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2024 International Conference on Future of Medicine and Biological information Engineering, MBIE 2024

作者： Song, Yaru Qin, Yingmei Li, Shanshan Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300350 China

ISBN: (纸本)9781510682825

Brain-computer interface technology (BCI) enables users to directly control external devices by establishing an information transmission path between the brain and external devices. Brain-computer interfaces based on the motor imagination paradigm have also begun to enter various fields. Therefore, the research on the brain-computer interface encoding and decoding algorithm of the motor imagination paradigm is particularly important. This paper proposes a model based on attention mechanism CBAM and EEGNet to classify motor imagination electroencephalogram signals (MI-EEG), and verified it on a public data set. Compared with a single EEGNet model, it improved by 3.7%, which is 8.1% higher than the traditional FBCSP model. The experimental results show the effectiveness of the new CBAM-EEGNet model on the four classification tasks of motor imagery. © 2024 SPIE.

关键词： Electroencephalography

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Research on financial social public opinion communication model based on variation mechanism 6

Research on financial social public opinion communication mo...

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6th International Conference on Electronic information technology and Computer Engineering, EITCE 2022

作者： Huang, Maojun Hong, Mei Dong, Lin Yuan, Da Key Laboratory of Intelligent Information Processing Shandong Technology and Business University Shandong Yantai264005 China

ISBN: (纸本)9781450397148

In the context of rapid development of the Internet, the place of spreading financial public opinion is converted from traditional offline places to major online social platforms. Mastering the development mechanism of financial social opinion dissemination on online social media can effectively estimate the length of influence of public opinion and the scope of affected people, and provide effective guidance to relevant staff. Based on the Susceptible Infected Recovered Model, this paper divides the people involved in opinion diffusion into commenters and discussers, and introduces the variation mechanism to design the Susceptible-comment-discussion-removal model, then simulates the model to study the effects of different initial states and parameters on the model, and finally verifies the validity of the model by combining the real data of stock bars. The simulation experiments and validation show that the model can effectively describe the spread of public opinion among the user groups of financial social platforms when it occurs, and provide a valid reference for related workers. However, the content of public opinion, the personal influence of communicators, and the lag effect of communication all have an impact on communication, and these issues need to be addressed in future research. © 2022 Association for Computing Machinery.

关键词： Social aspects

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Design of Automatic Control System for the Laying of Waterproof Plate in Tunnel Construction

Design of Automatic Control System for the Laying of Waterpr...

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International Conference on Mechanical Design, ICMD 2023

作者： Lu, Guangda Han, Xu Zhao, Zhuangzhuang Guo, Tinghang Han, Yu Li, Xiang Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9789819709212

Aiming at the problems of poor welding quality consistency and low construction efficiency of manually welded flashing, a set of automatic control system for welding flashing in tunnel construction is designed and manufactured. The equipment consists of hot melt liner storage module, hot melt liner conveying module, hot melt liner fixing module, hot melt liner welding module and automatic welding control module, introduces the working principle of the equipment as well as the workflow, and focuses on the analysis of the automatic control module of the equipment. The H5u PLC of Innovance is adopted as the core controller. Finally, based on the control module to complete the waterproof plate and 65 tunnel hot melt liner automation welding test, the results show that the equipment effectively reduce the burden of manual work, improve the quality of waterproof plate welding and construction efficiency, for the tunnel waterproof plate welding full automation welding provides an effective solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Controllers

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Multifeature attention detection algorithm based on power spectral density and differential entropy

Multifeature attention detection algorithm based on power sp...

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2024 International Conference on Future of Medicine and Biological information Engineering, MBIE 2024

作者： Yuan, Peng Bian, Yan Liu, Yanli Li, Shanshan Han, Chunxiao Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300350 China

ISBN: (纸本)9781510682825

Attention is closely related to human life. To detect attention states quickly and accurately with fewer resources, this research propose a method for attention state detection, it is based on differential entropy (DE) and power spectral density (PSD). Electroencephalogram (EEG) data from 15 participants. It was processed using the Fast Fourier Transform (FFT) to extract DE and PSD features, which was normalized. These features were input into a Support Vector Machine (SVM). After optimizing the model parameters, it achieved a well-performing attention state detection model. The proposed method achieved a maximum classification accuracy of 85% and an average accuracy of 67%, The model described in the statement surpasses traditional SVM models that are trained solely on DE or PSD features, as well as single-channel or multi-channel SVM models. The new method can be used to learn additional features for attention verification and generalizes well for the task of developing a robust deep learning system. © 2024 SPIE.

关键词： Electroencephalography

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Contextual feature aggregation pyramid vision transformer for polyp segmentation

Contextual feature aggregation pyramid vision transformer fo...

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2024 International Conference on Future of Medicine and Biological information Engineering, MBIE 2024

作者： Lin, Ziwei Zhang, Kaiyue Li, Shanshan Qin, Qing Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300350 China

ISBN: (纸本)9781510682825

Polyp segmentation has consistently been a difficult task because of the varying sizes of polyps and the significant intrinsic similarity between polyps and the surrounding tissues. To address the above problems, a contextual feature aggregation polyp segmentation algorithm combining Transformer and convolution is proposed. Firstly, the Pyramid Vision Transformer is used to extract image global features, and the stage bridging module(SBM) is employed to enhance the ability of the network to handle polyp details and aggregate high-level polyp features. Subsequently, a feature enhancement module (FEM) is used to explore shallow polyp information. Finally, cross-layer feature fusion is performed by a global adaptive module (GAM) to realize feature interaction. This algorithm is evaluated on the CVC-ClinicDB and Kvasir-SEG datasets and further tested for generalization capability on the CVC-ColonDB dataset. Compared to other classic segmentation networks, the proposed method demonstrates superior segmentation performance in both visual results and quantitative analysis. The method effectively segments colorectal polyp images, offering a new approach to diagnosing colorectal polyps. © 2024 SPIE.

