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

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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Comparative evaluation of region query strategies for dbscan clustering

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INFORMATION SCIENCES 2019年 502卷 76-90页

作者： Galan, Severino F. UNED Artificial Intelligence Dept C Juan del Rosal 16 Madrid 28040 Spain

Clustering is a technique that allows data to be organized into groups of similar objects. dbscan (Density-Based Spatial Clustering of Applications with Noise) constitutes a popular clustering algorithm that relies on a density-based notion of cluster and is designed to discover clusters of arbitrary shape. The computational complexity of dbscan is dominated by the calculation of the 6-neighborhood for every object in the dataset. Thus, the efficiency of dbscan can be improved in two different ways: (1) by reducing the overall number of 6-neighborhood queries (also known as region queries), or (2) by reducing the complexity of the nearest neighbor search conducted for each region query. This paper deals with the first issue by considering the most relevant region query strategies for dbscan, all of them characterized by inspecting the neighborhoods of only a subset of the objects in the dataset. We comparatively evaluate these region query strategies (or dbscan variants) in terms of clustering effectiveness and efficiency;additionally, a novel region query strategy is introduced in this work. The results show that some dbscan variants are only slightly inferior to dbscan in terms of effectiveness, while greatly improving its efficiency. Among these variants, the novel one outperforms the rest. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Clustering dbscan algorithm Region query strategy Comparative evaluation

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Analysis of Methicillin-resistant Staphylococcus Aureus Using Apriori, dbscan, and K-means algorithms 16

Analysis of Methicillin-resistant Staphylococcus Aureus Usin...

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3rd International Conference on Biomedical and Bioinformatics Engineering (ICBBE)

作者： Lee, Min Young Yoon, Taeseon Hankuk Acad Foreign Studies 50Oedaero 54beon Gil Yongin Gyeonggi Do South Korea

ISBN: (纸本)9781450348249

Known as the most virulent. super-bacteria,. methicillin-resistant staphylococcus aureus (MRSA) is a bacterium that has developed a multi-resistance to standard types of antibiotics, including the penicillins and cephalosporins. Such difficulty to treat with antibiotics exposes MRSA patients to a severe danger-even to death-especially those who possess weakened immune systems. In attempts to create effective antibiotics for MRSA, previous researches have focused on proteins in localized areas of the genome. This research, however, aims to suggest a new angle of MRSA by analyzing the entire genome through bioinformatics. The research utilizes three data mining techniques: Apriori algorithm, dbscan algorithm, and K-means algorithm. Apriori algorithm shows that Leucine is predominant in the genome, a characteristic similar to pathogens that cause malaria, dengue, and lung cancer. dbscan algorithm and K-means algorithm provide a surprising result: while MRSA genome may seem like a lump of leucine, it actually consists of two separate parts, with a ratio of 7 to 3. This result suggests that each region may have evolved differently from the original strain of MRSA, or, only one of the two regions may account for the multi-resistance of MRSA, while the other serving another purpose.

关键词： MRSA super-bacteria Apriori algorithm dbscan algorithm K-means algorithm leucine

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Automatic modal parameter identification of high arch dams:feasibility verification

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Earthquake Engineering and Engineering Vibration 2020年第4期19卷 953-965页

作者： Li Shuai Pan Jianwen Luo Guangheng Wang Jinting Department of Hydraulic Engineering Tsinghua UniversityBeijing 100084China

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(dbscan) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the dbscan algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

关键词： automatic modal parameter identification high arch dam dbscan algorithm stochastic subspace identification stabilization diagram ambient vibration

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Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data

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Petroleum Science 2022年第2期19卷 570-580页

作者： Lun Gao Ran-Hong Xie Li-Zhi Xiao Shuai Wang Chen-Yu Xu State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing102249China

In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water *** identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are *** this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale ***,different oil testing layers may have different causes of LRLC pay *** a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference *** densitybased spatial clustering algorithm with noise(dbscan)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 *** new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.

