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CiC-NET: a real-time semantic segmentation network for dam surface crack detection

Multimedia Tools and Applications 2024年 1-23页

作者： Li, Linjing Zhao, Hao Liu, Ran Nayyar, Anand Ali, Rashid Li, Yonglong Zhang, Hua School of Information Engineering Southwest University of Science and Technology Mianyang China University of Science and Technology of China Heifei China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Computer Science Duy Tan University Da Nang550000 Viet Nam Department of Computer Science University of Turbat Turbat Pakistan Department of Electronic Engineering Tsinghua University Beijing China Tianfu Institute of Research and Innovation Southwest University of Science and Technology Chengdu China

Crack detection is vital for maintaining hydraulic engineering infrastructure. However, achieving a balance between real-time processing and high precision in semantic segmentation models presents a significant challenge, especially given the intricate details of cracks and complex backgrounds. To tackle this issue, this paper proposes a real-time, high-precision crack segmentation model. Initially, a four-branch feature extraction structure is devised to capture the edge details of cracks, with a focus on enhancing segmentation accuracy. Subsequently, an image pyramid is constructed at the input end to feed small-scale samples into high-dimensional feature extraction branches, thereby reducing computational costs and improving segmentation speed. Finally, an effective feature fusion module is designed for the feature extraction structure to capture sufficient crack features and achieve precise crack localization. Extensive experiments validate the superior performance of the proposed method, with Pixel Accuracy, Recall, Intersection over Union, and F1 score reaching 68.45%, 67.04%, 51.22%, and 67.74%, respectively, while maintaining a modest parameter count of 13.12M. This model provides a reliable and efficient solution for the health monitoring and maintenance of hydraulic engineering. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Prediction of segregation characterization based on granular velocity and concentration in rotating drum

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Particuology 2023年第2期73卷 17-25页

作者： Wenzheng Xiu Ra Li Quan Chen Qicheng Sun Vladimir Zivkovic Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai200093China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China School of Engineering Newcastle UniversityNE17RUUnited Kingdom

In a binary granular system composed of two types of particles with different granule sizes and the same density,particle sorting occurs easily during the flow *** segregation pattern structure is mainly affected by the granular velocity and granular concentration in the flow *** paper reports on the experimental velocity and concentration measurement results for spherical particles in a quasi-two-dimensional rotating *** relationship between the granular velocity along the depth direction of the flow layer and granular concentration was established to characterize structures with different degrees of *** corresponding relationships between the granular velocity and concentration and the segregation pattern were further analyzed to improve the theoretical models of segregation(convection-diffusion model and continuous flow model)and provide a reference for granular segregation control in the production process.

关键词： Granular segregation Rotating drum Granular velocity Granular concentration

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

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computers, Materials & Continua 2024年第1期78卷 899-914页

作者： Chengsheng Yuan Baojie Cui Zhili Zhou Xinting Li Qingming Jonathan Wu Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of International Relations National University of Defense TechnologyNanjing210039China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable ***,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake *** of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial *** addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual *** response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for *** ridge texture area within the fingerprint image has been identified and designated as the region for perturbation ***,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient ***,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack *** results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

关键词： FLD adversarial attacks adversarial examples gradient optimization transferability

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Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Power Prediction Mode 8

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation Sys...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Badawi, Ahmed Elzein, I.M. Ali, Hassan Ismail, Nasser Zyoud, Alhareth Soliman, Mostafa College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine

ISBN: (纸本)9798350382150

In this research, an innovative approach is presented to enhance the power energy production of the wind turbines (WTs). Robust adaptive hill climbing search (HCS) algorithm based on variable step size is integrated with power prediction mode (PPM) technique. The objective is to achieve the maximum power point of the wind energy conversion system (WECS), thereby capturing the highest possible power from the wind source. Conventional methods of maximum power point tracking (MPPT) are unable to precisely track and reach the optimal point under unpredicted wind speeds. To address this issue, adaptive HCS is utilized due to its ability to overcome certain well-perceived challenges such as detection delays and the number of iterations in detecting the optimal peak point. More specifically, the PPM technique is combined with the adaptive HCS algorithm. The proposed Adaptive HCS-PPM technique is shown to demonstrate higher efficiency and faster trenching capability in reaching the optimal point, resulting in reduced power fluctuations, and improved overall performance of WECS as well as overcoming the oscillations around the optimal point. ©2024 IEEE.

