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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2024

作者： Liao, Yijie Li, Zhengxiu Jie, Lilin Cheng, Ruipeng Li, Ming Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330063 China School of Information Engineering Nanchang Hangkong University Nanchang330063 China

ISBN: (纸本)9781510689275

Electroencephalogram(EEG) signals have the potential to objectively analyze personality, but most of the existing personality analysis studies use a single EEG feature. In order to improve the accuracy of personality analysis, this paper proposes an EEG multi-feature fusion method for personality analysis. Firstly, a personality test paradigm which covers three emotional stimuli was designed, and the EEG data of 22 subjects were acquired;the preprocessed EEG data were extracted to contain multiple EEG features in the time domain, frequency domain, time-frequency domain, and nonlinear features. Then a multi-layer perceptual machine and sparse linear discriminant analysis were employed to integrate and reduce the extracted multi-electroencephalographic features, and the classification task was carried out using the deep learning network CNN-Transformer. Compared with other methods, the method proposed in this paper has an improvement in the analysis accuracy of the five personality dimensions of extraversion, agreeableness, conscientiousness, neuroticism, and openness by 4.6%-23.91%, 8.98%-34.5%, 7.89%-34.5%, 5.5%-30.5%, and 2.88%-22.88% respectively. © 2025 SPIE.

关键词： Bode diagrams

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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of Computer Science and Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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Efficient Construction of Delaunay Triangulations on Riemannian Manifolds with the Bowyer-Watson Method

Efficient Construction of Delaunay Triangulations on Riemann...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Dejiangmo Wang, Qian Zhou, Li Zhang, Xinyun Luo, Dawei Northwest Minzu University Colleage of Mathematics and Computer Science Lanzhou China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331509873

This paper explores the application of the Bowyer-Watson algorithm for constructing Delaunay triangulations on Riemannian manifolds, with a particular focus on karst terrain and channel detection scenarios. We define geodesic disks with sufficiently small radii and demonstrate the existence of a net within these disks that allows for the construction of triangles satisfying the Delaunay condition. By analyzing the insertion of points both inside and outside of triangles, we ensure proper point placement and perform edge flips as necessary to maintain the Delaunay property. Compared to other algorithms, the Bowyer-Watson approach offers notable advantages for achieving Delau-nay triangulation on Riemannian manifolds due to its flexibility and locality. This method effectively manages complex geometric structures by adaptively adjusting the triangulation, ensuring both accuracy and efficiency in non-Euclidean spaces. It is particularly well-suited for modeling karst terrain features and detecting channels, providing a crucial theoretical foundation and practical guidance for future numerical computations and geometric modeling on curved surfaces. © 2024 IEEE.

关键词： Triangulation

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Performance Analysis of Improved Sub-band Sparse Adaptive Algorithm for Acoustic Channel Identification

Performance Analysis of Improved Sub-band Sparse Adaptive Al...

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2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Alilouche, Ali Bendoumia, Redha Guessoum, Abderrezak University of Blida 1 Signal Processing and Image Laboratory BP N° 270 road of Soumaa Blida Algeria University of Blida 1 Detection Information and Communications Laboratory BP N° 270 road of Soumaa Blida Algeria

ISBN: (纸本)9798350353754

In this paper, we present the performance analysis of new Sub-band Improved Proportionate NLMS algorithm to improve the convergence rate of dispersive acoustical channel identification. Although the existing proportionate algorithms lately implemented had a good convergence speed in sparse systems, however they suffer from slow convergence speed in case of dispersive channels. This proposed algorithm based on Improved Proportionate NLMS incorporated into sub-band adaptive filtering structure denoted by S-IPNLMS shows good results in the latter case. In this approach, sub-band adaptive filtering is utilized to address challenges encountered in long adaptive filters, such as their slow convergence rate. Simulation results show that the sub-band IPNLMS performs better than the sub-band NLMS, PNLMS when the acoustical impulse response is dispersive. © 2024 IEEE.

