Journal articles
Yan X, Wilkinson R, Mleczko M, Brewin RJ, Gaston KJ, Mueller M, Shutler J, Anderson K (In Press). Environmental impacts of Earth observation data in the constellation and cloud computing era.
Science of the Total EnvironmentAbstract:
Environmental impacts of Earth observation data in the constellation and cloud computing era
Numbers of Earth Observation (EO) satellites have increased exponentially over the past decade reaching the current population of 1193 (January 2023). Consequently, EO data volumes have mushroomed and data processing has migrated to the cloud. Whilst attention has been given to the launch and in-orbit environmental impacts of satellites, EO data environmental footprints have been overlooked. These issues require urgent attention given data centre water and energy consumption, high carbon emissions for computer component manufacture, and difficulty of recycling computer components. Doing so is essential if the environmental good of EO is to withstand scrutiny. We provide the first assessment of the EO data life-cycle and estimate that the current size of the global EO data collection is ~807 PB, increasing by ~100 PB / year. Storage of this data volume generates annual CO2 equivalent emissions of 4101 tonnes. Major state-funded EO providers use 57 of their own data centres globally, and a further 178 private cloud services, with duplication of datasets across repositories. We explore scenarios for the environmental cost of performing EO functions on the cloud compared to desktop machines. A simple band arithmetic function applied to a Landsat 9 scene using Google Earth Engine (GEE) generated CO2 equivalent (e) emissions of 0.042 - 0.69 g CO2e (locally) and 0.13- 0.45 g CO2e (European data centre; values multiply by nine for Australian data centre). Computation-based emissions scale rapidly for more intense processes and when testing code. When using cloud services like GEE, users have no choice about the data centre used and we push for EO providers to be more transparent about the location-specific impacts of EO work, and to provide tools for measuring the environmental cost of cloud computation. The EO community as a whole needs to critically consider the broad suite of EO data life-cycle impacts.
Abstract.
Williams AJ, Menneer T, Sidana M, Walker T, Maguire K, Mueller M, Paterson C, Leyshon M, Leyshon C, Seymour E, et al (In Press). Fostering Engagement with Health and Housing Innovation: Development of Participant Personas in a Social Housing Cohort (Preprint).
Abstract:
Fostering Engagement with Health and Housing Innovation: Development of Participant Personas in a Social Housing Cohort (Preprint)
. BACKGROUND
. Personas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale.
.
.
. OBJECTIVE
. This study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community.
.
.
. METHODS
. The project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health–related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes.
.
.
. RESULTS
. The Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact.
.
.
. CONCLUSIONS
. Through the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public.
.
Abstract.
Maier M, Mueller M, Yan X (In Press). Introducing a Localised Spatio-temporal LCI Method with wheat production as exploratory case study. Journal of Cleaner Production
Menneer T, Mueller M, Townley S (2023). A cluster analysis approach to sampling domestic properties for sensor deployment. Building and Environment, 231, 110032-110032.
Robayo M, Mueller M, sharkh S, Abusara M (2023). Assessment of Supercapacitor performance in a hybrid energy storage system with an energy management based on the discrete wavelet transform. Journal of Energy Storage
Zheng L, Mueller M, Luo C, Menneer T, Yan X (2023). Variations in whole-life carbon emissions of similar buildings in proximity: an analysis of 145 residential properties in Cornwall, UK. Energy and Buildings, 296, 113387-113387.
Menneer T, Qi Z, Taylor T, Paterson C, Tu G, Elliott LR, Morrissey K, Mueller M (2021). Changes in Domestic Energy and Water Usage during the UK COVID-19 Lockdown Using High-Resolution Temporal Data.
International Journal of Environmental Research and Public Health,
18(13), 6818-6818.
