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IEEE General Meeting Power& Energy Society

作者： Mario D. Baquedano-Aguilar Arturo Bretas Sean Meyn Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Distributed Systems Group Pacific Northwest National Laboratory Richland WA USA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

In low-inertia power grids, traditional load-shedding methods can be ineffective, causing instability and unnecessary grid islanding. This work introduces a distributed tri-level optimization model for proactive load shedding to prevent grid islanding considering generator coherency identification. Preventing islanding is crucial because it reduces the need for additional load shedding and transmission line tripping, thereby maintaining frequency stability. This is especially important in systems heavily dependent on non-dispatchable resources like wind and solar. The model employs generator coherency identification through spectral clustering using synchronizing coefficients. The results are then used as constraints in a dynamic graph based on power flow measurements to identify coherent areas for load shedding under highly stressed conditions. This approach offers practical applications and holds promise for real-world use.

关键词： Wind Islanding Power transmission lines Load shedding Power system stability Transmission line measurements Generators Stability analysis Load modeling Stress measurement

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Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

arXiv

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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IEEE International Symposium on Biomedical Imaging

作者： Eugenia Mylona Dimitrios I. Zaridis Charalampos N. Kalantzopoulos Vasileios C. Pezoulas Nikolaos S. Tachos George K. Matsopoulos Daniele Regge Nikolaos Papanikolaou Manolis Tsiknakis Kostas Marias Dimitrios I. Fotiadis Biomedical Research Institute FORTH Ioannina Greece Unit of Medical Technology Intelligent Information Systems University of Ioannina Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Greece Institute of Computer Science FORTH Heraklion Greece Department of Radiology Candiolo Cancer Institute FPO-IRCCS Strada Provinciale Candiolo Italy Computational Clinical Imaging Group Champalimaud Foundation Portugal Computational Biomedicine Laboratory Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability.

关键词： Deep learning Support vector machines Analytical models Biological system modeling Feature extraction Transformers Lesions Prostate cancer Radiomics Principal component analysis

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UAV-Based Optimization for Fruit Counting in Greenhouses

UAV-Based Optimization for Fruit Counting in Greenhouses

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： João Vítor R. Vasconcelos Thayron M. Hudson André G. dos Santos Martin Saska Alexandre S. Brandão Department of Electrical Engineering Núcleo de Especialização em Robótica Graduate Program on Computer Science Federal Univer-sity of Vicosa Vicosa MG Brazil Graduate Program on Computer Science Federal University of Vicosa Vicosa MG Brazil Department of Cybernetics Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a solution for counting fruit in agricultural greenhouses using Unmanned Aerial Vehicles (UAV s). Initially, a heuristic based on Simulated Annealing was used to optimize the UAV's trajectory, ensuring efficient coverage of the beds. Next, digital image processing (DIP) techniques were implemented to count the fruit, including depth segmentation, application of bounding boxes, color filtering, and element counting. The DIP accuracy was evaluated in multiple scenarios and the results indicate high reliability in fruit counting, with the potential to optimize agricultural operations and provide valuable information to producers. Possible future improvements could include further refinements in image processing to increase the accuracy of counting other fruits. Ultimately, this work contributes to the advancement of automation in agriculture by offering a viable and efficient solution for counting fruit in greenhouses using UAV s.

关键词： Image segmentation Accuracy Image color analysis Greenhouses Simulated annealing Autonomous aerial vehicles Trajectory

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Comparative Study of Alternative-Pole Flux Switching and Inter-Modular Biased-Flux Permanent Magnet Motors

Comparative Study of Alternative-Pole Flux Switching and Int...

