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Braille Character Segmentation Algorithm Based on Gaussian Diffusion

computers, Materials & Continua 2024年第4期79卷 1481-1496页

作者： Zezheng Meng Zefeng Cai Jie Feng Hanjie Ma Haixiang Zhang Shaohua Li School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02%, respectively.

关键词： computer vision image recognition optical braille recognition

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YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

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computers, Materials & Continua 2024年第11期81卷 3261-3280页

作者： Chenghai Yu Zhilong Lu School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Railway turnouts often develop defects such as chipping,cracks,and wear during *** not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger *** advances in defect detection technologies,research specifically targeting railway turnout defects remains *** address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex *** enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex *** the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection ***,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection *** to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and *** on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.

关键词： YOLO railway turnouts defect detection mamba FPN(Feature Pyramid Network)

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A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

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Acta Oceanologica Sinica 2024年第12期43卷 13-33页

作者： Xiao Yin Taoxing Wu Jie Yu Xiaoyu He Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China

Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.

关键词： significant wave height forecasting dynamic seasonal variation dynamic graph neural networks

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Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors

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Chinese Journal of Electronics 2024年第6期33卷 1515-1525页

作者： Anhua MA Su PAN Weiwei ZHOU Broadband Wireless Communication College Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing Tech University

This paper proposes a Markov decision process based service migration algorithm to satisfy quality of service(QoS) requirements when the terminals leave the original server. Services were divided into real-time services and non-real-time services, each type of them has different requirements on transmission bandwidth and latency,which were considered in the revenue function. Different values were assigned to the weight coefficients of QoS parameters for different service types in the revenue and cost functions so as to distinguish the differences between the two service types. The overall revenue was used for migration decisions, rather than fixed threshold or instant *** Markov decision process was used to maximize the overall revenue of the system. Simulation results show that the proposed algorithm obtained more revenue compared with the existing works.

关键词： Energy consumption Costs Markov decision processes Simulation Quality of service Market research Cost function Real-time systems Energy efficiency Servers

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Sub-pixel multi-scale fusion network for medical image segmentation

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Multimedia Tools and Applications 2024年第41期83卷 89355-89373页

作者： Li, Jing Chen, Qiaohong Fang, Xian School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

CNNs and Transformers have significantly advanced the domain of medical image segmentation. The integration of their strengths facilitates rich feature extraction but also introduces the challenge of mixed multi-scale feature fusion. To overcome this issue, we propose an innovative deep medical image segmentation framework termed Sub-pixel Multi-scale Fusion Network (SMFNet), which effectively incorporates the sub-pixel multi-scale feature fusion results of CNN and Transformer into the architecture. In particular, our design consists of three effective and practical modules. Primarily, we utilize the Sub-pixel Convolutional Module to synchronize the extracted features at multiple scales to a consistent resolution. In the next place, we develop the Three-level Enhancement Module to learn features from adjacent layers and perform information exchange. Lastly, we leverage the Hierarchical Adaptive Gate to fuse information from other contextual levels through the Sub-pixel Convolutional Module. Extensive experiments on the Synapse, ACDC, and ISIC 2018 datasets demonstrate the effectiveness of the proposed SMFNet, and our method is superior to other competitive CNN-based or Transformer-based segmentation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Medical imaging

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Automated GUI widgets classification

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Frontiers of computer science 2023年第1期17卷 233-235页

作者： Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China School of Computer Science and Engineering Nanyang Technological UniversitySingap

1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these *** in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific *** authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI *** et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

关键词： GUI User classify

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Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

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computers, Materials & Continua 2023年第9期76卷 3379-3392页

作者： Shaohua Li Haixiang Zhang HanjieMa Jie Feng Mingfeng Jiang School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Scale variation is amajor challenge inmulti-person pose *** scenes where persons are present at various distances,models tend to perform better on larger-scale persons,while the performance for smaller-scale persons often falls short of ***,effectively balancing the persons of different scales poses a significant *** this paper proposes a newmulti-person pose estimation model called FSANet to improve themodel’s performance in complex *** model utilizes High-Resolution Network(HRNet)as the backbone and feeds the outputs of the last stage’s four branches into the DCB *** dilated convolution-based(DCB)module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each ***,the attention operation-based(AOB)module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of ***,predictions are made using the heatmap *** model can recognize images with diverse scales and more complex semantic *** results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets,validating the effectiveness of our proposed approach.

关键词： computer vision high-resolution network human pose estimation

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Interpreting Randomly Wired Graph Models for Chinese NER

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computer Modeling in Engineering & sciences 2023年第1期134卷 747-761页

作者： Jie Chen Jiabao Xu Xuefeng Xi Zhiming Cui Victor S.Sheng The School of Electronic and Information Engineering SuzhouUniversity of Science and TechnologySuzhouChina The School of Computer Science Texas Tech UniversityTexasUSA

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing(NLP)***,most existing approaches only focus on improving the performance of models but ignore their *** this work,we propose a Randomly Wired Graph Neural Network(RWGNN)by using graph to model the structure of Neural Network,which could solve two major problems(word-boundary ambiguity and polysemy)of ***,we develop a pipeline to explain the RWGNNby using Saliency Map and Adversarial *** results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.

关键词： Named entity recognition graph neural network saliency map random graph network interpretation

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Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

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computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering National Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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