Abstract:
Changes in Domestic Energy and Water Usage during the UK COVID-19 Lockdown Using High-Resolution Temporal Data
In response to the COVID-19 outbreak, the UK Government provided public health advice to stay at home from 16 March 2020, followed by instruction to stay at home (full lockdown) from 24 March 2020. We use data with high temporal resolution from utility sensors installed in 280 homes across social housing in Cornwall, UK, to test for changes in domestic electricity, gas and water usage in response to government guidance. Gas usage increased by 20% following advice to stay at home, the week before full lockdown, although no difference was seen during full lockdown itself. During full lockdown, morning electricity usage shifted to later in the day, decreasing at 6 a.m. and increasing at midday. These changes in energy were echoed in water usage, with a 17% increase and a one-hour delay in peak morning usage. Changes were consistent with people getting up later, spending more time at home and washing more during full lockdown. Evidence for these changes was also observed in later lockdowns, but not between lockdowns. Our findings suggest more compliance with an enforced stay-at-home message than with advice. We discuss implications for socioeconomically disadvantaged households given the indication of inability to achieve increased energy needs during the pandemic.
Abstract.
Williams AJ, Menneer T, Sidana M, Walker T, Maguire K, Mueller M, Paterson C, Leyshon M, Leyshon C, Seymour E, et al (2021). Fostering Engagement with Health and Housing Innovation: Development of Participant Personas in a Social Housing Cohort.
JMIR Public Health and Surveillance,
7(2), e25037-e25037.
Abstract:
Fostering Engagement with Health and Housing Innovation: Development of Participant Personas in a Social Housing Cohort
BackgroundPersonas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale.ObjectiveThis study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community.MethodsThe project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health–related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes.ResultsThe Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact.ConclusionsThrough the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public.
Abstract.
Menneer T, Mueller M, Sharpe RA, Townley S (2021). Modelling mould growth in domestic environments using relative humidity and temperature.
Building and EnvironmentAbstract:
Modelling mould growth in domestic environments using relative humidity and temperature
Damp and high levels of relative humidity (RH), typically above 70-80%, are known to provide mould-favourable conditions. Exposure to indoor mould contamination has been associated with an increased risk of developing and/or exacerbating a range of allergic and non-allergic diseases. The VTT model is a mathematical model of indoor mould growth that was developed based on surface readings of RH and temperature on wood in a controlled laboratory chamber. The model provides a mould index based on the environmental readings. We test the generalisability of this laboratory-based model to less-controlled domestic environments across different values of model parameters. Mould indices were generated using objective measurements of RH and temperature in the air, taken from sensors in a domestic setting every 3-5 minutes over 1 year in the living room and bedroom across 219 homes. Mould indices were assessed against self-reports from occupants regarding the presence of visible mould growth and mouldy odour in the home. Logistic regression provided evidence for relationships between mould indices and occupant responses. Mould indices were most successful at predicting occupant responses when the model parameters encouraged higher vulnerability to mould growth compared with the original VTT model. A lower critical RH level, above which mould grows, a higher sensitivity, and larger increases in the mould index all consistently increased performance. Using moment-to-moment time-series data for temperature and RH, the model and its developments could help inform smart monitoring or control of RH, for example to counter risks associated with reduced ventilation in energy efficient homes.
Abstract.
Walker T, Menneer T, Leyshon C, Leyshon M, Williams AJ, Mueller M, Taylor T (2020). Determinants of Volunteering Within a Social Housing Community.
VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations,
33(1), 188-200.
Abstract:
Determinants of Volunteering Within a Social Housing Community
AbstractIn general, research demonstrates that deprivation, education, health, and well-being are determinants of volunteering, and that volunteering can play an important role in building stronger communities and provides many benefits for individual health and well-being. This study concentrates on the effects of physical and mental health and well-being as predictors when the aspect of socio-economic impact has been minimised. It utilises a unique data set from a UK Housing Association community with generally high levels of deprivation. Data were analysed using bivariate probit regression. In contrast to previous findings, physical health and mental health were not significantly related to volunteering. The key finding was that mental well-being was significantly related to informal volunteering.
Abstract.
