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Finite-time complex projective synchronization of fractional-order complex-valued uncertain multi-link network and its image encryption application

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Chinese Physics B 2022年第11期31卷 244-255页

作者： Yong-Bing Hu Xiao-Min Yang Da-Wei Ding Zong-Li Yang School of Electronics and Information Engineering Anhui UniversityHefei 230601China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China

Multi-link networks are universal in the real world such as relationship networks,transportation networks,and communication *** is significant to investigate the synchronization of the network with *** this paper,considering the complex network with uncertain parameters,new adaptive controller and update laws are proposed to ensure that complex-valued multilink network realizes finite-time complex projective synchronization(FTCPS).In addition,based on fractional-order Lyapunov functional method and finite-time stability theory,the criteria of FTCPS are derived and synchronization time is given which is associated with fractional order and control ***,numerical example is given to verify the validity of proposed finite-time complex projection strategy and analyze the relationship between synchronization time and fractional order and control ***,the network is applied to image encryption,and the security analysis is carried out to verify the correctness of this method.

关键词： multi-links network fractional order complex-valued network finite-time complex projective synchronization image encryption

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Triple-Attention Residual Networks for Hyperspectral Image Classification 5

Triple-Attention Residual Networks for Hyperspectral Image C...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Zhang, Lu Ruan, Chao Zhao, Jinling Huang, Linsheng Anhui University Anhui Province Hefei China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui Province Hefei China

ISBN: (纸本)9798350373820

The attention mechanism has been widely applied in hyperspectral image classification, yet most current methods rely on a relatively simple form of attention, which fails to extract features deeply. To address this issue, this paper proposes a triple-attention residual networks (TARN) for hyperspectral image (HSI) classification. This method can not only emphasize the importance of different bands in hyperspectral image classification, but also capture pairwise relationships between spatial feature pixels at the pixel level. By integrating these attention mechanisms, we achieve efficient extraction of spectral-spatial features. Extensive experiments demonstrate that the proposed artificial neural network significantly outperforms state-of-the-art methods, exhibiting its superior performance in hyperspectral image classification. © 2024 IEEE.

关键词： Pixels

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A U-Shaped Multi-Scale Network for Defocus Blur Detection 17

A U-Shaped Multi-Scale Network for Defocus Blur Detection

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Li, Longrui Huang, Liqing College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China Digital Fujian Institute of Big Data Security Technology Fuzhou350117 China Fujian Provincial Engineering Research Center of Big Data Analysis and Application Fuzhou350117 China

ISBN: (纸本)9798331507398

Defocus blur detection (DBD) aims at accurately extract the in-focus sharp regions in natural images. Despite significant progress, the presence of uniform regions and gradient boundaries remain two of the most challenging problems. In this paper, we use a U-shaped multi-scale image input network for image DBD. The model is a U-shaped encoder-decoder structured network that incorporates inputs from multi-scale images to simultaneously extract features from images of different scales, and a feature fusion module (FFM) of the corresponding scales in the encoder part in order to better learn the features of these different scales. Experiments on two public datasets demonstrate the superior performance of the method in this paper in dealing with defocus blur detection. © 2024 IEEE.

关键词： Decoding

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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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Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

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Plant Phenomics 2024年第2期6卷 349-363页

作者： Yandong Wu Weiliang Wen Shenghao Gu Guanmin Huang Chuanyu Wang Xianju Lu Pengliang Xiao Xinyu Guo Linsheng Huang National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Nongxin Science&Technology(Beijing)Co. LtdBeijing 100097China

The 3-dimensional(3D)modeling of crop canopies is fundamental for studying functional-structural plant *** studies often fail to capture the structural characteristics of crop canopies,such as organ overlapping and resource *** address this issue,we propose a 3D maize modeling method based on computational *** initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture.

