检索结果-内蒙古大学图书馆

Few-Shot Class-Incremental Learning with Prior Knowledge

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jiang, Wenhao Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Kargobot of DiDi Shanghai201210 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: https://***/StevenJ308/LwPK. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

Real-Time Fault Detection Scheme for Industrial Chemical Tennessee Eastman Process

学校读者我要写书评

暂无评论

Real-Time Fault Detection Scheme for Industrial Chemical Ten...

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Institut Superieur des Sciences Appliques et de Technologie de Kasserine Kairouan University Tunisia The Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria The Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

关键词： Fault detection Power system dynamics Power system stability Feature extraction Electrical fault detection Real-time systems Stability analysis Robustness Safety Principal component analysis

Fuzzy Sandwich Control of Some Nonlinear Systems with a Number of Stochastically Occurring Impulses 13

学校读者我要写书评

暂无评论

Fuzzy Sandwich Control of Some Nonlinear Systems with a Numb...

13th International Conference on Information science and Technology, ICIST 2023

作者： Onasanya, Babatunde Oluwaseun Dai, Xiangguang Feng, Yuming Zhang, Wei School of Computer Science and Engineering Chongqing Three Gorges University Chongqing Wanzhou404100 China Department of Mathematics University of Ibadan Ibadan200284 Nigeria Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Chongqing Wanzhou404100 China Three Gorges Institute of Artificial Intelligence Chongqing Three Gorges University Chongqing Wanzhou404100 China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing Wanzhou404100 China

ISBN: (纸本)9798350313925

In this paper, rather than have a fixed constant matrix in the sandwich control of the nonlinear system, a fuzzy control matrix was used. In addition, simple inequalities that do not require additional computational time and skills in computer programming were used rather than the usual LMI. The result of this method is that the period T required in the control process is shorter and the control time is also reached much faster or reduced compared to the existing sandwich methods that used constant matrices. In other words, the control time of this method is shorter than the control time achieved when using constant matrices. Besides, this method also can be used to control more nonlinear chaotic systems such as Lorenz's, Rössler's and Chen's systems. Numerical simulations were used to demonstrate the effectiveness of this method. © 2023 IEEE.

关键词： Nonlinear systems

GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Song, Siyang Song, Yuxin Luo, Cheng Song, Zhiyuan Kuzucu, Selim Jia, Xi Guo, Zhijiang Xie, Weicheng Shen, Linlin Gunes, Hatice The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Baidu Inc Beijing100193 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Engineering Middle East Technical University Ankara Turkey School of Computer Science University of Birmingham Birmingham United Kingdom

Graph is powerful for representing various types of real-world data. The topology (edges’ presence) and edges’ features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, task-specific and crucial relationship cues may be disregarded by such manually defined topology and single-value edge features. In this paper, we propose the first general graph representation learning framework (called GRATIS) which can generate a strong graph representation with a task-specific topology and task-specific multi-dimensional edge features from any arbitrary input (i.e., graph or non-graph data). To learn each edge’s presence and multi-dimensional feature, our framework takes both of the corresponding vertices pair and their global contextual information into consideration, enabling the generated graph representation to have a globally optimal message passing mechanism for different down-stream tasks. The principled investigation results achieved for various graph analysis tasks (e.g., graph classification, vertex classification, and link prediction) on 11 graph and non-graph datasets show that our GRATIS can not only largely enhance pre-defined graphs but also learns a strong graph representation for non-graph data, with clear performance improvements on all tasks. In particular, the learned topology and multi-dimensional edge features provide complementary task-related cues for graph analysis tasks. Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data. Our code is made publicly available at https://***/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Feature

关键词： Graph neural networks

Joint Beamforming Optimization and Mode Selection for RDARS-Aided MIMO Systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Jintao Ma, Chengzhi Gong, Shiqi Yang, Xi Ma, Shaodan State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. To tackle this nonconvex problem with intractable binary and cardinality constraints, we propose an inexact block coordinate descent (BCD) based penalty dual decomposition (PDD) algorithm with the guaranteed convergence. Since the PDD algorithm usually suffers from high computational complexity, a low-complexity greedy-search-based alternating optimization (AO) algorithm is developed to yield a semi-closed-form solution with acceptable performance. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided

关键词： Mean square error

Efficient MedSAMs: Segment Anything in Medical images on Laptop

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

Assessing mechanisms for microbial taxa and community dynamics using process models