Faÿ F-X, Henriques JC, Kelly J, Mueller M, Abusara M, Sheng W, Marcos M (2019). Comparative assessment of control strategies for the biradial turbine in the Mutriku OWC plant. Renewable Energy, 146, 2766-2784.
Nicol‑Harper A, Dooley C, Packman D, Mueller M, Bijak J, Hodgson D, Townley S, Ezard T (2018). Inferring transient dynamics of human populations from matrix non-normality. Population Ecology
Guiver C, Mueller M, Hodgson D, Townley S (2016). Robust set-point regulation for ecological models with multiple management goals.
J Math Biol,
72(6), 1467-1529.
Abstract:
Robust set-point regulation for ecological models with multiple management goals.
Population managers will often have to deal with problems of meeting multiple goals, for example, keeping at specific levels both the total population and population abundances in given stage-classes of a stratified population. In control engineering, such set-point regulation problems are commonly tackled using multi-input, multi-output proportional and integral (PI) feedback controllers. Building on our recent results for population management with single goals, we develop a PI control approach in a context of multi-objective population management. We show that robust set-point regulation is achieved by using a modified PI controller with saturation and anti-windup elements, both described in the paper, and illustrate the theory with examples. Our results apply more generally to linear control systems with positive state variables, including a class of infinite-dimensional systems, and thus have broader appeal.
Abstract.
Author URL.
Guiver C, Edholm C, Jin Y, Mueller M, Powell J, Rebarber R, Tenhumberg B, Townley S (2016). Simple Adaptive Control for Positive Linear Systems with Applications to Pest Management. SIAM Journal on Applied Mathematics, 76(1), 238-275.
Rüffer BS, Van De Wouw N, Mueller M (2013). Convergent systems vs. incremental stability.
Systems and Control Letters,
62(3), 277-285.
Abstract:
Convergent systems vs. incremental stability
Two similar stability notions are considered; one is the long established notion of convergent systems, the other is the younger notion of incremental stability. Both notions require that any two solutions of a system converge to each other. Yet these stability concepts are different, in the sense that none implies the other, as is shown in this paper using two examples. It is shown under what additional assumptions one property indeed implies the other. Furthermore, this paper contains necessary and sufficient characterizations of both properties in terms of Lyapunov functions. © 2012 Elsevier B.V. All rights reserved.
Abstract.
Hackl CM, Hopfe N, Ilchmann A, Mueller M, Trenn S (2013). FUNNEL CONTROL FOR SYSTEMS WITH RELATIVE DEGREE TWO.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION,
51(2), 965-995.
Author URL.
Ruffer BS, Van De Wouw N, Mueller M (2012). From convergent dynamics to incremental stability. Proceedings of the IEEE Conference on Decision and Control, 2958-2963.
Li G, Weiss G, Mueller M, Townley S, Belmont MR (2012). Wave energy converter control by wave prediction and dynamic programming.
Renewable Energy,
48, 392-403.
Abstract:
Wave energy converter control by wave prediction and dynamic programming
We demonstrate that deterministic sea wave prediction (DSWP) combined with constrained optimal control can dramatically improve the efficiency of sea wave energy converters (WECs), while maintaining their safe operation. We focus on a point absorber WEC employing a hydraulic/electric power take-off system. Maximizing energy take-off while minimizing the risk of damage is formulated as an optimal control problem with a disturbance input (the sea elevation) and with both state and input constraints. This optimal control problem is non-convex, which prevents us from using quadratic programming algorithms for the optimal solution. We demonstrate that the optimum can be achieved by bang-bang control. This paves the way to adopt a dynamic programming (DP) algorithm to resolve the on-line optimization problem efficiently. Simulation results show that this approach is very effective, yielding at least a two-fold increase in energy output as compared with control schemes which do not exploit DSWP. This level of improvement is possible even using relatively low precision DSWP over short time horizons. A key finding is that only about 1 second of prediction horizon is required, however, the technical difficulties involved in obtaining good estimates necessitate a DSWP system capable of prediction over tens of seconds. © 2012 Elsevier Ltd.
Abstract.
