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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Li, Jiahang Yan, Qi Yuan, Rui Wang, Linhan Tian, Le Zhang, Weiwei Hua, Liru China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport Beijing China

ISBN: (纸本)9781510690585

Mountain roads serve as vital links connecting remote areas with the outside world. The stability of road slopes in these regions is particularly significant due to complex terrain and variable geological conditions. Accurately predicting slope displacement is crucial for disaster prevention and ensuring traffic safety. This paper proposes a predictive model that combines variational mode decomposition (VMD) and long short-term memory neural network (LSTM) to improve the accuracy and reliability of slope displacement prediction for mountain roads. The original slope displacement data is preprocessed using VMD, decomposing it into trend, periodic, and random modal components. Then, the LSTM model is utilized to predict each modal component. Experimental results show that the proposed VMD-LSTM model outperforms single-use LSTM, XGBoost, and ELM models in terms of prediction accuracy, with RMSE, MAE, and MAPE values of 0.102, 0.098, and 2.854%, respectively, demonstrating the effectiveness and superiority of the model. © 2025 SPIE.

关键词： Variational mode decomposition

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm

Research on Mining Association Rules for Classroom Concentra...

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IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： Jingqiu Bai Huafeng Kong Wenhan Hu Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology The Main Research Direction is the Computer Application

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future.

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Optimization of Performance Models for Interlayer Meltblown Nonwovens with Random Forest

Optimization of Performance Models for Interlayer Meltblown ...

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Health big data and Intelligent Healthcare (ICHIH), International Conference on

作者： PengJie Wang Ya Li YuHeng Liang Xiaolin Zhu TongFei Li Wei Lv Zhuhai College of Science and Technology Alibaba Cloud College of Big Data Application Zhuhai China Information Technology Application Innovation Platform Department YGSOFT INC. Zhuhai China

In the context of rapid societal and technological advancements, there has been a pronounced elevation in public health awareness. Among various protective measures, meltblown nonwoven materials have emerged as an indispensable component in protective masks, offering a crucial respiratory barrier for users. This study endeavors to provide an in-depth examination of this pivotal material. By systematically analyzing extensive datasets of meltblown fabric manufacturing processes and taking into account key parameters such as receiving distance, air velocity, and material thickness, this paper, grounded on historical production parameters, harnesses advanced machine learning techniques to meticulously design and optimize the performance control model for interlayer melt-blown nonwoven materials. The central aim of this research is to further enhance the efficacy of this material in health protection applications.

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Research on carbon emission prediction method for urban complexes based on knowledge graphs

Research on carbon emission prediction method for urban comp...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Yuan, Rui Zhuang, Qiushi Li, Jiahang Yuan, Zheng Huang, Yu Tang, Ruibo Guo, Xiaojun China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport PRC Beijing China

ISBN: (纸本)9781510690585

This study introduces a knowledge graph-based method for predicting carbon emissions in urban complexes, addressing the challenge of decoupling economic growth from CO2 emissions in cities. By integrating multi-source data, the model dynamically updates knowledge graphs to adapt to urban changes. Utilizing a Relational Evolutionary Graph Convolutional Network (RE-GCN) for knowledge graph embedding, the model captures complex relationships and temporal dynamics, enhancing prediction accuracy and reliability. Experiments, conducted on datasets from a major coastal city in eastern China spanning from 2019 to 2023, demonstrate the model's superior performance over baseline models, with notable improvements in Mean Squared Error (MSE), R-squared (R2), and Mean Absolute Error (MAE). The robustness of the model is further validated through sensitivity analysis with added Gaussian and Poisson noise, showing minimal performance decline. This research provides a powerful tool for urban planners and policymakers to formulate effective carbon reduction strategies, supporting the transition towards sustainable urban development and carbon neutrality goals. © 2025 SPIE.

关键词： Mean square error

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applications of Explainable Artificial Intelligent Algorithms to Medicine: A Bibliographic Study 2nd

Applications of Explainable Artificial Intelligent Algorithm...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Wang, Mini Han Xing, Lumin Yu, Xiangrong Yan, Chenyu Zhou, Ruoyu Chong, Kelvin K. L. Wang, Fengling Zhou, Xiaoshu Chen, Guoqiang Wu, Qing Lin, Zhiyuan Zeng, Peijin Xiao, Qide The Faculty of Data Science City University of Macau Macau China The Faculty of Medicine The Chinese University of Hong Kong Hong Kong Guanghui Hou Zhuhai China The Department of AI and Big Data Application Zhuhai Institute of Advanced Zhuhai China The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital Jina China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China Hezhou University Hezhou China Gree Polytechnic Zhuhai China Beijing Institute of Technology Zhuhai China Zhuhai Science and Technology Development Promotion Association Zhuhai China

