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2022 International Conference on Electrical, Electronics and information Engineering, EEIE 2022

作者： Lv, Meini Gan, Hui Liu, Xin Chen, Jia Yang, Qiuhui Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China

The auto focusing process is affected by the amount of image content, resulting in the focus curve not meeting the characteristics of the ideal focus curve. In this paper, aiming at the problem that the traditional autofocus cannot find the focal plane successfully in the case of microscopic sparse content, the qualitative analysis and quantitative analysis of 12 traditional focus evaluation functions are carried out. The experimental results show that the F Tenengrad function is suitable for the optimal focus evaluation function of sparse microscopic images. © Published under licence by IOP Publishing Ltd.

关键词： Focusing

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A Novel Two-Stage Method for Texture Defect Detection 4

A Novel Two-Stage Method for Texture Defect Detection

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Liu, Lu Jiang, Min Xu, Lifa School of Computer Science and Technology Wuhan University of Science and Technology Hubei WuHan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China

ISBN: (纸本)9798350384062

Texture defect detection has wide applications in both textiles and industry, where traditional defect detection methods focus on costly manual labeling and defect samples are complex to collect in real-world industrial scenarios. Furthermore, labeling errors are common in datasets from industrial scenarios, and the problem still needs to be explored. Therefore, we propose a novel texture defect detection method. It has two stages;the defective samples are synthesized in the first stage. In the second stage, we represent normal samples using a self-supervised learning method. Then, we construct a single classifier based on this learned representation. Subsequently, the model is trained to distinguish between normal and defective samples within the embedding space. To increase the robustness of our method, we adopt a novel strategy to construct defective samples and compute the feature similarity through a similarity-weighted aggregation method to achieve defect detection, which is more robust to noise. Experimental results on the DAGM2007 dataset show that our method performs comparably to the leading texture defect detection methods without label noise. In contrast, in the presence of noise, our method can effectively minimize the impact of noise on model performance. © 2024 IEEE.

关键词： Defects

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Vehicle Detection and Classification Algorithm Based on Improved Mask R-CNN 12th

Vehicle Detection and Classification Algorithm Based on Impr...

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12th International Conference on Computer Engineering and Networks, CENet 2022

作者： Deng, Hongwei Fu, Wenli Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811969003

Vehicle detection and classification technology is an important part of ITS system. It is often used in traffic flow statistics, ETC non-parking charging system and other scenarios, which solves a series of problems effectively in traffic control. In this paper, we design ResNet-HDC feature extraction network and New-FPN feature fusion network to address coarse-grained vehicle classification and recognition problem. We improve Mask R-CNN algorithm and propose a vehicle detection and classification algorithm based on improved Mask R-CNN to complete accurate recognition and classification of vehicles. To further optimize the training results, vehicle images are preprocessed before the algorithm model training and high-performance Tesla T4-GPU processor is used to enhance image recognition during the training process. After training and testing, the results show that the method enhance the precision of vehicle detection effectively, and the average accuracy is increased by about 10%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Event-Triggered Adaptive Finite-Time Control for Switched Cyberphysical Systems With Uncertain Deception Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 136-146页

作者： Wang, Xin Zhou, Yuhao Luo, Biao Jiang, Yuming Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Automation Central South University Changsha410083 China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7491 Norway T&M University at Qatar Doha23874 Qatar

This article is concerned with the event-triggered adaptive finite-time control problem for a class of switched cyber-physical systems (CPSs) subject to uncertain deception attacks. We deploy the finite time command filter to alleviate the possible explosion of complexity rendered by the virtual control signal’s duplicative differentiation. To stabilize the target system with a guaranteed disturbance rejection performance index, a disturbance observer is constructed by using fuzzy logic systems (FLS) with the hope of estimating the unknown external disturbance. Furthermore, the Nussbaum gain is utilized to resist the unknown control efficiency generated by uncertain deception attacks. Based on the common Lyapunov function (CLF) method, sufficient conditions are derived to ensure that all signals in the closed-loop system are uniformly bounded at a finite time. Finally, the simulation example shows the validity and efficacy of the developed control method. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Closed loop systems

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RESEARCH ON THE REWARDS AND PUNISHMENTS OF URBAN SEWAGE TREATMENT UNDER THE UNCERTAINTY THEORY

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 111-124页

作者： Ruixi, Yuan Lin, Zhang Li, Cun-Lin Masron, Tajul Ariffin Yang, Bao-Jun School of management North Minzu University Yinchuan750021 China Universiti Sains Malaysia Pinang 13500 Malaysia School of Mathematics and Information Science Guangzhou University Guangzhou510006 China School of Management The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Management Universiti Sains Malaysia Pinang 13500 Malaysia School of Business Northinzu University Yinchuan750021 China

This paper fills a gap in urban sewage treatment decision-making by exploring reward and punishment mechanisms within the uncertainty theory framework. Addressing challenges in characterizing sewage treatment capacity due to incomplete information, the study integrates incentive and punishment mechanisms in a decision-making model. Results indicate that government rewards encourage companies to treat more sewage, but a nuanced relationship exists between rewards, treatment capacity, and expected returns. The study emphasizes the need for a balanced approach, suggesting a focus on technology enhancement over sole reliance on rewards. Examining a double-headed sewage market, the research establishes an uncertain Cournot game model, providing insights into sewage treatment dynamics under asymmetric information. In conclusion, the paper contributes to practical reward and punishment strategies in sewage treatment policies, offering theoretical support for effective decision-making in urban sewage management within a concise framework.. © 2024, Politechnica university of Bucharest. All rights reserved.

