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arXiv 2025年

作者： Yang, Rui Wu, Xiaojun He, Shengfeng The School of Computer Sciences Shaanxi Normal University Xi'An China The School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song of the Ministry of Culture and Tourism Shaanxi Normal University Xi'An710119 China The School of Computing and Information Systems Singapore Management University Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture

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Congestion Control Method for Campus Opportunity Network Based on Ant Colony Algorithm

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Chinese Journal of Electronics 2025年第2期34卷 576-585页

作者： Li, Peng Cao, Yumei Jia, Huan Wang, Xiaoming Wu, Xiaojun School of Computer Science Shaanxi Normal University Xi an710119 China Key Lab. of Intelligent Comp. and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China

Due to the limited storage resources of portable devices, congestion control has become a hot direction in opportunity networks. To address the issue of heavy loads on certain nodes, which can impact routing efficiency and overall network performance, this paper proposes a load-balancing opportunity routing algorithm based on ant colony optimization (LBOR) in a campus environment. The congestion status is represented by the ratio of message drop receptions within a certain period and the occupancy of the cache. Path selection is based on the concentration of pheromones and the pheromones on the path are updated when a data transmission is completed. In the event of congestion, the algorithm prevents a large amount of data from entering the node and unloads the data to other nodes, even if they are not the optimal relay nodes. Experimental results demonstrate that the proposed algorithm effectively improves data transmission success rates, reduces network loads, and decreases the number of packet losses, especially under low latency conditions. © 2015 Chinese Institute of Electronics.

关键词： Ant colony optimization

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Modeling and Risk Analysis of Cooperative Adaptive Cruise Control Systems Based on Petri Nets and Distributed Edge Intelligence

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IEEE Internet of Things Journal 2025年

作者： Yu, Wangyang Cheng, Yumeng Fang, Xianwen Zhai, Xiaojun Wang, Yinglong Jing, Hongyuan Shaanxi Normal University Chang'an District Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Shaanxi Normal University School of Computer Science Chang'an District Xi'an710119 China Anhui University of Science and Technology Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui232001 China University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Beijing Union University College of Robotics Beijing100101 China

Fueled by advancements in intelligent transportation systems, the Internet of Vehicles (IoV) seeks to connect smart vehicles, road infrastructure, and users into a unified network, enhancing traffic efficiency and reducing accident risks. Centralized cloud data collection raises concerns about privacy and communication overhead. To address these, Distributed Edge Intelligence (DEI) reduces transmission costs and improves privacy by implementing machine learning at the network edge. In this context, Cooperative Adaptive Cruise Control (CACC) systems, combined with DEI in the IoV framework, enhance transportation system intelligence through real-time data processing and decentralized decision-making. This paper proposes a modeling and analysis method for CACC systems based on Petri nets. The datasets are automatically generated using tools developed by our team, and machine learning methods are utilized to perform risk prediction analysis on the CACC model. From the perspective of Petri nets synchronization, we propose risk mitigation strategies from a design standpoint. The research results show that the proposed method significantly reduces signal accumulation and enhances synchronization in CACC systems. This improvement provides new theoretical support and technical guidance for the design and implementation of CACC systems, ultimately enhancing their safety and reliability. © 2025 IEEE.

关键词： Adaptive cruise control

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FMR-GNet: Forward Mix-Hop Spatial-Temporal Residual Graph Network for 3D Pose Estimation

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Chinese Journal of Electronics 2024年第6期33卷 1346-1359页

作者： Honghong YANG Hongxi LIU Yumei ZHANG Xiaojun WU Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Shaanxi Normal University

Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation. However, comprehensively modeling consistent spatialtemporal dependencies among the body joints remains a challenging task. Current approaches are limited by performing graph convolutions solely on immediate neighbors, deploying separate spatial or temporal modules, and utilizing single-pass feedforward architectures. To solve these limitations, we propose a forward multi-scale residual graph convolutional network(FMR-GNet) for 3D pose estimation from monocular video. First, we introduce a mix-hop spatialtemporal attention graph convolution layer that effectively aggregates neighboring features with learnable weights over large receptive fields. The attention mechanism enables dynamically computing edge weights at each layer. Second,we devise a cross-domain spatial-temporal residual module to fuse multi-scale spatial-temporal convolutional features through residual connections, explicitly modeling interdependencies across spatial and temporal domains. Third, we integrate a forward dense connection block to propagate spatial-temporal representations across network layers, enabling high-level semantic skeleton information to enrich lower-level features. Comprehensive experiments conducted on two challenging 3D human pose estimation benchmarks, namely Human3.6M and MPI-INF-3DHP, demonstrate that the proposed FMR-GNet achieves superior performance, surpassing the most state-of-the-art methods.