Mueller M (2009). Normal form for linear systems with respect to its vector relative degree.
Linear Algebra and its Applications,
430(4), 1292-1312.
Abstract:
Normal form for linear systems with respect to its vector relative degree
For multi-input multi-output (MIMO) linear systems with existing vector relative degree a normal form is constructed. This normal form is not only structural simple but allows to characterize the system's zero dynamics for the design of feedback controllers. A characterization of the zero dynamics in terms of the normal form is given.
Abstract.
French M, Ilchmann A, Mueller M (2009). Robust stabilization by linear output delay feedback.
SIAM J. Control Optim.,
48(4), 2533-2561.
Abstract:
Robust stabilization by linear output delay feedback
The main result establishes that if a controller C (comprising of a linear feedback of the output and its derivatives) globally stabilizes a (nonlinear) plant P, then global stabilization of P can also be achieved by an output feedback controller C[h] where the output derivatives in C are replaced by an Euler approximation with sufficiently small delay h > 0. This is proved within the conceptual framework of the nonlinear gap metric approach to robust stability. The main result is then applied to finite dimensional linear minimum phase systems with unknown coefficients but known relative degree and known sign of the high frequency gain. Results are also given for systems with non-zero initial conditions.
Abstract.
Ilchmann A, Mueller M (2009). Robustness of funnel control in the gap metric.
SIAM J. Control Optim.,
48(5), 3169-3190.
Abstract:
Robustness of funnel control in the gap metric
For m-input, m-output, finite-dimensional, linear systems satisfying the classical assumptions of adaptive control (i.e. (i) minimum phase, (ii) relative degree one and (iii) positive high-frequency gain), the well known funnel controller k(t) = φ(t)/(
1−φ(t)|e(t)|) , u(t) = −k(t)e(t) achieves output regulation in the following sense: all states of the closed-loop system are bounded and, most importantly, transient behaviour of the tracking error e = y − y_ref is ensured such that the evolution of e(t) remains in a performance funnel with prespecified boundary 1/φ(t), where y_ref denotes a reference signal bounded with essentially bounded derivative. As opposed to classical adaptive high-gain output feedback, system identification or internal model is not invoked and the gain k(·) is not
monotone.
Invoking the conceptual framework of the nonlinear gap metric we show that the funnel controller is robust in the following sense: the funnel controller copes with bounded input and output disturbances and, more importantly, it may even be applied to a system not satisfying any of the classical conditions (i)–(iii) as long as the initial conditions and the disturbances are “small” and the system is “close” (in terms of a “small” gap) to a system satisfying (i)–(iii).
Abstract.
Ilchmann A, Mueller M (2008). Robustness of λ-tracking in the gap metric.
SIAM J. Control Optim.,
47(5), 2724-2744.
Abstract:
Robustness of λ-tracking in the gap metric
For m-input, m-output, finite-dimensional, linear systems satisfying the classical assumptions of adaptive control (i.e. (i) minimum phase, (ii) relative degree one and (iii) "positive" high-frequency gain), it is well known that the adaptive λ-tracker 'u = -ke, k' = max{0; |e|-λ}|e|' achieves λ-tracking of the tracking error e if applied to such a system: all states of the closed-loop system are bounded and |e| is ultimately bounded by λ, where λ > 0 is prespecified and may be arbitrarily small.
Invoking the conceptual framework of nonlinear gap metric, we show that the λ-tracker is robust. In the present setup this means in particular that the λ-tracker copes with bounded input and output disturbances and, more importantly, it may even be applied to a system not satisfying any of the classical conditions (i)-(iii) as long as the initial conditions and the disturbances are "small" and the system is "close" (in terms of "small" gap) to a system satisfying (i)-(iii).
Abstract.
Ilchmann A, Mueller M (2007). Time-varying linear systems: relative degree and normal form.
IEEE Trans. Aut. Control,
52(5), 840-851.