学校读者我要写书评

暂无评论

mLife 2023年第3期2卷 239-252页

作者： Linwei Wu Yunfeng Yang Daliang Ning Qun Gao Huaqun Yin Naija Xiao Benjamin YZhou Si Chen Qiang He Jizhong Zhou Institute of Ecology Key Laboratory for Earth Surface Processes of the Ministry of EducationCollege of Urban and Environmental SciencesPeking UniversityBeijingChina Institute for Environmental Genomics University of OklahomaNormanOKUSA Department of Microbiology and Plant Biology University of OklahomaNormanOKUSA State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina School of Minerals Processing and Bioengineering Central South UniversityChangshaChina Department of Mathematics Lunt HallNorthwestern UniversityEvanstonIllinoisUSA Department of Civil and Environmental Engineering The University of TennesseeKnoxvilleTennesseeUSA Institute for a Secure and Sustainable Environment The University of TennesseeKnoxvilleTennesseeUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA Earth and Environmental Sciences Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA School of Computer Science University of OklahomaNormanOKUSA

Disentangling the assembly mechanisms controlling community composition,structure,distribution,functions,and dynamics is a central issue in *** various approaches have been proposed to examine community assembly mechanisms,quanti-tative characterization is challenging,particularly in microbial ***,we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential *** time-series data from anaerobic bioreactors that target microbial 16S rRNA genes,we tested the applicability of three ecological models:the consumer–resource model,the neutral model,and the combined *** results revealed that model performances varied substantially as a function of population abundance and/or process *** combined model performed best for abundant taxa in the treatment bioreactors where process conditions were *** contrast,the neutral model showed the best performance for rare *** analysis further indicated that immigration rates decreased with taxa abundance and com-petitions between taxa were strongly correlated with phylogeny,but within a certain phylogenetic distance *** determinism underlying taxa and community dynamics were quantitatively assessed,showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system *** its mechanistic basis,the framework developed here is expected to be potentially applicable beyond microbial ecology.

关键词： community assembly mechanisms consumer-resource model neutral model species dynamics

Mathematical Model of Managing the Agricultural Holdings in Building a Digital Platform 14

学校读者我要写书评

暂无评论

Mathematical Model of Managing the Agricultural Holdings in ...

14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Kulba, Vladimir Medennikov, Viktor Laboratory of Modular Data Processing and Control Systems V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9781665412308

This work presents the original proprietary mathematical model for interregional agricultural holding management relying on the harmonized digital platform of the Russian agrarian sector that allows conceiving the general formalized approach to the strategic direction of various integrated establishments within the sector. The approach provides a theoretical substantiation for harmonized approaches to the long-term digital transformation of major agricultural associations followed by medium and small businesses. © 2021 IEEE.

关键词： Agriculture

Toward Zero-Shot Learning for Visual Dehazing of Urological Surgical Robots

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wu, Renkai Wang, Xianjin Liang, Pengchen Zhang, Zhenyu Chang, Qing Tang, Hao Department of Surgery Shanghai Key Laboratory of Gastric Neoplasms Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Urology Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai China School of Intelligent Science and Technology Nanjing University China

Robot-assisted surgery has profoundly influenced current forms of minimally invasive surgery. However, in transurethral suburethral urological surgical robots, they need to work in a liquid environment. This causes vaporization of the liquid when shearing and heating is performed, resulting in bubble atomization that affects the visual perception of the robot. This can lead to the need for uninterrupted pauses in the surgical procedure, which makes the surgery take longer. To address the atomization characteristics of liquids under urological surgical robotic vision, we propose an unsupervised zero-shot dehaze method (RSF-Dehaze) for urological surgical robotic vision. Specifically, the proposed Region Similarity Filling Module (RSFM) of RSF-Dehaze significantly improves the recovery of blurred region tissues. In addition, we organize and propose a dehaze dataset for robotic vision in urological surgery (USRobot-Dehaze dataset). In particular, this dataset contains the three most common urological surgical robot operation scenarios. To the best of our knowledge, we are the first to organize and propose a publicly available dehaze dataset for urological surgical robot vision. The proposed RSF-Dehaze proves the effectiveness of our method in three urological surgical robot operation scenarios with extensive comparative experiments with 20 most classical and advanced dehazing and image recovery algorithms. The proposed source code and dataset are available at https://***/wurenkai/RSF-Dehaze. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic surgery