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Artificial Intelligence and intelligent Information processing (AIIIP), International Conference on

作者： Yongheng Bian Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China

The purpose of domain adaptation is to transfer knowledge learned in the labeled source domain to unlabeled but related target domains without requiring a large number of target domain labels. The latest method of domain adaptation mainly adjusts the distribution of the source and target domains to adapt to the target domain. Despite significant success, when there are significant distribution differences between the source and target domains, the adaptability in the target domain still decreases, which may be due to insufficient exploration of domain specific features leading to domain offset issues. To address this issue, we propose the GSA-NET framework in this article. We designed an adversarial dropout method to improve the generalization performance of the model in the target domain by pushing the decision boundary away from the feature dense region. At the same time, in order to effectively match the distribution of advanced features across domains, we deploy feature adaptation modules before the classification layer, which can significantly reduce domain differences. Our experiments on two cross domain benchmarks, Office Home and Office-31, have shown that the proposed method is significantly superior to existing methods.

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A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI-SCALE AGGREGATION AND BOUNDARY-AWARE FOR LATERAL VENTRICLE SEGMENTATION ON MR IMAGES 47

A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI...

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2022 IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2022

作者： Ye, Fei Wang, Zhiqiang Zhu, Sheng Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Department of Radiology Affiliated Hospital of Xiangnan University Chenzhou423000 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781665405409

In this paper, we propose a novel convolutional neural network based on adaptive multi-scale feature aggregation and boundary-aware for lateral ventricle segmentation (MB-Net), which mainly includes three parts, i.e., an adaptive multi-scale feature aggregation module (AMSFM), an embedded boundary refinement module (EBRM), and a local feature extraction module (LFM). Specifically, the AMSFM is used to extract multi-scale features through the different receptive fields to effectively solve the problem of distinct target regions on magnetic resonance (MR) images. The EBRM is intended to extract boundary information to effectively solve blurred boundary problems. The LFM can make the extraction of local information based on spatial and channel attention mechanisms to solve the problem of irregular shapes. Finally, extensive experiments are conducted from different perspectives to evaluate the performance of the proposed MB-Net. Furthermore, we also verify the robustness of the model on other public datasets, i.e., COVID-SemiSeg and CHASE DB1. The results show that our MB-Net can achieve competitive results when compared with state-of-the-art methods. © 2022 IEEE

关键词： Magnetic resonance

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Identification of pseudo dissolution hysteresis coefficient of industrial aluminium electrolysis cell based on process mechanism knowledge

Identification of pseudo dissolution hysteresis coefficient ...

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第33届中国过程控制会议

作者： Zhaohui Zeng Weihua Gui Xiaofang Chen Yongfang Xie Renchao Wu School of Automation Central South University Key Laboratory of Intelligent Computing & Information Processing Ministry of EducationXiangtan University Peng Cheng Laboratory

To overcome the difficulties that are associated with the online recognition of the alumina dissolution properties in industrial aluminium electrolysis cells,a new parameter pseudo dissolution hysteresis coefficient(PDHC) is identified based on process mechanism *** process mechanism analysis and process semantic embedding,the proposed PDHC explicitly represents the process semantemes that are implied in the normalized cell voltage(NCV) and the feed *** PDHC quantifies the online dissolution performance of alumina and,thus,can overcome the inability to express the alumina online dissolution performance in industrial cells.

关键词： process mechanism analysis process semantic embedding explicit representation automatic knowledge acquisition pseudo dissolution hysteresis coefficient aluminium electrolysis

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Nonnegative Matrix Factorization Based on Topology-and-Attribute-Matching Degree for Community Detection 18th

Nonnegative Matrix Factorization Based on Topology-and-Attr...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Zeng, Ruolan Liu, Zhanghui Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is widely used in network analysis, which seeks to divide network nodes into distinct communities based on the topology structure and attribute information of the network. Due to its interpretability, nonnegative matrix factorization becomes an essential method for community detection. However, it decomposes the adjacency matrix and attribute matrix separately, which do not tightly incorporate topology and attributes. And in the problem of division inconsistency based on topology and attributes caused by the mismatch between the topology similarity and attribute similarity of paired nodes, it ignores the difference in the matching degree of each attribute and each node. In this paper, we propose a nonnegative matrix factorization algorithm for community detection (MTACD) based on the matching degree between topology and attribute. First, we employ an attribute embedding mechanism to enhance the node-attribute relationship. Second, we design an attribute matching degree and a node topology-and-attribute matching degree in order to resolve the mismatch between topology and attribute similarity. Experiments on both real-world and synthetic networks demonstrate the effectiveness of our algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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Interpretable Solutions for Multi-Physics PDEs Using T-NNGP 39

Interpretable Solutions for Multi-Physics PDEs Using T-NNGP

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cao, Lulu Lin, Zexin Tan, Kay Chen Jiang, Min Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Multiphysics simulation aims to predict and understand interactions between multiple physical phenomena, aiding in comprehending natural processes and guiding engineering design. The system of Partial Differential Equations (PDEs) is crucial for representing these physical fields, and solving these PDEs is fundamental to such simulations. However, current methods primarily yield numerical outputs, limiting interpretability and generalizability. We introduce T-NNGP, a hybrid genetic programming algorithm that integrates traditional numerical methods with deep learning to derive approximate symbolic expressions for multiple unknown functions within a system of PDEs. T-NNGP initially obtains numerical solutions using traditional methods, then generates candidate symbolic expressions via deep reinforcement learning, and finally optimizes these expressions using genetic programming. Furthermore, a universal decoupling strategy guides the search direction and addresses coupling problems, thereby accelerating the search process. Experimental results on three types of PDEs demonstrate that our method can reliably obtain human-understandable symbolic expressions that fit both the PDEs and the numerical solutions from traditional methods. This work advances multiphysics simulation by enhancing our ability to derive approximate symbolic solutions for PDEs, thereby improving our understanding of complex physical phenomena. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Numerical methods