Abstract:
Time-varying linear systems: relative degree and normal form
We define the relative degree of time-varying linear systems, show that it coincides with Isidori’s and with Liberzon/Morse/Sontag’s definition if the system is understood as a time-invariant nonlinear system, characterize it in terms of the system data and their derivatives, derive a normal form with respect to a time-varying linear coordinate transformation, and finally characterize the zero dynamics.
Abstract.
Conferences
Alharthi M, Hughes T, Mueller M (2022). Optimal Control of Volterra Difference Equations of the First Kind.
Abstract:
Optimal Control of Volterra Difference Equations of the First Kind
Abstract.
Walker T, Menneer T, Tu G, Mueller M, Leyshon C, Leyshon M, Morrissey K, Bland E, Buckingham S (2022). P44 Smarter social housing: user perspectives on technology adoption for healthy homes. Society for Social Medicine Annual Scientific Meeting Abstracts.
Walker T, Menneer T, Tu G, Mueller M, Leyshon C, Leyshon M, Morrissey K, Bland E (2022). Smarter social housing: user perspectives on technology adoption for healthy homes and health equity. European Health Economics Association: Health economics for sustainable welfare systems. 5th - 8th Jul 2022.
Abstract:
Smarter social housing: user perspectives on technology adoption for healthy homes and health equity
Abstract.
Zheng L, Mueller M, Luo C, Yan X (2020). A Review of Data-driven Approaches for Occupant’s Behaviour in Building Energy Conservation. Applied Energy Symposium 2020: Low carbon cities and urban energy systems. 10th - 17th Oct 2020.
Abstract:
A Review of Data-driven Approaches for Occupant’s Behaviour in Building Energy Conservation
Abstract.
Robayo M, Abusara M, Mueller M, Sharkh S (2020). A Smart Energy Management System for Battery-Supercapacitor in Electric Vehicles based on the Discrete Wavelet Transform and Deep Learning.
Abstract:
A Smart Energy Management System for Battery-Supercapacitor in Electric Vehicles based on the Discrete Wavelet Transform and Deep Learning
Abstract.
Williams A, Menneer T, Sidani M, Walker T, Maguire K, Mueller M, Paterson C, Leyshon M, Leyshon C, Seymour E, et al (2020). Using machine learning clustering techniques to support the understanding of populations and inform action. Public Health England Research and Science Conference - Application of scientific methods to improve and protect health.
Abstract:
Using machine learning clustering techniques to support the understanding of populations and inform action
Abstract.
Faÿ F-X, Kelly J, Henriques J, Pujana A, Abusara M, Mueller M, Touzon I, Ruiz-Minguela P (2018). Numerical Simulation of Control Strategies at Mutriku Wave Power Plant. ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. 17th - 22nd Jun 2018.
Faÿ F-X, Kelly J, Henriques J, Pujana A, Abusara M, Mueller M, Touzon I, Ruiz-Minguela P (2018). Numerical Simulation of Control Strategies at Mutriku Wave Power Plant. ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. 17th - 22nd Jun 2018.
Abstract:
Numerical Simulation of Control Strategies at Mutriku Wave Power Plant
Abstract.
Maier M, Mueller M, Yan X (2017). Introduction of a spatiotemporal Life Cycle Inventory method using a wind energy example.
Abstract:
Introduction of a spatiotemporal Life Cycle Inventory method using a wind energy example
Abstract.
Mueller M, Cantoni M (2010). Normalized coprime representations for time-varying linear systems. appears: Proc. 49th IEEE Conf. Decis. Control.
Abstract:
Normalized coprime representations for time-varying linear systems
Abstract.
Ilchmann A, Mueller M (2010). Robustness of funnel control in the gap metric. appears: Proc. 49th IEEE Conf. Decis. Control.
Abstract:
Robustness of funnel control in the gap metric
Abstract.
Ilchmann A, Mueller M (2009). Robustness of λ-tracking and funnel control in the gap metric. Proc. Joint 48th IEEE Conf. Decis. Control and 28th Chinese Contr. Conf.
Abstract:
Robustness of λ-tracking and funnel control in the gap metric
Abstract.