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An Errors Correction Model for the Errors of Non-word and Real-word in English Composition

Journal of Computers (Taiwan) 2022年第1期33卷 139-150页

作者： Huang, Guimin Li, Maolin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

In the procedure from composing English, it is inevitable to face the phenomenon of word writing errors. In recent years, English composition automatic correcting system has attracted much attention. However, the precision of the existing word errors correcting system is vague generalization. So as to move forward the accuracy of checking and correcting word errors, this paper designs a word errors correction model based on natural language processing technology. This model designs phoneme matching method based on an improved IDM algorithm, and combined with a non-word input errors correction method based on character distance. The accuracy of correcting non-word errors in this model reached 86.5%. The study also proposes a real-word errors correction method, which is implemented basing on the real-word confusion set and combining the binary statistical model and the GloVe word vector model, improving the real-word errors correction method based on feature annotation of the real-word confusion set, with an accuracy of 77.9%. © 2022 Computer Society of the Republic of China. All rights reserved.

关键词： Errors

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Adaptive Sliding Mode Synchronization of Different Hyperjerk Chaotic Systems Using RBF Neural Network 11

Adaptive Sliding Mode Synchronization of Different Hyperjerk...

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11th International Conference on intelligent control and Information processing, ICICIP 2021

作者： Zhang, Baojie Chongqing Three Gorges University College of Computer Science and Engineering Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control Chongqing Wanzhou 404100 China

ISBN: (纸本)9781665425155

In this paper, we consider the synchronization of hyperjerk chaotic systems with different structures. Besides the order, the systems are unknown with external disturbances. We use sliding mode control method to deal with the external disturbances. Radial basis function (RBF) neural network is proposed to approximate the unknown system. Based on RBF neural network, adaptive sliding mode synchronization of different hyperjerk chaotic systems are introduced. Numerical results show the effectiveness of the synchronization scheme. © 2021 IEEE.

关键词： Radial basis function networks

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An Analysis Model of Potential Topics in English Essays Based on Semantic Space

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Journal of Computers (Taiwan) 2022年第1期33卷 151-164页

作者： Huang, Guimin Zhang, Xiaowei Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

With the reform of English examination in China in recent years, the automatic evaluation of English essays as subjective questions has always been the focus and difficulty of research. The existing automatic essay evaluation system (AEE) has obtained good feedback on the vocabulary, syntactic and other features of English essays, but there is still a problem of low accuracy in the analysis of potential topic in English essay. In order to solve this problem, this paper takes relational triples as the carrier to analyze the potential topics of English essays. By constructing the hierarchical topic trees hybrid semantic spaces to carry out topic clustering, distributed representation of topic relational triples and topic set extension in English essays. Then, based on the improved on-topic analysis algorithm in this paper, the paper analyzes the topic of English essay in multiple dimensions to obtain more abundant potential on-topic semantic information. The experiment results show that the proposed model can reduce the noise caused by non-topic words effectively, and improve the fine-grained topic semantic space in English essays, and the proposed model has better performance than the current methods of on-topic analysis in English esssays. © 2022 Computer Society of the Republic of China. All rights reserved.

关键词： Semantics

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Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral image

arXiv

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arXiv 2025年

作者： Ding, Yao Kang, Weijie Yang, Aitao Zhang, Zhili Zhao, Junyang Feng, Jie Hong, Danfeng Zheng, Qinghe The Key Laboratory of Intelligent Control PLA Rocket Force University of Engineering Xi’an710025 China The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi’an710071 China The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral image (HSI) clustering has been a fundamental but challenging task with zero training labels. Currently, some deep graph clustering methods have been successfully explored for HSI due to their outstanding performance in effective spatial structural information encoding. Nevertheless, insufficient structural information utilization, poor feature presentation ability, and weak graph update capability limit their performance. Thus, in this paper, a homophily structure graph learning with an adaptive filter clustering method (AHSGC) for HSI is proposed. Specifically, homogeneous region generation is first developed for HSI processing and constructing the original graph. Afterward, an adaptive filter graph encoder is designed to adaptively capture the high and low frequency features on the graph for subsequence processing. Then, a graph embedding clustering self-training decoder is developed with KL Divergence, with which the pseudo-label is generated for network training. Meanwhile, homophily-enhanced structure learning is introduced to update the graph according to the clustering task, in which the orient correlation estimation is adopted to estimate the node connection, and graph edge sparsification is designed to adjust the edges in the graph dynamically. Finally, a joint network optimization is introduced to achieve network self-training and update the graph. The K-means is adopted to express the latent features. Extensive experiments and repeated comparative analysis have verified that our AHSGC contains high clustering accuracy, low computational complexity, and strong robustness. The code source will be available at https://***/DY-HYX. Copyright © 2025, The Authors. All rights reserved.

关键词： Adaptive filters

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Tracking control for High-order Fully Actuated System with Input Constraints by Explicit Reference Governor

Tracking Control for High-order Fully Actuated System with I...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Lin Yang Yuanlong Li Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

This paper studies the tracking control problem of high-order fully actuated system subject to input constraints. Since there exist input constraints, an inappropriate reference signal being tracked may cause actuator saturation and further cause system instability. For solving the problem, we firstly design a generalized proportional differential controller to ensure the stability of the closed-loop system without input constraints. Then, an explicit reference governor is constructed to modify the reference signal such that the applied reference signal never exceeds the saturation limits, and the equilibrium of the resulting closed-loop system is globally asymptotically stable. Furthermore, we prove that the applied reference will converge to the original reference as much as possible. Therefore, the system output finally converges to the original reference by tracking the applied reference. A numerical example demonstrates the validity of the proposed method in this paper.

关键词：

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Integrated Guidance and control Design Subject to Actuator Saturation Based on Fully Actuated System Approach

Integrated Guidance and Control Design Subject to Actuator S...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Lin Yang Yuanlong Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Based on fully actuated system approach, this paper proposes a design scheme for integrated guidance and control systems subject to actuator saturation. By elimination elevation-order way, the integrated guidance and control model in the pitch channel is transformed into a high-order fully actuated system with uncertain disturbances and actuator saturation. Firstly, a disturbance observer is constructed to estimate the disturbances generated by model nonlinearities, unknown target maneuvers, variations of aerodynamic parameters, etc. Then, an observer-based fully actuated controller is designed to stabilize the integrated guidance and control system and perform disturbance attenuation. Since the missile’s deflection angle for pitch control is constrained by actuator saturation, an anti-windup compensator is introduced into the designed fully actuated controller to reduce the negative impact of saturation. Moreover, the stability conditions of the closed-loop system are established by the Lyapunov stability theory and the invariant set principle. Finally, a numerical simulation is provided to verify the effectiveness of the proposed design scheme.

关键词： Actuators Missiles control design Estimation Numerical simulation Disturbance observers Stability analysis

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Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling

arXiv

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arXiv 2022年

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Tencent Shanghai China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well . In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2022, CC BY.

关键词： Deep neural networks

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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 96-105页

作者： Gang Bao Yide Zhang Zhigang Zeng Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New EnergyChina Three Gorges UniversityYichang 443002and Hubei Key Laboratory of Applied Mathematics(Hubei University)Wuhan 430074China Department of Medical Engineering California Institute of TechnologyPasadenaCalifornia 91125 USA School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan 430074China

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational *** neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear ***, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

关键词： Dopant drift memory memristive neural networks memristor

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