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Edge-fault-tolerance about the SM-λ property of hypercube-like networks

arXiv

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arXiv 2022年

作者： Liu, Dong Li, Pingshan Zhang, Bicheng Key Laboratory of Intelligent Computing & Information Processing Ministry of Education School of Mathematical and Computational Sicence Xiangtan University Hunan Xiangtan41105 China

The edge-fault-tolerance of networks is of great significance to the design and maintenance of networks. For any pair of vertices u and v of the connected graph G, if they are connected by min{degG(u), degG(v)} edge-disjoint paths, then G is strong Menger edge connected (SM-λ for short). The conditional edge-fault-tolerance about the SM-λ property of G, written smrλ(G), is the maximum value of m such that G − F is still SM-λ for any edge subset F with |F| ≤ m and δ(G−F) ≥ r, where δ(G−F) is the minimum degree of G−F. Previously, most of the exact value for smrλ(G) is aimed at some well-known networks when r ≤ 2, and a few of the lower bounds on some well-known networks for r ≥ 3. In this paper, we firstly determine the exact value of smrλ(G) on class of hypercube-like networks (HL-networks for short, including hypercubes, twisted cubes, crossed cubes etc.) for a general r, that is, smrλ(Gn) = 2r(n − r) − n for every Gn ∈ HLn, where n ≥ 3 and 1 ≤ r ≤ n − 2. Copyright © 2022, The Authors. All rights reserved.

关键词： Fault tolerance

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intelligent ultrasonic distance measurer system with temperature compensation algorithm

Intelligent ultrasonic distance measurer system with tempera...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Wu, Yuwen Yang, Qiang Tang, Kai Zou, Jianhang Zhuo, Jianming Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Xiamen Blue Ocean Technology Co. Ltd Xiamen361022 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

With regard to distance measurement in daily life, the traditional way is to measure the distance between objects through rulers. Nowadays, ultrasonic ranging, laser ranging, radar ranging, sonar ranging and other technologies have enriched the way of distance measurement. At the same time, it is possible to choose a suitable solution according to different actual environments. This paper design an intelligent ultrasonic ranging system through the research of ultrasonic characteristics and the application of existing ultrasonic ranging. The paper introduces the design ideas and process of the intelligent ultrasonic ranging system. Through the design of independent buttons, the normal mode ranging and the high-precision mode ranging are generated to achieve the division of the measurement range and accuracy. When the distance between the measured object and the intelligent ultrasonic ranging system is less than the preset value or exceeds the range of this mode, the buzzer will sound an alarm, so that the system can be used in more fields. The system detects the current ambient temperature in real time, so that the distance measurement can be compensated and modified by the temperature compensation algorithm in the system to achieve self-adaptation and reduce measurement errors. © 2022 IEEE.

关键词： Range finding

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Multipath Components Modeling and Measuring in Device-Free Underground Parking Localization

Multipath Components Modeling and Measuring in Device-Free U...

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IEEE International Conference on Communications in China (ICCC)

作者： Sihai Yang Xing Xiao Xing Wang Zhuoting Chen Menggang Sheng Zhiqiang Yao School of Automation and Electronic Information Xiangtan University Xiangtan China Hunan Province Key Laboratory of Credible Intelligent Navigation and Positioning Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan China School of Mathematics and Computational Science Xiangtan University Xiangtan China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The Device-Free Localization (DFL) system hold significant application value in NLOS (Non-Line-of-Sight) environments where GPS signals are unavailable. It is a challenge to accurately distinguish whether the changes in spatial signals are caused by the presence of users or the changes of environment, particularly in Non-Line-of-Sight (NLOS) environments without GPS signals. To address this issue, we proposes a DFL method utilizing Multipath Components (MPCs), which involves a detailed analysis of how the presence of users affects the MPCs. Specifically, a calculation method for signal propagation paths using virtual anchors has been developed to accurately construct a user-based diffraction model. An algorithm for extracting MPCs from actual signal measurements was also proposed. The maximum average absolute error between the simulated and measured MPC amplitudes with UWB was 0.9461 dB, validating the possibility of substituting simulation models for actual measurements. Moreover, by utilizing a deep learning network with MPCs as input features, the DFL method achieves a positioning accuracy of 0.44 m in an underground parking with only two sensors. This represents an improvement of $\mathbf{0. 3 5 m}$ over traditional RSS-based fingerprinting localization methods.

关键词： Location awareness Deep learning Accuracy Diffraction Measurement uncertainty Sensor phenomena and characterization Fingerprint recognition

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Community-Aware Heterogeneous Graph Contrastive Learning 19th

Community-Aware Heterogeneous Graph Contrastive Learning

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Li, Xinying Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Recently, heterogeneous graph contrastive learning, which can mine supervision signals from the data, has attracted widespread attention. However, most existing methods employ random data augmentation strategies to construct contrastive views, which may destroy the semantic information in heterogeneous graphs. Moreover, they often select positive and negative samples based solely on node-level proximity and overlook hard samples that are difficult to distinguish from anchors. To solve the above problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning model called CAHGCL. In particular, we design an adaptive data augmentation strategy to construct views, including feature augmentation and topology augmentation. To improve the quality of samples, we propose a dynamic sample weighting strategy based on node similarity and community information, capable of identifying both hard positive samples and hard negative samples. Finally, we introduce community-level contrast to improve community cohesion. Extensive experiments and analyses demonstrate that CAHGCL consistently outperforms state-of-the-art baselines on three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on Network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined Networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors Data models Ensemble learning Computer crime Software defined networking

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