关键词： intelligence modeling based canopies computational dimensional maize three

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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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A Diversion Path Planning Method of Waypoint Based on Flight Traffic Conduction

A Diversion Path Planning Method of Waypoint Based on Flight...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Hongfang Shi Ningning Le Jiahe Miao Qian Li Big Data Analysis and Application Center China Academy of Civil Aviation Science and Technology Beijing China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

Based on flight operation data, this paper constructs a diversion path planning method for busy waypoints by analyzing the relationship of flight traffic conduction between waypoints. Taking busy waypoint KHN as an example, a total of two diversion paths are planned, specifically one in the east-west direction and one in the north-south direction, and the two diversion paths are evaluated in terms of safety and economy, and the results of the evaluations are within the acceptable range. The method of this paper can improve the capacity of airspace resources without changing the existing air route network; at the same time, this method can also assist in identifying the bottleneck of airspace resources, which means identifying the key waypoints affecting the flow of diversion paths, so as to provide certain theoretical references for China to enhance the total number of flights, and to improve the utilization rate of airspace resources and operational efficiency.

关键词： Path planning Safety Planning Information technology

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Class-Dynamic and Hierarchy-Constrained Network for Entity Linking 28th

Class-Dynamic and Hierarchy-Constrained Network for Entity...

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28th International Conference on database Systems for Advanced applications, DASFAA 2023

作者： Wang, Kehang Liu, Qi Zhang, Kai Liu, Ye Tao, Hanqing Huang, Zhenya Chen, Enhong Auhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9783031306716

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge based systems

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A Revised Model for Sea-Surface Wind Inversion data with Joint Interpolation Optimization and Residual Network 18

A Revised Model for Sea-Surface Wind Inversion Data with Joi...

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Lin, Weihong You, Lijun Huang, Tianqiang Huang, Liqing Luo, Haifeng Xu, Chao College of Computer and Cyber Security Fujian Norm. Univ. Fujian Provincal Eng. Res. Center of Big Data Analysis and Application Digital Fujian Institute of Big Data Security Technology Fuzhou China Fujian Meteorological Information Center Fujian Key Laboratory of Severe Weather Fuzhou China

ISBN: (纸本)9798350318401

The sea-surface wind fields are pivotal for studies in both oceanography and meteorology. However, the acquisition of ocean wind data is often challenged by practical constraints, especially near coasts, leading to significant data gaps. This paper introduces a two-stage approach that combines interpolation optimization with a residual network to improve satellite-derived sea-surface wind fields data. Initially, the Kriging interpolation method is employed to fill missing regions. Subsequently, a network structure similar to ResNet-18 is tailored to revise the interpolated data using buoy anemometer observations. By establishing a correlation model between the interpolated results and ground truth through supervised training, the approach ensures the interpolated results gravitate towards the actual measurements. The efficacy of the proposed model is corroborated through experimental visual analysis and error cure assessments. © 2023 IEEE.

关键词： Interpolation

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Identification of Maize Leaf Diseases Based on Msdcnext Network

SSRN

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SSRN 2024年

作者： Zeng, Weihui Guo, Dandan Chen, Peng Bao, Wenxia Hu, Gensheng National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China

The complexity of the backgrounds, significant differences in the scale of the spots, and high similarity between the foregrounds and backgrounds result in low accuracy in identifying maize leaf diseases using existing methods. In the study, a multiscale deep neural network MSDCNeXt is proposed to identify maize diseases and solves this problems. In the MSDCNeXt network, each module constructs receptive fields of different sizes by stacking multiple layers of depthwise separable convolutional layers, improving the network's ability to express features of maize leaf diseases at different scales. CBAM attention mechanism is embedded in the MSDC block, enabling the network to more accurately locate key features of maize leaf diseases and effectively suppress interference from complex backgrounds. In addition, this study used data augmentation methods such as Mixup to further enhance the network's generalization *** verify the feasibility and effectiveness of the network in complex environments, we compared it with existing networks. The average identification accuracy of the network was 96.7%, and the proposed network outperformed the existing networks. This study demonstrates the capability for precise identification of images depicting diseases and pests affecting maize, thereby contributing to the development of diagnostic and management solutions for these afflictions in maize. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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