The University of Southampton

Published: 6 May 2021
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The modelling will support shipping companies as they aim for a net-zero emissions industry.

Artificial Intelligence technologies from the University of Southampton are helping to deliver more efficient shipping by providing insights, derived from millions of simulations, on the most effective strategies to achieve net-zero targets.

The research collaboration with Shell Shipping and Maritime analyses carbon tax scenarios before predicting the impact of green strategies such as adjusting voyage routes, installing energy efficient technologies and applying carbon offset incentives. The model is intended to provide information and support as shipping companies aim for a net-zero emissions industry.

Southampton AI experts including Professor Gopal Ramchurn and Dr Enrico Gerding are building a large scale agent-based simulation system that models and simulates elements of the shipping industry.

The expanding analysis is part of the University and Shell's partnership work in the Centre for Maritime Futures, which aims to transform the energy shipping industry to be safer, cleaner and more efficient through ground-breaking digital and technological advances.

Professor Ramchurn, Co-Director of the Centre for Maritime Futures, says: "Optimising the transition of shipping fleets to cleaner and more efficient technologies against a backdrop of uncertainty requires trialling a range of future scenarios. We are addressing this challenge through a range of AI and machine learning solutions embedded in a high fidelity simulation platform."

Dr Gerding, Director of Southampton's Centre for Machine Intelligence, adds: "Researchers have created a detailed carbon tax report, highlighting different carbon tax regimes at national, regional and international levels, currently enforced or planned. We developed an agent-based simulation to model different carbon tax regimes, shipping and commodity markets, and provide insights in order to help shipping companies better manage their emissions down."

The simulation system is being built in stages, with each stage adding new features to different apps allowing concurrent and independent development while also ensuring gradual enhancement in functionality, parallel deployment of resources based on their speciality.

To analyse the behaviours and better model the overall simulation system, a detailed data analysis was carried out on data provided by Shell for multiple vessels in their fleet.

The partners' next goal is to ensure that all user requirements are captured to further develop the platform to allow users to run 'what-if' scenarios. This will allow the creation of a multi-user testbed that will enable traders to make better trading decisions and shipping operators to predict the performance of future fleet profiles.

Professor Ramchurn says: "Our work will underpin the design of next generation Uber-freight models that will enable the dynamic routing of vessels and enable a smooth transition to net-zero shipping using green hydrogen both as a fuel and cargo."

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Publications

Mougan Navarro, Carlos, Kanellos, Georgios and Gottron, Thomas (2021) Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank. European Congress of Machine Learning (ECML PKDD) - 2nd Workshop on bias and fairness in AI, Online Event, Rome, Italy. 13 - 17 Sep 2021. 8 pp . (In Press)

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Published: 26 April 2021
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Andrius Matšenas (left) and Til Jordan (right) have launched the Stardust start-up.

Student entrepreneurs from the University of Southampton have created a free browser tool that automatically manages cookie pop-ups without sacrificing data privacy.

The Stardust start-up, founded by Computer Science and Mathematics students Til Jordan and Andrius Matšenas, is building a platform where users own, control, and communicate their personal data online.

Their new browser extension, called the Stardust Cookie Cutter, represents a first step toward this vision for people-centric personal data.

"Most of us have tried at least once to opt for the consents we are actually okay with but it's a forever repetitive task," Til says. "The reality is that after a short period of time most people give up trying and usually opt with 'Allow all'.

"Stardust, with its Cookie Cutter browser extension, proposes a solution - to only ask for a person's consent preferences once and automatically take care of the rest."

The plugin is free and accessible to everyone using Chrome, Firefox, Brave or Edge browsers. For other browsers, the instructions are given on the Stardust website.

The co-founders say that the start-up was born out of the billions of user records that are breached every year, rendering people’s personal data vulnerable online.

"Shifting data control from companies to individuals with transparent and independent technology is long overdue and probably the most sensible approach to alleviate the power of 'Big Tech' companies," Andrius says.

"It will firstly make online processes more convenient for the everyday person, like browsing the web without cookie pop-ups, but also in the long run cut server and infrastructure costs for companies and create many new business opportunities."

Til and Andius have previously demonstrated the benefits of personal data centred around the end-user in Garage48 Cybersecurity Hackathon in late 2020 where they secured a runner-up prize.

