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42nd Chinese control Conference, CCC 2023

作者： Dou, Lintao Wei, Haoyu Huang, Jian Pan, Lulu Shao, Haibin Li, Dewei College of Intelligent Science National University of Defense Technology Changsha410003 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581543

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation

CC-Net: Consistency Learning Combined with Contrastive Learn...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Luo, Yang Liu, Jun Ding, Mengqian School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Due to the scarcity of medical images with labels and the fact that medical image data is not easily accessible. Kidney ultrasound (KUS) images have speckle noise, low image quality, acoustic shadows, and other reasons, which pose significant challenges to the kidney ultrasound image segmentation task. To solve the above two problems, a novel consistency learning and contrastive learning-based kidney ultrasound image segmentation method (CC-Net) has been proposed. Specifically, the student-teacher model was adopted as the semi-supervised learning framework and uncertainty estimation was introduced. The Consensus Uncertainty Map (CUM) strategy was proposed to generate segmentation results for the teacher model when calculating uncertainty. To solve the problems of acoustic shadows and low image quality in kidney ultrasound images, a kidney ultrasound image data augmentation method (KUS-AUG) and a convolutional block (KUS-CONV) tailored to the characteristics of kidney ultrasound images were proposed. In addition, contrastive learning was implemented to enhance the encoder's ability to learn more semantic information, due to its inherent advantage in learning data distribution. Finally, a series of experiments were conducted on the ultrasound image dataset and the method achieved the best performance. © 2024 IEEE.

关键词： Contrastive Learning

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DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Dual-Teacher Semi-Supervised Network for Kidney Ultrasound Image Segmentation

DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Du...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

ISBN: (纸本)9781665410205

Accurate segmentation of kidney ultrasound (KUS) images is of great significance for the diagnosis of kidney diseases. However, KUS images have challenges such as low contrast, large size variations, and acoustic shadow interference. In addition, obtaining labeled data is a major challenge in the field of medical image segmentation. This paper proposes a dual-branch hybrid CNN Transformer combined with a dual teacher semi-supervised segmentation network (DHCT-DT) based on the MT model. The reliability of guidance can be improved between two teachers by constructing consistency regularization constraints. The U-Net architecture is employed between the main teacher and student model. The auxiliary teacher model adopts a dual-branch hybrid CNN Transformer architecture. The multi-scale channel fusion attention mechanism (MSCF) is introduced to capture the feature correlation information of KUS images at different scales and channels. Two different training methods can combine their advantages to fully extract local and global information from images. To demonstrate the applicability of the proposed method, experiments were conducted on two datasets: a KUS dataset and a breast ultrasound (BUS) dataset. Both datasets demonstrated the most competitive segmentation performance. For example, in a semi-supervised experiment using only 10% labeled data in the BUS dataset, the Dice coefficient, Jaccard coefficient, HD, and ASSD reached 82.28%, 76,36%, 13.36mm, and 3.71mm. © 2024 IEEE.

关键词： Image segmentation

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A research on cross-chain and interoperation methods of fusion protocol

IET Blockchain

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IET Blockchain 2024年第1期4卷 18-29页

作者： Ming, Li Wenpeng, Song Ankai, Li Ziming, Zhou Mianchen, Liu Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of Blockchain Shanghai China China Electronics Standardization Institute Beijing China School of Economics and Management Beihang University Beijing China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations Beijing China

At present, cross-chain technology and cross-chain system pay more attention to cross-chain security and performance, and have shown great improvement in security, scalability and efficiency. However, the generality of cross-chain technology in different scenarios is poor, and it may not fit in different business fields. In order to solve the generality problem of cross-chain technology, this paper sorts out and analyzes the research of blockchain cross-chain protocol, and proposes a fusion protocol cross-chain method based on relay-chain technology. This paper will introduce the cross-chain framework and cross-chain transaction process of fusion protocol. Cross-chain framework includes blockchain cross-chain interaction architecture, cross-chain interaction governance mechanism and cross-chain privacy protection scheme. The transaction process is divided into three different stages. Finally, combined with cross-chain key technologies and landing application requirements, the future research direction of blockchain cross-chain technology is given. The fusion protocol cross-chain method based on relay-chain technology proposed here is a groundbreaking innovation in the field of blockchain cross-chain technology. By integrating the advantages of different blockchain systems and protocols, the fusion protocol is able to achieve a high degree of generality and compatibility, making it suitable for various business scenarios and applications. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Accelerating the HS-type Richardson Iteration Method with Anderson Mixing

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Acta Mathematica Sinica,English Series 2022年第11期38卷 2069-2089页

作者： Zhi Zhi LI Huai ZHANG Le OU-YANG Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049P.R.China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Securityand Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060P.R.China

The Accelerated Hermitian/skew-Hermitian type Richardson(AHSR)iteration methods are presented for solving non-Hermitian positive definite linear systems with three schemes,by using Anderson *** upper bounds of spectral radii of iteration matrices are studied,and then the convergence theories of the AHSR iteration methods are ***,the optimal iteration parameters are provided,which can be computed *** addition,the application to the model convection-diffusion equation is depicted and numerical experiments are conducted to exhibit the effectiveness and confirm the theoretical analysis of the AHSR iteration methods.

