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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Chengyuan Liu, Kang Li, Xuelong School of Computer Science Northwestern Polytechnical University China Shanghai Artificial Intelligence Laboratory China Ministry of Industry and Information Technology Xi'an710072 China

ISBN: (纸本)9781728163277

The fatigue of riveted joints and bolts problem is extremely important in aircraft safety. In order to solve the problem of sample scarcity in the industrial field, this paper proposes a UNet++ based few-shot segmentation network Meta++ for aerospace metal structural component fatigue crack. The Meta++ consists of a base learner UNet++, a proxy extracting module, and a meta learner. Firstly, the base learner is trained by the semantic segmentation method. Secondly, we use the base learner predictions and ground truth masks to extract proxies. Thirdly, the meta learner is trained by the few-shot learning episode method, which uses the proxies and base learner intermediate features as inputs. The fatigue cracks are generated by a servohydraulic fatigue testing system and collated into a new aerospace crack dataset. Experiments on our proposed dataset demonstrate the superiority of Meta++ for the few-shot segmentation problem of aerostructures. © 2023 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Fatigue testing

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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Video Corpus Moment Retrieval with Query-specific Context Learning and Progressive Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5659-5670页

作者： Zhang, Long Song, Peipei Duan, Zhangling Wang, Shuo Chang, Xiaojun Yang, Xun School of Information Science and Technology Hefei230026 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230026 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Department of Computer Vision Abu Dhabi United Arab Emirates University of Science and Technology of China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition China

Video corpus moment retrieval (VCMR) aims to retrieve a moment from a large corpus of untrimmed videos corresponding to a given language query. However, existing methods often fall short due to their reliance on simple cross-modal attention mechanisms and one-stop localization, which fail to handle the complex multimodal information and large search space effectively. To address these challenges, we propose a novel VCMR method with Query-specific Context Learning and Progressive Localization (QCLPL). First, we construct query-specific multimodal contexts that capture complementary and consistent semantics across subtitles and frames, ensuring informative and efficient context building. We further introduce a semantic contrastive loss to refine these multimodal contexts, filtering out query-irrelevant information. Additionally, we introduce a progressive localization strategy that transforms the moment localization task into a two-stage process. By classifying frames into foreground and background regions, we present a simplified binary classification problem before boundary prediction, constrained by a region-aware loss. This progressive approach leverages region priors to improve subsequent moment localization. Extensive experiments on the TVR and DiDeMo datasets demonstrate that our method significantly outperforms existing approaches, setting a new state of the art for VCMR. © 1991-2012 IEEE.

关键词： Structured Query Language

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Image Dehazing Algorithm Based on Color Correction and Channel Compensation Prior 4

Image Dehazing Algorithm Based on Color Correction and Chann...

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4th International Conference on Information science, Electrical, and Automation Engineering, ISEAE 2022

作者： Xie, Shangyou Zhang, Jun School of Artificial Intelligence and Computer Science Jiangnan university Wuxi China School of Artificial Intelligence and Computer Science Jiangnan university China Jiangsu Key Laboratory of Media Design and Software Technology Wuxi China

ISBN: (纸本)9781510655157

The dark channel prior is a statistical conclusion based on outdoor haze-free images and it is widely used in haze removal because of its effectiveness. However, it does not work well when dealing with images containing sky areas . The reason is values of R,G,B channels are very high in this region, i.e. the dark channel prior does not hold . Therefore, the transmission of this region is overestimated, resulting in serious halo artifacts and noise amplification in the recovered image. To solve this problem, an image dehazing algorithm based on the inverse channel compensation prior is proposed in this paper. According to observation and experimental verification, for haze-free images, when the pixels in the sky region of the image have higher values in a certain channel, their inverse channel values are lower even tend to be close to zero. Therefore, when finding the dark channel of the image, the inverse channel is considered for compensation so that the pixels in the failed sky region also satisfy the dark channel prior assumption. It is worth noting that the method based on the inverse channel compensation in this paper can obtain the accurate transmittance, and the sky region will not produce distortion and noise amplification even if the lower constraint on transmission is not set . In addition, a simple and effective color cast judgment and adaptive correction method is proposed based on the characteristics of atmospheric light, and adaptive color correction if necessary, which makes the method in this paper effective for the enhancement of sand dust images, extending the applicable scenarios of the algorithm. © 2022 SPIE.

