检索结果-内蒙古大学图书馆

International Conference on Audio, Language and Image Processing

作者： Xiaoqing Yu Yupu Ding Wanggen Wan Etienne Thuillier Institute of Smart City Shanghai University School of Communication and Information Engineering University of Technology of Belfort-Montbeliar

ISBN: (纸本)9781479939046

Spatial clustering is one of the main methods of data mining and knowledge discovery. dbscan algorithm can be found in space with "noise" database clustering of arbitrary shape, is a kind of good clustering algorithm. This paper introduces the basic concept and principle of dbscan algorithm, and applies this algorithm to perform clustering analysis distributions of weibo location information. The article compare k-means algorithm with dbscan algorithm in order to prove the effectiveness of dbscan algorithm.

关键词： Socialnetwork dbscan algorithm Data mining K-means algorithm

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

Application of DBSCAN Algorithm in Precision Fertilization D...

引用

The International Conference on Intelligent Agriculture 2017(ICIA2017)

作者： Yang Li Guowei Wang Yu Chen Yang Jiao Haijiao Yu Guogang Zhao School of Information Technology Jilin Agricultural University School of Biological and Agricultural Engineering Jilin University Sixth Middle School in Changchun The City College of JLJU

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 dbscan algorithm to this field have not been reported so *** 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

Study on the mining based on the improved dbscan algorithm in pick-up hotspots areas

Study on the mining based on the improved DBSCAN algorithm i...

引用

2015 2nd International Workshop on Materials Engineering and Computer Sciences(IWMECS 2015)

作者： Zhipu Tang Xiaodong Wang Hao Liu Xiaowen Wang Zhiqiang Wei College of Information Science and Engineering Ocean University of China

The main business model of taxies called "Roadside Beckon" adopted widely is inefficientPassengers and drivers are ignorant of the location information of each otherThe rise of various types of taxi Apps also have made an effort to solve the problem of "difficult to take a taxi"However, these methods have not fundamentally gotten rid of the taxi status of "passive waiting for passengers"Therefore, this paper proposes a taxi cruise path recommendation method based on pick-up hotspots areasThe method can recommend a cruise path with short distance and high pick-up rate for taxisAs a result, it solves the problem of "passive waiting" and aimless random roaming for guests, balance the relationship between taxis and passengers.

关键词： pick-up hotspots route recommendation dbscan algorithm clustering

来源：评论

学校读者我要写书评

暂无评论

Retinal Optic Disk Segmentation and Analysis in Fundus Images using dbscan Clustering algorithm 23

Retinal Optic Disk Segmentation and Analysis in Fundus Image...

引用

23rd Iranian Conference on Biomedical Engineering / 1st International Iranian Conference on Biomedical Engineering (ICBME)

作者： Hamednejad, Golnoush Pourghassem, Hossein Islamic Azad Univ Najafabad Branch Dept Elect Engn Najafabad Iran

ISBN: (纸本)9781509034529

The Optic Disk (OD) detection as an important part of the retinal images are considered for diagnosing the some diseases such as diabetic retinopathy, high blood pressure and hemorrhages also for exudes analyzing. In this paper, a novel method for optic disk detection and segmentation is proposed based on the modified clustering algorithm. In our method, at first step by applying the masking procedure on the image, the candidate of ROI for region of optic disk is obtained. This ROI is improved with the combinational filtering. The improved sub image for convenient presentation is converted to the LAB color space. The Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm is used. In this clustering method with employing the square kernel with optimized parameters, the modified dbscan clustering is attained for the OD segmentation and recognition. Finally, for evaluating the proposed algorithm, several measures based on the structural content, similarity criterion and MSE of segmented images are computed. With this algorithm, the OD is finally segmented with the precision rate of 87.58% and the averaged variance of 0.0206 for the segmentation errors.

关键词： Optic Disk Segmentation Density-based Clustering dbscan algorithm

来源：评论

学校读者我要写书评

暂无评论

Discovery of rules in urban public facility distribution based on dbscan clustering algorithm

Discovery of rules in urban public facility distribution bas...

引用

Symposium on Remote Sensing and GIS Data Processing and Applications;and Innovative Multispectral Technology and Applications

作者： Li, Xinyan Li, Deren Huazhong Univ Sci & Technol Sch Architecture & Urban Planning 1037 Luoyu Rd Wuhan 430074 Peoples R China Wuhan Univ State Key Lab Info Engn Surveying Mapping & Remot Wuhan 430079 Peoples R China

ISBN: (纸本)9780819469540

Recently Spatial Data Mining (SDM) has been recognized as a powerful technology that can complement traditional GIS to facilitate urban planning and management since it can be used to discover interesting, implicit knowledge from spatial database. dbscan spatial clustering algorithm as a SDM method is able to effectively discover clusters of arbitrary shape in large database with noise points. In this paper we applied this algorithm to detect distribution patterns of urban public facilities in a developed city, including primary school, high school and commercial facilities. Both qualitative and quantitative analysis were carried out to investigate how to determine optimal values of input parameters for dbscan algorithm, and the distribution patterns of public facilities were assessed against urban planning design standard using the algorithm.

