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Robust co-design framework for buildings operated by predictive control

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arXiv 2024年

作者： Falugi, P. O’Dwyer, E. Zagorowska, M.A. Kerrigan, E.C. Nie, Y. Strbac, G. Shah, N. Engineering and Construction Department University of East London London United Kingdom Department of Chemical Engineering Imperial College London London United Kingdom Mechanical Engineering Department Delft University of Technology Delft Netherlands Department of Aeronautics Imperial College London London United Kingdom Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Cost-effective decarbonisation of the built environment is a stepping stone to achieving net-zero carbon emissions since buildings are globally responsible for more than a quarter of global energy-related CO2 emissions. Improving energy utilization and decreasing costs naturally requires considering multiple domain-specific performance criteria. The resulting problem is often computationally infeasible. The paper proposes an approach based on decomposition and selection of significant operating conditions to achieve a formulation with reduced computational complexity. We present a robust framework to optimise the physical design, the controller, and the operation of residential buildings in an integrated fashion, considering external weather conditions and time-varying electricity prices. The framework explicitly includes operational constraints and increases the utilization of the energy generated by intermittent resources. A case study illustrates the potential of co-design in enhancing the reliability, flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results demonstrate reductions in costs up to 30% compared to a deterministic formulation. Furthermore, the proposed approach achieves a computational time reduction at least 10 times lower compared to the original problem with a deterioration in the performance of only 0.6%. © 2024, CC BY.

关键词： Zero-carbon

Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach

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Real-Time Object Detection and Robotic Manipulation for Agri...

IEEE International Conference on Industrial Technology (ICIT)

作者： Hongyu Zhao Zezhi Tang Zhenhong Li Yi Dong Yuancheng Si Mingyang Lu George Panoutsos The Department of Physics Imperial College London United Kingdom The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom The Department of Electronics and Computer Science University of Southampton Southampton United Kingdom The Department of Economics Fudan University Shanghai China The Center for Nondestructive Evaluation (CNDE) Iowa State University Ames IA United States

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

关键词： YOLO Visualization Crops Grasping Production Robustness Real-time systems

A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy

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arXiv 2023年

作者： Mamalakis, Michail Macfarlane, Sarah C. Notley, Scott V. Gad, Annica K.B. Panoutsos, George Insigneo Institute for in-silico Medicine University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease Department of Computer science Sheffield United Kingdom Department of Psychiatry Cambridge University Cambridge United Kingdom Department of Oncology and Metabolism The Medical School University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Madeira Chemistry Research Centre University of Madeira Funchal Portugal Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden

We developed a transparent computational large-scale imaging-based framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells, using a combination of multiattention channels network and global explainable techniques. We test a classification between normal cells (Bj primary fibroblast), and their isogenically matched, transformed and invasive counterpart (BjTertSV40TRasV12). Manual annotation is not trivial to automate due to the intricacy of the biologically relevant features. In this research, we utilized established deep learning networks and our new multi-attention channel architecture. To increase the interpretability of the network - crucial for this application area - we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The significant results from our analysis unprecedently allowed a more detailed, and biologically relevant understanding of the cytoskeletal changes that accompany oncogenic transformation of normal to invasive and metastasizing cells. We also paved the way for a possible spatial micrometre-level biomarker for future development of diagnostic tools against metastasis (spatial distribution of vimentin). © 2023, CC BY-SA.

关键词： Cells

Modelling the connection between propagating disturbances and solar spicules

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arXiv 2024年

作者： Skirvin, Samuel Skirvin, S.J. Samanta, Tanmoy Van Doorsselaere, Tom Centre for mathematical Plasma Astrophysics Department of Mathematics KU Leuven Celestijnenlaan 200B bus 2400 LeuvenB-3001 Belgium Plasma Dynamics Group Department of Automatic Control & Systems Engineering The University of Sheffield United Kingdom Indian Institute of Astrophysics Koramangala Bangalore India

