检索结果-内蒙古大学图书馆

Load Spectra Extrapolation by Bandwidth-Optimized Kernel Density Estimation Based on dbscan algorithm

JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第2期12卷 1445-1456页

作者： Yang, Xuefeng Zhou, Xiaojun Wan, Bowen Fu, Yimeng Zhejiang Univ Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

Load spectra extrapolation is the basis of fatigue analysis and life prediction in engineering. This paper extrapolates the loads based on the kernel density estimation method, and a new bandwidth calculation method of kernel density estimation is proposed by introducing the dbscan algorithm. This bandwidth calculation method has high accuracy and efficiency. Torque load time history in the brake stabilization phase was first collected through the data acquisition system. Then, the mean and amplitude distributions of loads were obtained by means of the rain flow counting method. The dbscan algorithm was used to cluster the load data and divide them into different clusters. Data in the same cluster share the same bandwidth, and the rule of thumb is used for calculation. Experiments were conducted with fixed bandwidth, adaptive bandwidth, and the bandwidth calculation method proposed in this article, respectively. The statistical load amplitude extremes were extrapolated with 100 folds by three different methods, respectively, and 100 braking trials were conducted as a comparison. The results showed that the method of this article has much higher accuracy than the method based on the rule of thumb, meanwhile, it has similar accuracy and lower algorithm complexity compared with the adaptive method. The research content in this paper has a certain guiding significance for the extrapolation of load spectrum in practical engineering.

关键词： Load spectra extrapolation Kernel density estimation Bandwidth dbscan algorithm Brake stabilization phase

来源：评论

学校读者我要写书评

暂无评论

An improved dbscan algorithm for hazard recognition of obstacles in unmanned scenes

引用

SOFT COMPUTING 2023年第24期27卷 18585-18604页

作者： Zhang, Wenying Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Henan Peoples R China

The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved dbscan algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional dbscan by refining the core point definition using an adaptive density threshold. It identifies the "elbow point" in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance.

关键词： Hazard identification Obstacles dbscan algorithm Bounding box AI image processing Unmanned

来源：评论

学校读者我要写书评

暂无评论

K-dbscan: An improved dbscan algorithm for big data

引用

JOURNAL OF SUPERCOMPUTING 2021年第6期77卷 6214-6235页

作者： Gholizadeh, Nahid Saadatfar, Hamid Hanafi, Nooshin Univ Birjand Birjand South Khorasan Iran

Big data storage and processing are among the most important challenges now. Among data mining algorithms, dbscan is a common clustering method. One of the most important drawbacks of this algorithm is its low execution speed. This study aims to accelerate the dbscan execution speed so that the algorithm can respond to big datasets in an acceptable period of time. To overcome the problem, an initial grouping was applied to the data in this article through the K-means++ algorithm. dbscan was then employed to perform clustering in each group separately. As a result, the computational burden of dbscan execution reduced and the clustering execution speed increased significantly. Finally, border clusters were merged if necessary. According to the results of executing the proposed algorithm, it managed to greatly reduce the dbscan execution time (98% in the best-case scenario) with no significant changes in the qualitative evaluation criteria for clustering.

关键词： Data mining Clustering Big data dbscan algorithm K-means++ algorithm

来源：评论

学校读者我要写书评

暂无评论

Consumer clusters detection with geo-tagged social network data using dbscan algorithm: a case study of the Pearl River Delta in China

引用

GEOJOURNAL 2021年第1期86卷 317-337页

作者： Fan, Tianhui Guo, Naijing Ren, Yujie Grenoble Ecole Management Dept Mkt Grenoble France Nanjing Forestry Univ Coll Landscape Architecture Nanjing Peoples R China Kyushu Univ Grad Sch Human Environm Studies Fukuoka Japan