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Electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Afrank Alireza Eikani Ehsan Farmahini Farahani Mojtaba Mirsalim Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K

This paper executes a comparative analysis between alternative-pole flux-switching and inter-modular biased-flux permanent magnet (PM) motors. Despite their close structural similarities, the former puts magnets not only between stator modules, but also close to the armature windings. However, the inter-modular PM (IMPM) motor only occupies spaces between stator modules. This type of occupation allows this structure to provide a safe operation point for PMs. Not considering the PM’s recoil-phenomenon, the torque density of the IMPM motor is not as significant as the alternative-pole flux-switching PM (AP-FSPM) motor. This is because of the four-operating mode of the AP-FSPM structure which enables it to increase the torque density by utilizing the PMs in every moment of the operation. But, when the demagnetization of the AP-FSPM’s PMs is taken into account, the torque density and torque per PM values of the AP-FSPM structure plummets. A complete full-load demagnetization analysis is done in order to get a better understanding of how the performance of the IMPM structure supersedes the AP-FSPM structure. 2D finite element analysis is used to extract the most important indexes of both topologies.

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Drivers, the Missed Attack Surface of Connected Vehicles 6

Drivers, the Missed Attack Surface of Connected Vehicles

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6th Smart Cities Symposium, SCS 2022

作者： Adnane, Asma Hardy, James Ahmad, Farhan Morris, Andrew Filtness, Ashleigh Department of Computer Science Loughborough University United Kingdom College of Science and Engineering University of Derby United Kingdom Systems Security Group Centre for Future Transport and Cities Coventry University United Kingdom Loughborough University Transport Safety Research Centre School of Design and Creative Arts Loughborough United Kingdom

ISBN: (纸本)9781839538544

The purpose of this paper is to analyse the human factors in cyberattacks targeting connected vehicles. To achieve this purpose, we utilised qualitative research methods and analysed multiple attack cases against connected vehicles. These cases demonstrated a number of different attack vectors and vulnerabilities. Our paper demonstrated that the most widely used Risk Assessment standards are flawed in this case;each assumes that certain considerations are either managed by other standards or that they are common knowledge and do not necessitate standardisation. This problem results in unresolved voids in the overall standards structure. The main gap identified was around Human Factors;none of the regulations or standards considered the impact of the owner/user/driver on the connected elements of vehicle safety and cybersecurity. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Risk assessment

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Digital Shadows for Robotic Assembly in the World Wide Lab

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Procedia CIRP 2023年 120卷 165-170页

作者： Mohamed Behery Philipp Brauner Aline Kluge-Wilkes Ralph Baier Alexander Mertens Robert H. Schmitt Martina Ziefle Gerhard Lakemeyer Knowledge-Based Systems Group Ahornstraße 55 52074 Aachen Germany Human-Computer Interaction Centre Campus-Boulevard 57 52074 Aachen Germany Laboratory of Machine Tools and Production Engineering Campus-Boulevard 30 52074 Aachen Germany Institute of Industrial Engineering and Ergonomics Eilfschornsteinstraße 18 52062 Aachen Germany

Due to the lacking standardisation of Digital Shadow (DS) of changeable assembly processes, these processes have not yet been fully integrated into the World Wide Lab (WWL). We propose generalising, standardising, and using Behaviour Trees (BTs) as standard DS for assembly in the WWL. A BT models processes in a modular, human-readable tree structure that has shown great success in many applications. BT monitor task execution giving real-time feedback to their operators. We show that BTs cross several developmental milestones of DSs and give future recommendations for further development and integration into the WWL.

关键词： Digital Shadows Behaviour Trees Robotic Assembly Industry 4.0 Industrial Internet of Things

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ProProv: A Language and Graphical Tool for Specifying Data Provenance Policies

ProProv: A Language and Graphical Tool for Specifying Data P...