关键词： Location awareness Three-dimensional displays Correlation Graph convolutional networks Fuses Aggregates Pose estimation Semantics Benchmark testing Skeleton

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Review of GAN-Based Research on Chinese Character Font Generation

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Chinese Journal of Electronics 2024年第3期33卷 584-600页

作者： Xuanhong WANG Cong LI Zengguo SUN Luying HUI Xi'an University of Posts and Telecommunications School of Computer Science Shaanxi Normal University Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism

With the rapid development of deep learning,generative adversarial network(GAN) has become a research hotspot in the field of computer *** has a wide range of applications in image *** by GAN,a series of models of Chinese character font generation have been proposed in recent *** this paper,the latest research progress of Chinese character font generation is analyzed and *** and its development history are ***-based methods for Chinese character font generation are clarified as well as their improvements,based on whether the specific elements of Chinese characters are *** public datasets used for font generation are summarized in detail,and various application scenarios of font generation are *** evaluation metrics of font generation are systematically summarized from both qualitative and quantitative *** paper contributes to the in-depth research on Chinese character font generation and has a positive effect on the inheritance and development of Chinese culture with Chinese characters as its carrier.

关键词： Generative adversarial network Font generation Image-to-image translation Deep learning

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TFSWA-UNet: Temporal-Frequency and Shifted Window Attention Based UNet for Music Source Separation

TFSWA-UNet: Temporal-Frequency and Shifted Window Attention ...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Yao, Zhenyu Su, Yuping Yang, Honghong Wu, Xiaojun Zhang, Yumei Shaanxi Normal University Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an China

ISBN: (纸本)9798350380347

The performance of music source separation (MSS) has been greatly improved in recent years due to the rapid development of various neural network architectures. Spectrogram features are widely used in MSS tasks which consists of both time and frequency information. However, the time and frequency correlations and the local patterns of spectrogram have not been fully explored. In this paper, we propose a novel U-Net architecture named TFSWA-UNet in which a temporal-frequency and shifted window attention (TFSWA) based block is designed as bottleneck block. In the proposed TFSWA block, time sequence attention (TSA) block and frequency sequence attention (FSA) block are used to capture the global correlation of music spectrogram features within time and frequency sequences respectively. To further capture the local correlations of spectrogram features, shifted window attention based Swin transformer is also introduced into the TFSWA module, which computes self-attention within local non-overlapping windows and capture correlations from both temporal and frequency dimensions. Experiment results on MUSDB18 dataset indicate that the proposed TFSWA-UNet model outperforms the state-of-the-art MSS methods with SDR of 9.16dB on vocals. © 2024 IEEE.

关键词： Spectrographs

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folk melody generation based on CNN-BiGRU and Self-Attention 4

Folk melody generation based on CNN-BiGRU and Self-Attention

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4th International Conference on Communications, Information System and Computer Engineering, CISCE 2022

作者： Su, Yuping Han, Rong Wu, Xiaojun Zhang, Yumei Li, Yue Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

Using various deep learning networks to generate music is a research hotspot in the field of human intelligence. Due to the limitation of network structure in the existing melody generation models, the quality of melody generation is poor. In this paper, for folk melody, we propose a melody generation network based on CNN-BiGRU and Self-Attention. First, the processed data is input into the CNN module consisting of one-dimensional convolution layer and pooling layer to extract local features. Then, the features extracted by CNN module are constructed as time series and is input into BiGRU network to extract global features. Finally, Self-Attention assigns different weights to the features of BiGRU output to highlight important feature information during note generation. Experimental results show that the prediction accuracy of the proposed model is improved by 3.61 % compared with BiGUR, 5.26% compared with CNN, and 6.53% compared with CNN-BiGRU. Meanwhile, the model presented in this paper also achieves improvement in other evaluation measures. © 2022 IEEE.