关键词： spatial data mining dbscan algorithm GIS urban public facility

来源：评论

学校读者我要写书评

暂无评论

Research on the Parallelization of the dbscan Clustering algorithm for Spatial Data Mining Based on the Spark Platform

引用

REMOTE SENSING 2017年第12期9卷 1301-1301页

作者： Huang, Fang Zhu, Qiang Zhou, Ji Tao, Jian Zhou, Xiaocheng Jin, Du Tan, Xicheng Wang, Lizhe Univ Elect Sci & Technol China Sch Resources & Environm 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Inst Remote Sensing Big Data Big Data Res Ctr 2006 Xiyuan Rd Chengdu 611731 Sichuan Peoples R China Texas A&M Univ Texas A&M Engn Expt Stn College Stn TX 77843 USA Texas A&M Univ High Performance Res Comp College Stn TX 77843 USA Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing 2 Xueyuan Rd Fuzhou 350116 Fujian Peoples R China Wuhan Univ Int Sch Software 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 10094 Peoples R China

Density-based spatial clustering of applications with noise (dbscan) is a density-based clustering algorithm that has the characteristics of being able to discover clusters of any shape, effectively distinguishing noise points and naturally supporting spatial databases. dbscan has been widely used in the field of spatial data mining. This paper studies the parallelization design and realization of the dbscan algorithm based on the Spark platform, and solves the following problems that arise when computing macro data: the requirement of a great deal of calculation using the single-node algorithm;the low level of resource-utilization with the multi-node algorithm;the large time consumption;and the lack of instantaneity. The experimental results indicate that the proposed parallel algorithm design is able to achieve more stable speedup at an increased involved spatial data scale.

关键词： spatial data mining dbscan algorithm parallel computing spark platform traffic congestion area discovery

来源：评论

学校读者我要写书评

暂无评论

Improved LSTM-AE-dbscan motor controller temperature abnormality label recognition based method

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年

作者： Zhou, Juan Zhou, Shengwei Wang, Peng China Jiliang Univ Sch Qual & Safety Engn 258 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China China Automot Engn Res Inst Chongqing Peoples R China

In order to solve the problem that the unlabeled data of the temperature part of the motor controller cannot be utilized in the motor troubleshooting of new energy vehicles, LSTM-autoencoding density spatial clustering (LSTM-AE-dbscan) motor controller temperature anomaly label recognition method is proposed. Firstly, the text outlines a process whereby the neurons in the hidden layer are replaced with LSTM neurons using the autoencoder (AE) algorithm to extract time series data features while preserving the key information of the data structure. Secondly, the motor controller monitoring data is divided by using the sliding window approach to obtain optimal LSTM-AE model performance. Then, the temporal features of the new energy vehicle motor controller no-abnormality monitoring data are extracted and reconstructed from the input data. Besides, the improved dbscan algorithm trains the difference between the reconstructed value and the actual value in an anomalous manner. The EPS and Min-Samples parameters are generated by chaotic sequences, and the silhouette coefficients are used as fitness values. These values are then optimized by the chaotic game optimization (CGO) algorithm to obtain the optimal parameters of the dbscan algorithm. This approach reduces human involvement and minimizes clustering errors. Finally, the time series data from the motor controller during the operation of the real vehicle is input into the model described above. Comparative experiments have been conducted and the proposed enhanced method has a higher precision of 0.985, a recall of 1, and a recognition time of 1.47 s compared to K-means, LOF, and single dbscan anomaly recognition methods, proving the effectiveness of the proposed model.

关键词： Motor controller temperature dbscan algorithm chaotic game optimization algorithm anomaly recognition

来源：评论

学校读者我要写书评

暂无评论

An aero-engine remaining useful life prediction model based on clustering analysis and the improved GRU-TCN

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷 016001-016001页

作者： Chen, Fudan Yu, Yang Li, Yuanjiang Jiangsu Univ Technol Coll Mech Engn Changzhou 213000 Peoples R China Jiangsu Univ Sci & Technol Sch Oceanog Mengxi Rd Zhenjiang Peoples R China

Accurately predicting the remaining useful life (RUL) of engines is paramount for implementing effective preventive maintenance strategies, preventing injuries and fatalities caused by equipment failures, and significantly reducing routine repair and replacement costs. However, existing deep learning models often ignore the variable operating conditions in real engineering applications and do not sufficiently consider the interaction between time series and degradation laws, which directly leads to the inability to effectively extract to degradation feature extraction. To address this problem, this study developed a novel combined network model named CA-DRGRU-TTCN, aimed at accurately predicting the RUL of engines. Firstly, a density-based spatial clustering of applications with noise algorithm is used to identify multiple operating conditions, and incorporate the recognition results into the model as additional new features. The first degradation time point is determined by Jensen-Shannon divergence. Secondly, the deep connectivity of the residual gated recurrent unit (GRU) module is designed to extract deeper degradation features, and an improved time-based mean square error loss function based on the first degradation time point is applied to temporal convolutional networks (TCN) to better capture the dependency between the time series and the real degradation degree of the engine. Finally, experiment results on the C-MAPSS dataset show that the proposed method achieves better performance compared to existing state-of-the-art methods.