ISBN: (纸本)9789819721436

In recent years, Explainable Artificial Intelligent (XAI) algorithms have been widely applied and deeply studied in the field of medicine. Based on the bibliometric method, this paper summarizes and analyses 315 non-repetitive related literature retrieved from the literature databases of Scopus and Web of Science (WOS). Developing trends, key authors, contributed countries, journals, references, and keywords are explored. Based on the method of text mining, representative articles, and their core opinions are qualitatively analyzed. Methods are clustered, applications, strengths, and drawbacks are discussed, and issues and future directions are exhibited. Findings show that applications of the interpretable algorithm to the medical field have vital potential clinical value. Medical diagnosis based on computer vision with an explainable mechanism is aimed at breaking the trust between artificial intelligence and medicine disciplines. In addition, cooperative research and talent training in this interdisciplinary area are also worthy of attention in the future. Thus, a reference significance for XAI applied to the medical field is contributed by this study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medicinal chemistry

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PTCR-PJF: A Person-Job Fit Model for Structured Resumes

PTCR-PJF: A Person-Job Fit Model for Structured Resumes

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiaming Li Jingfa Yao Guifa Teng Mingyue Yan School of Information Science and Technology Hebei Agricultural University Baoding China Hebei Software Institute Baoding China Intelligent Interconnection Equipment and Multi-Modal Big Data Application Technology Research and Development Center Baoding China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the declining efficiency of online recruitment and the increasing trend of structured job seeker information, existing Person-job Fit (PJF) methods are challenged by insufficient matching accuracy and the complexity of data structures. Traditional models often fail to utilize information in structured resumes fully and are inefficient in processing large volumes of heterogeneous data. To address these issues, we propose a novel Person-Job Fit model (PTCR-PJF) based on an improved Text Convolutional Neural Network (TextCNN) and Roformer joint modeling. PTCR-PJF employs two position-aware strategies: sine-cosine absolute position encoding (SPE) and rotary relative position encoding (RoPE), effectively enhancing the recognition of specific positional information in job postings. Additionally, the model employs a dual-layer text convolution module and applies chunk max pooling and adaptive max pooling to extend its perception of positional information further. It also integrates a Roformer based on RoPE to capture long-distance dependencies. Extensive experiments conducted on the large-scale real-world dataset CareerBuilder2012 and comparisons with 12 baseline models show that PTCR-PJF achieves state-of-the-art (SOTA) performance. The results indicate a significant advantage of the PTCR-PJF model in processing structured resume data, offering a new technological path for online intelligent recruitment.

关键词： Adaptation models Resumes Neural networks Predictive models Market research Transformers data models

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WOBTree:a write-optimized B+-tree for non-volatile memory

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Frontiers of Computer Science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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An Interactive Annotation Method Based on Incremental Learning 2

An Interactive Annotation Method Based on Incremental Learni...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Duan, Xiusheng Sun, Guohua Cao, Jingya Zhang, Yuyang Yang, Jingchao Han, Duyu Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center Shijiazhuang China Shijiazhuang Tiedao University School of Mechanical Engineering Shijiazhuang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

ISBN: (纸本)9798350331417

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time. © 2023 IEEE.

关键词： data annotation incremental learning interaction sample augmentation

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TAPFixer: Automatic Detection and Repair of Home Automation Vulnerabilities based on Negated-property Reasoning

arXiv

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arXiv 2024年

作者： Yu, Yinbo Xu, Yuanqi Huang, Kepu Liu, Jiajia National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Northwestern Polytechnical University China Research & Development Institute Northwestern Polytechnical University Shenzhen China

Trigger-Action Programming (TAP) is a popular end-user programming framework in the home automation (HA) system, which eases users to customize home automation and control devices as expected. However, its simplified syntax also introduces new safety threats to HA systems through vulnerable rule interactions. Accurately fixing these vulnerabilities by logically and physically eliminating their root causes is essential before rules are deployed. However, it has not been well studied. In this paper, we present TAPFixer, a novel framework to automatically detect and repair rule interaction vulnerabilities in HA systems. It extracts TAP rules from HA profiles, translates them into an automaton model with physical and latency features, and performs model checking with various correctness properties. It then uses a novel negated-property reasoning algorithm to automatically infer a patch via model abstraction and refinement and model checking based on negated-properties. We evaluate TAPFixer on market HA apps (1177 TAP rules and 53 properties) and find that it can achieve an 86.65% success rate in repairing rule interaction vulnerabilities. We additionally recruit 23 HA users to conduct a user study that demonstrates the usefulness of TAPFixer for vulnerability repair in practical HA scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Model checking

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DESIGN AND ANALYSIS OF WIND TURBINE FAULT DIAGNOSIS SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

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Diagnostyka 2025年第1期26卷

作者： Luo, Xiaoli Ning, Jinye Wu, Min Scientific Research Department Hunan Electrical College of Technology Xiangtan411101 China Institute of Big Data and Artificial Intelligence Application Technology Hunan Electrical College of Technology Xiangtan411101 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan City411201 China

Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation. © 202x by the Authors.

关键词： Convolutional neural networks

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