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International Conference on Informatics Education and Computer Technology Applications (IECA)

作者： Chen, Shanshan Liu, Qian Suzhou Univ Sch Foreign Languages Suzhou 234000 Anhui Peoples R China Anhui Univ Finance & Econ Sch Languages & Media Bengbu 233000 Anhui Peoples R China

ISBN: (纸本)9798350375343;9798350375336

When classifying educational resources, the lack of analyzing the correlation relationship of the resources leads to the low reliability of the classification results, for this reason, we propose a method for classifying university English teaching resources based on density clustering algorithm. Firstly, the correlation between resources between neighboring grids is fully considered, and a weighted grid is constructed for each resource partition;secondly, the corresponding weights are set based on the correlation of resources, and the density parameter of the grid cell is calculated, then the C0MC0RE-MR algorithm is used to determine the range of Key-value parameter values;finally, when the Key-value parameter values are within the given density threshold parameter range of the grid cell, the density threshold parameter value of the grid cell will be set to zero. Finally, when the Key-value parameter value is within the range of the given density parameter of the grid cell, the corresponding educational resources are classified as the same kind of resources with the center target grid object. The test results show that the accuracy of the classification results of the designed method is stable at more than 95.49%, which has obvious advantages compared with the control group.

关键词： density clustering algorithm university English teaching resources classification methods

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A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 523卷 702-713页

作者： Jiang, Jianhua Chen, Yujun Meng, Xianqiu Wang, Limin Li, Keqin Jilin Univ Finance & Econ Dept Data Sci Changchun 130117 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

density Peaks clustering (DPC) algorithm is a kind of density-based clustering approach, which can quickly search and find density peaks. However, DPC has deficiency in assignment process, which is likely to trigger domino effect. Especially, it cannot process some non-spherical data sets such as Spiral. The research results indicate that assignment process appears to be the most significant step in deciding the success of the clustering performance. Therefore, we propose a density peaks clustering based on nearest neighbors (DPC-KNN) which aims to overcome the weakness of DPC. The proposed DPC-KNN integrates the idea of nearest neighbors into the distance computation and assignment process, which is more reasonable. It can be seen from experimental results that the DPC-KNN algorithm is more feasible and effective, compared with K-means, DBSCAN and DPC. (C) 2019 Elsevier B.V. All rights reserved.

关键词： density clustering algorithm density peaks k nearest neighbors

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Sentiment Distribution of Topic Discussion in Online English Learning: An Approach Based on clustering algorithm and Improved CNN

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023年第2期16卷 1页

作者： Yang, Qiujuan Zhang, Jiaxiao Weinan Normal Univ Weinan Peoples R China Xidian Univ Xian Peoples R China

Online English teaching resources have recently surged, highlighting the exigency for efficient organization and categorization. This manuscript introduces an innovative strategy to classify university-level English teaching resources, employing a sophisticated density clustering algorithm. Initially, student discourse was mined within a teaching platform comment section, and in-depth textual analysis was conducted. Subsequently, the term frequency-inverse document frequency (TF-IDF) feature extraction algorithm was enhanced, while emotive attributes were seamlessly integrated into the textual manifestation layer during the classification procedure. This enabled the distribution of topics and emotions to be acquired for each comment, facilitating subsequent analyses of emotion feature extraction and model training. An improved weight calculation was designed based on TF-IDF to evaluate the importance of feature items for each corpus file. The simulation results demonstrate the proposed scheme's effectiveness. The algorithm facilitates faster scholarly access to educational resource information and effectively classifies data for high research adaptability.

关键词： density clustering algorithm IF-IDF Teaching Resource Classification Text Mining Word Frequency Analysis

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A bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings

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JOURNAL OF BUILDING ENGINEERING 2024年 87卷

作者： Xu, Kang Li, Qiu-Sheng Zhou, Kang Han, Xu-Liang Cent South Univ Sch Civil Engn Changsha Peoples R China City Univ Hong Kong Architecture & Civil Engn Res Ctr Shenzhen Res Inst Shenzhen Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Hong Kong Peoples R China Hefei Univ Technol Coll Civil Engn Hefei Peoples R China

This paper proposes a bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings. Firstly, the stochastic subspace method in combination with the bootstrap technique enables the estimation of multiple sets of modal parameters from raw data series. Then, a bootstrap-based stabilization diagram is used to extract the physical modes. Finally, the modal identification and associated uncertainty quantification results are determined via statistical analysis. Through a numerical study of high-rise buildings, the performance of the proposed method is validated, demonstrating that it can provide reliable modal parameter identification and uncertainty quantification as well as has good noise immunity. Furthermore, the developed approach is employed to identify modal parameters of a 600-m-tall skyscraper during a typhoon, proving its applicability to field measurements and structural health monitoring of high-rise buildings. This paper aims to present a novel tool for modal parameter identification and associated uncertainty quantification of high-rise buildings.

