检索结果-内蒙古大学图书馆

image Denoising Using Bandelets and Hidden Markov Tree Models

Chinese Journal of Electronics 2023年第4期19卷 646-650页

作者： Wenge Zhang Suang Wang Fang Liu Xinbo Gao Licheng Jiao School of Computer Science and Technology Xidian University Xi'an China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, both the marginal and joint statistics of second generation Orthogonal bandelet transform (OBT) coefficients of natural images are firstly studied, and the highly non-Gaussian marginal statistics and strong interscale, interlocation and interdirection dependencies among OBT coefficients are found. Then a Hidden Markov tree (HMT) model in OBT domain which can effectively capture all dependencies across scales, locations and directions is developed. The main contribution of this paper is that it exploits the edge direction information of OBT coefficients, and proposes an image denoising algorithm (B-HMT) based on HMT model in OBT domain. We apply B-HMT to denoise natural images which contaminated by additive Gaussian white noise, and experimental results show that B-HMT outperforms the Wavelet HMT (W-HMT) and Contourlet HMT (C-HMT) in terms of visual effect and objective evaluation criteria.

关键词： Geometry Analytical models Additives Hidden Markov models Transforms White noise Visual effects

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Visual localization and map construction based on ground texture 19th

Visual localization and map construction based on ground tex...

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19th Chinese intelligent Systems Conference, CISC 2023

作者： Chen, Xin Yu, Lei School of Mechanical and Electric Engineering Soochow University Suzhou China Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education Lanzhou Jiaotong University Lanzhou China Jiangsu Key Laboratory of Advanced Manufacturing Technology Huaiyin Institute of Technology Huai’an China

ISBN: (纸本)9789819968817

This paper describes a visual localization and map construction method based on ground texture. The ground texture is scanned twice in a zigzag pattern and divided into a grid to avoid distortion of the stitched image. A global optimization method is used to calculate the image pose by reprojection constraint and key frame selection to stitch the global map. For positioning, the global positioning is transformed into a local positioning process through global search and local optimization, which improves the speed and accuracy of positioning. For map updating, local optimization is performed by constructing loopbacks for possible stain occlusion or breakage problems to solve tracking loss and repositioning problems. Experimental results show that the ground texture stitching and localization results after global optimization match well with the true values, and the global maps constructed by stitching are aligned with the true physical locations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Textures

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Efficient Fabric Defect Detection Algorithm Based on Improved YOLOv5

Efficient Fabric Defect Detection Algorithm Based on Improve...

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Chinese Automation Congress (CAC)

作者： LiHua Tian AoZe Wang ZhiGang Sun Li Xiao dept. School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Huazhong University of Science and Technology Wuhan China

In order to address the issues of real-time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes the ESE_YOLOv5 network based on YOLOv5. Firstly, to address the relative redundancy of the neck detection network feature channels, a relatively lightweight and efficient convolution module is adopted to ensure accuracy while reducing computation and parameter volume. Furthermore, the Efficient Squeeze-Excitation (ESE) module is introduced into the backbone to optimize the dependency of feature channels, which enhances the model's feature extraction capacity and improves detection accuracy. Experimental results show that compared to YOLOv5, the proposed ESE_YOLOv5 model reduces computation and parameter volume while improving accuracy, meeting the needs of fabric defect detection for recognizing fabric defects that have similar characteristics to the background while maintaining real-time performance.

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A Novel Hybrid Model for Online Prediction of Rate of Penetration (ROP) in Drilling Process

A Novel Hybrid Model for Online Prediction of Rate of Penetr...

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2023 china Automation Congress, CAC 2023

作者： Wang, Yao Gan, Chao Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9798350303759

In this paper, a novel hybrid model is proposed for online prediction of rate of penetration (ROP) in drilling process, which including two parts (online data pre-processing and online hybrid modeling). In the first part, threshold filtering and Savitzky Golay (SG) filtering are both employed to enhance the quality of drilling data considering the expert experience and data characteristics. In the next part, a novel hybrid model with error compensation is established, which is combined the Bingham sub-model and gradient boosting decision tree (GBDT) sub-model. To better capture the dynamic changes of ROP, the hybrid model is updated with moving window strategy. Finally, compared simulation results with well-known ROP prediction models indicate the efficiency of the hybrid model. © 2023 IEEE.

关键词： Error compensation

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Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

arXiv

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arXiv 2025年

作者： Liu, Dingkun Li, Siyang Wang, Ziwei Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Transfer learning

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image Super-Resolution of Residual-Dense Connected Networks in Unmanned Deep-Sea Exploration Equipment 42

Image Super-Resolution of Residual-Dense Connected Networks ...