关键词： Image enhancement

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CONVERGENCE ANALYSIS OF SOME FINITE ELEMENT PARALLEL ALGORITHMS FOR THE STATIONARY INCOMPRESSIBLE MHD EQUATIONS

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Journal of Computational Mathematics 2024年第1期42卷 49-70页

作者： Xiaojing Dong Yinnian He Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105China Institute of Applied Physics and Computational Mathematics Beijing 100088China School of Mathematics and Statistics Xi'an Jiaotong UniversityXi'an 710049China

By combination of iteration methods with the partition of unity method(PUM),some finite element parallel algorithms for the stationary incompressible magnetohydrodynamics(MHD)with different physical parameters are presented and *** algorithms are highly *** first,a global solution is obtained on a coarse grid for all approaches by one of the iteration *** parallelized residual schemes,local corrected solutions are calculated on finer meshes with overlapping *** subdomains can be achieved flexibly by a class of *** proposed algorithm is proved to be uniformly stable and ***,one numerical example is presented to confirm the theoretical findings.

关键词： Partition of unity method Local and parallel algorithm Finite element method Iteration methods Magnetohydrodynamics

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Lower limb motor imagery EEG signals classification based on 1D-CNN-LSTM algorithm

Lower limb motor imagery EEG signals classification based on...

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2024 International Conference on Future of Medicine and Biological information Engineering, MBIE 2024

作者： Han, Chunxiao Hu, Yue Ren, Hui Chen, Wenjin Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781510682825

With the continuous development of the brain-computer interface (BCI) technology, the lower limb rehabilitation system based on Motor Imagery (MI) has graduallybecome a researchhotspot in the field of *** recognize the lower limbs MI, this paper designed an experimental paradigmfor MIlower limb MI and used the 1D-CNN-LSTMdeep learning algorithm to classify lower limb movement features from MI EEG signals. Compared with classical machine learning algorithms, the results showed that 1D-CNN-LSTM has relatively higher accuracy. Meanwhile, the paper built a real-time lower limb rehabilitation system based on the 1D-CNN-LSTM algorithm, which verifies the effectiveness and feasibility of the algorithm. The system provides an advanced and effective solution for brain-computer interfaces based on MI. © 2024 SPIE.

关键词： Image reconstruction

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AI-Based Text Detection and Classification for Smart City Monitoring Using BERT and TextCNN Models

AI-Based Text Detection and Classification for Smart City Mo...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Liu, Yunxia Li, Mingyuan Ye, Zhonglin Shi, Xiaoan Wang, Weijie Zhao, Haixing College of Computer Qinghai Normal University Xining810000 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9798331505516

This paper proposes an Artificial Intelligence (AI) text detection and classification model that combines Bidirectional Encoder Representations from Transformers (BERT) and Text Convolutional Neural Network (TextCNN), applicable for smart city social media monitoring. By leveraging BERT's powerful contextual semantic understanding capabilities and TextCNN's local feature extraction abilities, the model achieves efficient text detection and classification. The model first utilizes BERT to perform semantic representation of the input text, capturing rich contextual features. These features are then fed into the TextCNN model, where multiple convolutional and pooling operations extract and compress the features. Finally, a fully connected layer converts the extracted features into fixed-length vectors for precise classification prediction. Experimental results show that this hybrid model significantly outperforms traditional baseline models on two public datasets, Human ChatGPT Comparison Corpus (HC3) and sharegpt_gpt4. The hybrid model demonstrates notable improvements in key metrics such as Accuracy (ACC), Precision (PREC), Recall (REC), and F1-score (F1). For example, on the sharegpt_gpt4 dataset, the accuracy reaches 0.8490, making a significant improvement over the baseline model. This validates the effectiveness and superiority of combining BERT and TextCNN in text classification tasks. The innovation of this paper lies in the novel integration of BERT and TextCNN, harnessing the contextual semantic understanding of BERT with the local feature extraction of TextCNN, resulting in enhanced text classification performance. The use of diverse datasets such as HC3 and sharegpt_gpt4 showcases the model's robustness across various text types. Additionally, the model's application in smart city social media monitoring demonstrates its practical relevance, providing accurate and efficient text detection crucial for monitoring public sentiment and emerging issues in real-t

关键词： Smart city

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Enhanced fully convolutional network based on external attention for low-dose CT denoising 24

Enhanced fully convolutional network based on external atten...

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6th International Conference on Control and Computer Vision, ICCCV 2024

作者： Zhang, Haining Dong, Jian Tianjin Key Laboratory of information sensing and intelligent control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9798400718045

In recent decades, researchers have concentrated their efforts on developing techniques to denoise low-dose, sparse-angle CT images. Currently, mainstream low-dose CT methods include traditional iterative reconstruction algorithms and deep learning algorithms. However, these methods still exhibit transition smoothing, noise, and artifact residue. Inspired by the idea of deep learning, we propose an improved fully convolutional neural network using external attention. The network incorporates a novel external attention module, and use perceptual loss as the loss function which is used to construct a fully convolutional model with dense connections. This model fuses extracted feature information to achieve image denoising. Following training, the proposed EA-FCN demonstrated superior performance on actual clinical CT images. Furthermore, our method achieved favorable subjective and objective evaluations in terms of noise and artifact suppression. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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