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2024 2nd International Conference on Internet of Things and Cloud Computing Technology, IoTCCT 2024

作者： Xu, Jun Feng, Bao Shi, Jiangfeng Lu, Senliang Chen, Yehang Chen, Xiangmeng School of Computer and Electronic Information Guangxi University Nanning China Laboratory of Intelligent Detection and Information Processing Guilin University of Aerospace Technology Guilin China Department of Radiology Jiangmen Central Hospital Jiangmen China

ISBN: (纸本)9798400710148

The problem of imbalanced distribution in multi-center medical data leads to suboptimal model performance. Therefore, to address the heterogeneity issue in multi-center data, a low heterogeneity feature transfer-based federated learning algorithm is proposed. In this algorithm, considering the influence of heterogeneous feature information in the global model, a low heterogeneity feature selection network (LHFS-Net) is introduced to construct local models and reduce the impact of heterogeneity features on local model performance. Additionally, an dynamic aggregation network (DA-Net) is proposed to filter heterogeneous feature information from each local model and aggregate the global model, thereby improving its performance. Moreover, to validate the performance of the proposed algorithm, the study collected early-stage lung cancer data from four medical institutions for the classification of early stage lung cancer medical images. The experimental results demonstrate that the proposed algorithm outperforms three existing methods across the four early stage lung cancer data centers, with AUC values of 0.8108, 0.7894, 0.7467, and 0.7527 for each respective test set. © 2024 Copyright held by the owner/author(s).

关键词： Lung cancer

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Quantitative matching method for network traffic features 18

Quantitative matching method for network traffic features

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18th International Conference on Computational Intelligence and Security, CIS 2022

作者： Hu, Zhihui Liu, Caiming School of Electronic Information and Artificial Intelligence Leshan Normal University Intelligent Network Security Detection and Evaluation Laboratory Leshan China Leshan Normal University Internet Natural Language Intelligent Processing Key Laboratory of Education Department of Sichuan Province Leshan China

ISBN: (纸本)9798350346275

The heterogeneity of network traffic features brings quantitative calculation problems to the matching between network data. In order to solve the above fuzzy matching problem between the heterogeneous network feature data, a quantitative matching method for network traffic features is proposed in this paper. By constructing the numerical expression method of network traffic features, the numerical expression of key features of network data is realized. By constructing the suitable section calculation methods for the similarity of different network traffic features, the personalized quantitative matching for heterogeneous network data features is realized according to the actual meaning of different features. By defining the weight of network traffic features, the quantitative importance value of different features is realized. The weighted sum mathematical method is used to accurately calculate the overall similarity value between network data. The effectiveness of the proposed method through experiments is verified. The experimental results show that the proposed matching method can be used to calculate the similarity value between network data, and the quantitative calculation purpose of network traffic feature matching with heterogeneous features is realized. © 2022 IEEE.

关键词： Heterogeneous networks

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Federated Learning based on Feature Transfer: Multi-center Nuclear Segmentation of Histological Images 5

Federated Learning based on Feature Transfer: Multi-center N...

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5th International Conference on Frontiers Technology of information and Computer, ICFTIC 2023

作者： Lu, Senliang Feng, Bao Chen, Yehang Huang, Zijun School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Laboratory of Intelligent Detection and Information Processing Guilin University of Aerospace Technology Guilin City Guangxi Province541004 China

ISBN: (纸本)9798350309034

Federated learning has enabled multiple medical data centers to collaboratively train segmentation models without sharing data. However, the existence of Non-IID data across different data centers leads to poor model generalization. To address this issue, we propose a personalized federated learning approach based on feature transfer, suitable for multi-center medical image segmentation tasks. Firstly, to construct personalized local segmentation models and reduce the influence of heterogeneous feature information in the global segmentation model, we employ a meta-network to automatically identify and select transferable features from the encoding layers of U-Net. It generates feature transfer weights and constructs a feature transfer loss to enhance the local models' attention to informative features. Secondly, to filter out heterogeneous feature information from each local model and enable the global model to identify and transfer robust features from local models, the meta-network computes feature transfer weights based on the representations of each local model and construct an aggregation loss. This loss is then used to update the feature transfer weights based on the variation of soft dice loss of the global segmentation model. Experimental evaluations are conducted on six central datasets for cell nuclei segmentation tasks, and comparisons are made with three existing federated learning algorithms. The experimental results demonstrate that the proposed method achieves the highest dice coefficient in centers A, B, C, E, and F, with a nearly 10% improvement in the dice coefficient for center F. © 2023 IEEE.

关键词： Image segmentation

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Blind Separation of Heart Sound Convolutive Mixtures Utilizing Independent Vector Analysis 40

Blind Separation of Heart Sound Convolutive Mixtures Utilizi...

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40th Chinese Control Conference, CCC 2021

作者： Xie, Yuan Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing School of Automation Guangzhou510006 China Ministry of Education of the P.R.C. Guangdong Key Laboratory of IoT Information Processing Jt. Intl. Research Laboratory of Intelligent Information Processing and System Integration of IoT Guangzhou510006 China Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Guangzhou510006 China

ISBN: (纸本)9789881563804

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then, using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards, the unmixng matrix is updated using Newton's method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline methods. Especially, it has better superiority in the strong reverberant environment. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Time domain analysis

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Matching Algorithm for Data Similarity of Public Dataset of Network Intrusion detection 4

Matching Algorithm for Data Similarity of Public Dataset of ...