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42nd Chinese control Conference, CCC 2023

作者： Chu, Jiajun Cao, Weihua Gan, Chao Yang, Yulong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581543

Deep-sea unmanned exploration equipment is an important tool for exploring and developing the resources in the ocean, and it can survey the deep-sea environment more visually with the help of visual images. However, the complex and variable environment and the low resolution of the underwater lens lead to the poor resolution of the images acquired by the equipment. In this paper, we propose a residual-dense connected method applied to unmanned deep-sea exploration equipment to improve it's image resolution. The method uses dense connections within the residual structure to improve the model detail information acquisition to ensure accuracy and model stability of model. Secondly, through the study of the model performance, a high precision residual-dense connected model with less computational effort is designed. Finally, the model is trained and tested using environmental images in deep-sea conditions, and it is demonstrated that the method can be applied to deep-sea unmanned exploration equipment for fast, accurate, and stable image super-resolution processing. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： image resolution

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A Feature Difference Network for Defect Detection on the Surface with Complex Pattern of Ceramic Tile 42

A Feature Difference Network for Defect Detection on the Sur...

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42nd Chinese control Conference, CCC 2023

作者： Xiong, Jianru An, Jianqi Wang, Xing-Ao Ou, Huimin Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581543

The surface defects of ceramic tile greatly affect the service life of ceramic tile. At present, many detection methods of ceramic tile surface defects are mostly used for ceramic tiles with monochrome background or simple texture. However, many tiles with complex and irregular surface patterns are used in practical applications, but many methods cannot effectively detect surface defects in such tiles. This paper presents a double input feature difference network structure to overcome the limitation. First, a double input channel is constructed to extract features from the template image and the defect image respectively. Next, a method of feature difference is performed at different depths to suppress the background interference and prevent misclassification between different defect categories. Then a parameter-free attention module is embedded in the backbone to improve the ability of feature extraction. Experimental results show that this model effectively improves the mean average accuracy of 8.3% and the recall rate of 11.7%. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Surface defects

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MVCNet: Multi-View Contrastive Network for Motor imagery Classification

arXiv

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arXiv 2025年

作者： Wang, Ziwei Li, Siyang Chen, Xiaoqing Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has received significant attention due to its intuitive mechanism. However, most existing models rely on single-stream architectures and overlook the multi-view nature of EEG signals, leading to limited performance and generalization. We propose a multi-view contrastive network (MVCNet), a dual-branch architecture that parallelly integrates CNN and Transformer models to capture both local spatial-temporal features and global temporal dependencies. To enhance the informativeness of training data, MVCNet incorporates a unified augmentation pipeline across time, frequency, and spatial domains. Two contrastive modules are further introduced: a cross-view contrastive module that enforces consistency of original and augmented views, and a cross-model contrastive module that aligns features extracted from both branches. Final representations are fused and jointly optimized by contrastive and classification losses. Experiments on five public MI datasets across three scenarios demonstrate that MVCNet consistently outperforms seven state-of-the-art MI decoding networks, highlighting its effectiveness and generalization ability. MVCNet provides a robust solution for MI decoding by integrating multi-view information and dual-branch modeling, contributing to the development of more reliable BCI systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Analysis of MFCS topology for high efficiency and long lifetime

Analysis of MFCS topology for high efficiency and long lifet...

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第43届中国控制会议

作者： Hongfu Xiang Jiajian Wu Zhen Wang Jie Wang Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC)technology,MFCS can provide higher system power and longer service *** the number of stacks increases,different topologies can be formed between stacks,including parallel structures and series structures,and the physical characteristics of MFCS will also *** this study,the degradation of the stacks is first considered,and the system model of series structure and parallel structure including the degradation of the stacks is *** parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of *** the simulation of systems with different topologies,it is found that the power generation efficiency of the systems caused by the topologies varies greatly in different output power *** efficiency of the parallel structure in the low power stage is significantly better than that of the series structure,and the parallel structure is more conducive to the high efficiency of the system in the whole *** topological structures also have a greater impact on the degradation of *** degradation rates of stacks in parallel structures are close to each other,and the state of stacks is more *** overall life of the system in parallel structures is also better than that in series structures.

关键词： SOFC MFCS topological structure stack degradation

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Template Matching and Trend Feature Analysis-Based Data Pre-processing Method for Seismic Wave Detection

Template Matching and Trend Feature Analysis-Based Data Pre-...

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2023 china Automation Congress, CAC 2023

作者： Xu, Zheng Gan, Chao Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

In this paper, a template matching and trend feature analysis-based data pre-processing method for seismic wave detection is proposed with two stages. In the first stage, it involves extracting the rock physical parameters from seismic wave detection results using OCR (Optical Character Recognition) method, and extracting the original rock physical parameters from the raw rock property table using keyword matching method. Using the rock physical parameters as a template, a template matching approach is employed to eliminate abnormal values from the original rock physical parameters. In the next stage, a technique is proposed to extract trend features of rock physical parameters for conducting advanced geological forecasting, which considered the expertise of experts in interpreting seismic wave detection data. Finally, the effectiveness of the proposed method is verified by the compared simulation results. © 2023 IEEE.

关键词： Seismic waves

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