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Optimal Design and Operation of Long-Distance Deep-Water HVAC Transmission for Offshore WECS Integration With FPSO Unit

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IEEE Transactions on Industry Applications 2025年

作者： Schmitz, Lenon Viglus, Francisco Jose de Andrade, Jessika Melo Asta, Matheus Schramm Dall Evald, Paulo Jefferson Dias de Oliveira Heldwein, Marcelo Lobo Lazzarin, Telles Brunelli Federal University of Santa Catarina Department of Computing Araranguá Brazil Federal Technological University of Paraná Academic Department of Electronics Campo Mourão Brazil Federal University of Santa Catarina Department of Mobility Eng. Joinville Brazil Federal University of Santa Catarina Department of Electrical and Electronics Engineering Florianopólis Brazil Federal University of Pelotas Int. Sys. and Control Group Pelotas Brazil Technical University of Munich High-Power Converter Systems Munich Germany

This paper evaluates the technical feasibility of integrating an offshore wind energy conversion system (WECS) into a floating production storage and offloading (FPSO) unit. The WECS is situated closer to the Brazilian mainland, requiring a 150 km long subsea umbilical cable for the integration with the FPSO unit. High voltage alternating current (HVAC) transmission is mandatory due to the substantial water depths, introducing challenges such as power losses and significant reactive capacitive current generated by the cable. This paper proposes a comprehensive approach to dealing with these challenges. Firstly, the umbilical cable section is sized based on ampacity and maximum current under short-circuit conditions. A power flow optimization is thus employed to determine the optimal transmission voltage that minimizes power losses. The optimization results define the reactive power that the back-to-back converter of the WECS must absorb based on the active power. This relationship is represented as a lookup table control, which ensures efficient operation by adjusting the reactive power reference in the WECS power control unit. Using this approach, the WECS back-to-back converter compensates for a portion of the reactive power generated by the umbilical, reducing the need for additional infrastructure such as shunt reactors or reactive compensation substations. Each component of the system is modeled, and a complete simulation is conducted using MATLAB/Simulink to validate the proposal. The results demonstrate the effectiveness of this approach in enhancing system efficiency while significantly reducing carbon dioxide emissions during oil and gas exploration through the integration of WECS with FPSO units. © 1972-2012 IEEE.

关键词： Floating production storage and offloading

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Performance Analysis of Multi-RIS-Aided LoRa Systems With Outdated and Imperfect CSI

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IEEE Transactions on Communications 2024年

作者： Liang, Zhaokun Cai, Guofa He, Jiguang Kaddoum, Georges Huang, Chongwen Guangdong University of Technology School of Information Engineering China Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control Chongqing University of Posts and Telecommunications Chongqing400065 China Great Bay University School of Computing and Information Technology China University of Oulu Centre for Wireless Communications Oulu90014 Finland University of Quebec Electrical Engineering Department École de Technologie Supérieure MontrealQCH2L 2C4 Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

Although LoRa has emerged as the leading technology among the rapidly developing low-power wide-area networks, the performance of the LoRa system severely deteriorates over fading channels. To address this problem, in this paper, we introduce multiple reconfigurable intelligent surfaces (multi-RISs) into the LoRa system to improve its performance. Our specific focus is on the impact of outdated channel state information (CSI), the imperfection of estimated CSI, and the design of RIS discrete phase shifts on the performance. To this end, we first use the moment-matching method to obtain the end-to-end (E2E) channel coefficient of the joint outdated channels and erroneous channels over Nakagami-m fading. Moreover, the closed-form bit error rates (BERs) of the proposed system with non-coherent and coherent detections are derived. The results reveal that, in the high signal-to-noise ratio (SNR) regime, coherent detection encounters the error floor and performs worse than non-coherent detection. Furthermore, we also analyze delay outage rate, throughput, and achievable diversity order of the proposed system. The results show that, despite the presence of outdated CSI and channel estimation errors, the proposed system is still superior to RIS-aided LoRa systems adopting blind transmission and RIS-free ones. Finally, we also thoroughly investigate the effects of various important factors such as the correlation factor, channel estimation errors, the number of RIS reflecting elements, and the number of quantization bits for RIS discrete phase shifts on the performance. © 1972-2012 IEEE.

关键词： Fading channels

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Multi-Robot System for Autonomous Cooperative Counter-UAS Missions: Design, Integration, and Field Testing

Multi-Robot System for Autonomous Cooperative Counter-UAS Mi...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Antonella Barisic Marlan Ball Noah Jackson Riley McCarthy Nasib Naimi Luca Strässle Jonathan Becker Maurice Brunner Julius Fricke Lovro Markovic Isaac Seslar David Novick Jonathan Salton Roland Siegwart Stjepan Bogdan Rafael Fierro Laboratory for Robotics and Intelligent Control Systems (LAR-ICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA ETH Zurich Autonomous Systems Lab (ASL) Zurich Switzerland Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (纸本)9781665456814

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS.