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IEEE International Conference on Trust, Privacy and Security in Intelligent systems and Applications (TPS-ISA)

作者： Kevin Dennis Shamaria Engram Tyler Kaczmarek Jay Ligatti Department of Computer Science and Engineering University of South Florida Tampa USA Secure Resilient Systems and Technology Group MIT Lincoln Laboratory Lexington USA

The Function-as-a-Service cloud computing paradigm has made large-scale application development convenient and efficient as developers no longer need to deploy or manage the necessary infrastructure themselves. However, as a consequence of this abstraction, developers lose insight into how their code is executed and data is processed. Cloud providers currently offer little to no assurance of the integrity of customer data. One approach to robust data integrity verification is the analysis of data provenance—logs that describe the causal history of data, applications, users, and non-person entities. This paper introduces ProProv, a new domain-specific language and graphical user interface for specifying policies over provenance metadata to automate provenance *** evaluate the convenience and usability of the new ProProv interface, 61 individuals were recruited to construct provenance policies using both ProProv and the popular, general-purpose policy specification language Rego—used as a baseline for comparison. We found that, compared to Rego, the ProProv interface greatly increased the number of policies successfully constructed, improved the time taken to construct those policies, and reduced the failed-attempt rate. Participants successfully constructed 73% of the requested policies using ProProv, compared to 41% using Rego. To further evaluate the usability of the tools, participants were given a 10-question questionnaire measured using the System Usability Scale (SUS). The median SUS score for the graphical ProProv interface was above average and fell into the “excellent” category, compared to below average and “OK” for Rego. These results highlight the impacts that graphical domain-specific tools can have on the accuracy and speed of policy construction.

关键词： Cloud computing Data integrity Sociology Metadata Particle measurements Real-time systems Security

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Speech Separation with Pretrained Frontend to Minimize Domain Mismatch

arXiv

引用

arXiv 2024年

作者： Wang, Wupeng Pan, Zexu Li, Xinke Wang, Shuai Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore Alibaba Group Singapore189554 Singapore Industrial Systems Engineering and Management National University of Singapore Singapore119077 Singapore Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Guangdong518172 China

Speech separation seeks to separate individual speech signals from a speech mixture. Typically, most separation models are trained on synthetic data due to the unavailability of target reference in real-world cocktail party scenarios. As a result, there exists a domain gap between real and synthetic data when deploying speech separation models in real-world applications. In this paper, we propose a self-supervised domaininvariant pretrained (DIP) frontend that is exposed to mixture data without the need for target reference speech. The DIP frontend utilizes a Siamese network with two innovative pretext tasks, mixture predictive coding (MPC) and mixture invariant coding (MIC), to capture shared contextual cues between real and synthetic unlabeled mixtures. Subsequently, we freeze the DIP frontend as a feature extractor when training the downstream speech separation models on synthetic data. By pretraining the DIP frontend with the contextual cues, we expect that the speech separation skills learned from synthetic data can be effectively transferred to real data. To benefit from the DIP frontend, we introduce a novel separation pipeline to align the feature resolution of the separation models. We evaluate the speech separation quality on standard benchmarks and realworld datasets. The results confirm the superiority of our DIP frontend over existing speech separation models. This study underscores the potential of large-scale pretraining to enhance the quality and intelligibility of speech separation in real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Hybrid Ensemble Deep Learning Approach for Emotion Classification

A Hybrid Ensemble Deep Learning Approach for Emotion Classif...

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IEEE International Conference on Big Data

作者： Christos Karras Aristeidis Karras Dimitrios Tsolis Markos Avlonitis Spyros Sioutas Computer Engineering and Informatics Department Decentralized Systems Computing (DSC) Group University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781665480468

Speech processing, the field of analysing input speech signals and methods of processing them has emerged in the recent days. Additionally, the development of a speech processing system involves several components in the design phase with probabilistic approximations for enhanced audio sampling and de-noising. In this work, we focus into use of Gaussian random variables while modelling and filtering noise that gets added after being passed through an additive noise channel in a communication system, and the applications of Hidden Markov models. Moreover, we apply deep learning methods for emotion classification via a robust and accurate ensemble learning scheme that is applied to a joint deep network which incorporates audiovisual inputs and generates the emotion prediction effectively reaching satisfactory accuracy.

关键词： Deep learning Additive noise Filtering Hidden Markov models Speech recognition Markov processes Big Data

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