Aims. Propagating (intensity) disturbances (PDs) are well reported in observations of coronal loops and polar plumes in addition to recent links with co-temporal spicule activity in the solar atmosphere. However, despite being reported in observations, they are yet to be studied in depth and understood from a modelling point of view. Methods. In this work, we present results from a 3D MHD numerical model featuring a stratified solar atmosphere which is perturbed by a p-mode wave driver at the photosphere, subsequently forming spicules described by the rebound shock model. Results. Features with striking characteristics to those of detected PDs appear consistent with the co-temporal transition region dynamics and spicular activity resulting from nonlinear wave steepening and shock formation. Furthermore, the PDs can be interpreted as slow magnetoacoustic pulses propagating along the magnetic field, rather than high speed plasma upflows, carrying sufficient energy flux to at least partially heat the lower coronal plasma. Using forward modelling, we demonstrate the similarities between the PDs in the simulations and those reported in observations from IRIS and SDO/AIA. Conclusions. Our results suggest that, in the presented model, the dynamical movement of the transition region is a result of wave dynamics and shock formation in the lower solar atmosphere, and that PDs are launched co-temporally with the rising of the transition region, regardless of the wave-generating physical mechanisms occurring in the underlying lower solar atmosphere. However, it is clear that signatures of PDs appear much clearer when a photospheric wave driver is included. Finally, we present the importance of PDs in the context of providing a source for powering the (fast) solar wind. © 2024, CC BY.

关键词： Magnetohydrodynamics

Pre-stabilised Predictive Functional control for Open-loop Unstable Dynamic systems

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IFAC-PapersOnLine 2021年第6期54卷 147-152页

作者： Muhammad Saleheen Aftab John Anthony Rossiter Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street S1 3JD UK

Predictive functional control (PFC) is the simplest model-based algorithm, equipped with the attributes of a fully fledged predictive controller but at the cost and complexity threshold of a standard PID regulator. It has proven benefits in controlling stable SISO dynamic systems, but similarly to its competitor PID, it loses efficacy when a challenging application is introduced. In this paper, we present a modified PFC approach, especially tailored for open-loop unstable processes, using pre-stabilisation to efficiently control the undesirable dynamics at hand. This is essentially a two-stage design scheme with implications for PFC tuning and constraint handling. The proposal, nevertheless, is straightforward and intuitive, and provides improved closed-loop control in the presence of external perturbations against the standard PFC, and significantly better performance overall compared to the common PID algorithm, as demonstrated in a numerical case-study.

关键词： PFC coincidence point feedback compensation pre-stabilisation

MI3SE: A Multi-User Index-Based Searchable Symmetric Encryption Scheme for Improving Security of Data in Connected Electronic Devices

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IEEE Transactions on Consumer Electronics 2024年

作者： Soleymani, Seyed Ahmad Goudarzi, Shidrokh Anisi, Mohammad Hossein Xiao, Pei Mihaylova, Lyudmila Wang, Wenwu University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of West London School of Computing and Engineering LondonW5 5RF United Kingdom University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom GuildfordGU2 7XH United Kingdom GuildfordGU2 7XH United Kingdom

In the Industrial Internet of Things (IIoT), outdoor electronic devices serve crucial roles across sectors, providing vital data for decision-making. However, their exposure to open outdoor environments makes them vulnerable to unauthorized access, physical theft, or compromise, endangering both the device and its data. Ensuring the security of outdoor devices and their data is thus critical. This study addresses data security in outdoor IIoT devices by supporting the encryption of all IIoT-related data in device memory. Accessing and retrieving this data requires operations on encrypted data. Hence, we introduce a Searchable Symmetric Encryption (SSE) scheme called MI3SE, which ensures each device's encryption key is unique and valid for a period based on the device's security sensitivity. Moreover, MI3SE meets key security requirements, including confidentiality, integrity, forward secrecy, and backward secrecy. It is specifically designed to mitigate physical compromise and query pattern analysis through a two-keyword query approach and withstand various attacks, as validated by rigorous security analysis. Comparative evaluations against benchmark schemes underscore the efficacy of MI3SE in terms of both security and performance. Moreover, comprehensive non-mathematical security analysis and simulation experiments affirm the enhanced accuracy and efficacy of MI3SE in securing sensitive data stored in outdoor IIoT devices. © 1975-2011 IEEE.

关键词： Data Security IIoT Outdoor Electronic Devices Searchable Symmetric Encryption (SSE)

Towards error categorisation in BCI: Single-trial EEG classification between different errors

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Towards error categorisation in BCI: Single-trial EEG classi...

