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Understanding and probing progression of localized corrosion on inner walls of steel pipelines:an overview

Journal of Iron and Steel research International 2025年第1期32卷 1-18页

作者： Ming-yu Wang Hai-yuan Yao Yong-fei Liu Ye-sen Zhu Wan-bin Chen Yun-ze Xu Yi Huang School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian 116024LiaoningChina China National Offshore Oil Corporation Research Institute Co. Ltd.Beijing 100028China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024LiaoningChina

Corrosion poses a major threat to the safety of transportation ***,it is crucial to have an in-depth understanding of corrosion mechanisms in pipeline steels for the effective management of pipeline *** research on corrosion mechanisms relies on the use of efficient and reliable corrosion monitoring and analysis *** advancements in corrosion monitoring techniques specifically designed for the localized corrosion monitoring were aimed to be introduced,and a comprehensive overview of recent progress in understanding the localized corrosion mechanisms in pipeline steels was *** on the different corrosive environments encountered,the localized corrosion issues inside pipelines are classified into two categories:localized corrosion primarily influenced by electrochemical processes and localized corrosion controlled by both electrochemical and mechanical ***,a thorough analysis of the synergistic effects between micro-cell and macro-cell currents,as well as the interplay of mechanics and electrochemistry is ***,recommendations for future research on the mechanisms of internal localized corrosion in pipelines are provided.

关键词： Pipeline steel Localized corrosion Corrosion type Corrosion monitoring Corrosion mechanism

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Keyword extraction method based on complex network

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International Journal of Information and Communication technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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TSDDISCOVER: Discovering Data Dependency for Time Series Data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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Model Checking for Probabilistic Multiagent Systems

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Journal of computer Science & technology 2023年第5期38卷 1162-1186页

作者：付辰 Andrea Turrini 黄小炜宋磊冯元张立军 State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Intelligent Software Guangzhou 511455China Department of Computer Science University of LiverpoolLiverpool L693BXU.K Microsoft Research Beijing 100190China Centre for Quantum Software and Information University of Technology SydneySydney 2007Australia

In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called *** this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic ***,the problem is undecidable in *** show that it becomes decidable when restricted to memoryless uniform ***,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever *** algorithms have been implemented in an existing model checker and then validated on *** experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.

关键词： probabilistic multiagent system model checking uniform scheduler probabilistic epistemic temporal logic

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HIET:Hybrid Information Enhancement Transformer Network for Single-Photon Image Reconstruction

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Journal of Beijing institute of technology 2025年第1期34卷 1-17页

作者： Yiming Liu Xuri Yao Tao Zhang Yifei Sun Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Center for Quantum Technology Research and Key Laboratory of Advanced Optoelec-tronic Quantum Architecture and Measurements(MOE) School of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Uniview Technology Hangzhou 310056China

Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal ***,these advantages also make them highly susceptible to ***,single-photon cameras face severe quantization as low as 1 bit/*** factors make it a daunting task to recover high-quality scene information from noisy single-photon *** current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm *** this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel ***,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution ***,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon *** results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.

关键词： single-photon images hybrid information enhancement structual feature enhancement data simulation pipeline

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Agile development research based on software engineering to software testing 9

Agile development research based on software engineering to ...

引用

9th International Conference on Systems and Informatics, ICSAI 2023

作者： Chen, QiaoLi Guo, LiWen Feng, YunChen Shaanxi Institute of Technology College of Computer and Software Xi'an China Shanghai Radio Equipment Research Institute Shanghai China

ISBN: (纸本)9798350383706

This article conducts in-depth research from software engineering to software testing teaching, introducing the agile development model and XP method of software development models, Scrum mode and its process, TDD, automated testing and other new trends and technologies. The purpose is to improve software development efficiency, reduce software testing costs, improve software quality and user experience, and finally advocate the use of Scrum mode and TDD in agile development, combined with the CDIO concept to strengthen team cooperation, So as to more efficiently complete the implementation and delivery tasks of software projects. © 2023 IEEE.

关键词： software testing

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DeepFilter: A Deep Learning Based Variant Filter for VarDict

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Tsinghua Science and technology 2023年第4期28卷 665-672页

作者： Hao Zhang Zekun Yin Yanjie Wei Bertil Schmidt Weiguo Liu School of Software Shandong UniversityJinan 250100China Shenzhen Research Institute of Shandong University Shenzhen 518057China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Institute for Computer Science Johannes Gutenberg UniversityMainz 55128Germany

With the development of sequencing technologies,somatic mutation analysis has become an important component in cancer research and *** is a commonly used somatic variant caller for this *** the heuristic-based VarDict algorithm exhibits high sensitivity and versatility,it may detect higher amounts of false positive variants than callers,limiting its clinical *** address this problem,we propose DeepFilter,a deep-learning based filter for VarDict,which can filter out the false positive variants detected by VarDict *** approach trains two models for insertion-deletion mutations(InDels)and single nucleotide variants(SNVs),*** show that DeepFilter can filter at least 98.5%of false positive variants and retain 93.5%of true positive variants for InDels and SNVs in the commonly used tumor-normal paired *** code and pre-trained models are available at https://***/LeiHaoa/DeepFilter.

关键词： variant filter deep learning somatic variant

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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain Science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research Institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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