With the advent of the Big Data era, multi-source geo-tagged data provide a new perspective and data source for urban spatial analysis. In order to accurately identify the location and characteristics of consumer clusters in urban area and explore their formation mechanism, this study collects Sina Weibo check-in data and *** electronic word of mouth (e-WOM) data generated in the catering consumer space in the core region of Pearl River Delta, Guangdong and identifies location and characteristics of clusters with the help of dbscan clustering algorithm and CSA indices. In addition, the formation mechanism of these catering space clusters is explained by non-spatial and global spatial regression models. The result revealed that 4 levels of 19 catering space clusters are identified in the study area. The size and heat of consumer clusters are mainly affected by the geometric form, diversity of check-in, population density, distance from the city center and e-WOM corresponding to each cluster. The present study suggests that the new dbscan-based clustering method has a high accuracy. Compared with the traditional factors that reflect the objective attributes of cities and non-spatial models, the unstructured information elements contained in e-WOM and spatial error models can better explain the formation mechanism of the consumer clusters.

关键词： Geo-tagged data dbscan algorithm Consumer clusters Spatial error model

来源：评论

学校读者我要写书评

暂无评论

An Improved dbscan algorithm for Adaptively Determining Parameters in Multi-density Environment 2021

An Improved DBSCAN Algorithm for Adaptively Determining Para...

引用

2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Chen, Feiya Beijing Univ Technol Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9781450390200

The density-based spatial clustering dbscan algorithm can realize clustering of arbitrary shapes in noisy data sets. The dbscan algorithm needs to manually determine the Eps and Min Pts parameters. As a result, the accuracy of the clustering results directly depends on the user's choice of parameters. In the face of large-scale irregular data, fixed parameters lead to ineffective multi-density data clustering. ideal. In response to the above situation, an improved dbscan algorithm for adaptively determining parameters in a multi-density environment is proposed. The non-parametric kernel density estimation theory is used to analyze the distribution characteristics of data samples to automatically determine the Eps parameters, preprocess the data, and identify each Based on the density around the data object, the density threshold MinPts suitable for the density of the area is automatically generated. The manual intervention of the clustering process is avoided, and the automation of the clustering process is realized. This method can select reasonable Eps and MinPts parameters, and obtains clustering results with higher accuracy.

关键词： dbscan algorithm multi-density kernel density estimation adaptive

来源：评论

学校读者我要写书评

暂无评论

A fast dbscan algorithm for big data based on efficient density calculation

引用

EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Hanafi, Nooshin Saadatfar, Hamid Univ Birjand Comp Engn Dept Birjand Iran

Today, data is being generated with a high speed. Managing large volume of data has become a challenge in the current age. Clustering is a method to analyze data that is generated in the Internet. Various approaches have been presented for data clustering until now. Among them, dbscan is a most well-known density-based clustering algorithm. This algorithm can detect clusters of different shapes and does not require prior knowledge about the number of clusters. A major part of the dbscan run-time is spent to calculate the distance of data from each other to find the neighbors of each sample in the dataset. The time complexity of this algorithm is O(n2);Therefore, it is not suitable for processing big *** this paper, dbscan is improved so that it can be applied to big datasets. The proposed method calculates accurately each sample density based on a reduced set of data. This reduced set is called the operational set. This collection is updated periodically. The use of local samples to calculate the density has greatly reduced the computational cost of clustering. The empirical results on various datasets of different sizes and dimensions show that the proposed algorithm increases the clustering speed compared to recent related works while having similar accuracy as the original dbscan algorithm.

关键词： Data Mining Clustering Big Data dbscan algorithm

来源：评论

学校读者我要写书评

暂无评论

A Semi-supervised Fuzzy Clustering Approach via Modifications of the dbscan algorithm 10th

A Semi-supervised Fuzzy Clustering Approach via Modification...

引用

10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions (ICSCCW)

作者： Bedalli, Erind Mancellari, Enea Rada, Rexhep Epoka Univ Tirana Albania Univ Elbasan Elbasan Albania

ISBN: (纸本)9783030352493;9783030352486

In the data mining context, semi-supervised learning is applicable in circumstances where only a scarce amount of information on the intrinsic structure of a dataset is available. This information may be in the form a few labelled instances or a relatively small set of constraints on the pairwise memberships of particular instances. In this study we are providing a semi-supervised fuzzy clustering model which modifies versions of conventional dbscan algorithm in order to generate soft clusters which foreclose the noise points. The employed modifications are mostly related to the control parameters of the algorithm intending to utilize the additional information (which in our case is in the form of a few labelled instances) and adaptations towards the fuzzy clustering approach. Finally, several experimental procedures have been conducted on synthetic and real-world benchmark datasets in order to assess the accuracy of our employed model and to compare it to the conventional algorithms of the respective domain.

