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4th International Conference on control and computer Vision, ICCCV 2021

作者： Qiu, Hao Su, Pan Jiang, Shanshan Yue, Xingyu Zhao, Yitian Liu, Jiang Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China School of Control and Computer Engineering North China Electric Power University China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781450390477

Modern deep neural networks are able to beat human annotators in several medical image processing tasks. In practical manual annotation for medical image segmentation tasks, the labels of annotators often show inter-observer variability (IOV) which is mainly caused by annotators' different understandings of expertise. In order to build a trustworthy segmentation system, robust models should consider how to capture uncertainty in samples and labels. Different from the conventional way of handling IOV with label fusion such as majority voting, a fuzzy integral based ensemble framework of multiple deep learning models for optic disc segmentation is proposed. Each component segmentation model is trained with respect to an annotator. Then, a powerful nonlinear aggregation function, the Choquet integral, is employed in form of a neural network to integrate the segmentation results of multiple annotators. The proposed method is validated on the public RIM-ONE dataset consisting of 169 fundus images and each image is annotated by 5 experts. Compared with conventional segmentation ensemble methods, the proposed methods achieves a higher Dice score (98.69%). © 2021 ACM.

关键词： Deep neural networks

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Research Progress of Parallel control and Management

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 355-367页

作者： Gang Xiong Xisong Dong Hao Lu Dayong Shen State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Systems Engineering National University of Defense TechnologyChangsha 410073China

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex *** paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application *** future research,application directions,and suggestions are also discussed.

关键词： ACP methodology artificial systems computational experiments parallel control parallel management parallel systems

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

Model predictive control for tracking using artificial refer...

引用

IEEE Conference on Decision and control

作者： P. Krupa J. Köhler A. Ferramosca I. Alvarado M.N. Zeilinger T. Alamo D. Limon Computer Science Group Gran Sasso Science Institute (GSSI) L’Aquila Italy ETH Zürich Institute for Dynamic Systems and Control Zürich Switzerland Department of Management Information and Production Engineering University of Bergamo Via Marconi 5 Dalmine (BG) Italy Department of Systems Engineering and Automation Universidad de Sevilla Seville Spain

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.

关键词： Surveys Asymptotic stability Costs Tutorials Optimization Predictive control

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Optical Data Center Implementation within Data Center Networks Context Based on Mininet 2

Optical Data Center Implementation within Data Center Networ...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmahdi, Esraa Ali Ali, Adnan Hussein Ibrahim, Raed Khalid Al-Askery, Ali Alhamdane, Hayder Jasim Mahmood, Amal Ibrahim Electrical Engineering College Middle Technical University Student at Dept. of Computer Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Electrical Engineering College Middle Technical University Department of Control and Automation Technical Engineering Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Electrical Engineering College Middle Technical University Dept. of Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350373363

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time. © 2023 IEEE.

关键词： Optical switches

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A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model 8

A Simplified Variant for Graph Convolutional Network Based K...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Wang, Yashen Li, Qiang Zhang, Yi Zhang, Xuecheng Ju, Zhuoya Sun, Jian Beijing China Scientific and Technological Innovation Center of Ari Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Cetc Academy of Electronics and Information Technology Group Co. Ltd. Beijing China Khoury College of Computer Science Northeastern University United States Scientific and Technological Innovation Center of Ari Beijing China Qi An Xin Technology Group Inc. Beijing China

ISBN: (纸本)9798350331035

Knowledge graphs (KGs) serve as useful resources for various applications including machine learning, data mining, and artificial intelligence. Knowledge Graph Completion (KGC) aims at reasoning over known (observed) facts and infer the missing (unobserved) relationships, to improve KG's coverage. Many recent works have laid stress on leveraging Graph Convolutional Networks (GCNs) for KGC task, which obtain good representation learning ability for graph-structured data, and have achieved satisfactory results. However, GCN-based researches continue to improve these lines of models by introducing an increasingly complex feature space. Especially, the hyper-parameter space of conventional GCN-based KGC model is too huge, resulting in low computational efficiency and excessive storage pressure. To overcome this problem, this paper investigates a simplified and generic variant for GCN-based KGC model, and shows that simple changes in the architecture of the underlying architecture (with a Subtraction Operator and an Addition Operator), can compte with state-of-the-art GCN-based models. Therefore, this paper proposes a easy-to-accomplish plug-in unit, which could be loaded into any current GCN-based KGC architectures, and help them maximize the maintenance of native model's original performance while significantly reducing parameter scale. © 2023 IEEE.

