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Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities
Get PDFPeer-to-peer (P2P) energy trading and energy communities have garnered much attention over in recent years due to increasing investments in local energy generation and storage assets. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency to be gained from P2P trading, and the structure of local energy markets raise many important challenges. To analyse the efficiency of P2P energy markets, in this work, we consider two different popular approaches to peer-to-peer trading: centralised (through a central market maker/clearing entity) vs. fully decentralised (P2P), and explore the comparative economic benefits of these models. We focus on the metric of Gains from Trade (GT), given optimal P2P trading schedule computed by a schedule optimiser. In both local market models, benefits from trading are realised mainly due to the diversity in consumption behaviour and renewable energy generation between prosumers in an energy community. Both market models will lead to the most promising P2P contracts (the ones with the highest Gains from Trade) to be established first. Yet, we find diversity decreases quickly as more peer-to-peer energy contracts are established and more prosumers join the market, leading to significantly diminishing returns. In this work, we aim to quantify this effect using real-world data from two large-scale smart energy trials in the UK, i.e. the Low Carbon London project and the Thames Valley Vision project. Our experimental study shows that, for both market models, only a small number of P2P contracts i.e. less than 10% of the possible P2P contracts are required to achieve the majority of the maximal potential Gains from Trade. Similarly, only a fraction of prosumers are required to participate in energy trading to realise significant GT; namely we found that 60% of the maximal GT can be realised with only 30% of prosumers’ participation, with the percentage of maximal GT reaching 80% when participation increases to 50% of prosumers. Finally, we study the effect that diversity in consumption profiles has on overall trading potential and dynamics in an energy community. We show that in a community with a DF(load diversity factor) 1, 80% of potential maximal GT can be achieved by 10% of prosumers engaging in P2P trading, while in a community with DF 1.5, it is beneficial for 40% of the prosumers to trade
Knowledge extraction in the quantum random-Oracle model
No full textThe aim of this thesis is to present novel techniques for proving cryptographic schemes secure against quantum adversaries. Most results are within the context of an idealized model called the ‘quantum random-oracle model’. A particular challenge is to extract some piece of knowledge an adversary possesses just from its interaction with an oracle, while mitigating the effects of the collapse of the wave function caused by the observation of a quantum state
Delay threshold for social interaction in volumetric eXtended Reality communication
Get PDFImmersive technologies like eXtended Reality (XR) are the next step in videoconferencing. In this context, understanding the effect of delay on communication is crucial. This article presents the first study on the impact of delay on collaborative tasks using a realistic Social XR system. Specifically, we design an experiment and evaluate the impact of end-to-end delays of 300, 600, 900, 1,200, and 1,500 ms on the execution of a standardized task involving the collaboration of two remote users that meet in a virtual space and construct block-based shapes. To measure the impact of the delay in this communication scenario, objective and subjective data were collected. As objective data, we measured the time required to execute the tasks and computed conversational characteristics by analyzing the recorded audio signals. As subjective data, a questionnaire was prepared and completed by every user to evaluate different factors such as overall quality, perception of delay, annoyance using the system, level of presence, cybersickness, and other subjective factors associated with social interaction. The results show a clear influence of the delay on the perceived quality and a significant negative effect as the delay increases. Specifically, the results indicate that the acceptable threshold for end-to-end delay should not exceed 900 ms. This article additionally provides guidelines for developing standardized XR tasks for assessing interaction in Social XR environments
Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast
Get PDFObject: In a typical MR session, several contrasts are acquired. Due to the sequential nature of the data acquisition process, the patient may experience some discomfort at some point during the session, and start moving. Hence, it is quite common to have MR sessions where some contrasts are well-resolved, while other contrasts exhibit motion artifacts. Instead of repeating the scans that are corrupted by motion, we introduce a reference-guided retrospective motion correction scheme that takes advantage of the motion-free scans, based on a generalized rigid registration routine. Materials and methods: We focus on various existing clinical 3D brain protocols at 1.5 Tesla MRI based on Cartesian sampling. Controlled experiments with three healthy volunteers and three levels of motion are performed. Results: Radiological inspection confirms that the proposed method consistently ameliorates the corrupted scans. Furthermore, for the set of specific motion tests performed in this study, the quality indexes based on PSNR and SSIM shows only a modest decrease in correction quality as a function of motion complexity. Discussion: While the results on controlled experiments are positive, future applications to patient data will ultimately clarify whether the proposed correction scheme satisfies the radiological requirements
Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields
Get PDFUncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated. Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability, it is essential that practitioners pay attention to the issues concerned. Here a UQ study is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters, which affect key quantities of interest, including material properties and binding free energy predictions in drug discovery and personalized medicine. Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes, the sensitivity of the input parameters is ranked. Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed. This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters
How auto-differentiation can improve CT workflows: Classical algorithms in a modern framework
Get PDFMany of the recent successes of deep learning-based approaches have been enabled by a framework of flexible, composable computational blocks with their parameters adjusted through an automatic differentiation mechanism to implement various data processing tasks. In this work, we explore how the same philosophy can be applied to existing “classical” (i.e., non-learning) algorithms, focusing on computed tomography (CT) as application field. We apply four key design principles of this approach for CT workflow design: end-to-end optimization, explicit quality criteria, declarative algorithm construction by building the forward model, and use of existing classical algorithms as computational blocks. Through four case studies, we demonstrate that auto-differentiation is remarkably effective beyond the boundaries of neural-network training, extending to CT workflows containing varied combinations of classical and machine learning algorithms
A survey on machine learning in ship radiated noise
Get PDFThe utilization of machine learning in analyzing ship radiated noise (SR-N) is undergoing rapid evolution. Because the omnipresent background noise strongly depends on the highly variable environment, the application of such techniques poses challenges. Furthermore, publicly available labeled datasets are scarce. Motivated by this, there has been a surge in the number of publications regarding the implementation of machine learning in the monitoring of SR-N within the past few years. This comprehensive survey delineates the state-of-the-art machine learning techniques applied to SR-N, with a specific focus on passive measurements. Recent developments are categorized into several sub-areas, namely; publicly available datasets, data augmentation, signal denoising, feature extraction, detection, localization, and recognition of SR-N. Additionally, future research directions are explored
Proving correctness of parallel implementations of transition system models
Get PDFThis article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems
Software for the paper: Temporal true and surrogate fitness landscape analysis for expensive bi-objective optimisation
No full textMobile touch: Responsive wireless tactile communication for portable pneumatic wearables
No full textPneumatic actuation is an effective method for recreating human touch sensations because it can apply pressure to the skin and has a natural softness. However, integrating this technology into fully wearable devices has proven to be difficult due to the lack of responsiveness with smaller pneumatic components, which limits the number of effective touches that can be portrayed.
Mobile Touch is a fully wearable system designed to deliver touch sensations to the wrist through a combination of pneumatic and vibrotactile actuation. By compensating for the inflation time of the pneumatic component with vibrations, the system's responsiveness is increased, allowing for the creation of more varied touch patterns. In addition, a wireless controller was developed that allows for programming touch patterns and directly transmitting touch wirelessly to the wearable device.
In the user study, the pneumatic-vibrotactile modality generally received significantly higher ratings for its similarity to human touch and pattern recognition by leveraging the strengths of each individual modality (n=35). Specifically, for longer touches, it was more effective due to its ability to mimic the softness of human skin and the pressure of a touch. For short taps, the vibrotactile component was clearer and enhanced the perceived realism