关键词： Sewage treatment

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer 29

Improving Fake News Detection of Influential Domain via Doma...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Fake detection

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Research on financial social public opinion communication model based on variation mechanism 22

Research on financial social public opinion communication mo...

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Proceedings of the 2022 6th International Conference on Electronic information Technology and Computer Engineering

作者： Maojun Huang Mei Hong Lin Dong Da Yuan Key Laboratory of Intelligent Information Processing Shandong Technology and Business University China

ISBN: (纸本)9781450397148

In the context of rapid development of the Internet, the place of spreading financial public opinion is converted from traditional offline places to major online social platforms. Mastering the development mechanism of financial social opinion dissemination on online social media can effectively estimate the length of influence of public opinion and the scope of affected people, and provide effective guidance to relevant staff. Based on the Susceptible Infected Recovered Model, this paper divides the people involved in opinion diffusion into commenters and discussers, and introduces the variation mechanism to design the Susceptible-comment-discussion-removal model, then simulates the model to study the effects of different initial states and parameters on the model, and finally verifies the validity of the model by combining the real data of stock bars. The simulation experiments and validation show that the model can effectively describe the spread of public opinion among the user groups of financial social platforms when it occurs, and provide a valid reference for related workers. However, the content of public opinion, the personal influence of communicators, and the lag effect of communication all have an impact on communication, and these issues need to be addressed in future research.

关键词： variation mechanism financial social opinion SIR model public opinion dissemination SCDR model

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Image Repair Methods Based on Deep Residual Networks 11th

Image Repair Methods Based on Deep Residual Networks

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Deng, Hongwei Lin, Ziyu Li, Jinxia Yao, Ming Wang, Taozhi Luo, Hongkang Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811665530

In recent years, deep learning has shown significant advantages in image restoration. Compared with the traditional repair method, the image repair method based on deep learning can better solve the problem of image missing blur, but it will also cause the problem of local color difference of the repair image. In this paper, an image repair model based on deep residual network is proposed, which is divided into repair module and mitigation module. The repair module uses part of the convolution network to repair the missing area of image blur, the mitigation module uses the deep residual network to adjust the color difference of the repaired image, and the two modules coordinate with each other to make the image repair effect closer to the real image. The experimental results show that the method proposed in this paper has better effect on image repair. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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Generative Negative Sample Enhancement Based Few-Shot Named Entity recognition 11th

Generative Negative Sample Enhancement Based Few-Shot Named ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Duan, Zhen Xiao, Shenghua Chen, Jie Zhao, Shu Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230601 China

ISBN: (纸本)9783031927409

Few-shot Named Entity recognition (Few-shot NER) aims to identify and classify unseen named entity types with a limited labeled samples. In recent years, large language models have achieved remarkable results in various NLP tasks due to their excellent zero/few-shot generalization capabilities. Recent research has extensively focused on using large language models to study the named entity recognition task. Negative sample entities have been demonstrated to be effective when performing this task using in-context learning paradigm. However, the problem of low efficiency and poor quality in manually constructing negative sample entities has been exposed. To address this issue, in this paper, we propose a Few-shot NER method based on generative negative sample enhancement (GNSE). Specifically, GNSE first utilizes a large language model to pre-identify potential entity spans in the support set text paragraphs in a zero-shot manner and generate the semantics of the spans based on the paragraph context. Then, the pre-identified candidate spans are compared with the ground-truth labels to determine positive or negative sample entity spans. Finally, based on Chain-of-Thought, the span semantics, positive and negative sample entities, and ground-truth labels are inserted into the example template for the in-context learning paradigm, and the test text from the query set is concatenated to construct a complete prompt. GNSE was evaluated on three widely used datasets: CoNLL’03, WNUT’17 and GUM. Experiments show that GNSE has achieved better performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Few-shot Named Entity recognition In-context Learning Large Language Model Prompt Engineering

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Geometric Numerical Integral Method in Compact Lie Group 6

Geometric Numerical Integral Method in Compact Lie Group

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6th IEEE International Conference on intelligent Computing and Signal processing, ICSP 2021

作者： Liu, Chao Yang, Shengyi Guizhou Minzu University Key Laboratory of Pattern Recognition Intelligent System of Guizhou Province Gui Yang China

ISBN: (纸本)9780738143705

Three dimensions special orthogonal group SO (3) is widely used to describe the rotation kinematics of the rigid body without local coordinates, which can avoid rotation singularity and unwinding in traditional methods. Propagating the rotation kinematics in SO (3) with a specific geometric integration method is not only to obtained numerical results with improved qualitative behavior, but also provided more accurate long-time integration results. While many studies have focused on geometric integration algorithms to preserve the geometric structure, this work has the additional objective of studying result accuracy. Integral curves on SO (3) obtained using the third-order Crouch-Grossman Lie group method are compared with numerical results using the third-order RKMK algorithms, the exponential coordinates method, and the simple projection method. Results show that the use of the Crouch-Grossman Lie group method better preserves the geometric structure of SO (3) for the larger time steps considered. It is also found that the third-order Crouch-Grossman algorithm is more accurate than the RKMK except for the smallest time step used. © 2021 IEEE.

关键词： Geometry

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