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4th IEEE Eurasia Conference on IoT, Communication and Engineering, ECICE 2022

作者： Hu, Zhihui Liu, Caiming Li, Caixiu Leshan Normal University School of Electronic Information and Artificial Intelligence Leshan China Leshan Normal University Intelligent Network Security Detection and Evaluation Laboratory Leshan China Leshan Normal University Internet Natural Language Intelligent Processing Key Laboratory of Education Department of Sichuan Province Leshan China

ISBN: (纸本)9781665482080

In order to provide the basis for the improvement and optimization of the public network intrusion detection data set, a matching algorithm for data similarity of the KDD CUP ' 99 data set is proposed in this paper. According to the characteristics of the feature record of the network flow of the data set, the structure is defined to save a complete network flow feature record. The sub-algorithm of reading flow feature record, constructing flow feature record queue, calculating flow feature record similarity, and matching data set similarity are designed for the process of data set similarity matching including reading, parsing, similarity calculation, and mutual matching. With the program based on the above matching algorithm, repeated experiments are carried out. The experiment result shows that the matching algorithm can calculate data similarity accurately. © 2022 IEEE.

关键词： Intrusion detection

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Adaptive Neural Consensus Tracking Control for Nonlinear Multiagent Systems with Dead Zone and Sensor Faults 42

Adaptive Neural Consensus Tracking Control for Nonlinear Mul...

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Chengjie Chen, Ci Liu, Zhi Li, Zhenni Guangdong University of Technology Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581543

In this paper, an adaptive neural tracking control design is investigated for uncertain multiagent systems (MASs) with actuator dead zone and sensor faults. The adaptive control technique based on neural networks is employed to handle the uncertainty in the systems, which, however, results in a bounded tracking error from most existing works. To achieve the asymptotic tracking performance, we proposed a direct adaptive strategy via the tuning function method. Technically, a Lyapunov function with a series of smooth functions is designed, which benefits proposing of an asymptotic controller. Then, under the framework of direct adaptive control, an inverse compensation scheme, as well as a fault-tolerant mechanism, are considered for disposing of the actuator dead zone and sensor failures. As a result, user-preset tracking performance is achieved and the system error in the MASs converges to a predefined range. An example in the simulation section illustrates the effectiveness of the proposed method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Multi-center solitary solid pulmonary nodule classification algorithm based on mutual information of graph federation 5

Multi-center solitary solid pulmonary nodule classification ...

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5th International Conference on Frontiers Technology of information and Computer, ICFTIC 2023

作者： Shi, Jiangfeng Feng, Bao Liu, Yu Chen, Xiangmeng Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin541004 China Guilin University of Aerospace Technology Laboratory of Intelligent Detection and Information Processing Guangxi Province Guilin City541004 China Jiangmen Central Hospital Department of Radiology Jiangmen529000 China

ISBN: (纸本)9798350309034

With the continuous growth of medical image data and the imbalance in distribution across multiple medical centers, accurate and reliable classification of Solitary solid pulmonary nodules has become increasingly important. Firstly, to address the issue of data silos, a federated learning framework is employed, which ensures data security while improving the diagnostic performance of the model. Secondly, to tackle the non-independent and identically distributed (non-IID) nature of multi-center data, a graph federated personalized strategy based on mutual information is proposed. Graph convolutional algorithms and mutual information are utilized to explore the topological structure and correlation information among the data for personalized modeling. Additionally, considering the variations in shape and size of pulmonary nodules in different CT images, this paper combines a Diverse Branch Block (DBB) convolutional structure with ResNet18, serving as the framework for local and global models, to enhance the model's capability of extracting multi-scale features. The results demonstrate promising classification performance of the proposed algorithm across five pulmonary nodule data centers, with AUC values of 0.7572, 0.8378, 0.7616, 0.7813, and 0.9130 on the respective test sets. © 2023 IEEE.

关键词： Diverse Branch Block Graph Convolution Mutual information Personalized Federated Learning Solitary solid pulmonary Nodule Classification

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A Low Complexity Beam Selection Method for MmWave Massive MIMO Systems

A Low Complexity Beam Selection Method for MmWave Massive MI...

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Ubiquitous Communication (Ucom), International Conference on

作者： Chunhua Zhu Qinwen Ji Xinying Guo The Key Laboratory of Grain Information Processing and Control The Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China

The levels of inter-user interference (IUI) usually vary due to the different geographical locations of users, but, most existing beam selection methods apply the same beam selection criteria for all users, which is detrimental to maintain an attractive trade-off between complexity and system performance. Considering the discrepancy of IUI comprehensively, a low complexity beam selection method for millimeter-wave massive multiple-input multiple-output (MIMO) systems is proposed. Firstly, a candidate beam set (CBS) is constructed in light of the sparsity and power distribution of the beamspace channel, thereby all users are classified into non-interfering users (NIUs), low-interference users (LIUs) and high-interference users (HIUs). For NIUs, the beams with large power are selected from CBS under the magnitude maximization (MM) criterion; for LIUs, a novel MM criterion with a tabu list is designed, which can effectively tackle the drawback of assigning shared beams in traditional MM; for HIUs, the incremental algorithm based on the sum-rate maximization criterion is developed to find the optimal beams from CBS. Simulation results validate that the proposed method can achieve almost the same sum-rate performance as the existing methods with the lower computational complexity.

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Multi-Channel Based on Attention Network for Infrared Small Target detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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