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

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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

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

Application of DBSCAN Algorithm in Precision Fertilization D...

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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

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Application of dbscan algorithm in Precision Fertilization Decision of Maize

Application of DBSCAN Algorithm in Precision Fertilization D...

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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

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A Parallel dbscan algorithm Based On Spark 6

A Parallel DBSCAN Algorithm Based On Spark

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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

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

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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

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

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

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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

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

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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

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DGBPSO-dbscan: An Optimized Clustering Technique Based on Supervised/Unsupervised Text Representation

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IEEE ACCESS 2024年 12卷 110798-110812页

作者： Abdulsahib, Asma Khazaal Balafar, M. A. Baradarani, Aryaz Univ Tabriz Fac Elect & Comp Engn Tabriz *** East Azerbaijan Iran Tessonics Inc Ctr Diagnost Imaging Res Windsor ON N9A Canada

Density-based spatial clustering of noisy applications (dbscan), a widely used density-based clustering technique, faces challenges in determining its key parameter, Eps, leading to manual specification and suboptimal clustering outcomes. Additionally, its time complexity poses limitations. This study introduces a novel approach to enhance the algorithm's performance. We combined the dbscan algorithm with another approach, the particle swarm optimization, based on a novel way to represent text, termed a dependency graph;this method is recognized as DGBPSO-dbscan (Dependency Graph Based Particle swarm algorithm for dbscan). This paper focuses on employing a novel approach for PSO variable upgrading to explore the Eps range more rapidly and effectively. This method offers a selection of informative elements from the text, where the initial groups are derived from the graph-based degree centrality, the PSO method is used to choose the most compelling features, and the dbscan algorithm is used for text clustering. The experimental findings indicate that the modified PSO is used to improve dbscan. We compared the outcomes of the suggested technique to those of the standard clustering algorithm. According to the assessment criteria MSE, accuracy, precision, recall, and F-measure, our technique has been demonstrated to be superior to the conventional method.

关键词： Clustering algorithms Reviews Particle swarm optimization Accuracy Partitioning algorithms Optimization Noise measurement Decision trees dbscan algorithm decision tree degree centrality dependency graph

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Identification and spatio-temporal analysis of earthquake clusters using SOM-dbscan model

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NEURAL COMPUTING & APPLICATIONS 2023年第11期35卷 8081-8108页

作者： Sharma, Ashish Vijay, Rahul Kumar Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Jaipur Dept Elect & Commun Engn Jaipur 302017 Rajasthan India Banasthali Vidyapith Dept Comp Sci Tonk 304022 Rajasthan India

Seismic catalogs are vital to understanding and analyzing the progress of active fault systems. The background seismicity rate in a seismic catalog, strongly associated with stressing rate, is the critical parameter in seismic hazard analysis. Estimating background seismicity is a complex task due to the high correlation with aftershock sequences which may dominate the background seismicity rate. In this paper, identification of the significant earthquake aftershocks and independent background events is performed using a two-stage clustering approach. It works in two phases: Self-Organized Map and Density-based Temporal Clustering. The event's location and depth information in the earthquake catalog is used to identify the major hot spots (SOM prototypes) in the region (Spatial domain). Later, density-based temporal clustering is applied to decipher the neighborhood events of each SOM prototype. The proposed two-level clustering approach performs effective spatio-temporal analysis and identifies the aftershock clusters and background. The experimental study is carried out on the prominent earthquake catalogs of Taiwan, Afghanistan, California, the Himalayas, Indonesia, Chile, and Japan. The statistical parameters: Coefficient of Variation (time-domain) and m-Morisita index (spatial domain) justify and validate the accuracy of the presented approach. The proposed model is compared with benchmark de-clustering algorithms for mainshock and background detection.

关键词： Earthquake clusters Self-organized map dbscan algorithm Coefficient of variance m-Morisita index

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Genetic-Based Keyword Matching dbscan in IoT for Discovering Adjacent Clusters

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Computer Modeling in Engineering & Sciences 2023年第5期135卷 1275-1294页

作者： Byoungwook Kim Hong-Jun Jang Department of Computer Engineering Dongshin UniversityNaju58245Korea Department of Computer Science and Engineering Jeonju UniversityJeonju55069Korea

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(dbscan).Existing dbscan research focuses on efficiently finding clusters in numeric data or categorical *** this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based dbscan *** existing dbscan algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the *** solve this problem,we developed the Genetic algorithm-based Keyword Matching dbscan(GKM-dbscan)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each *** order to improve the performance of GKM-dbscan,we improved the general genetic algorithm by performing a genetic operation in *** conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-dbscan than the brute-force *** experimental results show that GKM-dbscan outperforms the brute-force method by up to 21 ***-dbscan with the index number binarization(INB)is 1.8 times faster than GKM-dbscan with the cluster number binarization(CNB).

关键词： Spatial clustering dbscan algorithm genetic algorithm textual information

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