关键词： Graph Convolutional Network Knowledge Graph Knowledge Graph Completion Representation Learning

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AWGUNET: ATTENTION-AIDED WAVELET GUIDED U-NET FOR NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

arXiv

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arXiv 2024年

作者： Roy, Ayush Pramanik, Payel Kaplun, Dmitrii Antonov, Sergei Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://***/AyushRoy2001/AWGUNET © 2024, CC BY.

关键词： Image segmentation

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Awgunet: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images

Awgunet: Attention-Aided Wavelet Guided U-Net for Nuclei Seg...

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IEEE International Symposium on Biomedical Imaging

作者： Ayush Roy Payel Pramanik Dmitrii Kaplun Sergei Antonov Ram Sarkar Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

关键词： Image segmentation Convolution Histopathology Microprocessors Noise computer architecture Feature extraction

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Contrastive Conditional Adversarial Autoencoder with Class-specific Forces for Imbalanced Open-set Fault Detection

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IEEE Transactions on automation Science and Engineering 2025年 22卷 15757-15767页

作者： Zuo, Zhonglin Meng, Tao Li, Zheng Zhang, Hao Liu, Tong Chen, Zhansheng Pang, Zhibo Zhejiang University College of Control Science and Engineering Hangzhou310027 China Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Hainan Institute of Zhejiang University Sanya572025 China Chongqing University School of Automation Chongqing400044 China University of Sheffield Department of Computer Science SheffieldS1 4DP United Kingdom Shanghai Aerospace Space Technology Co. Ltd Shanghai201109 China ABB Corporate Research Sweden Department of Automation Technology Vasteras Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm Sweden

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection. © 2004-2012 IEEE.

关键词： Contrastive Learning

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Hydrone: Modeling of a Hybrid Unmanned Aerial Underwater Vehicle Considering Trans-Media Dynamics

SSRN

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SSRN 2024年

作者： de Oliveira Evald, Paulo Jefferson Dias Brião, Stephanie Loi Aoki, Vivian Misaki da Silva, César Bastos dos Santos Cardoso, Dayana Pinheiro, Pedro Miranda de Oliveira, Felipe Gomes André Barbosa Cunha, Mauro Jorge Drews, Paulo Lilles Intelligent Systems and Control Group Federal University of Pelotas RS Pelotas Brazil Intelligent Robotics and Automation Group Federal University of Rio Grande RS Rio Grande Brazil Department of Computer Engineering and Industrial Automation State University of Campinas SP Campinas Brazil Federal Institute of Education Science and Technology in Rio Grande do Sul RS Pelotas Brazil Institute of Exact Sciences and Technology Federal University of Amazonas Itacoatiara AM Amazonas Brazil Brazil

Research in mobile robotics is growing into applications for difficult-to-access environments, such as in rescue and transport missions. Furthermore, autonomous vehicles can perform data collection in complex approach areas. Therefore, the study of hybrid unmanned vehicles is relevant for several areas, since these vehicles can act in different domains. The developed vehicle, called Hydrone, is designed in a quadrotor-type structure and equipped with a set of aerial and underwater propellers, which are fixed and independent between them. In this sense, this work presents the mathematical modeling of an unmanned hybrid vehicle, which can navigate underwater and aerial environments. Furthermore, the modeling considers the ground effect due to the influence on the vehicle’s trajectory change and the dynamics of the transition zone between water-air and air-water. These dynamics are important to evaluate the controller’s performance. Numerical simulations consider both transitions (water-air and air-water) and they are performed to discuss the influence of acting forces on the vehicle stability and position-tracking performance using a classical control system. © 2024, The Authors. All rights reserved.

关键词： Hybrid vehicles

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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