关键词： Modal identification Uncertainty quantification Stochastic subspace identification Bootstrap method density clustering algorithm High-rise building

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SURFACE-TO-AIR MISSILE SITES DETECTION AND RECOGNITION WITH LARGE-SCALE REMOTE SENSING IMAGES

SURFACE-TO-AIR MISSILE SITES DETECTION AND RECOGNITION WITH ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhao, Tianming Gao, Peng Tao, Zeyuan Tian, Tian Tian, Jinwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

As an important part of the air defense and anti-missile system, the surface-to-air missile sites (SAMSs) have important military application value. The existing deep learning-based detection algorithms have the problems of high false alarm rate and low efficiency when applied to large-scale remote sensing images. To address this issue, in this work we propose a multi-task detection and recognition network, including classification branch and detection branch. The classification branch selects the suspected target area from the large-scale remote sensing image, and the detection branch detects and recognizes the target in the suspected area, achieving precise positioning from coarse to fine. In addition, we propose a density clustering algorithm to post-process the detection results, which effectively reduces the false alarm rate of the algorithm. Finally, we propose a SAMS detection and recognition dataset (DSAMS), which divides the SAMSs into three parts: the launch fielding, the launch bunker and the control and guide plain. Comparing our algorithm with other current mainstream target detection algorithms on the DSAMS dataset, our algorithm has significant advantages.

关键词： SAMS Detection Multi-task network density clustering algorithm

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Characteristics of tropical cyclones formed in the Eastern Pacific Northwest

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NATURAL HAZARDS 2021年第3期106卷 2619-2633页

作者： Zhang, Lijie Zhu, Huiyun Liu, Jiancheng Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China

Typhoon is one of the main natural disasters affecting China. Every year typhoons cause lots of property losses and casualties. If the early warning department can issue early warning more time, it may reduce more losses. In this paper, we use TC path data, TC potential impact index method and density clustering algorithm, to study the characteristics of TC generated on the sea area east of 150 degrees E in the Northwest Pacific Ocean (Eastern Northwest Pacific Ocean, ENPO). The results show that although the ENPO is far from China, the proportion of impact TCs is not high, but the impact on Zhejiang, Fujian, Jiangxi, Taiwan Province and other provinces accounts for 10-30% of the total impact of the Northwest Pacific TCs;some provinces in the north are mainly affected by the TCs generated in the region;there is a flat X path in the Northwest Pacific Ocean, and the high impact TC mainly affects China through this path;from July to September, near the flat path, it is more likely to observe high impact TC.

关键词： Tropical cyclone Disaster Path density clustering algorithm

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Research on Emergency Material Optimization Scheduling and Importance Decision in Cluster Supply Chain 32

Research on Emergency Material Optimization Scheduling and I...

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第32届中国控制与决策会议

作者： Xue Hong Wang Yuan Liao Zhifeng Xu Ruidi Xu Zhuoran School of Computer and Information Engineering Beijing Technology and Business University

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to solve the problem of optimal scheduling and reasonable allocation of limited materials in a short time after a natural disaster,a clustering supply chain emergency material distribution priority decision algorithm based on density clustering algorithm is *** clustering as a factor indicator to determine the priority level of emergency material distribution in each supply chain in the clustered supply *** on the material importance,timeliness,and gap index factors,a fuzzy C-means clustering algorithm for supply chain emergency material demand importance decision algorithm is proposed to classify a variety of emergency materials required in disaster areas when an emergency occurs.,And decide the importance of each type of emergency *** results of simulation experiments verify the feasibility of the emergency supply materials scheduling and importance decision-making method for the clustered supply *** decision results guarantee the optimal scheduling and allocation of limited supplies in the shortest possible *** transportation programs provide theoretical support.

关键词： Emergency material distribution priority decision Emergency material demand importance decision density clustering algorithm Fuzzy C-means clustering algorithm Cluster supply chain

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China's tropical cyclone disaster risk source analysis based on the gray density clustering

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NATURAL HAZARDS 2014年第2期71卷 1053-1065页

作者： Zhang, L. J. Zhu, H. Y. Sun, X. J. Nanjing Univ Informat Sci & Technol China Inst Mfg Dev Nanjing 210044 Jiangsu Peoples R China

In recent years, tropical cyclones on the Pacific Northwest have decreased. We cannot infer that tropical cyclones impact China have reduced, because the Pacific Northwest is not homogeneous, and the variation characteristics of tropical cyclones in different sea areas are not clear. This paper uses gray relational density clustering algorithm to cluster tropical cyclone data sets between 1949 and 2008, according to the generated position of tropical cyclones, generated density and the possibility of landing. The Pacific Northwest is divided into different sea areas. Then, we analyze the risk of tropical cyclones generated in these sea areas. The results show that the probability of tropical cyclones landing generated in some sea areas is very high, reached 74 %, but the probability of tropical cyclones landing generated in other sea areas is only 2 %. Tropical cyclones generated in some sea areas are more likely to develop into typhoons, strong typhoons and so on, but the intensity of tropical cyclones generated in other sea areas is lower, there is little risk for China. Finally, according to the climate change stage trends, we divide the period 1949-2008 into three stages and analyze the tropical cyclone risk of each sea areas.

关键词： density clustering algorithm Gray relational Tropical cyclones Risk source

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