关键词： remaining useful life prediction (RUL) dbscan algorithm JS divergence gated recurrent network (GRU) temporal convolutional networks (TCN)

来源：评论

学校读者我要写书评

暂无评论

From 2D to 3D Urban Analysis: An Adaptive Urban Zoning Framework That Takes Building Height into Account

引用

BUILDINGS 2025年第7期15卷 1182-1182页

作者： Shen, Tao Kong, Fulu Yuan, Shuai Wang, Xueying Sun, Di Ren, Zongshuo Beijing Univ Civil Engn & Architecture Sch Geomatics & Urban Spatial Informat Beijing 100044 Peoples R China China Acad Railway Sci Inst Comp Technol Beijing 100081 Peoples R China

The vertical heterogeneous structures formed during the evolution of urban agglomerations, driven by globalization, pose challenges to traditional two-dimensional spatial analysis methods. This study addresses the vertical heterogeneity and spatial multiscale problem in three-dimensional urban space and proposes an adaptive framework that takes into account building height for multiscale clustering in urban areas. Firstly, we established a macro-, meso- and micro-level analysis system for the characteristics of urban spatial structures. Subsequently, we developed a parameter-adaptive model through a dynamic coupling mechanism of height thresholds and average elevations. Finally, we proposed a density-based clustering method that integrates the multiscale urban analysis with parameter adaptation to distinguish urban spatial features at different scales, thereby achieving multiscale urban regional delineation. The experimental results demonstrate that the proposed clustering framework outperforms traditional density-based and hierarchical clustering algorithms in terms of both the Silhouette Coefficient and the Davies-Bouldin Index, effectively resolving the problem of vertical density variation in urban clustering.

关键词： data-driven urban analysis multiscale spatial clustering urban 3D models dbscan algorithm urban spatial structure

来源：评论

学校读者我要写书评

暂无评论

A New IoT-Driven Coherency Assessment Strategy for Enhancing Power System Stability in Power Grids with Renewable Energy Sources

引用

SMART GRIDS AND SUSTAINABLE ENERGY 2025年第1期10卷 1-14页

作者： Okasha, Aliaa A. Mansour, Diaa-Eldin A. Zaky, Ahmed B. Suehiro, Junya Megahed, Tamer F. Egypt Japan Univ Sci & Technol E JUST Fac Engn Dept Elect Power Engn Alexandria Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Tanta Univ Fac Engn Dept Elect Power & Machines Engn Tanta Egypt Egypt Japan Univ Sci & Technol Comp Sci & Informat Technol Programs CSIT Alexandria Egypt Benha Univ Fac Engn Shoubra Banha Egypt Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan Mansoura Univ Fac Engn Elect Engn Dept Mansoura Egypt

Identification of coherent generators (CGs) is crucial for situational awareness of electrical power systems under emergencies. Sudden disturbances, in addition to uncertainties of renewables, can perturb the equilibrium state among synchronous generators, unleashing subsequent electromechanical oscillations. Under this situation, recognizing coherent generators is an essential step for proper control actions to maintain system stability and security. This study proposes an IoT-based strategy for coherency assessment in hybrid power systems. Coherency analysis is performed based on frequency deviation signals from all generating units, including renewables. Phasor measurement units (PMUs) measure the frequency of each generator, and then frequency deviations are calculated locally at edge devices. This step significantly enhances the accuracy of calculations while also reducing the amount of data sent to the cloud, thereby decreasing network latency. Then, Spearman's rank correlation coefficient is adopted to measure the similarity level between all pairs of generators. Finally, the density-based spatial clustering of applications with noise (dbscan) algorithm is adopted to define coherent generators autonomously based on the obtained similarity indices. The proposed approach is tested on the modified IEEE 39-bus system. This study also investigates the impact of integrating renewable energy sources (RESs) on the obtained coherency patterns. All time-domain simulations are performed on the DIgSILENT PowerFactory software connected to the ThingSpeak platform for cloud computing. Simulation results demonstrate the efficiency of the proposed strategy. It also illustrates the potential of coherent generating groups to change due to the insertion of low inertia resources.

关键词： Coherency identification IoT Renewable energy sources Spearman's rank correlation coefficient dbscan algorithm Electromechanical oscillations

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：