关键词： Convolutional neural networks

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Joint Extraction of Entities and Relations by Adversarial Training and Mixup Data Augmentation 7

Joint Extraction of Entities and Relations by Adversarial Tr...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Chen, Hongjing Lu, Gang Wu, Xiaojun Zhang, Yumei SNNU Key Laboratory of Intelligent Computing and Service Technology For Folk Song Ministry of Culture and Tourism Shaanxi Normal University School of Computer Science Xi'an China

ISBN: (纸本)9781665409506

Joint extraction of entities and relations is an important task for building a knowledge graph and information extraction. However, small interference in the text can greatly change the semantics of words and sentences in natural language, thereby affecting the model's prediction results. To solve the sensitivity of text to noise, in this paper, we present a method, called BERT of Adversarial Training and Sentence Mixup Data Augmentation (BERT-AT-SMDA), which use the BERT pre-training model to obtain the embedding vector representation of the text and then to incorporate the FGM adversarial training strategy into the fine-tuning of Bert to construct adversarial samples. Both the adversarial sample and the original sample into the encoder to training. Then, to prevent the over-fitting phenomenon and the additional interference caused by the instability of adversarial training, we introduce the mixup data augmentation method after obtaining the hidden layer of the encoder output, which performs a linear interpolation between the output hidden layer. In addition, we also study the effect of linear interpolation at different positions on the model's joint extraction of entities and relations. We conduct experiments on a public dataset produced by the distant supervision method. The experimental results show that our method is effective for the joint extraction method based on tagging scheme. Not only improves the accuracy of entity and relation extraction, and ensures the robustness and generalization of the model. © 2021 IEEE.

关键词： Interpolation

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A Dataset for Generating Chinese Landscape Painting

A Dataset for Generating Chinese Landscape Painting

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2023 International Conference on Culture-Oriented Science and technology, CoST 2023

作者： Sun, Zengguo Li, Haoyue Wu, Xiaojun Zhang, Yumei Guo, Rong Wang, Biyu Dong, Liren Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (纸本)9798350357998

A dataset was constructed for intelligent generation of Chinese landscape paintings based on deep learning, which provides the scientific assistance for inheriting the traditional Chinese culture. This dataset is originated from the two of top ten paintings in ancient China, i.e., 'Dwelling in the Fuchun Mountains' with ink wash style and 'A Thousand Li of Rivers and Mountains' with style of blue and green. This dataset contains landscape paintings and their corresponding sketches, as well as photos of natural landscapes. The samples of landscape paintings are achieved with the size of 256× 256 due to the technique of image preprocessing, the samples of sketches are generated by the canny edge detector, and the samples of landscape photos are formed by image flipping. The above samples of landscape paintings, sketches, and photos make the dataset more flexible for the generation of landscape paintings. Based on this dataset, landscape paintings can be generated from sketches and photos respectively, and landscape paintings can be transferred from one style to another. The experimental results demonstrate the suitability of the dataset for the field of landscape painting generation. The constructed dataset will be publicly available to the community. © 2023 IEEE.

关键词： Generative adversarial networks

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Relation Transformation based on Graph Convolution Network for Entity Alignment

Relation Transformation based on Graph Convolution Network f...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Yang, Luheng Wang, Zhihui Zhu, Tingting Chen, Jianrui Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Entity alignment has lately received great attention for its key role in knowledge fusion, which has a goal that links entities with the same meaning from different knowledge graph. Most recently, many novel methods based on graph convolution network (GCN) have emerged as interesting models for entity alignment. Although these existing methods can further improve the quality of entity embedding, they ignored the influence of the embeddings of multiple relationships between entities on entity alignment. In addition, current models do not considerably learn the embeddings of relationship. To address these problems, we adopt a novel Relation Transformation based on Graph Convolution Network for entity alignment, named RT-GCN. Specifically, we point out a new GCN to aggregate the information of entities, which aims to strikingly obtain entities embeddings. Moreover, we develop a novel relational transformation method to generate relational embeddings. The crucial role of relational transformation is to strengthen the process of alignment and improve the robustness of the model. Experimental results on three datasets state that the proposed model RT-GCN performs surprisingly better than the state-of-the-art methods. © 2022 IEEE.

关键词： Convolution

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