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Surface-PMSM Sensorless Control Strategy Based on Reduced-Order Linear Kalman Filter Cooperating With Prediction Error Rolling Compensation

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IEEE Transactions on Power Electronics 2025年第8期40卷 10804-10815页

作者： Xu, Sang Shen, Anwen Tang, Qipeng Wang, Miaomiao Luo, Pan Luo, Xin Xu, Jinbang National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors due to its strong robustness against measurement and system noise. However, the conventional method suffers from poor position and speed estimation accuracy under dynamic conditions and high computational cost. In order to solve these problems, an enhanced sensorless control strategy based on reduced-order linear Kalman filter (RLKF) cooperating with prediction error rolling compensation is introduced in this article. Differing from conventional methods, a reduced-order linear state equation encompassing rotor position, speed, and torque disturbance is constructed. Then, by obtaining the prediction error of system output using the motor current equation in the predictive reference frame, the optimal estimation of rotor position and speed in the KF algorithm is achieved based on the reduced-order model. This not only simplifies the calculation process but also enhances the position and speed estimation accuracy under dynamic conditions. Meanwhile, a prediction error rolling compensation algorithm is developed to minimize the estimation deviations caused by motor parameter variations. In addition, the system's dynamic performance is further improved with an adopted speed controller based on torque disturbance feedforward and acceleration feedback. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy. © 1986-2012 IEEE.

关键词： Permanent magnets

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A text dependent copy-paste plagiarism and text-rewriting plagiarism model

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Journal of Intelligent and Fuzzy Systems 2025年第6期48卷 849-858页

作者： Guo, Qingkai Huang, Guimin Wang, Yabing Zhou, Ya School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin China

Plagiarism is common in English writing exams. Researchers classify plagiarism into copy-paste and text-rewriting plagiarism, but existing models need help with problems such as the single way of checking and unsatisfactory results. Aiming at the copy-paste problem in English writing plagiarism, this paper proposes a digital fingerprint model based on the N-Gram window jumping mechanism. The model incorporates a sliding window and an improved matching tool to solve the problems of excessive fingerprint density and low checking efficiency in text extraction. Meanwhile, the model adds a Fisher-Yates shuffle algorithm with a salt parameter to crack the hash collision in text matching. The experiments show that the model can detect copy-paste plagiarism in English composition. For text rewriting plagiarism, this paper designs a TextCNN-BiGRU-based model, which combines TextCNN and BiGRU so that the extracted text semantic information considers the text’s local and contextual features. The experiments show that the model improves the accuracy by 1.9 percentage points and the F1 value by 1.2 percentage points on the MRPC dataset compared with other models. © 2025 – IOS Press. All rights reserved.

关键词： copy and paste Plagiarism checking text rewriting

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Causal Representation Enhances Cross-Domain Named Entity Recognition in Large Language Models

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Computers, Materials & Continua 2025年第5期83卷 2809-2828页

作者： Jiahao Wu Jinzhong Xu Xiaoming Liu Guan Yang Jie Liu School of Artificial Intelligence Zhongyuan University of TechnologyZhengzhou450007China School of Computer Science Zhongyuan University of TechnologyZhengzhou450007China Zhengzhou Key Laboratory of Text Processing and Image Understanding Zhengzhou450007China School of Information Science and Technology North China University of TechnologyBeijing100144China

Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and *** order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively *** results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.

关键词： Large language model entity bias causal graph structure

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Dynamic Prediction of Intraoperative Hypotension Based on Hemodynamic Monitoring Data With a Transformer-Based Deep Learning Model

Dynamic Prediction of Intraoperative Hypotension Based on He...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Kai Ren, Mucheng Xu, Jun Zeng, Xian Nanjing University of Information Science and Technology Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing China