关键词： Wind turbines

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Stacking Ensemble Techniques for Productivity Forecasting in Bangladesh's Garment Industry

Stacking Ensemble Techniques for Productivity Forecasting in...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hosen, Md. Hamid Tasnia, Naima Amran, Mohammed Chowdhury, Rituparna Uddin, Altaf Saha, Arnob East Delta University Computer Science and Engineering Chittagong Bangladesh Bangladesh University of Engineering and Technology Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Predicting productivity in garment manufacturing is important for optimizing workforce management and operational efficiency of garments. The prediction of productivity enables industries to adapt proactively to market dynamics, thereby sustaining growth and profitability. This research, based on a Kaggle dataset, explores the application of five machine-learning algorithms and a stacking ensemble technique to predict garment worker productivity. We evaluate Random Forest, Decision Tree, Support Vector Machine with Radial Basis Function, XGBoost Regressor, Gradient Boost Regressor, and Stacking through five error metrics: MAE, MSE, RMSE, MAPE, and R-squared error. The analysis includes a comparison with existing research and a demonstration of hyperparameter tuning for attaining the best-performing model. The results reveal that the stacking ensemble model achieves the lowest error values, particularly an MAE of 0.04, RMSE of 0.09, and R-squared error of 0.65, outperforming individual ML algorithms. This research contributes insights into effective model selection and optimization for enhancing productivity prediction in garment manufacturing settings. © 2024 IEEE.

关键词： Garment industry

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Detection of Edge-blurred Small Targets of Steel Surface Defects Based on SAM-YOLO 2

Detection of Edge-blurred Small Targets of Steel Surface Def...

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2nd IEEE International Conference on electrical, Automation and computer engineering, ICEACE 2024

作者： Wu, Yanxu Liu, Kai Zhou, Xianghao Zhang, Xiumei University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China South-Central Minzu University School of Computer Science Wuhan China Beihang University School of Automation Science and Electrical Engineering Beijing China University of Science and Technology Liaoning School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350368208

To address the issue of challenging detection tasks for tiny and medium-sized objects because of backdrop confusion and inadequate feature representation of steel surface flaws, this paper proposes an efficient feature-enhanced, feature-fused SAM-YOLO detector based on the YOLOv8 model. It adds a new feature fusion module MSFF based on the original YOLOv8 model and replaces the bottleneck module in the C2f module with a feature enhancement module SACM. These two modules improve the enhancement and detection of defect edge features, local area sensing, and the network capability of multi-scale feature fusion, and the effect of SAM-YOLO is verified through several experiments on the NEU-DET steel surface defect dataset. The mAP50 of SAM-YOLO reaches 77.9%. mAP50:95 reaches 49.1%, exceeding the original YOLOv8 baseline model by 6.9% and surpassing several other benchmark models. To confirm the legitimacy of each SAM-YOLO model module, ablation tests were conducted on the model in the interim. © 2024 IEEE.

关键词： Target tracking

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Critical Separation Hashing for Cross-Modal Retrieval 5th

Critical Separation Hashing for Cross-Modal Retrieval

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5th EAI International Conference on 6G for Future Wireless Networks, 6GN 2022

作者： Wang, Zening Sun, Yungong Liu, Liang Li, Ao School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Electrical Engineering and Computer Science Pennsylvania State University New York United States

ISBN: (纸本)9783031360107

With the development of Internet technology, unimodal retrieval techniques are no longer suitable for the current environment, and mutual retrieval between multiple modalities is needed to obtain more complete information. Deep hashing has clearly become a simpler and faster method in cross-modal hashing. In recent years, unsupervised cross-modal hashing has received increasing attention. However, existing methods fail to exploit the common information across modalities, thus resulting in information wastage. In this paper, we propose a new critical separation cross-modal hashing (CSCH) for unsupervised cross-modal retrieval, which explores the similarity information across modalities by highlighting the similarity between instances to help the network learn the hash function, and we carefully design the loss function by introducing the likelihood loss commonly used in supervised learning into the loss function. Extensive experiments on two cross-modal retrieval datasets show that CSCH has better performance. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Hash functions

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