检索结果-内蒙古大学图书馆

Vision Sensor-Based Shoe Detection for Human Tracking in a Human-Robot Coexisting Environment: A Photometric Invariant Approach Using dbscan algorithm

引用

IEEE SENSORS JOURNAL 2019年第12期19卷 4549-4559页

作者： Paral, Pritam Chatterjee, Amitava Rakshit, Anjan Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Human-tracking problems are considered as one of the major problems in a human-robot coexisting environment. Such systems often employ a sensor fusion mechanism involving a vision sensing-based system along with ultrasonic or IR sensing systems. In this paper, we consider a very important sub-problem, where vision sensing is employed in such a human-robot coexisting environment for shoe detection purposes, by approximating the target person's positions in successive frames captured by the onboard camera of a robot, during human tracking. This paper considers a more challenging scenario of shoe detection under photometric changes and proposes a novel variant of the CFAsT-match algorithm, called photometric-invariant CFAsT-match (PICFAsT-match) algorithm. Both the CFAsT-match and PICFAsT-match have been implemented using the dbscan algorithm, a density-based clustering approach. Performance evaluations carried out for real-life tracking of human shoes show the supremacy of the proposed PICFAsT-match algorithm.

关键词： Human-robot coexisting environment shoe detection vision sensing CFAsT-match photometric-invariant CFAsT-match dbscan algorithm

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

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

Scaling up the dbscan algorithm for Clustering Large Spatial Databases Based on Sampling Technique

引用

Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 467-473页

作者： Guan Ji hong 1, Zhou Shui geng 2, Bian Fu ling 3, He Yan xiang 1 1. School of Computer, Wuhan University, Wuhan 430072, China 2.State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China 3.College of Remote Sensin School of Computer Wuhan University Wuhan China State Key Laboratory of Software Engineering Wuhan University Wuhan China College of Remote Sensing and Information Engineering Wuhan University Wuhan China

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with dbscan algorithm to cluster large spatial databases, and two sampling based dbscan (Sdbscan) algorithms are developed. One algorithm introduces sampling technique inside dbscan, and the other uses sampling procedure outside dbscan. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

关键词： spatial databases data mining clustering sampling dbscan 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

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

引用

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

Memory Effect in dbscan algorithm

Memory Effect in DBSCAN Algorithm

引用

4th International Conference on Computer Science and Education

作者： Li Jian Yu Wei Yan Bao-Ping Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Bei Hang Univ Software Coll Beijing Peoples R China

ISBN: (纸本)9781424435197

As a density-based clustering algorithm, dbscan plays an important role in data mining. Normally dbscan algorithm is computationally expensive, limiting its performance in large scale data sets, especially in high dimensional data sets. The high complexity is rooted from the region queries, a very common operation in density-based algorithms, which brings the complexity of the algorithms to O(n(2)), where a is the number of database objects. With the help of index structure the complexity can be reduced to O(nlogn), however it is inefficient to create the index structureespecially for high dimensional data sets or large scale databases. In this paper we propose a new concept named memory effect (ME). ME can be used to shrink the scope of region queries to neighboring objects. Based on ME we have improved dbscan algorithm evidently, and empirical experiments have shown the improvement in both effectiveness and efficiency. At last, we give the theoretical analysis of MEdbscan algorithm and talk about the influence of parameters.

关键词： Density-based clustering dbscan algorithm MEdbscan algorithm Memory Effect

来源：评论

学校读者我要写书评

暂无评论

Forecast The Distribution Of Urban Water Point By Using Improved dbscan algorithm

Forecast The Distribution Of Urban Water Point By Using Impr...

