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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Wei, Hua-Liang Dept. Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom Energy Institute The University of Sheffield Sheffield United Kingdom Centre of Machine Intelligence The University of Sheffield Sheffield United Kingdom Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9798350322972

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting. © 2023 IEEE.

关键词： Electric power utilization

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System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Hua-Liang Wei Dept. Automatic Control and Systems Engineering Energy Institute Centre of Machine Intelligence Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield UK

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Joint Sensor Scheduling and Target Tracking with Efficient Bayesian Optimisation

Joint Sensor Scheduling and Target Tracking with Efficient B...

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Sensor signal processing for Defence (SSPD)

作者： Xingchi Liu Chenyi Lyu Seyed Ahmad Soleymani Wenwu Wang Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey Guildford UK

The received signal strength measurement has been widely used in search and tracking applications and its benefit is linked with the distance between the transmitter and receiver. This paper proposes an online Bayesian optimisation-based approach that relies on signal strength measurements to schedule multiple sensors for searching and tracking a moving target, without any prior knowledge of the target’s state or motion model. A unique contribution lies in incorporating the Gaussian processes factorisation method into the Bayesian optimisation framework, which enhances the effectiveness of the proposed approach. Numerical results obtained from different sizes of measurements demonstrate that the proposed approach can efficiently schedule two unmanned aerial vehicles. Particularly, it achieves at most 21% lower computational time for deciding measurement locations and 79% lower time for updating the surrogate model as compared to the benchmark approach.

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Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Al...

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Sensor signal processing for Defence (SSPD)

作者： Seyed Ahmad Soleymani Shidrokh Goudarzi Xingchi Liu Lyudmila Mihaylova Wenwu Wang Pei Xiao Centre for Vision Speech and Signal Processing (CVSSP) The University of Surrey UK School of Computing and Engineering University of West London UK Department of Automatic Control and Systems Engineering The University of Sheffield UK Institute for Communication Systems (5GIC) The University of Surrey UK

This paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Minimal order disturbance estimator design for aircraft load alleviation control

Minimal order disturbance estimator design for aircraft load...

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IEEE Conference on control Technology and Applications

作者： Daniel Ossmann Charles Poussot-Vassal Institute of System Dynamics and Control German Aerospace Center Germany Systems and Signal Processing Department ONERA Centre de Toulouse Toulouse France

In this paper a novel approach to design linear disturbance estimators is presented. The model based design approach relies on advanced nullspace computation techniques and explicit facilitates an estimators design of low state dimension. This reduces the dependencies on the underlying design models and thereby increases robustness against modeling errors. To further support this low order characteristics an advanced model approximation technique for preprocessing high order design models is presented. The proposed approaches are applied to a generic model of a business jet aircraft. The derived disturbance estimator is used in a control algorithm to reduce the wing bending moments on the aircraft in case of wind gusts. Both, the disturbance estimator together and the load alleviation controller are verified in a non-linear closed loop aircraft simulation model.

关键词： presented proposed approaches applied

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

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IOP Conference Series: Materials Science and Engineering 2018年第1期342卷

作者： Tiagrajah V Janahiraman Nooraziah Ahmad Farah Hani Nordin Centre for Signal Processing and Control Systems Dept. of Electronics and Communication Engineering College of Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN 43000 Kajang Selangor Malaysia.

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

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Human pluripotent stem cells as tools for high-throughput and high-content screening in drug discovery

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International Journal of High Throughput Screening 2015年 5卷 1-13页

作者： Thomas F Allison Nicola S Powles-Glover Veronica Biga Peter W Andrews Ivana Barbaric 1 Centre for Stem Cell Biology Department of Biomedical Science University of Sheffield Sheffield 2 AstraZeneca Alderley Edge 3 Centre for Signal Processing and Complex Systems Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

:A significant bottleneck in drug discovery is the lack of suitable models for sensitive, reliable, and rapid assessment of lead molecules in preclinical stages of drug discovery. Human pluripotent stem cells (hPSCs) derived either from early human blastocysts (human embryonic stem cells) or by reprogramming somatic cells to a pluripotent state (human-induced pluripotent stem cells) can be propagated extensively in vitro while retaining the ability to differentiate into any specialized cell type within the body. In this review, we discuss how these unique features of hPSCs could offer a way of producing relevant in vitro models amenable to high-throughput testing for drug discovery. We summarize recent progress in inducing differentiation of hPSCs to specific cell types, and describe the ongoing efforts in applying hPSCs and their differentiated derivatives in disease modeling, drug discovery, and developmental toxicology. Moreover, we review the applications of high-content imaging assays in detecting the changes in the phenotype of hPSCs and their differentiated progeny. Finally, we highlight challenges that need to be overcome in order for the application of hPSC technology to fully benefit drug discovery.

关键词： human pluripotent stem cells drug discovery high-content assays

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A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization

A comparison on optimization of surface roughness in machini...

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IEEE Conference on System, Process and control, ICSPC 2015

作者： Ahmad, Nooraziah Janahiraman, Tiagrajah V. Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan Bachok Campus Locked Bag 1 Kelantan Malaysia Centre of Signal Processing and Control Systems Department of Electronics and Communication Engineering College of Engineering Universiti Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor43000 Malaysia

ISBN: (纸本)9781467376556

AISI 1045 steel is one of the most widely used steel in the manufacturing industry. In order to have the best quality of turned AISI 1045 steel product, surface roughness is being considered as output parameter. The two purposes of this research are to model the surface roughness using response surface methodology and to compare the different types of optimization approaches in order to identify the optimum surface roughness with particular combination of cutting parameters in turning operation. The result obtained from this study showed that the values from RSMs' prediction are 99.3% similar to the experimental values. While, particle swarm optimization give the lowest surface roughness when compared to Taguchi method and genetic algorithm and it can optimize faster than genetic algorithm. © 2015 IEEE.

关键词： Surface roughness

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