关键词： Pixels

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DTN Routing Algorithm Based on Social Center and Classifier 20th

DTN Routing Algorithm Based on Social Center and Classifie...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Cui, Jianqun Gao, Mengnan Chang, Yanan Yan, Huiran Ma, Zhiyuan Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Hubei Wuhan430079 China Department of Computer Science Central China Normal University Wuhan China

ISBN: (纸本)9789819628636

Opportunistic Mobile Social Networks (OMSNs) are intermittent complex networks consisting of mobile nodes equipped with communication devices, characterized by unstable connections, high node mobility, and limited resources. Existing OSMN routing algorithms, when selecting the next hop node, either focus on limited information within the network and employ simple fusion techniques or consider excessive redundant information in complex designs. To address these issues, this paper proposes an OSMN routing algorithm RFSC based on social centrality and classifier. The algorithm introduces a social circle partitioning algorithm and designs the routing algorithm SCRouter by considering the social attributes of nodes, node-specific situations, and message properties. Subsequently, the SCRouter algorithm is integrated with classifier to enhance the algorithm’s anti-interference capabilities and reduce the consideration of redundant metrics. Furthermore, the RFSC algorithm introduces an adaptive cache replica allocation strategy to improve data transmission efficiency. Experimental results demonstrate that compared to CARA, Prophet, SprayAndWait, CPN_ASW and INR_PRA algorithms, the RFSC enhances delivery rates by 139.6%, 16.6%, 39.5%, 12.0% and 12.7% respectively. It effectively reduces transmission delays and alleviates network congestion. © IFIP International Federation for Information Processing 2025.

关键词： Routing algorithms

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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A Deep Learning Method for Colon Polyp Segmentation with Better Universality 1

A Deep Learning Method for Colon Polyp Segmentation with Bet...

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1st IEEE International Conference on Medical artificial intelligence, MedAI 2023

作者： Wang, Yuanyuan Deng, Zhaohong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi China ZJLab Wuxi China

ISBN: (纸本)9798350358780

Colon polyp segmentation is an important work in the adjuvant treatment of colon diseases. Traditional colon polyp segmentation methods are mainly designed according to the underlying characteristics of polyps, but the size of polyp tissues is usually different. Thus the segmentation accuracy of such methods is low. In recent years, deep learning colon polyp segmentation methods developed based on vision Transformer have higher segmentation accuracy than traditional methods on some colon polyp datasets. However, this method is more specific to a certain dataset and is not universal. In order to address the above challenges, this paper proposes a general colon polyp segmentation method-MixFormNet, which can effectively improve the segmentation accuracy of multiple datasets. MixFormNet is a encoder-decoder structure. MixFormNet utilizes a Mix Transformer with multi-scale output as an encoder. Then this paper designs a new decoder structure for multi-scale output. The decoder designed in this paper processes the characteristics of the different scale feature maps output from the encoder separately, with the aim of maximizing the use of the feature information contained in the deep and shallow feature maps. Specifically, the low-level output of the encoder has a large resolution but contains weak semantic information. Therefore, the convolutional neural network is constructed in the decoder and applied to the low-level output in order to obtain richer local feature information. A large number of experiments have shown that MixFormNet effectively improves the segmentation accuracy of multiple colon polyp datasets. In particular, the mDice of the CVC-ClinicDB dataset reached 0.94. It is proved that MixFormNet has good universality and effectiveness in improving the segmentation accuracy of colon polyp datasets. © 2023 IEEE.

关键词： Large datasets

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Grounding Planning Tasks Using Tree Decompositions and Iterated Solving 33

Grounding Planning Tasks Using Tree Decompositions and Itera...

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33rd International Conference on Automated Planning and Scheduling, ICAPS 2023

作者： Corrêa, Augusto B. Hecher, Markus Helmert, Malte Longo, Davide Mario Pommerening, Florian Woltran, Stefan University of Basel Switzerland TU Wien Institute of Logic and Computation Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

ISBN: (纸本)1577358813

Classical planning tasks are commonly described in a first-order language. However, most classical planners translate tasks by grounding them and then rewriting them into a propositional language. In recent years, the grounding step has become a larger bottleneck. In this work, we study how to improve it. We build on top of the most common grounder for planning tasks which uses Datalog to find all reachable atoms and actions. Inspired by recent progress in lifted planning, database theory, and algorithmics, we develop a new method to ground these Datalog programs. Our algorithm can ground more instances than the baseline, and most tasks it cannot ground are out of reach from any ground planner. Copyright © 2023, Association for the Advancement of artificial intelligence (***). All rights reserved.

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How Does Oversampling Affect the Performance of Classification Algorithms 28

How Does Oversampling Affect the Performance of Classificati...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Xiang, Zhizhen Xu, Yingying Tang, Zhenzhou College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Wenzhou University Wenzhou Key Laboratory for Intelligent Networking 325035 China

ISBN: (纸本)9798350300482

To address the issue of imbalanced datasets classification, this study explores how different oversampling algorithms and imbalance ratios affect the performance of classification algorithms. Two oversampling algorithms, including random oversampling and Synthetic Minority Oversampling Technique (SMOTE), are used to adjust the imbalance ratio of the training dataset to 999:1, 99:1, 9:1, 3:1, and 1:1. Four classification methods, including the Convolutional Neural Network, Vision Transformer, XGBoost and CatBoost, are evaluated using performance metrics such as precision, recall, AUC, and F2-Score. We conduct more than 240 experiments and observe that oversampling ratio has a significant positive impact on AUC and recall rate, but a negative impact on precision. The study also identifies the best oversampling algorithm and imbalance ratio for each classification algorithm. It is noteworthy that the Vision Transformer algorithm used in this study has not been employed in previous research on imbalanced data classification. © 2023 IEEE.

关键词： Intrusion detection

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