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Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

computer Modeling in engineering & sciences 2023年第8期136卷 1835-1856页

作者： Yan Zhu Lili Tian Fan Ye Gaofeng Sun Xianyong Fang Department of Sports and Military Education Anhui UniversityHefei230601China School of Computer Science and Technology Anhui UniversityHefei230601China

Non-rigid registration of point clouds is still far from stable,especially for the largely deformed *** initial correspondences are often adopted to facilitate the ***,there are few studies on how to build them ***,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is *** priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region *** further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift *** method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different *** and quantitative experiments demonstrate the advantages of the proposed method.

关键词： Non-rigid registration point clouds coherent point drift

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Large model-guided multimodal face image super-resolution

Large model-guided multimodal face image super-resolution

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Jiang, Zhihao Chu, Yuezhong Liu, Heng School of Computer Science and Technology Anhui University of Technology 243000 China

ISBN: (纸本)9781510681514

Aiming at the issue of single-image super-resolution methods relying solely on the input images of poor resolution, which struggle to reconstruct satisfactory results when the details in the low-resolution images are insufficient, this paper proposes a text-guided images of low quality. The shortcomings of inadequate information in images with low resolution are compensated for by introducing text information as prior knowledge into the network. Specifically, a multi-modal network structure is proposed, which utilizes a text-image cross-modal pre-trained large model to extract shallow image feature information of different modalities. After deep feature extraction, sub-pixel convolution is employed for magnifying the images and reconstruct high-quality images. Experimental results indicate that the proposed method can utilize text information to reconstruct abundant details missed in low-resolution image scenarios. NIQE and PI metrics confirm the superior super-resolution performance of the approach. © 2024 SPIE.

关键词： Image reconstruction

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ST-SNNCA: Spatio-Temporal Shared Nearest Neighbors Clustering Algorithm Based on Spatio-Temporal Neighbourhood Analysis 4

ST-SNNCA: Spatio-Temporal Shared Nearest Neighbors Clusterin...

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4th International Conference on computer science, Electronic Information engineering and Intelligent Control technology, CEI 2024

作者： Zhang, Fengling Huang, Shengqiang Luo, Yonglong The School of Computer Science and Information Technology Anhui Normal University Anhui China

ISBN: (纸本)9798350378542

With the widespread use of mobile devices and GPS technology, the amount of trajectory data has been significantly accumulated. Therefore, how to properly analyze and mine these data has become an essential but challenging problem to be solved. Traditional trajectory clustering algorithms primarily focus on spatial similarity, disregarding the temporal relationship between different trajectories. To tackle this problem, a novel clustering algorithm called Spatio-temporal Shared Nearest Neighbors Clustering Algorithm is proposed in this work. By introducing temporal attributes during the trajectory segmentation stage, this algorithm effectively improves the reliability of segmentation points. Additionally, a Spatio-temporal Shared Nearest Neighbors method is developed by integrating temporal attributes with the Shared Nearest Neighbors approach, and it is found that this method can overcome the existing limitations of distance metrics by constructing the similarity matrix used for clustering. Finally, two different datasets of the hurricane track and animal track, respectively, are considered to verify the clustering validation of our proposed method. © 2024 IEEE.

关键词： Spatio-temporal data

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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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Recent advances and applications of single atom catalysts based electrochemical sensors

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Nano Research 2024年第4期17卷 2994-3013页

作者： Mingyue Wang Mingfu Ye Jieyue Wang Yong Xu Zhendong Wang Xinyue Tong Xinya Han Kui Zhang Wenhai Wang Konglin Wu Xianwen Wei Institute of Clean Energy and Advanced Nanocatalysis(iClean) School of Chemistry and Chemical EngineeringAnhui University of TechnologyMaanshan 243002China School of Materials Science and Engineering Anhui University of TechnologyMaanshan 243002China Anhui International Research Center of Energy materials green manufacturing and biotechnology School of Chemistry and Chemical EngineeringAnhui University of TechnologyMaanshan 243002China Carbon Cycle and Emission Control Research Center Low-Carbon Research InstituteAnhui University of TechnologyMaanshan 243002China

Single atom catalysts(SACs)have attracted considerable attention due to their unique structures and excellent catalytic performance,especially in the area of catalysis science and energy conversion and *** recent years,SACs have emerged as a new type of sensing material for constructing electrochemical sensors(ECSs),presenting excellent sensitivity,selectivity,and ***,we review the recent advances of SACs in electrochemical sensing and discuss the status quo of current SAC-based ***,the fundamentals of SAC-based ECSs are outlined,including the involved central metal atoms and various supports of SACs in this field,the detection mechanisms,and improving strategies of SAC-based ***,the important applications of SAC-based ECSs are listed and classified,covering the detection of reactive oxygen and nitrogen species,environmental pollutants,disease biomarkers,and ***,based on abundant reported cases,the current conundrums of SAC-based ECSs are summarized,and the prediction of their future developing trends is also put forward.

