Skip to main content

Research Repository

Advanced Search

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems (2022)
Journal Article
Zhao, H., Yu, H., & Peng, L. (in press). Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/tnnls.2022.3174885

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a... Read More about Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems.

Towards Predictive Design: Tracking a CNC Fixture Design Process to Identify the Requirements (2022)
Conference Proceeding
Vasantha, G., Purves, D., Corney, J., Canavan, M., Quigley, J., & Sherlock, A. (in press). Towards Predictive Design: Tracking a CNC Fixture Design Process to Identify the Requirements. In International Conference on Manufacturing Research ICMR 2022

Training novice production engineers to design manufacturing fixtures is a time-consuming process involving significant input from experienced experts. Motivated by the vision of providing an intelligent support system for general mechanical design,... Read More about Towards Predictive Design: Tracking a CNC Fixture Design Process to Identify the Requirements.

Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems (2022)
Journal Article
Zhao, H., Shan, J., Peng, L., & Yu, H. (in press). Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems. IEEE Transactions on Industrial Electronics, https://doi.org/10.1109/tie.2022.3174275

This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled syst... Read More about Learning-based Robust Bipartite Consensus Control for a Class of Multiagent Systems.

Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks (2022)
Journal Article
Zhao, H., Shan, J., Peng, L., & Yu, H. (in press). Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks. IEEE Transactions on Industrial Informatics, https://doi.org/10.1109/tii.2022.3157595

This paper studies fully distributed data-driven problems for nonlinear discrete-time multi-agent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model b... Read More about Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks.

Automating the modular construction process: A review of digital technologies and future directions with blockchain technology (2021)
Journal Article
Olawumi, T. O., Chan, D. W., Ojo, S., & Yam, M. C. (2022). Automating the modular construction process: A review of digital technologies and future directions with blockchain technology. Journal of Building Engineering, 46, https://doi.org/10.1016/j.jobe.2021.103720

Modular integrated construction (MiC) method has come to limelight in recent years due to its enormous potentials. Although several digital tools and technologies (DTT) have been employed in MiC projects, no previous research study has critically rev... Read More about Automating the modular construction process: A review of digital technologies and future directions with blockchain technology.

A Probabilistic Design Reuse Index for Engineering Designs (2020)
Journal Article
Vasantha, G., Corney, J., Stuart, S., Sherlock, A., Quigley, J., & Purves, D. (2020). A Probabilistic Design Reuse Index for Engineering Designs. Journal of Mechanical Design, 142(10), https://doi.org/10.1115/1.4046435

Many companies offer a range of related products that are constructed using similar components and processes. This enables them to meet customer expectations of product variety while minimising the overheads (e.g. development and manufacturing costs)... Read More about A Probabilistic Design Reuse Index for Engineering Designs.

An AI approach to Collecting and Analyzing Human Interactions with Urban Environments (2019)
Journal Article
Ferrara, E., Fragale, L., Fortino, G., Song, W., Perra, C., di Mauro, M., & Liotta, A. (2019). An AI approach to Collecting and Analyzing Human Interactions with Urban Environments. IEEE Access, 7, 141476-141486. https://doi.org/10.1109/access.2019.2943845

Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifi... Read More about An AI approach to Collecting and Analyzing Human Interactions with Urban Environments.

Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach (2019)
Journal Article
Di Mauro, M., & Liotta, A. (2019). Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach. IEEE Transactions on Network and Service Management, 16(4), 1493-1506. https://doi.org/10.1109/tnsm.2019.2943776

The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provisioning platforms such as the IP Multimedia Subsystem (IMS). The NFV concept is... Read More about Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach.

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks (2019)
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserv... Read More about Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks.

Curvilinear MetaSurfaces for Surface Wave Manipulation. (2019)
Journal Article
La Spada, L., Spooner, C., Haq, S., & Hao, Y. (2019). Curvilinear MetaSurfaces for Surface Wave Manipulation. Scientific Reports, 9(1), https://doi.org/10.1038/s41598-018-36451-8

Artificial sheet materials, known as MetaSurfaces, have been applied to fully control both space and surface waves due to their exceptional abilities to dynamically tailor wave fronts and polarization states, while maintaining small footprints. Howev... Read More about Curvilinear MetaSurfaces for Surface Wave Manipulation..

Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities (2019)
Journal Article
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., …Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217

The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing dat... Read More about Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities.

Metasurfaces for Advanced Sensing and Diagnostics (2019)
Journal Article
La Spada, L. (2019). Metasurfaces for Advanced Sensing and Diagnostics. Sensors, 19(2), 355. https://doi.org/10.3390/s19020355

Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and ac... Read More about Metasurfaces for Advanced Sensing and Diagnostics.

A Probabilistic Design Reuse Index (2018)
Conference Proceeding
Vasantha, G., Sherlock, A., Corney, J., & Quigley, J. (2018). A Probabilistic Design Reuse Index. In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

The benefits of being able to create a number of product variations from a limited range of components, or sub-assemblies, are widely recognized. Indeed it is clear that companies who can effectively reuse elements of existing designs when creating n... Read More about A Probabilistic Design Reuse Index.

The shifting terrain of commercial aviation: Implications for HRM (2018)
Conference Proceeding
Grant, K., & Maxwell, G. (2018). The shifting terrain of commercial aviation: Implications for HRM. In Conference Proceedings

Safety is of paramount importance in commercial aviation. Human factors account for up to 80% of aviation accidents and incidents. Commercial aviation is experiencing rapid growth and change while facing intense competition from a large number of new... Read More about The shifting terrain of commercial aviation: Implications for HRM.

Interference graphs to monitor and control schedules in low-power WPAN (2018)
Journal Article
van der Lee, T., Liotta, A., & Exarchakos, G. (2019). Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014

Highlights • This study presents the complete and slotted interference graph model. • The service uses the complete interference graph to evaluate the network. • Slotted interference graphs are used to reschedule problematic connections. • Rea... Read More about Interference graphs to monitor and control schedules in low-power WPAN.

Cross-modality interactive attention network for multispectral pedestrian detection (2018)
Journal Article
Zhang, L., Liu, Z., Zhang, S., Yang, X., Qiao, H., Huang, K., & Hussain, A. (2019). Cross-modality interactive attention network for multispectral pedestrian detection. Information Fusion, 50, 20-29. https://doi.org/10.1016/j.inffus.2018.09.015

Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between... Read More about Cross-modality interactive attention network for multispectral pedestrian detection.

Helicopter Lands on Uneven Terrain by means of Articulated Robotic Legs-Modelling, Simulation and Control Approach (2018)
Conference Proceeding
Boix, D. M., Goh, K., & McWhinnie, J. (2018). Helicopter Lands on Uneven Terrain by means of Articulated Robotic Legs-Modelling, Simulation and Control Approach. In 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). https://doi.org/10.1109/aim.2018.8452431

In this paper, a 3D mathematical model of a helicopter attached to a robotic landing gear is proposed to investigate into helicopter landing on uneven terrain. The landing gear consists of four articulated legs connected to the helicopter fuselage. E... Read More about Helicopter Lands on Uneven Terrain by means of Articulated Robotic Legs-Modelling, Simulation and Control Approach.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2018)
Journal Article
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-04316-3

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-... Read More about Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.