关键词： Scalability Robot vision systems Autonomous aerial vehicles Cameras Software Safety Security

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Correction: Towards automated remote sizing and hot steel manufacturing with image registration and fusion

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Journal of Intelligent Manufacturing 2023年第1期36卷 439-439页

作者： Lin, Yueda Wang, Peng Wang, Zichen Ali, Sardar Mihaylova, Lyudmila Automatic Control and Systems Engineering The University of Sheffield Sheffield UK Department of Computing and Mathematics Manchester Metropolitan University Manchester UK

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Simulations and Experiments of Time-Varying Takagi Factorization via Zhang Neural Network 5

Simulations and Experiments of Time-Varying Takagi Factoriza...

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5th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2021

作者： Tang, Zanyu Zeng, Tianyu Li, Zhenyu Chen, Jianrong Zhang, Yunong Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China College of Information Science and Engineering Jishou University Jishou416000 China School of Public Health Administration Youjiang Medical University for Nationalities Baise533000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

The problem of Takagi factorization (TF) for complex time-varying symmetric matrix is proposed in this paper. To solve this difficult and challenging problem, a continuous-time TF (CTTF) model of derivative equation system form is proposed via Zhang neural network (ZNN) method. For the sake of digital hardware implementation, we also build up the corresponding discrete-time (DT) ZNN solution models with different precisions of the derivative equation system discretization. Specifically, an eight-instant DTTF (EIDTTF) model are proposed. We also comparatively use two other multi-instant (e.g., four-instant and six-instant, i.e., FIDTTF and SIDTTF, respectively) models by discretizing the CTTF model. The effective solution step-size intervals of the proposed DTTF models are further discussed lastly. The results of simulations and experiments substantiate the correctness, effectiveness and superiority of the proposed CTTF and DTTF models. © 2021 IEEE.

关键词： Complex networks

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LQG Differential Stackelberg Game under Nested Observation Information Pattern

arXiv

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arXiv 2022年

作者： Li, Zhipeng Marelli, Damián Fu, Minyue Zhang, Huanshui College of Engineering Qufu Normal University Rizhao276826 China School of Guangdong University of Technology Guangzhou510006 China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Electrical Engineering and Computing University of Newcastle NSW2308 Australia School of Control Science and Engineering Shandong University Jinan250061 China

We investigate the linear quadratic Gaussian Stackelberg game under a class of nested observation information pattern. Two decision makers implement control strategies relying on different information sets: The follower uses its observation data to design its strategy, whereas the leader implements its strategy using global observation data. We show that the solution requires solving a new type of forward-backward stochastic differential equations whose drift terms contain two types of conditional expectation terms associated to the adjoint variables. We then propose a method to find the functional relations between each adjoint pair, i.e., each pair formed by an adjoint variable and the conditional expectation of its associated state. The proposed method follows a layered pattern. More precisely, in the inner layer, we seek the functional relation for the adjoint pair under the σ-sub-algebra generated by follower's observation information;and in the outer layer, we look for the functional relation for the adjoint pair under the σ-sub-algebra generated by leader's observation information. Our result shows that the optimal open-loop solution admits an explicit feedback type representation. More precisely, the feedback coefficient matrices satisfy tuples of coupled forward-backward differential Riccati equations, and feedback variables are computed by Kalman-Bucy filtering. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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Modelling, control Methods and Applications of Chaotic Oscillators

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Discrete Dynamics in Nature and Society 2023年第1期2023卷

作者： Vaidyanathan, Sundarapandian Sambas, Aceng Lien, Chang-Hua Mamat, Mustafa Centre for Control Systems Vel Tech University 400 Feet Outer Ring Road Chennai 600062 India Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Gong Badak 21300 Terengganu Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Jawa Barat 46196 Indonesia Department of Marine Engineering National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan

Some typical oscillations relate to nonlinear pendulums, population models, business cycles, and jerk systems [5]. Since the discovery of chaotic oscillations in a nonlinear weather model by Lorenz in 1963, chaotic oscillators, their modelling, and control have received much attention in the literature [1]. [...]the MATLAB simulation results of the buck converter with memristive load are shown to be in good agreement with the analog results obtained with PSIM. [...]the authors describe simulation results for an example of a remotely operated vehicle (ROV) with three degrees of freedom to demonstrate the efficacy of the suggested control approach in their research article. A unique feature of the proposed model is that the designed Chen chaotic oscillation system is integrated in a single chip with the advantages of smaller chip area, lower supply voltage, and power consumption.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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