关键词： Low-resistivity-low-contrast(LRLC)pay zones Conventional well logging Machine learning dbscan algorithm Multi-layer perceptron

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Integrating automatic order determination with response prediction error minimization for nonlinear subspace identification in structural dynamics

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NONLINEAR DYNAMICS 2024年第24期112卷 21881-21900页

作者： Jiang, Dong Li, Ang Wang, Yusheng Xie, Shitao Cao, Zhifu Zhu, Rui Nanjing Forestry Univ Sch Mech & Elect Engn Nanjing 210037 Peoples R China Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Tech Univ Munich Sch Engn & Design D-80539 Munich Germany Southeast Univ Inst Aerosp Machinery & Dynam Nanjing 211189 Peoples R China

The singular value decomposition order determination method used in nonlinear subspace identification may encounter challenges due to the noise in signals, resulting in the omission of modes or the occurrence of spurious mode. Additionally, the lack of an iterative process in the nonlinear subspace identification, which primarily relies on matrix operations, will lead to suboptimal solutions. To address these challenges, an improved framework for nonlinear subspaces identification is proposed in this paper. False modes are eliminated through data preprocessing and modal stability criteria, followed by the clustering of stable modes using the Density-Based Spatial Clustering of Applications with Noise algorithm for automatic order determination;Simultaneously, an iterative optimization approach based on response prediction error minimization is introduced to enhance the accuracy of the state-space model estimation results. The effectiveness of proposed method is validated through two simulation cases and one experimental verification. The results show that the clustering algorithm effectively distinguishes real modes from false ones and achieves automatic system order determination across various SNR conditions. The iterative optimization process notably enhances state-space model estimation accuracy. Compared to original nonlinear subspace identification, proposed method significantly improves identification accuracy.

关键词： Nonlinear subspace identification dbscan algorithm Automatic order determination State-space model optimization

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A three-stage automated modal identification framework for bridge parameters based on frequency uncertainty and density clustering

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ENGINEERING STRUCTURES 2022年 255卷 113891-113891页

作者： He, Yi Yang, Judy P. Li, Yi-Feng Chongqing Univ Res Ctr Engn Vibrat & Disaster Prevent Chongqing 400045 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Natl Yang Ming Chiao Tung Univ Dept Civil Engn Hsinchu 300093 Taiwan Xiamen Acad Bldg Res Grp Co Ltd Xiamen 361005 Fujian Peoples R China

As the automated modal analysis is crucial for a continuous monitoring system, this study proposes a framework for automated modal identification of bridge parameters based on the uncertainty of estimated frequencies and density-based clustering algorithm, which consists of the following three stages: First, the modal parameters and standard deviations of the estimated frequencies are calculated in a wide range of model orders to construct the stabilization diagram using the reference-based covariance-driven stochastic subspace identification algorithm. Second, the criteria of frequency uncertainty and stabilization are adopted to eliminate the spurious modes. Third, for present purpose, the modified version of an unsupervised density-based clustering algorithm is introduced to group physical modes and detect outliers to reach automated identification of bridge modal parameters. From the analysis, it has shown that the proposed framework is powerful in eliminating the spurious modes and robust in the presence of interference caused by spurious modes while a simple procedure for clustering physical modes with desired statistical reliability is employed.

关键词： Automated modal identification Frequency uncertainty dbscan algorithm Spurious mode Bridge modal analysis

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Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

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Computer Systems Science & Engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(dbscan)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the dbscan technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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Single Image Based Algal Bloom Detection Using Water Body Extraction and Probabilistic Algae Indices

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IEEE ACCESS 2019年 7卷 84468-84478页

作者： Park, Cheol Woo Jeon, Jong Ju Moon, Yong Ho Eom, Il Kyu Pusan Natl Univ Dept Elect Engn Pusan 46241 South Korea Gyeongsang Natl Univ Dept Aerosp & Software Engn ReCAPT Jinju 52828 South Korea

Algal blooms are collections of algae that exist on the surface of the water. Because of their negative effects on aquatic organisms and humans, extensive studies have been performed to detect harmful algal blooms (HABs). However, most of the detection methods are based on remote-sensing imaging and have limitations with regard to resolution, time, and cost. In this paper, we present a new cyanobacterial algal bloom detection algorithm in inland water from a single image. The proposed method can be used as a first step in automatic early detection, warning, and rapid response systems that can be employed to mitigate the detrimental effects of HAB contamination in inland water bodies. We first divide an image into homogeneous regions via a density-based spatial clustering (dbscan) algorithm. From the segmented regions, we extract water bodies using wavelet leader-based texture analysis. The entropy and the number of zero wavelet coefficients are used as measures for the water body extraction. For images with a sky region, we introduce a simple sky-region removal method using the average brightness of segmented regions. We propose three probabilistic indices bases on an RGB-based vegetation index, a hue-based index, and a saturation-based index for estimating the degree of green algae in the extracted water body. The final index is obtained via multiplication of these three indices. In experiments on various types of images, our proposed algorithm achieves 94% accuracy for water body extraction. The proposed approach achieves better green algae estimation performance than the conventional vegetation index-based methods.

关键词： Algal bloom detection dbscan algorithm entropy probabilistic algae index water body extraction wavelet leader

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