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arXiv 2019年

作者： Xia, Weiguo Ye, Mengbin Liu, Ji Cao, Ming Sun, Xi-Ming School of Control Science and Engineering Dalian University of Technology China Faculty of Science and Engineering ENTEG University of Groningen Netherlands Department of Electrical and Computer Engineering Stony Brook University United States

—This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual’s degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate, and strong bias. The opinions of the individuals lie between 0 and 1. It is shown that for strongly connected networks, the equilibria with all elements equal identically to the extreme value 0 or 1 is locally exponentially stable, while the equilibrium with all elements equal to the neutral consensus value of 21 is unstable. Regions of attraction for the extreme consensus equilibria are given. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is unstable for all strongly connected networks if individuals all have weak bias, becomes locally exponentially stable for complete and two-island networks if individuals all have strong bias, and its stability heavily depends on the network topology when individuals have intermediate bias. Analysis on star graphs and simulations show that additional equilibria may exist where individuals form clusters. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Iterative learning control of the displacements of a cantilever beam

Iterative learning control of the displacements of a cantile...

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IEEE Annual Conference on Decision and control

作者： Maciej Patan Kamil Klimkowicz Robert Maniarski Krzysztof Patan Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra Poland Eric Rogers is with the School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper develops a numerically effective approach to control synthesis for repetitive transverse loads in cantilever beam using an iterative learning control. In particular, the optimal tracking problem for the transverse displacements in the beam are considered. The system dynamics are modelled by a partial differential equation with a biharmonic operator with suitable boundary conditions at the clamped and free ends of the beam. The key novelty is the regulation of the temporal dynamics of the system state by a D-type iterative learning control law based on the second derivative of the beam displacement measured at a priori chosen reference spatial locations. The main systems theory contributions of this paper are the analysis of the convergence properties of control law and the development of a numerical scheme for implementation based on a finite element method. A physically relevant numerical example is given to illustrate the application of the new design in the form of dynamic displacement control for a vibrating cantilever beam.

关键词： Structural beams Iterative learning control Displacement measurement Convergence Actuators Boundary conditions control design

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Data-Driven Model Order Reduction for T-Product-Based Dynamical Systems

arXiv

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arXiv 2025年

作者： Mei, Shenghan He, Ziqin Mei, Yidan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems the Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics the Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

— Model order reduction plays a crucial role in simplifying complex systems while preserving their essential dynamic characteristics, making it an invaluable tool in a wide range of applications, including robotic systems, signal processing, and fluid dynamics. However, traditional model order reduction techniques like balanced truncation are not designed to handle tensor data directly and instead require unfolding the data, which may lead to the loss of important higher-order structural information. In this article, we introduce a novel framework for data-driven model order reduction of T-product-based dynamical systems (TPDSs), which are often used to capture the evolution of third-order tensor data such as images and videos through the T-product. Specifically, we develop advanced T-product-based techniques, including T-balanced truncation, T-balanced proper orthogonal decomposition, and the T-eigensystem realization algorithm for input-output TPDSs by leveraging the unique properties of T-singular value decomposition. We demonstrate that these techniques offer significant memory and computational savings while achieving reduction errors that are comparable to those of conventional methods. The effectiveness of the proposed framework is further validated through synthetic and real-world *** Codes 93C05, 15A69, 93B30, 65F99 © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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Fuzzy Domain Adaptation via Variational Inference for Evolving Concept Drift

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IEEE Transactions on Fuzzy Systems 2025年

作者： Wang, Chuang Wang, Zidong Sheng, Weiguo Liu, Qingqiang Dong, Hongli Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China Northeast Petroleum University Sanya Offshore Oil & Gas Research Institute Sanya572025 China Brunel University London Department of Computer Science Middlesex UxbridgeUB8 3PH United Kingdom Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China Northeast Petroleum University School of Electrical & Information Engineering Daqing163318 China

The concept of fuzzy domain adaptation (FDA) isfocused on transferring a model trained in a source domainto a target domain, where intrinsic distribution discrepanciesexist in non-stationary and non-deterministic environments. Inthis paper, a novel drift decoupling-based variational adaptationnetwork (DD-VAN) is proposed for FDA, allowing for the learningof intra-domain evolutionary patterns and inter-domain uncer-tainties. The DD-VAN algorithm is implemented in three mainsteps: (1) an intra-domain evolutionary trend modeling module isfirst employed to capture unknown temporal variations throughan autoencoder architecture with variational inference;(2) aprototype-assisted fuzzy clustering module is used to estimate themembership degree of the target data, characterizing the inherentuncertainty and imprecision present in real-world distributions;and (3) a membership-aware domain fuzzy matching module isutilized to learn the gradual transitions between category-relateddata pairs in the source and target domains by introducing un-certainties. Furthermore, it is theoretically demonstrated that theinferred posterior distributions of latent codes can be optimizedto align with the corresponding prior distributions by minimizingthe Kullback-Leibler divergence. Extensive experiments are con-ducted on cross-domain tasks involving both synthetic and real-world datasets, and the experimental results suggest that the DD-VAN algorithm outperforms existing state-of-the-art methods. © 1993-2012 IEEE.

关键词： Variational techniques

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BNAS: An efficient neural architecture search approach using broad scalable architecture

arXiv

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arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Sun, Zhiquan Philip Chen, C.L. State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electric Engineering University of Science and Technology Beijing Beijing100083 China School of Computer Science & Engineering South China University of Technology Guangzhou Guangdong510006 China College of Navigation Dalian Maritime University Dalian116026 China

Efficient Neural Architecture Search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning. In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of search cost. Moreover, time consuming of model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from prohibitive performance drop in the phase of architecture estimation. In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt reinforcement learning and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS who modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in reinforcement learning-based NAS approaches, 2) compared with small-size (0.5 millions parameters) and medium-size (1.1 millions parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-1

关键词： Topology

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Visualizing and Predicting Culex Tarsalis Trapcounts for West Nile Virus (WNV) Disease Incidence using Machine Learning Models

Visualizing and Predicting Culex Tarsalis Trapcounts for Wes...

