Meredith, and Axel Bindel. “A framework for automatically realizing assembly sequence changes in a virtual manufacturing environment.” Procedia CIRP 50 (2016): 129- 134, Journal, Open Access
2016: Ahmad, Mussawar, Bugra Alkan, Bilal Ahmad, Daniel Vera, Robert Harrison, James Meredith, and Axel Bindel. “The use of a complexity model to facilitate in the selection of a fuel cell assembly sequence.” Procedia CIRP 44 (2016): 169-174. Best Paper Award, 6th CIRP Conference on Assembly Technologies and Systems (CATS 2016), Gothenburg, Sweden, Journal, Open Access
2016: Alkan, Bugra, Daniel Vera, Mussawar Ahmad, Bilal Ahmad, and Robert Harrison. “A lightweight approach for human factor assessment in virtual assembly designs: an evaluation model for postural risk and metabolic workload.” Procedia CIRP 44 (2016): 26-31, Journal, Open Access
2016: Ahmad, Mussawar, Bilal Ahmad, Bugra Alkan, Daniel Vera, Robert Harrison, James Meredith, and Axel Bindel. “Hydrogen fuel cell pick and place assembly systems: Heuristic evaluation of reconfigurability and suitability.” Procedia CIRP 57 (2016): 428- 433, Journal, Open Access
2016: Alkan, Bugra, Daniel Vera, Mussawar Ahmad, Bilal Ahmad, and Robert Harrison. “A model for complexity assessment in manual assembly operations through predetermined motion time systems.” Procedia CIRP 44 (2016): 429-434, Journal, Open Access
2016: Alkan, Bugra, Daniel Vera, Mussawar Ahmad, Bilal Ahmad, and Robert Harrison. “Design evaluation of automated manufacturing processes based on complexity of control logic.” Procedia CIRP 50 (2016): 141-146, Journal, Open Access
2013: Alkan, Bugra. “Hydrodynamic design optimization of an autonomous underwater vehicle based on response surface methodology.“ 7th International Advanced Technologies Symposium (IATS’13), 30 October-1 November 2013, Istanbul, Turkey (2013). Best Student Paper Award, 7th International Advanced Technologies Symposium (IATS’13), Istanbul, Turkey, Preprint
2013: Alkan, Bugra. “Aerodynamic Analysis of Rear Diffusers for a Passenger Car Using CFD.” 7th International Advanced Technologies Symposium (IATS’13), 30 October-1 November 2013, Istanbul, Turkey (2013), Preprint
Google Scholar for a full list.
Modules Taught
CSI-7-ICS Industrial Cyber-Physical Systems: (2021/22, 2022/23, 2023/24 - Role: Module Leader) Level-7 Computer Science MSc, Module Description: This module specifically addresses the design and development of industrial cyber-physical systems. It covers various aspects such as CPS architectural frameworks, CPS modelling, finite-state automata, M2M communication technologies, IoT analytics, and the basic implementation of predictive maintenance.
CSI-6-ARI Artificial Intelligence: (2021/22, 2022/23, 2023/24 - Role: Module Leader) Level-6 Computer Science BSc, Module Description: This module presents a comprehensive introduction to the fundamental principles that form the foundation of artificial intelligence and machine learning. It covers the concepts of machine learning and biologically inspired computation, as well as explores artificial and convolutional neural networks and their practical uses. The practical tutorials are designed and delivered with an emphasis on Python programming, integrating various libraries such as Numpy, Pandas, Tensorflow, and Keras.
CSI-6-SCS Systems and Cybersecurity: (2021/22, 2022/23 - Role: Module Leader) Level-6 Computer Science and Information Technology BSc, Module Description: This module provides a comprehensive overview of cybersecurity in engineering systems, including cryptographic techniques, hashing algorithms, and digital signatures; conducting penetration tests; evaluating and managing vulnerabilities; wireless and wired security protocols; and assessing and managing cybersecurity risks.
Tutorials
CSI-7-RME Research Methods: (2023/24 - Role: Tutor) Level-7 MSc in Data Science and MSc in Applied Artificial Intelligence; • CSI-7-SAM Statistical Analysis and Modelling: (2022/23, 2023/24 - Role: Tutor) Level-7 M.Sc. in Data Science and M.Sc. in Applied AI, • CSI-7-ICS Industrial Cyber-physical Systems: (2022/23, 2023/24 - Role: Tutor) Level-7 MSc in Applied Artificial Intelligence; • CSI-4-DMA Discrete Mathematics: (2021/22, 2022/23, 2023/24 - Role: Tutor) Level-4 Computer Science and Information Technology BSc students; • CSI-4-FCS Fundamentals of Computer Science: (2021/22 - Role: Tutor) Level-4 Computer Science BSc students. • CSI-4-DSA Data Structures and Algorithms (2021/22: Role: Tutor) Level-4 Computer Science BSc students. • Introduction to PLC Programming (2018–19), at the WMG, University of Warwick.
Robotic system programming for auto-sorting applications (CSI-7-ICS Tutorial)
Agent-based modelling - Swarm Robotics ExC Activities
Agent-based modelling - Swarm Robotics ExC Activities - II
Open-day seminar given to prospective students - Introduction to Robotics and Programming
Editorial
Associate Editor - Heliyon (Engineering, IF:4, Cite Score: 2.1, Q1, Multidisciplinary) (2023 - present)
Associate Editor - Industrial Robot Journal (Engineering, IF:1.9, Cite Score: 3.7, Q2, Manufacturing and Industrial Engineering) (2023 - present)
Reviewer Editor - Sensors (2020 - present) and Frontiers of Industrial Engineering (Systems Engineering) (2022 - present)
Guest Special Issue Editor - Machines, Special Issue ”AI-Integrated Advanced Robotics towards Industry 5.0” (ISSN 2075-1702)
Guest Special Issue Editor - Machines, Special Issue ”Smart Manufacturing Systems Towards Sustainability and Zero-Defect Manufacturing” (ISSN 2075-1702)
Conference Technical chair - IEEE ICPS 22, 23 (Chair - CPS technologies)
Technical Program Committee Member - IEEE ICPS 22, 23,24, TICEC 22, 23, CLAWAR 22, 23,24 IEEE INDIN 22, IEEE ISIE 21
Reviewer
Reviewer (Peer-reviewed Journals): Journal of Manufacturing Systems, International Journal of Production Research, Journal of Intelligent Manufacturing, International Journal of Advanced Manufacturing Technology, Journal of Engineering Design, IEEE Access, Neural Computing and Applications, Soft Computing, Sensors, Machines, Micromachines, Sustainability, Applied Sciences (more than 60 verified article reviews - according to WoS)
Fellow - Advance HE
Fellowship Reference: PR21073
Member - IEEE
Institute of Electrical and Electronics EngineersBugra Alkan, Ph.D.
Phone: +44 (0) 20 7815 7585
E-mail: alkanb (at) lsbu.ac.uk
Director of the Human-Centred Industrial Intelligence Lab
Computer Science and Digital Technologies
College of Technology and Environment
London South Bank University
Faraday Wing, FW-104 & FW-301, Keyworth Street
London, SE1 6NG, UK