ISBN: (纸本)9798350386226

Timely prediction and intervention for Intraoperative Hypotension (IOH), a prevalent complication associated with general anesthesia, is crucial to prevent severe postoperative outcomes. While existing machine learning methods for IOH prediction have shown promise, they face limitations such as reliance on single data sources and disregard for crucial features like the trend in Arterial Blood Pressure (ABP) waveforms. To address these challenges, this paper proposes a novel multichannel deep learning framework that combines Convolutional Neural Networks (CNN) and Transformers for automatic IOH prediction. The model leverages four types of physiological waveforms, including ABP, ElectroCardioGram (ECG), photoplethysmography (PLE), and Carbon Dioxide (CO2), to capture both local and global information, enhancing information representation. Experimental results on retrospective data of 14,140 adult patients undergoing non-cardiac surgery from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital (from January 6, 2005 to March 1, 2014), demonstrate that the proposed model consistently outperforms other methods, achieving superior AUROC values of 0.943, 0.928, and 0.923 at 5, 10, and 15 minutes before the event, respectively. These results demonstrate the powerfulness of multi-modal data source as well as the importance of ABP waveform trends. Additionally, the incorporation of multi-task learning and the attention mechanism validates the effectiveness and superiority of our model, highlighting its potential for proactive clinical interventions and improved postoperative patient outcomes. © 2024 IEEE.

关键词： Blood pressure

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Dilation-Supervised Learning: A Novel Strategy for Scale Difference in Retinal Vessel Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1693-1707页

作者： Wang, Huadeng Zuo, Wenbin Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China

Retinal fundus image segmentation based on deep learning is an important method for auxiliary diagnosis of ophthalmic diseases. Due to the scale difference of the blood vessels and the imbalance between foreground and background pixels in the fundus image, the deep learning network will inevitably ignore thin vessels when downsampling and feature learning. For the scale difference problem, this article aims to tackle its limitation from two perspectives: changing the supervised approach and adapting the feature learning. Correspondingly, a dilation-supervised learning method and an adaptive scale dimensional attention mechanism which are used to construct a two-stage segmentation model is proposed. Moreover, we introduce a quantitative approach to evaluate the scale difference of the blood vessels. With the help of the proposed weighted loss function, the segmentation results are refined, and the class imbalance problem between foreground and background pixels is resolved. Finally, the proposed adaptive threshold selection method is used in the postprocessing of segmentation results. The experiments on DRIVE, STARE, CHASE_DB1, and HRF datasets show that the proposed method achieves better segmentation performance compared with other state-of-the-art methods, and has good generalization ability and robustness. © 2020 IEEE.

关键词： Semantics

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Dynamic Student Classification with Forgetting Mechanisms on Memory Networks for Knowledge Tracing 5

Dynamic Student Classification with Forgetting Mechanisms on...

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5th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Huang, Guimin Qin, Xiangqian School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350368284

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. Therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods. © 2024 IEEE.

关键词： Contrastive Learning

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Context-Aware Grammatical Error Correction Model 5

Context-Aware Grammatical Error Correction Model

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Lin, Jianxing Huang, Guimin Wang, JiaHao Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331506612

The pursuit of accurate and fluent English communication is the cornerstone of global academic exchange and expression. The accuracy of written English is crucial for effective discourse;however, despite advancements in grammatical error correction (GEC) technology, these systems often fall short when analyzing coherent text. This paper delves into the complexities of English writing and the challenges posed by current GEC systems, advocating for a shift toward understanding document-level context in error correction. We propose a dual-encoder architecture within an encoder-decoder model, referred to as the Context-Aware Grammatical Error Correction (CAGEC) model. The CAGEC model employs an innovative dual-encoder structure, combining the encoder of the Transformer model with a Bi-GRU (Bidirectional Gated Recurrent Unit) neural network, and integrates encoding into the decoder through attention and gating mechanisms to achieve a deep understanding of the source sentence and its context. © 2024 IEEE.

关键词： Decoding

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Zhou, Ya Chen, Chen Huang, Guimin Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331506612

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%. © 2024 IEEE.

关键词： Semantics

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5350-5367页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Layout Three-dimensional displays image reconstruction Estimation Cameras Floors Cognition Transforms Training Semantics

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： image fusion

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