Stardust is part of this spring’s Founders Cohort for the University’s Future Worlds start-up accelerator, where a four-month acceleration programme will rapidly develop the business toward market.

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Email:
w.hsuan-yang@soton.ac.uk

 

Hsuan-Yang Wang is a PhD student at the Centre of Doctoral Training (CDT) of Machine Intelligence for Nano-Electronic Devices and Systems (MINDS) at the University of Southampton. 

Hsuan-Yang graduated with an MEng degree in Acoustical Engineering in 2015 from the Institute of Sound and Vibration Research (ISVR) at the University of Southampton. He has previously worked in the industry as a senior acoustic consultant at MACH Acoustics. In 2019, he joined the 'Embedded AI' research theme of the MINDS CDT and started a PhD under the supervision of Dr Christine Evers and Prof Philip Nelson. His research interest is in utilising the latest machine learning and artificial intelligence technologies in audio applications. He is currently working on developing an artificial source localisation model that emulates human perceptual behaviour to evaluate binaural technology, such as hearable devices and augmented reality.

Research

Research interests

Embedded AI, Machine Learning, Signal Processing, Binaural Source Localisation, Cocktail Party Problem

Publications

Wang, Hsuan-Yang, Nelson, Philip and Evers, Christine (2021) Excitation-inhibition Cell activity patterns for binaural source localisation. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Mohonk Mountain House, New Paltz, United States. 17 - 20 Oct 2021. 5 pp . (In Press)

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Publications

Batt, D. A. and Currie, A. J. (1984) A VLSI Design Language Incorporating Self-timed Concurrent Processes. Proc. IEE Conf. on Electronic Design Automation. pp. 199-203 .

Allerton, D. J., Batt, D. A., Currie, A. J. and Nichols, K. G. (1984) Functional Simulation as an Adjunct to Silicon Compilation. Proc. IEE Conf. on Computer Aided Engineering. pp. 36-41 .

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Published: 1 April 2021
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The Southampton design is the winner of the 2021 UK University CanSat competition.

A team of students from the University of Southampton have secured first place in a nationwide engineering contest with an effective design for a can-sized satellite.

The Soton CanSat team designed and built the cylindrical satellite simulation to be launched hundreds of metres into the air before returning to ground by parachute.

Judges ranked the Southampton design first in UK University CanSat competition, with a 95.8% rating that was over five percent greater than its nearest rivals.

The spacecraft wasn't launched in this spring's contest, owing to lockdown restrictions, however the Southampton team already have their sights set on a maiden flight at this July's Mach-21 competition at Machrihanish Airbase in Scotland.

This was the Southampton team's debut entry in the UK University CanSat competition.

The Soton CanSat team, run by the Southampton University Spaceflight Society (SUSF), included fourth year Electronic Engineering with Industrial Studies student Adrian Kraft, second year Electronic Engineering student Harry Snell, first year Aerospace Electronic Engineering with Industrial Studies student Oli Perez, second year Aeronautics and Astronautics student Nicholas Horsman and first year Computer Science student Thomas Cross.

"First place is an amazing result and I believe it really shows what a determined and organised team can achieve," project lead Adrian says. "Our ability to collaborate effectively online in the face of challenges posed by national lockdowns helped us attain a winning design.

"Our aim was to fulfil the competition requirements to the fullest without having to overcomplicate the design. We focused on splitting sections of our design into different subsystems such as flight software and electrical power. Rigorous testing and reshaping were key. I'm really looking forward to seeing what we can achieve in the next Mach-21 event."

The CanSat competition is designed to reflect various aspects of real-world missions, including telemetry requirements, communications, and autonomous operations. The experience allows students to get a feel for project-work in an engineering related career, develop their time and project management skills, and learn how to work effectively in a team.

"It was a challenging but exciting experience," Adrian says. "Elements of the electronic and mechanical build were particularly difficult without access to laboratory equipment. Considering we were not able to meet in person, I am very proud of what the team has accomplished."

The Soton CanSat team is sponsored and supported by SUSF, the University’s School of Electronics and Computer Science, and industry partner Cirium.

Second year Aerospace Electronic Engineering student Harry Hancock is joining the team to bring their numbers to six ahead of this summer's competition. Mach-21 will take place from 14-16 July.

