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IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Roman Voliansky Borys Kuznetsov Ihor Bovdui Yuliya Averyanova Ivan Ostroumov Olha Sushchenko Maksym Zaliskyi Oleksandr Solomentsev Kostiantyn Cherednichenko Viktoriia Ivannikova Olena Sokolova Yurii Bezkorovainyi Tatyana Nikitina Oleksii Holubnychyi Department of Electromechanical Systems Automation and Electric Drives National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Department of magnetism of technical objects Anatolii Pidhornyi Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine Kharkiv Ukraine Air Navigation Systems Department National Aviation University Kyiv Ukraine Aerospace Control Systems Department National Aviation University Kyiv Ukraine Department of telecommunication and radioelectronic systems National Aviation University Kyiv Ukraine Air Transportation Management Department National Aviation University Kyiv Ukraine Business School Dublin City University Dublin Ireland Electromechanical and computer systems department Educational scientific professional pedagogical Institute Ukrainian Engineering Pedagogical Academy Kharkiv Ukraine

ISBN: (数字)9798350368178

ISBN: (纸本)9798350368185

The paper deals with the design of a variable-structure chaotic oscillator for path-planning applications of autonomous mobile vehicles. This oscillator is designed as a result of analysis of the generalized dynamical system. We use interval methods to model such a system and take into consideration its nonlinearities. Our approach offers to replace exactly defined system nonlinear functions with domains where these functions are localized. We define lower and upper boundaries for these domains by using piecewise linear functions. The usage of these functions allows us to rewrite initial nonlinear system equations into linear-like form with linear terms that are defined in some intervals and thus simplify the system model. Piecewise linearity of this model gives us the possibility to analyze the system features by using various control methods. Such a model can be used in both real and complex domains. The consideration of system dynamics in a complex domain allows us to increase the order of the considered system and define a set of operation modes to design a variable-structure system. We show the benefits of our approach by studying the Duffing pendulum and designing its base variable-structure system.

关键词： Performance evaluation Trajectory planning System dynamics Linearity Switches Mathematical models Trajectory Nonlinear systems Oscillators Nanotechnology

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

引用

Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Recursive Time-Sensor Domain Data Decomposition: A Novel Method for Time-Varying Process Monitoring

SSRN

引用

SSRN 2024年

作者： Gui, Yufei Lang, Zi-Qiang Liu, Zepeng Laalej, Hatim Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Advanced Manufacturing Research Centre University of Sheffield SheffieldS10 2TN United Kingdom

Process monitoring aims to timely detect faults or abnormal conditions, ensuring safety and achieving satisfactory product quality. Time-varying processes are common in various industries, including chemical, semiconductor, and advanced manufacturing. A key challenge in time-varying process monitoring is how to accommodate deterministic but unknown time-varying trends in process responses. Existing multivariate statistical process monitoring (MSPM) approaches designed for time-varying processes face challenges including intensive computation and worse adaption to unknown process changes. In this study, a new process monitoring approach is proposed based on a novel idea referred to as recursive time-sensor decomposition (RTSD). The multi-sensor data are compressed into dominant time and sensor domain components. In the time domain, the feature vectors are extracted to represent process states. In the sensor domain, the process information distributed across different sensors is used to accommodate the normal time-varying trend and determine the control limit. The proposed approach is validated first by analysing a simulated time-varying process and then by an application of monitoring tool wear states in a real milling experiment. The results demonstrate that the proposed approach is highly effective, has higher fault detection rates and lower false alarm rates when compared to existing methods, and is applicable in a real time-varying industrial process. © 2024, The Authors. All rights reserved.

关键词： Process monitoring

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Power control of Cascaded Doubly Fed Induction Generator Integrated Into Wind Power Generation System Using Sliding Mode control

Power Control of Cascaded Doubly Fed Induction Generator Int...

引用

Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET), International Conference on

作者： Ali Mazari Kouider Laroussi Amar Maafa Hamou Ait Abbas Abdelah Kouzou Billel Naceri Laboratory of The Applied and Automation and Industrial Diagnostic University Of Djelfa Djelfa Algeria Department of Electrical Engineering University Of Bouira Bouira Algeria Laboratory of Electrical and Automatic Systems Engineering (LGSEA) University of Bouira Bouira Algeria Laboratory of Identification Commande Control and Communication (LI3CUB) University of Biskra Biskra Algeria

ISBN: (数字)9798350346336

ISBN: (纸本)9798350346343

This paper aims to study the performance of combining two doubly fed induction generators (DFIG) without changing their design, therefore the benefit of eliminating the brushes. The sliding mode control (SMC) is used for controlling the active and reactive powers for the cascaded doubly fed induction generator (CDFIG) integrated into wind power generation system (WPGS). Additionally, the system incorporates a maximum power point tracking strategy (MPPT) to optimize energy generation and a pitch control mechanism for the wind turbine's blades to adjust to changing wind conditions.

