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Constructing Three-way Decision with Fuzzy Granular-ball Rough Sets Based on Uncertainty Invariance

IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1781-1792页

作者： Yang, Jie Liu, Zhuangzhuang Wang, Guoyin Zhang, Qinghua Xia, Shuyin Wu, Di Liu, Yanmin Zunyi Normal University School of Physics and Electronic Science Zunyi563002 China Jiangsu University of Science and Technology School of Computer Zhenjiang212100 China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China Zunyi Normal University School of Mathematics Zunyi563002 China

Granular-ball computing (GBC) proposed by Xia adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility. Moreover, GBC greatly improves the efficiency by replacing point input with granular-ball. However, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. In this paper, we introduce three-way decision (3WD) into GBC to construct a novel three-way decision with fuzzy granular-ball rough sets (3WD-FGBRS) from the perspective of uncertainty. This helps to construct reasonable multi-granularity spaces for handling complex decision problems with uncertainty. Firstly, 3WD-FGBRS is constructed in a data-driven method based on fuzziness, which avoids the subjective definition of certain risk parameters when calculating the threshold pairs. Based on 3WD-FGBRS, we further propose a sequential three-way decision with fuzzy granular-ball rough sets (S3WD-FGBRS) and analyze the fuzziness loss of multilevel decision result in S3WD-FGBRS. Then, the optimal granular-ball space selection mechanism of S3WD-FGBRS is introduced by combining fuzziness and granular-ball space distance. Finally, extensive comparative experiments are conducted with 3 state-of-the-art GB-based classifiers and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our models almost outperform other comparison methods in terms of effectiveness, efficiency and robustness. © 1993-2012 IEEE.

关键词： Granular computing

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DEFECT PREDICTION METHOD FOR POWER TRANSMISSION EQUIPMENT BASED ON GREY WOLF OPTIMIZATION ALGORITHM AND LSTM-HNA

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer science 2024年第4期2024卷 125-140页

作者： Yang, Zhennan Zhang, Jie Zhang, Ke Sun, Youqiang Huang, Wenli Zou, Le School of Artificial Intelligence and Big Data Hefei University Hefei230601 China Institute of Intelligent Machine Hefei Institute of Physical Science Chinese Academy of Sciences Hefei230031 China Anhui NARI Jiyuan Power Grid Technology Co. Ltd Hefei230088 China

Predicting defects in power transmission equipment is crucial for ensuring the stable operation of the power grid. However, existing methods suffer from shortcomings such as ignoring temporal features, susceptibility to irrelevant feature interference, and lack of hyperparameter optimization. To address this, this paper proposes a novel method for defect prediction in power transmission equipment that combines the Grey Wolf Optimization (GWO) algorithm with Long Short-Term Memory networks and an attention mechanism. This method utilizes LSTM networks to extract temporal feature information, innovatively designs a Hidden-layer Neuron Attention module (HNA) to reduce the impact of irrelevant features, incorporates the Grey Wolf Optimization algorithm for automatic hyperparameter tuning, and proposes a joint training strategy for GWO and LSTM-HNA to improve efficiency. Extensive experiments validate the effectiveness of the proposed method, achieving a high accuracy of 97.37% in defect prediction tasks for power transmission equipment. Precision, recall, F1 score, and other metrics outperform other methods, providing a new approach to enhancing the monitoring and fault prevention capabilities of power transmission equipment. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Long short-term memory

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Multi-view Unsupervised Feature Selection Guided by Diversity and Consensus Structure 11th

Multi-view Unsupervised Feature Selection Guided by Diversi...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Zou, Jinlin Ma, Yingcang Yang, Xiaofei Xing, Zhiwei School of Science Xi’an Polytechnic University Xi’an710048 China Xi’an International Science and Technology Cooperation Base for Big Data Analysis and Algorithms Xi’an710048 China

ISBN: (纸本)9783031927430

The core challenge in multi-view feature selection lies in identifying discriminative features that capture both consensus and diversity information. Feature selection methods based on consensus learning have garnered significant attention due to their ability to reveal the common underlying structure across different views. To effectively capture both homogeneous and heterogeneous information across views, this paper proposes a novel multi-view unsupervised feature selection method guided by diversity and consensus structure, referred to as GDCSMuFS. The diversity structure learning across multiple views is crucial for preserving the heterogeneous information in the selected features. Building on this concept, the proposed method begins by incorporating both homogeneous and heterogeneous information. It first learns the multi-view cluster structure matrix through non-negative matrix factorization. Then, multi-view spectral analysis is employed to integrate the cluster structure matrices into a consensus pseudo-label matrix, enabling the extraction of consensus information at the label level. Subsequently, a feature selection matrix is constructed under the shared constraints of consensus labels and graph structures. Finally, sparse constraints are applied to the feature selection matrix, and the model is solved using an alternating iterative algorithm. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method, with comparative results showing its superiority over existing algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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PACDNet:A landslide extraction network based on parallel attention 3

PACDNet:A landslide extraction network based on parallel att...

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3rd International Conference on Environmental Remote Sensing and Geographic Information technology, ERSGIT 2024

作者： Zhu, Meijiang Wang, Lei Chen, Fang Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

Landslides pose a serious threat to the economy and human safety. Rapid and efficient landslide extraction is essential for post-disaster relief and reconstruction efforts. Current landslide extraction techniques still face challenges in accurately identifying landslides boundaries. To address this issue, we propose the Parallel Attention Change Detection Network (PACDNet), a network designed to extract landslides by calculating changes between pre-disaster and post-disaster images. Our method is an encoder-decoder network that utilizes a Siamese network structure with StarBlock from StarNet in the encoding stage, allowing for higher feature extraction capability with lower computational costs. In the decoding phase, the Parallel Attention Module (PAM) enhances the model's landslide boundaries extraction capability by simultaneously computing spatial attention and channel attention. To validate the effectiveness of our proposed PACDNet, we used the Global Very-High-Resolution Landslide Mapping (GVLM) dataset. This dataset includes pre-disaster and post-disaster images of 17 different landslide events. After comparing our model with four recently proposed models, PACDNet demonstrated improvements of 2.31% and 3.41% in Intersection over Union (IoU) on the Kaikoura and Tbilisi datasets, respectively. This indicates that PACDNet has a significant advantage in landslide extraction across different landslide events. © 2025 SPIE.