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Telephone:
+442380596782
Email:
K.E.Chamberlain@soton.ac.uk

 

Manage CHB Labs based in Building 85 supporting Prof. Morgan's Biomedical Electronics Research group. Cleanroom fabrication in Zepler Institute Building 53 to make a wide range of devices used in Biomedical electronics research my speciality are bond-aligned and thermally compressed wafer processing to make microfluidic devices which are used in impedance experiments by the researchers in Building 85 CHB to study cell behaviour and in the marine environment. Provide technical support and demonstration teaching to undergraduates in the CHB labs and ECS summer Schools and postgraduate teaching in the TRC Cleanroom. I provide H&S advice to the School of ECS Research groups’ facilities, and this includes workplace inspections.

 

Other ,voluntary roles. I ran the Tech FEPS Forum to network with Technicians across the five faculty Schools since Feb 2018 till June 2020 and recently been involved with the Technician Commitment Implementation Group's 'Visibility group' to support Southampton University’s Technician Commitment action plan involves raising profiles of TAE staff internally and externally and to help organise the first ever University event for Technical staff (30th March 2022).

Research

Research interests

Cleanroom fabrication and lithography supporting projects iFAST, EVFoundry, Tech Oceans

Teaching

ECS Biomedical Electronics Engineering,  PGT and ECS Summer Schools provides technical support and demonstration on modules ELEC1211, ELEC2230, BIOL2051, ELEC6205

Publications

Painter, Stuart C., Sanders, Richard, Waldron, Howard N., Lucas, Michael I., Woodward, E. Malcolm S. and Chamberlain, Katie (2008) Nitrate uptake along repeat meridional transects of the Atlantic Ocean. Journal of Marine Systems, 74 (1-2), 227-240. (doi:10.1016/j.jmarsys.2007.12.009).

Huang, Xi, Pascal, Robin W., Chamberlain, Katie, Banks, Christopher J., Mowlem, Matthew and Morgan, Hywel (2011) A miniature, high precision conductivity and temperature sensor system for ocean monitoring. IEEE Sensors Journal, 11 (12), 3246-3252. (doi:10.1109/JSEN.2011.2149516).

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Publications

Shankar, F., Sonnenfeld, A., Grylls, P., Zanisi, L., Nipoti, C., Chae, K.-H., Bernardi, M., Petrillo, C.E., Huertas-Company, M., Mamon, G.A. and Buchan, S. (2018) Revisiting the bulge-halo conspiracy - II. Towards explaining its puzzling dependence on redshift. Monthly Notices of the Royal Astronomical Society, 475 (3), 2878-2890. (doi:10.1093/mnras/stx3086).

Grylls, Philip J, Shankar, F, Zanisi, L and Bernardi, M (2019) A statistical semi-empirical model: satellite galaxies in groups and clusters. Monthly Notices of the Royal Astronomical Society, 483 (2), 2506-2523. (doi:10.1093/mnras/sty3281).

Shankar, Francesco, Weinberg, David H, Marsden, Christopher, Grylls, Philip J., Bernardi, Mariangela, Yang, Guang, Moster, Benjamin, Fu, Hao, Carraro, Rosamaria, Alexander, David M., Allevato, Viola, Ananna, Tonima T., Bongiorno, Angela, Calderone, Giorgio, Civano, Francesca, Daddi, Emanuele, Delvecchio, Ivan, Duras, Federica, Lafranca, Fabio, Lapi, Andrea, Lu, Youjun, Menci, Nicola, Mezcua, Mar, Ricci, Federica, Rodighiero, Giulia, Sheth, Ravi K, Suh, Hyewon, Villforth, Carolin and Zanisi, Lorenzo (2020) Probing black hole accretion tracks, scaling relations, and radiative efficiencies from stacked X-ray active galactic nuclei. Monthly Notices of the Royal Astronomical Society, 493 (1), 1500-1511. (doi:10.1093/mnras/stz3522).

Chapman, Age, Ugwudike, Pamela, Grylls, Philip, Gammack, David and Ayling, Jacqueline, Anne (2022) A data-driven analysis of the interplay between criminological theory and predictive policing algorithms. In ACM Conference on Fairness, Accountability, and Transparency: FaCCT. ACM Press. 14 pp .

Grylls, Philip J, Shankar, F and Conselice, C. J. (2020) The significant effects of stellar mass estimation on galaxy pair fractions. Monthly Notices of the Royal Astronomical Society, 499 (2), 2262-2275. (doi:10.1093/mnras/staa2966).

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