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IEEE International Conference on Electro-Information Technology

作者： Radhakrishnan Angamuthu Chinnathambi Alex Marquette Tyler Clark Aaron Johnson Daisy Flora Selvaraj Jeff Vaughan Todd Hanson Scott Hanson Prakash Ranganathan Naima Kaabouch School of Electrical Engineering and Computer Science (SEECS) University of North Dakota Grand Forks ND USA University of North Dakota Grand Forks USA ND EPSCoR Tribal Colleges Liaison and NATURE Manager Grand Forks USA City of Grand Forks Mosquito Control Grand Forks USA

ISBN: (数字)9781728153179

ISBN: (纸本)9781728153186

This paper discusses how visualization and machine learning models can be effectively used to track and forecast trap counts of Culex Tarsalis, female mosquitoes responsible for spreading the West Nile Virus (WNV). This paper applies four different machine learning models namely, Support vector machines (SVM), Regression tree (RT), Partial Least Square Regression (PLSR), and a hybrid combination of SVM-PLSR to multi-year WNV data sets. Precisely, historical data sets ranging from 2005-2015 was used to predict trap counts for 2016. This paper also discusses a tree-based data visualization technique for displaying historical trap counts. The visualization model is designed to focus on identifying trends in the behavior of Culex tarsalis by tracking parameters such as meteorological data, dead birds, WNV cases, human cases, and deaths. The preliminary results indicate that SVM model outperforms interms of accuracy than other machine learning models. Keywords: Culex Tarsalis, Partial Least Squares Regression (PLSR), Support Vector Machines(SVM), SVM-PLSR, West Nile Virus (WNV), Vector control.

关键词： Data visualization Support vector machines Viruses (medical) Libraries Predictive models Meteorology Machine learning

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Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

Copy and Paste GAN: Face Hallucination From Shaded Thumbnail...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yang Zhang Ivor W. Tsang Yawei Luo Chang-Hui Hu Xiaobo Lu Xin Yu School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China Southeast University Nanjing China Australian Centre for Robotic Vision Australian National University Australia

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternatively in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.

关键词： Face Lighting Gallium nitride Image resolution Feature extraction Generative adversarial networks Automation

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Blockchain based Power Transaction Asynchronous Settlement System

Blockchain based Power Transaction Asynchronous Settlement S...

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IEEE Conference on Vehicular Technology (VTC)

作者： Songpu Ai Diankai Hu Tong Zhang Yunpeng Jiang Chunming Rong Junwei Cao Research Institute of Information Technology Tsinghua University Beijing China Blockchain Innovation Lab/School of information and control engineering MingByte Technology (Qingdao) Co. Ltd./Qingdao University of Technology Qingdao China Blockchain Innovation Lab/School of Computer Science and Technology MingByte Technology (Qingdao) Co. Ltd./Shandong University Qingdao China Blockchain Innovation Lab MingByte Technology (Qingdao) Co. Ltd. Qingdao China Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

The popularization and rapid development of distributed energy becomes a trend of the times. Distributed energy prosumers should be able to trade with each other to reduce losses, increase efficiency, flexibility and economy. The traditional centralized power settlement scheme is not suitable for the utilization on the situation of distributed energy transaction settlement. Energy internet as the next generation energy system integrating cuttingedge information technologies with energy system could realize peerto- peer energy services. The distributed interactive concept of the energy trading is highly consistent with the principle of blockchain. In this paper, aiming at the problems of information disunity, trust system difficult to establish, power deviation waste and cost advance caused by power pre-sale, a power transaction asynchronous settlement system for microgrid is proposed based on blockchain technology. The experiment results illustrate that the system obtains promising performance by reasonable set grid structure which could meet the requirements of practical applications.

关键词： Blockchain Peer-to-peer computing Smart contracts Power supplies Microgrids

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Automatic Brain Tumor Segmentation Using 3D Architecture Based on ROI Extraction

Automatic Brain Tumor Segmentation Using 3D Architecture Bas...

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IEEE International Conference on Robotics and Biomimetics

作者： Jing Huang Minhua Zheng Peter X. Liu School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing P. R. China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

MRI (magnetic resonance image) analysis is crucial for diagnosis, monitoring, and treatment of brain tumors. Manual MRI segmentation of brain tumors requires professional knowledge and costs huge amount of time. Automatic segmentation for multimodal 3D MR images is thus very desirable for clinical applications. In this paper, we present an automatic brain tumor segmentation method based on the U-Net architecture, which is composed of ROI (region of interest) extraction and 3D segmentation networks with long and short skip connections. The introduced method is able to capture detailed features and improves the problem of the imbalanced classification of tumors' sub-regions. The method was evaluated on the BRATS 2017 dataset and the results were promising.

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PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm

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IFAC-PapersOnLine 2018年第4期51卷 877-881页

作者： Ates, Abdullah Yuan, Jie Dehghan, Sina Zhao, Yang Yeroglu, Celaleddin Chen, YangQuan Inonu University Computer Engineering Department Malatya44280 Turkey School of Automation Southeast University Nanjing210096 China UC Merced Mechanical Engineering Departments MESA Lab MercedCA95301 United States School of Control Science and Engineering at Shandong University Jinan250061 China

This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions. © 2018

关键词： Multiobjective optimization

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