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Graph Deep Active Learning Framework for Data Deduplication

Big Data Mining and Analytics 2024年第3期7卷 753-764页

作者： Huan Cao Shengdong Du Jie Hu Yan Yang Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of EducationChengdu 611756China College of Information and Electric Engineering Asia UniversityChongsheng 41359China

With the advent of the era of big data,an increasing amount of duplicate data are expressed in different *** order to reduce redundant data storage and improve data quality,data deduplication technology has never become more significant than *** is usually necessary to connect multiple data tables and identify different records pointing to the same entity,especially in the case of multi-source data *** learning trains the model by selecting the data items with the maximum information divergence and reduces the data to be annotated,which has unique advantages in dealing with big data ***,most of the current active learning methods only employ classical entity matching and are rarely applied to data deduplication *** fill this research gap,we propose a novel graph deep active learning framework for data deduplication,which is based on similarity algorithms combined with the bidirectional encoder representations from transformers(BERT)model to extract the deep similarity features of multi-source data records,and first introduce the graph active learning strategy to build a clean graph to filter the data that needs to be labeled,which is used to delete the duplicate data that retain the most *** results on real-world datasets demonstrate that the proposed method outperforms state-of-the-art active learning models on data deduplication tasks.

关键词： data deduplication active learning similarity calculation

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A smoothed assumed enhanced strain method for frictional contact with constant strain elements

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4369-4385页

作者： Fushen Liu Wei Sun Minghui Li Xiaonan Shang Computing Center for Geotechnical Engineering Zhejiang UniversityHangzhou310058China Research Center of Coastal and Urban Geotechnical Engineering Zhejiang UniversityHangzhou310058China Engineering Research Center of Urban Underground Development of Zhejiang Province Hangzhou310058China School of Civil Engineering Sun Yat-sen UniversityGuangzhou510275China School of Energy Resource China University of Geosciences(Beijing)Beijing100083China

This paper presents a finite element framework for imposing frictional contact conditions on embedded fracture faces,implemented by the constant-strain assumed enhanced strain(AES)method,where penalty method is used to impose both non-penetration constraint and Coulomb’s law of *** proposed constant-strain AES method for modeling embedded frictional contact can be cast into an integration algorithm similar to those used in the classical plasticity theory,where displacement jump is calculated from the local traction equilibrium at Gauss point,so the method does not introduce any additional global degrees of ***,constant-strain elements are often desirable in practice because they can be easily created automatically for large-scale engineering applications with complicated *** encountered in other enriched finite element methods for frictional contact,the problem of normal contact pressure oscillations is also observed in the constant-strain AES ***,we developed a strain-smoothing procedure to effectively mitigate the *** investigated and verified the proposed AES framework through several numerical examples,and illustrated the capability of this method in solving challenging nonlinear frictional contact problems.

关键词： Assumed enhanced strain(AES)method Frictional contact Strain-smoothing method Penalty method

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Corn Plant Counting Using Deep Learning and UAV Images

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IEEE Geoscience and Remote Sensing Letters 2024年 1-5页

作者： Kitano, Bruno T. Mendes, Caio C. T. Geus, Andre R. Oliveira, Henrique C. Souza, Jefferson R. Faculty of Computing Federal University of Uberlêndia Uberlêndia 38408-100 Brazil School of Civil Engineering Architecture and Urban Planning University of Campinas Campinas 13083-970 Brazil

The adoption of new technologies, such as unmanned aerial vehicles (UAVs), image processing, and machine learning, is disrupting traditional concepts in agriculture, with a new range of possibilities opening in its fields of research. Plant density is one of the most important corn (Zea mays L.) yield factors, yet its precise measurement after the emergence of plants is impractical in large-scale production fields due to the amount of labor required. This letter aims to develop techniques that enable corn plant counting and the automation of this process through deep learning and computational vision, using images of several corn crops obtained using a low-cost unmanned aerial vehicle (UAV) platform assembled with an RGB sensor. IEEE

关键词： Computer architecture

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A low-power consumption security data fusion model for Industrial Internet of Things

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International Journal of Computers and Applications 2025年第3期47卷 312-322页

作者： Zeng, Mengjia Cheng, Zhaolin Huang, Xu Xu, Huibin School of Electronic Information Huzhou College Huzhou China Huzhou City Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou China School of Economic and Management Huzhou University Huzhou China School of Information Engineering Huzhou University Huzhou China

