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Inductive Relation Prediction by Disentangled Subgraph Structure

Tsinghua Science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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State-of-Charge Estimation of Lithium Battery Based on Deep Residual Shrinkage Networks and a Variant Long Short Term Memory Neural Network

State-of-Charge Estimation of Lithium Battery Based on Deep ...

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第35届中国控制与决策会议

作者： Penghua Li Yihui Zhang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Accurate state-of-charge(SOC) estimation,which is critical to ensuring the safe and reliable operation of battery management systems in electric vehicles,is still a challenging task due to sophisticated battery dynamics and ever-changing ambient *** paper proposes a method combining DRSN and ASTLSTM neural networks to estimate lithium batteries' state of charge(SOC).The proposed method can not only utilize the historical information of the data but also eliminate unimportant features by inserting soft thresholding into the deep *** reveal the performance of the proposed method,the results are compared with several other methods from the *** comparative experimental results show that the proposed method achieves a lower root mean square error(RMSE) of 0.83% with normal data and RMSE of 0.98% with noisy data,respectively.

关键词： Lithium Battery State of Charge Recurrent Neural Network Deep Residual Shrinking Network

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LAGIM:A Label-Aware Graph Interaction Model for Joint Multiple Intent Detection and Slot Filling

LAGIM:A Label-Aware Graph Interaction Model for Joint Multip...

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第35届中国控制与决策会议

作者： Penghua Li Ziheng Huang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Multi-intent spoken language understanding joint model can handle multiple intents in an utterance and is closer to complicated real-world scenarios,attracting increasing ***,existing research(1) usually focuses on identifying implicit correlations between utterances and one-hot encoding while ignoring intuitive and explicit original label characteristics;(2) only considers the token-level intent-slot interaction,which results in the limitation of the *** this paper,we propose a Label-Aware Graph Interaction Model(LAGIM),which captures the correlation between utterances and explicit labels' semantics to deliver enriched ***,a global graph interaction module is constructed to model the sentence-level interaction between intents and ***,we propose a novel framework to model the global interactive graph based on the injection of the original label semantics,which can fuse explicit original label features and provide global *** results show that our model outperforms existing approaches,achieving a relative improvement of 11.9% and 2.1%overall accuracy over the previous state-of-the-art model on the MixATIS and MixSnips datasets,respectively.

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State of Health Estimation of Lithium-ion Battery Based On Improved Transfer Learning

State of Health Estimation of Lithium-ion Battery Based On I...

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第35届中国控制与决策会议

作者： Penghua Li Kangheng Shan Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications

Many existing methods of forecasting the stateof-health(SOH) assume that training and testing data follow the same *** model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution *** order to meet this challenge,this paper proposes an improved method Mutual Information Domain-Adversial Neural Networks(DANN) based on domain adaptation,which improves the domain discriminator to better extract domain invariant *** addition,to avoid the loss of target information,the mutual information among target features,source features,and original target data is calculated to fix the features on the target site during the migration *** from the traditional methods,we only use 40% of the data sets for training,and the rest are used for prediction,so we can complete the prediction of more *** results show that this method can accurately and stably predict SOH.

关键词： transfer learning state-of-health lithium-ion battery neural network

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

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Multimedia Tools and Applications 2024年第42期83卷 90553-90572页

作者： Jiang, Xiaohui Zhu, Haijiang An, Ning Xie, Binjian Wei, Hao Tang, Fulin Wu, Yihong College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Multimodal Artificial Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing100013 China Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China

In many 3D reconstruction, Simultaneous Localization and Mapping (SLAM), AR, and VR systems, the camera focal length constantly changes slightly due to loose hardware, causing their performances to deteriorate. To address this problem, we propose a novel camera self-calibration method that incorporates clustering prior and nonlinear optimization. By leveraging the Kruppa equation, we roughly estimate multiple analytical solutions for the camera focal length from an image sequence. Then, robust clustering based on the Gaussian mixture model is introduced to obtain an initial camera focal length value. Finally, a nonlinear cost function is proposed to refine the initial camera focal length value. Extensive experimental results on simulation and real data demonstrate that the proposed method outperforms the state-of-the-art methods, exhibiting good robustness and high accuracy. Moreover, the proposed method can be used in real-time videos for SLAM systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cost functions

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State-of-Charge Estimation of Lithium Battery Based on Deep Residual Shrinkage Networks and a Variant Long Short Term Memory Neural Network

State-of-Charge Estimation of Lithium Battery Based on Deep ...

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Chinese Control and Decision Conference, CCDC

作者： Penghua Li Yihui Zhang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

Accurate state-of-charge (SOC) estimation, which is critical to ensuring the safe and reliable operation of battery management systems in electric vehicles, is still a challenging task due to sophisticated battery dynamics and ever-changing ambient conditions. This paper proposes a method combining DRSN and ASTLSTM neural networks to estimate lithium batteries' state of charge (SOC). The proposed method can not only utilize the historical information of the data but also eliminate unimportant features by inserting soft thresholding into the deep architectures. To reveal the performance of the proposed method, the results are compared with several other methods from the literature. The comparative experimental results show that the proposed method achieves a lower root mean square error (RMSE) of 0.83% with normal data and RMSE of 0.98% with noisy data, respectively.

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State of Health Estimation of Lithium-ion Battery Based On Improved Transfer Learning

State of Health Estimation of Lithium-ion Battery Based On I...

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Chinese Control and Decision Conference, CCDC

作者： Penghua Li Kangheng Shan Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

Many existing methods of forecasting the state- of-health(SOH) assume that training and testing data follow the same distribution. The model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution discrepancy. In order to meet this challenge, this paper proposes an improved method Mutual Information Domain-Adversial Neural Networks (DANN) based on domain adaptation, which improves the domain discriminator to better extract domain invariant features. In addition, to avoid the loss of target information, the mutual information among target features, source features, and original target data is calculated to fix the features on the target site during the migration process. Different from the traditional methods, we only use 40% of the data sets for training, and the rest are used for prediction, so we can complete the prediction of more scenes. Experimental results show that this method can accurately and stably predict SOH.

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