关键词： single atom catalysts(SACs) electrochemical sensors(ECSs) reactive oxygen and nitrogen species environmental pollutants disease biomarkers pharmaceuticals

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Anisotropic exchange coupling interaction between hard-hard magnetic grains in sintered SrFe_(12)O_(19)ferrites

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Journal of Advanced Ceramics 2023年第4期12卷 815-821页

作者： Shuang Zhang Chunxiang Cao Shubing Su Ailin Xia Huiyan Zhang Hailing Li Zhiyuan Liu Chuangui Jin School of Materials Science and Engineering Anhui University of TechnologyMaanshan 243002China Analysis and Testing Central Facility Anhui University of TechnologyMaanshan 243002China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315016China

Exchange coupling interaction in sintered magnetic materials is generally *** this study,the anisotropic exchange coupling interaction was found in sintered oblate cylindrical SrFe_(12)O_(19)(SrM)specimens obtained by the SrM nanopowders synthesized via a hydrothermal *** to Henkel plots,the exchange coupling interaction between hard-hard magnetic grains was found in both as-pressed and sintered ***,the exchange coupling interaction can only be found in the in-plane direction but not in the out-of-plane direction for thel sintered *** building a model of a grain configuration,this anisotropy of the exchange coupling interaction was ascribed to the vertically arranged plate-like SrM grains with micrometers in width but nanometers in thickness,which was confirmed by morphologies of cross sections in fractured specimens.

关键词： SrFe_(12)O_(19)(SrM)ferrite hydrothermal method anisotropic exchange coupling interaction Henkel plot

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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computer Modeling in engineering & sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Analysis of SSVEP Frequency-spatial Characteristics based on EhythmNet and its Application in Identity Recognition 15

Analysis of SSVEP Frequency-spatial Characteristics based on...

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15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Ma, Shuanglin Zhang, Chao Wu, Xiaopei Shi, Chenyun School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9781510675056

The frequency space characteristic is an important characteristic of steady-state visually evoked potential (SSVEP). The frequency space characteristic of traditional SSVEP is generally obtained by analyzing the power spectrum of EEG of scalp electrodes, but it is vulnerable to noise interference and data quality. This article proposes a new SSVEP frequency space feature analysis method. The proposed method consists of two main parts: 1) A shallow convolutional neural network, called EhythmNet in this paper, is designed for EEG rhythm analysis;2) Based on multi channels EEG data obtained from a set of stimulus frequencies, single channel EEG of SSVEP dominant electrodes (such as Oz) was used as the training data for EhythmNet, and data from other channels were used as the test set to obtain the recognition rate of eachchannel, namely single channel recognition rate (SCA). The experimental results indicate that SCAs can accurately reflect the spatial distribution of SSVEP in the scalp electrode, and it is found that the spatial distribution of SCA has good individual stability and differences between individuals. In order to verify this feature, an identity recognition test based on SCA was conducted in the article, and more than 98.5% of the recognition results were achieved. © 2024 SPIE.

关键词： Electrodes

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Active Learning Based on Prototype Vector Feature Adaptive Fusion 3rd

Active Learning Based on Prototype Vector Feature Adaptive F...

引用

3rd International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

作者： Fang, Xiaohao Cheng, Wenjie Ji, Xia The School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789819721993

From successfully embedding the KNN classifier into a network using prototype vectors, to exploring various active learning methods in deep learning to find uncertain and representative samples for active learning, feature fusion in the learned feature space has been continuously investigated. In this paper, we aim to further combine prototype vectors with feature fusion and propose an active learning based on prototype vector feature adaptive fusion. Firstly, we explore the features of prototype vectors in the NCE-Net model, where they represent common features for each class. Secondly, we construct a prototype vector matrix based on the feature space. Then, we fuse the prototype vector matrix with the feature matrix of all unlabeled samples and add the samples with inconsistent pseudo-labels before and after feature fusion to the candidate set. Finally, we cluster the candidate samples to consider the representativeness of the selected samples in active learning. Experimental results on classic datasets for image classification show that the proposed method achieves higher accuracy compared to traditional active learning methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vectors

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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