Industrial Internet of Things (IIOT) is an important part of manufacturing industry in the future, and the front-end sensor data acquisition network is the core component of it. Due to the limited energy of IIOT, reducing transmission interaction, energy consumption, and computational complexity in the process of data transmission is the key and difficult point in designing model of IIOT. In this paper, a lightweight green security data fusion model for IIOT is proposed, called LCSDF model, which aims to achieve data security and integrity verifiability by using less energy consumption. The simulation results show that the proposed model can ensure data security and verifiability, and meet the energy consumption requirements of the system. The performance of the algorithm is also satisfactory, reflecting the lightweight, green, and security of the model. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensor data fusion

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Coupled THMC model‑based prediction of hydraulic fracture geometry and size under self‑propping phase‑transition fracturing

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International Journal of Coal Science & Technology 2024年第6期11卷 29-53页

作者： Nanlin Zhang Fushen Liu Liangliang Jiang Pinqiang Mo Jingwen Xiao Qi Song Yuhao Luo Computing Center for Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Research Center of Coastal and Urban Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Engineering Research Center of Urban Underground Development of Zhejiang Province Hangzhou 310058China Department of Chemical and Petroleum Engineering University of CalgaryCalgaryAB T2N1N4Canada State Key Laboratory of Intelligence Construction and Healthy Operation and Maintenance of Deep Underground Engineering School of Mechanics and Civil EngineeringChina University of Mining and TechnologyXuzhou 221116JiangsuChina Safety Environment and Technology Supervision Research InstitutePetro China Southwest Oil and Gas Field CompanyChengdu 610041China

The Self-Propping Phase-transition Fracturing Technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,especially in the context of a carbon-constrained sustainable *** involves the coupling of Thermal,Hydraulic,Mechanical,and Chemical(THMC)fields,which makes it challenging to understand the mechanism and path of hydraulic fracture *** study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System(PFFS).An algorithm,integrating the Finite Element Method,Discretized Virtual Internal Bonds,and Element Partition Method(FEM-DVIB-EPM),is proposed and validated through a case *** results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving *** length of the hydraulic fracture increases with the quantity and displacement of PFFS,and excessive displacement may result in uncontrolled fracture *** the parameters considered,a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m^(3),that means the fracture length tends to *** study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT,offering insights for optimizing hydraulic fracturing technology and treatment parameters.

关键词： Sustainable development,Self Propping phase Transition fracturing technology,Thermal Hydraulic Mechanical Chemical coupling,FEM DVIB EPM,Hydraulic fracture propagation

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DiagLLM: multimodal reasoning with large language model for explainable bearing fault diagnosis

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Science China(Information Sciences) 2025年第6期68卷 41-55页

作者： Jie WANG Tianrui LI Yan YANG Shiqian CHEN Wanming ZHAI School of Computing and Artificial Intelligence Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education State Key Laboratory of Rail Transit Vehicle System

Accurate and reliable bearing fault diagnosis is critical for ensuring the safe operation of mechanical *** data-driven methods encounter challenges in training advanced deep learning models, primarily due to the scarcity of fault data and inconsistencies in data distributions. Additionally, these methods often suffer from limited interpretability and reliability, as they lack constraint-guided learning based on the physical mechanisms underlying bearing failures, which hampers their utility in machine condition monitoring. Recent advancements in large language models(LLMs) demonstrate their potential to address these challenges. To this end, we aim to enhance the generalization and interpretability of bearing fault diagnosis by leveraging the capabilities of multimodal LLMs. Specifically, a novel framework called DiagLLM is designed to achieve this goal. DiagLLM leverages the powerful reasoning capabilities of large language models and incorporates contextual information from both envelope spectrum images and expert knowledge to accurately diagnose bearing faults. To effectively tune DiagLLM, diagnostic visual instruction-following data are constructed to link fault feature descriptions with signal characteristics, and the entire model is fine-tuned using a parameter-efficient training pipeline. Extensive experiments are conducted on two publicly available bearing fault diagnosis datasets, and the results show that DiagLLM outperforms leading baselines, particularly in scenarios with limited data and cross-data generalization.

关键词： large language model fault diagnosis cross-dataset generalization multimodal learning multimodal fusion

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Research on rapid measurement technology of railway line based on 3D coordinate acquisition trolley 6

Research on rapid measurement technology of railway line bas...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Zhang, Maolin Cui, Mengzhen Li, Congxu Chen, Yuan Beijing University of Civil Engineering and Architecture School of Geomatics and Urban Spatial Informatics Beijing100044 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100015 China Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9781510688018

The traditional method of remeasurement of operational railroad has many processes, complicated operations and low efficiency, and the time of railroad maintenance days is decreasing, so new demands are made on the remeasurement method of operational railroad. In response to this problem, this paper researches the rapid measurement technology of operational railroad to achieve the rapid and high-precision acquisition of spatial measurement data in a wide range of operational railroad and the rapid and high-precision measurement of railroad track line information. This paper mainly studies the railroad line 3D coordinate acquisition trolley and its acquisition system, mainly including the key technology research of data synchronization technology, data fusion Kalman filter technology, combined navigation solution technology and track flat and longitudinal section fitting optimization research. Through this research, the three-dimensional coordinate acquisition trolley of railroad line can obtain high-precision railroad track alignment information without relying on CPⅢ control network, reduce the number of times on the track, reduce the operation safety hazards, save cost and human resources. © 2025 SPIE.

