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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Li, Gege Zhou, Lin Ye, Zhonglin Zhao, Haixing College of Computer Qinghai Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Xining China Key Laboratory of Tibetan Information Processing Ministry of Education Qinghai Xining China

ISBN: (纸本)9789819959709

Today's link prediction methods are based on the network structure using a single-channel approach for prediction, and there is a lack of link prediction algorithms constructed from a multichannel approach, which makes the features monotonous and noncomplementary. To address this problem, this paper proposes a link prediction algorithm based on multichannel structure modelling (MCLP). First, the network is sampled three times to construct its three subgraph structures. Second, the node representation vectors of the network are learned separately for each subgraph on a single channel. Then, the three node representation vectors are combined, and the similarity matrix is calculated for the combined vectors. Finally, the performance of the MCLP algorithm is evaluated by calculating the AUC using the similarity matrix and conducting multiple experiments on three citation network datasets. The experimental results show that the proposed link prediction algorithm has an AUC of 98.92%, which is better than the performance of the 24 link prediction comparison algorithms used in this paper. The experimental results sufficiently prove that the MCLP algorithm can effectively extract the relationships between network nodes, and confirm its effectiveness and feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Local Feature-Emphasizing Transformer for Cloth-Changing Person Re-identification 6

Local Feature-Emphasizing Transformer for Cloth-Changing Per...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Zhou, Jieqiong Zhang, Guoqing Zheng, Yuhui Zhang, Fuguo Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Tibetan Information Processing Qinghai Normal University Xining China Suzhou Kunke Intelligent Equipment Co. Ltd. Suzhou China

ISBN: (纸本)9798400712739

Cloth-changing person re-identification (CC Re-ID) is a challenging problem for the prevalence of clothing texture information which occupies most of the pixels in the image, becoming invalid information or even misleading information. Recently, many methods introduce Vision Transformer into the field of Re-ID to establish global feature relationships between image patch sequences. However, Transformer tends to scatter critical local features, resulting in decreased performance within cloth-changing scenarios. In order to solve this problem, we propose a Local Feature-Emphasizing Transformer for CC Re-ID (LFET) that centers on the association of local features. Specifically, we propose a Patch Exchange Fusion Module (PEFM), which aims to improve the generalization ability of the reconstructed patch sequence by strengthening the correlation between image patches. Thus, we promote a closer interpretation of the spatial relationship within the image, and when environmental conditions change and various interference exist, the robustness of the model is enhanced. Furthermore, we design a dynamic feature weighting strategy (DyFW) to reduce noise and emphasize discriminative features by applying dynamically generated weighted tensors to sequences. We carry out experiments on various public datasets, demonstrating that our proposed method delivers strong performance in addressing CC Re-ID task. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： cloth-changing person re-identification Vision Transformer

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM with Attention Mechanism 6

Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM w...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Sun, Feihu Zhong, Yanru Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China

ISBN: (纸本)9798350377798

The existing power load data exhibits complex multidimensionality, while existing techniques often focus on learning and forecasting from single load data. To fully exploit the complex multidimensionality of the existing power load data and enhance the accuracy of short-term load forecasting, a smart grid short-term load forecasting model based on attention mechanism and CNN-BiLSTM is proposed. Firstly, the one-dimensional convolutional network (1D-CNN) slides along the temporal dimension to extract multidimensional features such as temperature and humidity from the raw power load data. Secondly, the Bidirectional Long Short-Term Memory network (BiLSTM) captures both historical and future information of the data through gate mechanisms and the bidirectional network structure. Lastly, the attention mechanism allocates weights to focus on important parts of the load sequence data, enhancing the model's ability to identify key information and further improving the accuracy of short-term power load forecasting. The method is experimented on a real-world multivariate load dataset and compared with existing learning methods. The proposed model achieved lower RMSE in 24-hour load forecasting compared to CNN-LSTM, CNN-BiLSTM, LSTM-Attention, BiLSTM-Attention and CNN-LSTM-Attention models by 13.2%, 10.6%, 6.7%, 5.7% and 5.9% respectively. © 2024 IEEE.