关键词： Semi-supervised learning Density-based clustering dbscan algorithm Fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

Evaluation Method of Switchgear State Based on Adaptive dbscan algorithm 5

Evaluation Method of Switchgear State Based on Adaptive DBSC...

引用

5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Li, Yangyang Hu, Jinlei Lai, Junju Wang, Wei Yang, Fan Huang, He Guangdong Power Grid Corp Qingyuan Power Supply Bur Qingyuan Peoples R China Shanghai Univ Elect Power Sch Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728152813

In order to better identify the outlier in the detection data and reasonably evaluate the state of the switchgear, we propose a method for evaluating the state of switchgear based on the Density-Based Spatial Clustering of Applications with Noise (dbscan). First, the feature quantities of the switchgear is composed of the insulation state index, the Transient Earth Voltage (TEV), the Ultrasonic Testing (UT), the ambient temperature, the ambient humidity and the operation life. Standardization method is carried out for all feature quantities to form multi-dimensional feature *** the k-average nearest neighbor method and mathematical expectation method are used to generate the R radius and the minimum number of MinPts parameters. The density threshold (Den) is introduced to automatically find the stable range of cluster number. Finally, In this paper, the field test data is used as an example to verify the feasibility of the method and provide a theoretical basis for the evaluation of switchgear.

关键词： switchgear state evaluation adaptive optimization dbscan algorithm outlier

来源：评论

学校读者我要写书评

暂无评论

AT_dbscan: dbscan algorithm with Self-Variable Threshold

International Core Journal of Engineering

引用

International Core Journal of Engineering 2021年第4期7卷 409-417页

作者： Haifeng Ma Aihua Wu

Based on the analysis of the shortcomings of the density-based clustering algorithm dbscan and its improved algorithm, an improved algorithm of dbscan based on self-variation of threshold value is proposed: AT_dbscan algorithm. The algorithm calculates the distribution characteristics of the shortest neighbor distance of each node in the data set, and then calculates the distribution of different densities in the data set according to the distribution characteristics, and finally determines the different radius thresholds of different density distribution data areas based on this. In this way, different radius thresholds ε can be used for data in different areas to accept dbscan algorithm clustering. On the whole, it basically solves the unreasonable clustering results caused by the unfavorable selection of the parameter value ε at the initial stage of the dbscan algorithm, and the unreasonable clustering of data with different density distributions. The experimental results show that the algorithm has a good clustering effect, and the algorithm first obtains the radius threshold ε without manual setting, which is of great significance.

关键词： Data Mining kd-tree Clustering dbscan algorithm

来源：评论

学校读者我要写书评

暂无评论

Restricted Randomness dbscan : A faster dbscan algorithm 21

Restricted Randomness DBSCAN : A faster DBSCAN Algorithm

引用

2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)

作者： Sashakt Pathak Arushi Agarwal Ankita Ankita Mahendra Kumar Gurve Jaypee Institute of Information Technology India

ISBN: (纸本)9781450389204

Data Mining is the process of extracting useful and accurate information or patterns from large databases using different algorithms and methods of machine learning. To analyze the data, Clustering is one of the methods in which similar data is grouped together and dbscan clustering algorithm is the one, which is broadly used in numerous practical applications. This paper presents a more efficient density based clustering algorithm, which has the ability to discover cluster faster than the existing dbscan algorithm. The efficiency is achieved by restricting the randomness of choosing points from the dataset. Our proposed algorithm named Restricted Randomness dbscan (RR dbscan) is compared with conventional dbscan algorithm over 9 datasets on the basis of Silhouette Coefficient, Time taken in formation of clusters and accuracy. The results show that RR dbscan performs better than traditional dbscan in terms of accuracy and time taken to form clusters.

关键词： dbscan algorithm Clustering Restricted randomness

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：