关键词： Network coding

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shi, Chenglong Geng, Haibin Jiang, Wenfeng Li, Jinbao Wu, Chao Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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Spatial-Temporal Motion Compensation for Learned Video Compression

Spatial-Temporal Motion Compensation for Learned Video Compr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Qian Liu, Wenting Lu, Hao Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing211100 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China

ISBN: (纸本)9781665410205

In the past few years, learned video compression has received increasing attention. However, most current learned methods rely on the bilinear warping operations in motion compensation, which is equivalent to a low-pass filtering operation and brings the distortion of reconstruction. To address this problem, we propose a spatial-temporal motion compensation for learned video compression (STMC-LVC). STMC-LVC uses a spatial-temporal motion compensation network (STMC-Net) to fully consider the spatial-temporal correlation between successive frames, performing accurate motion compensation. Specifically, STMC-Net mainly consists of the initial feature prediction module and the fusion module. The initial feature prediction module predicts reference features based on DCN to provide sufficient information for motion compensation. The fusion module obtains temporal attention information by calculating the similarity between features, performs spatial attention operations, and finally performs spatial-temporal fusion, further improving the robustness of motion compensation. In addition, STMC-LVC uses a conditional coding framework. We use a concatenation of the current feature and predicted feature as context to explore spatial-temporal correlations in feature space. Experimental results show that our method effectively improves video compression. Specifically, our model achieves an average of 33.86% and 58.70% bitrate savings than x265 (veryslow) on PSNR and MS-SSIM, respectively. © 2024 IEEE.

关键词： Low pass filters

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Study on the Differences of Civil Flight Regularity Indicators under Different Statistical Standards 4

Study on the Differences of Civil Flight Regularity Indicato...

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4th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2022

作者： Li, Qian Zhang, Xinyue Miao, Jiahe China Academy of Civil Aviation Science and Technology Big Data Analysis and Application Center Beijing China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Flight regularity rate is one of the important indicators to describe flight normalcy. With the development of civil aviation, several important changes have been made to the flight regularity rate statistical standards. How to measure the flight regularity rate fairly and accurately is related to the correctness of the subsequent research direction. To this end, this paper is based on the data of domestic flight operation in summer and autumn seasons of 2021, uses python to clean and pre-process the data, establishes indicators and clusters through Tableau, then finds the differences of flight regularity rate under different statistical standards, and finally makes suggestions on how to improve the statistical method of flight normalcy and improve the level of flight normalcy effectively. © 2022 IEEE.

关键词： Statistics

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Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 IEEE.

关键词： coordinate network cross attention flow pyramid learned video compression

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Engineering Vehicle Recognition and Multi-Object Tracking System based on YOLOv5 and DeepSort Algorithm 5

Engineering Vehicle Recognition and Multi-Object Tracking Sy...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Wu, Yanbo Liang, Xintao Cui, Quan Wang, Qingyan Heilongjiang Government Affairs Big Data Center Harbin150028 China School Of Measurement And Control Technology And Telecommunications Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

This study explores traditional object detection algorithms and deep neural network-based engineering vehicle detection algorithms. We applied preprocessing algorithms such as image denoising, enhancement, and edge detection to process the images and constructed our own trainable dataset through labeling. Next, we used the YOLOv5 algorithm, which has high detection accuracy and real-time capability, for engineering vehicle detection. To address the issues of missed targets and predicted bounding box misalignment, we introduced the DeepSORT algorithm for target prediction and tracking. This algorithm utilizes Kalman filtering for estimation and updates and employs the Hungarian algorithm to associate data between consecutive frames, thereby achieving engineering vehicle tracking. To tackle the problem of frequent identity switching due to camera motion and non-uniform vehicle movements, we adopted the modified GIoU to calculate the intersection over union between trajectories and detected target bounding boxes, reducing identity jumps during the tracking process. Finally, we designed an engineering vehicle detection and tracking system and applied this algorithm to practical production. © 2023 Owner/Author.

关键词： Deep neural networks

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A Novel Global-Local Representations Network for Speech Steganalysis 5

A Novel Global-Local Representations Network for Speech Steg...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Xu, Shulong Tian, Hui Quan, Hanyu Lu, Jing College of Computer Science and Technology Xiamen Key Laboratory of Data Security and Blockchain Technology Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University China

ISBN: (纸本)9781450396899

With the popularity of the internet and the development of network services, Steganography based on speech stream has become a research hotspot in information hiding. To improve the detection performance of steganalysis of multiple steganography methods, in this paper, we proposed the Global-Local Representations Network (GLRN), which consists of a Global Correlation Extraction (GCE) module and a Local Correlation Enhancement (LCE) module. Firstly, considering the inter-class differences of different coding elements, the GCE module is used to capture the global correlation of different coding elements by using multi-channel modeling. Then, we realize that the process of global correlation extraction suffers from the loss of detailed information, so the LCE module is used to capture local correlations to complement the global features. The experiments show that the GLRN achieves the start-of-art detection performance. © 2022 ACM.

关键词： Steganography

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