关键词： Long short-term memory

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A High-Quality and Efficient Bus-Aware Global Router

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Chinese Journal of Electronics 2025年第2期34卷 444-456页

作者： Liu Genggeng Wei Ling Yu Yantao Xu Ning College of Computer and Data Science Fuzhou University Fuzhou 350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China School of Information Engineering Wuhan University of Technology Wuhan 430070 China

As advanced technology nodes enter the nanometer era, the complexity of integrated circuit design is increasing, and the proportion of bus in the net is also increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between bus and non-bus in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-bus and the deviation value of bus;A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation;We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution;And a depth-first search (DFS)-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithms can effectively reduce bus deviation compared with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation.

关键词： Global routing Layer assignment Bus deviation Physical design Bus routing

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A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

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Journal of Earth Science 2023年第5期34卷 1374-1389页

作者： Qinjun Qiu Bin Wang Kai Ma Hairong Lü Liufeng Tao Zhong Xie Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450046China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural ResourcesZhengzhou 450046China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Department of Automation Tsinghua UniversityBeijing 100084China

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for *** of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new *** knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making *** this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling *** on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic ***,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph *** research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

关键词： mineral resource report geological knowledge knowledge graph ontology hierarchical knowledge representation model

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CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chen, Gong Liu, Jun Li, Chen-Qian Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337020

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images. © 2023 IEEE.

关键词： class imbalance multi-task learning pulmonary nodule

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PCRTAM-Net:A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

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Journal of Computer Science & technology 2023年第3期38卷 567-581页

作者：汪华登李紫正保罗黎兵兵潘细朋刘振丙蓝如师罗笑南 Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin 541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China Department of Pathology Ganzhou Municipal HospitalGanzhou 341000China

Retinal images play an essential role in the early diagnosis of ophthalmic *** segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast *** the same time,automated models struggle to capture representative and discriminative retinal vascular *** fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the *** addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature *** evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and *** model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.

关键词： retinal image segmentation triple attention mechanism atrous convolution residual network

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Multi-objective Balanced Task Offloading in Vehicular Networks Based on Edge Computing 27th

Multi-objective Balanced Task Offloading in Vehicular Networ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Lingjiao Meng, Lingwei Guo, Hua Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

ISBN: (纸本)9783031783821

The increasing number of vehicular networking devices and application demands has made the limited computing and communication resources a significant challenge. The heuristic task offloading strategy mechanism was proposed to improve the efficiency of task offloading in vehicular networks. This strategy mechanism utilized the Nondominated Sorting Genetic Fireworks Algorithm (NSGFA) based on the characteristics of the problem, integrated consideration of the multi-target balance between system offloading costs and load balancing. Experimental data results demonstrate that this method performs well in reducing latency, energy consumption, and load balancing, effectively enhancing the quality of service for vehicular network users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

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A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

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Defence technology（防务技术） 2023年第3期21卷 164-175页

作者： Shi-qi Liu Bing Li Bo Zhao Lei Huang Yue-zhou Wu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China School of Computer Science Civil Aviation Flight University of ChinaGuanghan618307China China Aerospace Science and Technology Corporation Beijing100048China

The excellent remote sensing ability of synthetic aperture radar(SAR)will be misled seriously when it encounters deceptive jamming which possesses high fidelity and *** this paper,the dynamic synthetic aperture(DSA)scheme is used to extract the difference between the true and false targets.A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing *** system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be *** iterative reweighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework,where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its *** dictionary parameter learning,the grid mismatching problem in a fixed grid based method is ***,the proposed method can reap a super resolution ***,a simple yet effective DSA section scheme is developed for the SAR data excerpting,in which only two DSAs are *** results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

关键词： channel resolution separation

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