作者： Wirth, C. Dockree, P.M. Harty, S. Lacey, E. Arvaneh, M. Automatic Control and Systems Engineering Department University of Sheffield Sheffield United Kingdom Institute of Neuroscience Trinity College Dublin Dublin Ireland

Objective. Error-related potentials (ErrP) are generated in the brain when humans perceive errors. These ErrP signals can be used to classify actions as erroneous or non-erroneous, using single-trial electroencephalography (EEG). A small number of studies have demonstrated the feasibility of using ErrP detection as feedback for reinforcement-learning-based brain-computer interfaces (BCI), confirming the possibility of developing more autonomous BCI. These systems could be made more efficient with specific information about the type of error that occurred. A few studies differentiated the ErrP of different errors from each other, based on direction or severity. However, errors cannot always be categorised in these ways. We aimed to investigate the feasibility of differentiating very similar error conditions from each other, in the absence of previously explored metrics. Approach. In this study, we used two data sets with 25 and 14 participants to investigate the differences between errors. The two error conditions in each task were similar in terms of severity, direction and visual processing. The only notable differences between them were the varying cognitive processes involved in perceiving the errors, and differing contexts in which the errors occurred. We used a linear classifier with a small feature set to differentiate the errors on a single-trial basis. Main results. For both data sets, we observed neurophysiological distinctions between the ErrPs related to each error type. We found further distinctions between age groups. Furthermore, we achieved statistically significant single-trial classification rates for most participants included in the classification phase, with mean overall accuracy of 65.2% and 65.6% for the two tasks. Significance. As a proof of concept our results showed that it is feasible, using single-trial EEG, to classify these similar error types against each other. This study paves the way for more detailed and efficient learning in BCI, a

关键词： Errors

Self-consistent equilibrium of a helical magnetic flux rope in a finite-pressure plasma

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arXiv 2024年

作者： Cheremnykh, Oleg K. Fedun, Viktor Lashkin, Volodymyr M. Space Research Institute Pr. Glushkova 40 k.4/1 Kyiv03187 Ukraine Plasma Dynamics Group Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Institute for Nuclear Research Pr. Nauki 47 Kyiv03028 Ukraine

We present an analytical model of the self-consistent equilibrium of a magnetic flux rope which is obtained in cylindrical geometry. The equilibrium azimuthal magnetic field and plasma pressure are determined in a self-consistent way through the current density which is derived as a solution of a nonlinear equation. By minimizing the energy functional, it was shown that the constrained equilibrium state is stable. The obtained results are also applicable to the cylindrical tokamak magnetic configurations. It is shown that the analytically predicted radial profiles of equilibrium quantities are in good agreement with the experimental data. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear equations

Reliability of Tumour Classification from Multi-Dimensional DCE-MRI Variables using Data Transformations

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arXiv 2023年

作者： Notley, S.V. Thacker, N.A. Horsley, L. Little, R.A. Watson, Y. Mullamitha, S. Jayson, G.C. Jackson, A. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Division of Informatics Imaging and Data Science University of Manchester Manchester United Kingdom Department of Medical Oncology The Christie NHS Foundation Trust Wilmslow Road ManchesterM20 4BX United Kingdom

Summary mean DCE-MRI variables show a clear dependency between signal and noise variance, which can be shown to reduce the effectiveness of difference assessments. Appropriate transformation of these variables supports statistically efficient and robust comparisons. The capabilities of DCE-MRI based descriptions of hepatic colorectal tumour classification was assessed, with regard to their potential for use as imaging biomarkers. Four DCE-MRI parameters were extracted from 102 selected tumour regions. A multi-dimensional statistical distance metric was assessed for the challenging task of comparing intra- and inter- subject tumour differences. Statistical errors were estimated using bootstrap resampling. The potential for tumour classification was assessed via Monte Carlo simulation. Transformation of the variables and fusion into a single chi-squared statistic shows that inter subject variation in hepatic tumours is measurable and significantly greater than intra-subject variation at the group level. However, reliability analysis shows that, at current noise levels, individual tumour assessment is not possible. Appropriate data transforms for DCE-MRI derived parameters produce an improvement in statistical sensitivity compared to conventional approaches. Reliability analysis shows, that even with data transformation, DCI-MRI variables do not currently facilitate good tumour discrimination and a doubling of SNR is needed to support non-trivial levels of *** Codes 92-08 © 2023, CC BY.

关键词： Tumors