关键词： Anderson mixing Hermitian/skew-Hermitian splitting the Richardson iteration convergence analyses optimal parameters the model convection-diffusion equation

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Distributed Model Predictive control Framework for Consensus of Multi-Agent System 20

Distributed Model Predictive Control Framework for Consensus...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Li, Huaqing Zheng, Lifeng Wang, Huiwei Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering No. 2 Tiansheng Road Beibei District Chongqing China

ISBN: (纸本)9798350320831

Considering the widespread applications of the model predictive control (MPC), such as industrial process control and robotic operations, this paper proposes an MPC-based asynchronous distributed algorithm to solve the consensus problem of multi-agent system (MAS) by using the operator splitting method. The proposed algorithm is asynchronous, which means that all agents do not require to update and communicate simultaneously. When the positive uncoordinated step sizes satisfy the given condition, the optimal solution can be worked out by using the proposed algorithm. Moreover, all agents are allowed to set the step sizes locally based on local information. Numerical simulation validates the feasibility and effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Model predictive control

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Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning 29

Improving Abstractive Dialogue Summarization with Speaker-Aw...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Geng, Zhichao Zhong, Ming Yin, Zhangyue Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University Key Laboratory of Intelligent Information Processing Fudan University

Pre-trained models have brought remarkable success on the text summarization task. For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed. As a result, existing summarization systems based on pretrained models are unable to recognize the unique format of the speaker-utterance pair well in the dialogue. To investigate this issue, we conduct probing tests and manual analysis, and find that the powerful pre-trained model can not identify different speakers well in the conversation, which leads to various factual errors. Moreover, we propose three speaker-aware supervised contrastive learning (SCL) tasks: Token-level SCL, Turn-level SCL, and Global-level SCL. Comprehensive experiments demonstrate that our methods achieve significant performance improvement on two mainstream dialogue summarization datasets. According to detailed human evaluations, pretrained models equipped with SCL tasks effectively generate summaries with better factual consistency. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Text processing

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Entity Alignment Through Joint Utilization of Multiple Pretrained Models for Attribution Relationship 11

Entity Alignment Through Joint Utilization of Multiple Pretr...

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11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Yujia, Liu Tao, Xu Zhehan, Su Xinsong, Li Kaipeng, Xue Yuxin, Liu Northwest Minzu University Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou China Northwest Minzu University Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou China College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350376982

Most existing models for entity alignment tasks are based on a single pre-training mode. Through multiple experiments, it has been verified that a single pre-training model is no longer sufficient to meet the needs of current entity alignment tasks. Based on considerations of multiple pre-training models, this paper proposes an entity alignment method that utilizes multiple pre-training models. The purpose of this method is to address the limited accuracy in entity alignment tasks, with a particular focus on separately pre-training the embedding of entity attribute information and link relationship information for matching. To achieve the goal of improving entity alignment accuracy, this paper adopts joint alignment using pre-training with BERT and GloVe word embeddings. Experimental evaluations are conducted on the DBP15k dataset, and the results demonstrate that leveraging multiple pre-training models for attribute and relationship joint alignment can enhance model performance. The evaluation metrics of the model on three different language datasets, Hits@1 and MRR, are as follows: 0.899, 0.930, 0.964, and 0.932, 0.951, 0.975, respectively. These metrics indicate that the model performs well in aligning entities and outperforms current mainstream entity alignment methods. © 2024 IEEE.

关键词： Embeddings

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Semantic enhancement methods for image captioning

Semantic enhancement methods for image captioning

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Luming Li, Lin School of Computer Science Qinghai Normal University Xining810016 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Normal University Xining810008 China

ISBN: (纸本)9781510662988

Image captioning, a cross-modal study, aims to generating a description for a given image, which plays an important role in many fields like image retrieval and computer-assisted instruction. Currently, the challenge in image captioning is the limited quality of generated descriptions including insufficient utilization of image feature information and the limited language learning ability of the decoder. In this paper, we address the above problems by constructing a semantic enhancement module and a multi-round decoding mechanism to enhance the decoding ability of the model, which uses the Transformer model as the primary structure. To validate the efficacy of the model, we conducted intensive experiments on the MSCOCO2014 benchmark and evaluated its performance using five evaluation metrics. The experimental results show that the proposed method in this paper has improved to varying degrees on all five-evaluation metrics. © 2023 SPIE. All rights reserved.

关键词： Decoding

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