关键词： Data fusion

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Optimization of Fair Materials Distribution Considering the Integrated Road Network 24

Optimization of Fair Materials Distribution Considering the ...

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3rd Asia Conference on Algorithms, computing and Machine Learning, CACML 2024

作者： Liu, Fang Liang, Tao Li, Feng Xia, Qiang Ma, Fangfang Beijing Jinghang Research Institute of Computing and Communication Beijing China Academy of System Engineering Academy of Military Sciences Beijing100166 China Beijing Institute of Surveying and Mapping Beijing Key Laboratory of Urban Spatial Information Engineering Beijing100074 China

ISBN: (纸本)9798400716416

To address the issue of balancing timeliness and fairness in the distribution process of materials, a road network is constructed and an improved proportional sharing comprehensive cost variable neighborhood ant colony algorithm is proposed. A comparative analysis of distribution based on traditional ant colony algorithm and improved algorithm under the road network is designed. The analysis shows that the constructed road network is closer to the real path than a single road network. Secondly, the improved algorithm reduces the number of convergence iterations and improves operational efficiency. Thirdly, adding population weight and spatial clustering grouping can obtain a more objective spatial division to achieve fair distribution. The research results can be used for road network planning in complex situations, especially for quickly developing disaster relief plans and transportation support in regional wars or emergencies. It can provide a theoretical reference for the planning of road networks in complex situations. © 2024 ACM.

关键词： Ant colony optimization

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Quantifying the performance of urban sewer network using inverse-problem models: An approach for synchronous determination of in-sewer groundwater infiltration and pollutant degradation

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Journal of Hydrodynamics 2025年第1期37卷 1-13页

作者： Hui-jin Zhang Zu-xin Xu Wan-qiong Wang Shou-hai Peng Chong Li Shuai Fang Danlu Guo Hai-long Yin Key Laboratory of Urban Water Supply Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water ResourcesTongji UniversityChinaShanghai 200092China State Key Laboratory of Pollution Control and Resource Reuse Tongji UniversityShanghai 200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China China Three Gorges Corporation Wuhan 430010China School of Engineering College of EngineeringComputing and CyberneticsAustralian National UniversityCanberraAustralia

The performance of sewer network is associated with both clean water infiltration and in-sewer pollutant *** their contributions in large-scale sewer network remains challenging due to the infeasibility of numerous on-site measurements of water flows and water quality concentrations in the whole *** study developed a physically inverse problem approach to address this challenge,which was tested in an actual sewer network system(25.66 km^(2))with gridding-based in-sewer flow rate and water quality *** optimization framework was integrated into sewer hydrodynamic and water quality models to inversely estimate source parameters including source flow rates and source discharge *** simulated annealing algorithm can demonstrate 20.6%-54.2% higher accuracy compared with the other methods,due to its progressive instead of fast and steep convergence toward the true *** the developed approach,the infiltrated clean water infiltration and mass loss of chemical oxygen demand(COD)within the sewer network were quantified ***,the condition of sewer structural defects was assessed,and a reference value for allowable in-sewer COD degradation was also presented,which was 4%-5%COD mass per hour of sewage hydraulic ***,this methodology can provide cost-effective solution for comprehensive assessment of sewer network conditions.

关键词： Sewer network mathematical model inverse problem approach pollutant degradation hydraulic transport

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Analysis of Machine Learning (ML) and Internet of Things (IoT): A Scientometric Review 20

Analysis of Machine Learning (ML) and Internet of Things (Io...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Legaspino, Rosie Fe B. Gimeno, Anabella R. Ypanto, Leomar A. Baird, Jeem Clyde C. Adediran, Anthonia Oluwatosin Sunway University School Of Engineering And Technology Department Of Computing And Information Systems Selangor Malaysia Cebu Technological University Cebu Moalboal Philippines Federal University Of Uberlandia Faculty Of Architecture And Urban Design Uberlândia Brazil

ISBN: (纸本)9798350382310

The objective of this paper is to critically examine the publication's trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis (BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of Technology (India). However, the top country and research funder are;India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics. © 2024 IEEE.

关键词： Machine learning

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