关键词： Maximum power point trackers Wind energy generation Brushes Uncertainty Wind speed Power control Doubly fed induction generators

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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Robust learning via ensemble density propagation in deep neural networks

arXiv

引用

arXiv 2021年

作者： Carannante, Giuseppina Dera, Dimah Rasool, Ghulam Bouaynaya, Nidhal C. Mihaylova, Lyudmila Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States University of Sheffield Department of Automatic Control and Systems Engineering United Kingdom

Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for robust learning that builds upon Bayesian estimation and Variational Inference. We formulate the problem of density propagation through layers of a DNN and solve it using an Ensemble Density Propagation (EnDP) scheme. The EnDP approach allows us to propagate moments of the variational probability distribution across the layers of a Bayesian DNN, enabling the estimation of the mean and covariance of the predictive distribution at the output of the model. Our experiments using MNIST and CIFAR-10 datasets show a significant improvement in the robustness of the trained models to random noise and adversarial attacks. © 2021, CC BY.

关键词： Deep neural networks

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

arXiv

引用

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

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ROSIE: A ROS Adapter for a Modular Digital Twinning Framework

ROSIE: A ROS Adapter for a Modular Digital Twinning Framewor...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Gianmarco Pisanelli Mariusz Tymczuk James A Douthwaite Jonathan M. Aitken James Law Advanced Manufacturing Research Centre The University of Sheffield Sheffield UK Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK Department of Computer Science Advanced Manufacturing Research Centre The University of Sheffield Sheffield UK

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

As robotic systems become more interactive and complex, there is a need to standardise interfaces and simplify development processes. This is particularly pertinent in the field of manufacturing, where human-robot collaboration is on the increase, but where standards and proprietary software are key barriers to deployment and *** this article we present the ROSIE Adapter, a general-purpose, modular adapter developed in ROS designed to support the creation and connection of industry-ready digital twins. Together with our previous work on the modular CSI digital-twin framework, we demonstrate how the ROSIE Adapter creates a versatile "plug-and-play" interface that simplifies the development of new robotic processes, and improves accessibility to novice users. Furthermore, the adaptor supports integration of intuitive interface devices, such as speech and augmented reality interfaces, which enable more natural collaboration. We describe the adaptor and its use in two real-world applications, demonstrate the ease of use via a three-day hackathon event, and provide results showing the faithfulness of the arising digital twins to their connected physical systems.

关键词： Adaptation models Collaboration Process control Data models Digital twins Safety Speech processing

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Open access resources to support the first course in feedback, dynamics and control

引用

IFAC-PapersOnLine 2022年第17期55卷 1-6页

作者： A. Serbezov K. Zakova A. Visioli J.A. Rossiter B. Douglas J. Hedengren Department of Chemical Engineering Rose-Hulman Institute of Technology Terre Haute IN USA Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Slovakia Department of Mechanical and Industrial Engineering University of Brescia Italy Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Engineering Media LLC Kirkland WA 98034 USA Department of Chemical Engineering Brigham Young University Provo UT 84602 USA

The Technical Committees for control Education of IFAC and IEEE have started a collaborative initiative to collect, curate, and disseminate high quality freely available resources that support instruction of the first control course for undergraduate students in engineering and applied sciences programs. An initial survey with limited distribution provided 64 resources that cover all topics in a typical introductory control curriculum. This paper gives an initial glimpse of the resources collected thus far. It also suggests possible ways for categorization and dissemination.

关键词： engineering education automatic control curriculum E-learning in control engineering Open access resources in automatic control automatic control virtual learning environments

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Peak Value-at-Risk Estimation for Stochastic Differential Equations Using Occupation Measures

Peak Value-at-Risk Estimation for Stochastic Differential Eq...

引用

IEEE Conference on Decision and control

作者： Jared Miller Matteo Tacchi Mario Sznaier Ashkan Jasour Automatic Control Laboratory (IfA) ETH Zürich Zürich Switzerland ECE Department Robust Systems Lab Northeastern University Boston MA Automatic Control Laboratory EPFL Lausanne Switzerland GIPSA-lab Univ. Grenoble Alpes CNRS Grenoble INP (Institute of Engineering Univ. Grenoble Alpes) Grenoble France Team 347T-Robotic Aerial Mobility Jet Propulsion Lab Pasadena CA

This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Value-at-Risk (VaR) of a state function along SDE state distributions. The VaR problem is upper-bounded by an infinite-dimensional Second-Order Cone Program in occupation measures through the use of one-sided Cantelli or Vysochanskii-Petunin inequalities. These upper bounds on the true quantile statistics may be approximated from above by a sequence of Semidefinite Programs in increasing size using the moment-Sum-of-Squares hierarchy when all data is polynomial. Effectiveness of this approach is demonstrated on example stochastic polynomial dynamical systems.

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