关键词： Adaptive filtering

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain 5

Motion Planning and Tracking Control Method Based on Coppeli...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Huang, Jiale Chen, Zhihua Zhong, Yongbo Zeng, Wenrui Ji, Chuanmin Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China

ISBN: (纸本)9798350307320

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATLAB and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm. © 2023 IEEE.

关键词： Motion planning

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink 13

Research on Collaborative Filtering Algorithm of E-commerce ...

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13th International Conference on Intelligent Control and Information processing, ICICIP 2025

作者： Zheng, Haohao Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In online shopping, a person's interest in a product is closely related to whether they will purchase it Analyzing people's interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user- item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the issue of matrix sparsity, but its accuracy in recommending products based on implicit feedback alone is insufficient This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by summing the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product's maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm's accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). © 2025 IEEE.

关键词： Collaborative filtering

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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2023 IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Xia, Shaoqi Huang, Hua Li, Qishen He, Yifan Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Jiangxi Nanchang China Nanchang Hangkong University School of Information Engineering Nanchang China Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Software Nanchang China

ISBN: (纸本)9798350399028

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52. © 2023 IEEE.

关键词： Semantic Segmentation

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship 4

An Analysis Model of English Text Coherence Based on RST Dep...

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Huang, Guimin Shuai, Wanying Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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GSCL-RVT: Generalized supervised contrastive learning with global–local feature fusion for micro-expression recognition

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Pattern Recognition Letters 2025年 196卷 169-176页

作者： Fan Song Junhua Li Zhengxiu Li Ming Li Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China

Micro-expressions (MEs) are instantaneous facial expressions that appear quickly after an emotionally evocative event and are difficult to suppress, and they can reveal one’s genuine feelings and emotions. With their spontaneous and transient nature, MEs provide a unique perspective for sentiment analysis. However, their subtle and transient nature, coupled with the scarcity and lack of diversity of existing datasets, brings great challenges in discriminative feature learning and model generalization. To address these issues, this paper proposes a novel micro-expression recognition (MER) framework. This framework integrates a feature fusion network by blending residual blocks with a vision transformer (RVT), which can capture local details and integrate global contextual information in images across multiple levels. Furthermore, a generalized supervised contrastive learning (GSCL) strategy is introduced in this paper, wherein traditional one-hot labels are transformed into mixed labels. This strategy then proceeds to compare the similarity between the mixed labels and anchors, with the aim of minimizing the cross-entropy between the label similarity and the potential similarity. This approach aims to optimize the semantic spatial metrics between different MEs and enhance the model’s feature learning capabilities. In addition, we propose a method for augmenting data through region substitution, based on the local features of samples belonging to the same category. This approach works synergistically with a generalized supervised contrastive learning framework, with the objective of addressing the issue of limited micro-expression (ME) data availability. Lastly, we conduct a series of experiments with both Single Database Evaluation (SDE) and Composite Database Evaluation (CDE) protocols, obtaining either optimal or near-optimal results. We also provide sufficiently interpretable analyses to demonstrate the superiority and effectiveness of our proposed methodology.

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A Convolutional Knowledge Tracing Method based on Graph Embedding 4

A Convolutional Knowledge Tracing Method based on Graph Embe...

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4th International Symposium on Computer Engineering and Intelligent communications, ISCEIC 2023

作者： Huang, Guimin Li, Ying Guo, Qingkai Deng, Nanxiao Lin, Jianxing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350306323

With online education rapidly developing, it is a significant issue to evaluate students' grasp of knowledge more accurately. Knowledge tracing models are good at it, in which convolutional knowledge tracing (CKT) based on convolutional neural networks(CNN) gets better performance. However, we found that the CKT model ignores the relation between the knowledge points, which we believe has an important impact on the correctness of students' answers. Therefore, we put forward a graph embedding based convolutional knowledge tracing model (CKT-GE), which feeds the relation between the knowledge points with other inputs into model for improving the effectiveness of knowledge tracing. Our model outperforms existing models on two classic datasets, and improves the performance over the original CKT model, allowing for more accurate assessment of student performance. © 2023 IEEE.

关键词： Students

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