引用

3rd International Conference on Intelligent System Design and Engineering Applications (ISDEA)

作者： Yan Jianzhuo Qi Mengyao Fang Liying Wang Ying Yu Jianyun Beijing Univ Technol Elect Informat & Control Engn Inst Beijing 100124 Peoples R China Capital Univ Econ & Busines Educ & Technol Ctr Beijing 100070 Peoples R China

ISBN: (纸本)9780769549231;9781467348935

Spatial clustering is an important method for spatial data mining and knowledge discovery. According to the deficiency existing in density-based clustering algorithm dbscan, such as the I/O overhead, memory consumption etc. This paper improves the dbscan algorithm, which proposed directional density algorithm, the algorithm reduces lots of points which need to be queried. By taking Geographic Information System for the application background, we successfully applied to forecast the distribution of urban water points. Compared with the traditional dbscan algorithm, the results conformed to the actual situation, and efficiency increased by 20%.

关键词： Spatial Data Mining Density Clustering dbscan algorithm Distribution of Urban Water Point

来源：评论

学校读者我要写书评

暂无评论

Application of dbscan algorithm in Precision Fertilization Decision of Maize 11th

Application of DBSCAN Algorithm in Precision Fertilization D...

引用

11th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Li, Yang Wang, Guowei Chen, Yu Jiao, Yang Yu, Haijiao Zhao, Guogang Jilin Agr Univ Sch Informat Technol Changchun 130118 Peoples R China Jilin Prov Res Ctr Changchun 130118 Peoples R China Jilin Univ Sch Biol & Agr Engn Changchun 130022 Peoples R China Sixth Middle Sch Changchun Changchun 130000 Peoples R China JLJU City Coll Changchun 130000 Peoples R China

ISBN: (纸本)9783030061791;9783030061784

In the current era of big data, information technology is developing quite rapidly, the most important data mining technology in information technology is also widely used, and now it is applied to the field of agricultural production, what can solve many problems such as agricultural production, fertilization and so on. In this paper, data mining technology is applied to the process of corn fertilization, because in corn production, effective and reasonable amount of fertilizer can make corn grow better, however, if there is no specific fertilization according to the soil properties of the corn, it will lead to the soil which needs fertility can not be with enough fertility, and the soil without fertility will be added more and more. In view of this problem, the soil planted with corn was graded and treated with different levels of soil, so as to achieve the purpose of effective utilization of soil fertility. In this paper, the dbscan algorithm in clustering analysis is used to classify the soil, the DB SCAN algorithm to this field have not been reported so far. By applying the nutrient balance method, the amount of soil fertilizer was calculated at each level, and the fertilizer was targeted according to the amount of fertilizer. Through the pilot application in Nong'an County of Jilin province Chen hometown, compared with the traditional fertilization results, Fertilizer reduced by 25%, corn production increased by about 15%, effectively reducing the input of chemical fertilizer and increasing the output of crops.

关键词： Data mining Cluster analysis Soil classification dbscan algorithm

来源：评论

学校读者我要写书评

暂无评论

A Parallel dbscan algorithm Based On Spark 6

A Parallel DBSCAN Algorithm Based On Spark

引用

2016 IEEE International Conferences on BDCloud/SocialCom/SustainCom 2016

作者： Luo, Guangchun Luo, Xiaoyu Gooch, Thomas Fairley Tian, Ling Qin, Ke Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781509039364

With the explosive growth of data, we have entered the era of big data. In order to sift through masses of information, many data mining algorithms using parallelization are being implemented. Cluster analysis occupies a pivotal position in data mining, and the dbscan algorithm is one of the most widely used algorithms for clustering. However, when the existing parallel dbscan algorithms create data partitions, the original database is usually divided into several disjoint partitions;with the increase in data dimension, the splitting and consolidation of high-dimensional space will consume a lot of time. To solve the problem, this paper proposes a parallel dbscan algorithm (S_dbscan) based on Spark, which can quickly realize the partition of the original data and the combination of the clustering results. It is divided into the following steps: 1) partitioning the raw data based on a random sample, 2) computing local dbscan algorithms in parallel, 3) merging the data partitions based on the centroid. Compared with the traditional dbscan algorithm, the experimental result shows the proposed S_dbscan algorithm provides better operating efficiency and scalability.

关键词： Spark Data Mining Parallel algorithms dbscan algorithm Data Partition

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：