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APPLYING CLUSTERING TECHNIQUES IN HYBRID NETWORK IN THE PRESENCE OF 2D AND 3D OBSTACLES

COMPUTING AND INFORMATICS 2013年第6期32卷 1170-1191页

作者： Ibrahim, Lamiaa Fattouh Salman, Hesham Abou El Fetouh King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21551 Saudi Arabia Cairo Univ Inst Stat Studies & Res Cairo Egypt

Clustering spatial data is a well-known problem that has been extensively studied. In the real world, there are many physical obstacles such as rivers, lakes, highways, and mountains, whose presence may substantially affect the clustering result. Although many methods have been proposed in previous works, very few have considered physical obstacles and interlinking bridges. Taking these constraints into account during the clustering process is costly, yet modeling the constraints is paramount for good performance. Owing to saturation in existing telephone networks and the ever increasing demand for wire and wireless services, telecommunication engineers are looking at technologies that can deliver sites and satisfy the demand and level of service constraints in an area with and without obstacles. In this paper, we study the problem of clustering in the presence of obstacles to solve the network planning problem. As such, we modified the Net-Plan algorithm and developed the COD-NETPLAN (Clustering with Obstructed Distance - Network Planning) algorithm to solve the problem of 2D and 3D obstacles. We studied the problem of determining the location of the multi service access node in an area with many mountains and rivers. We used a reachability matrix to detect 2D obstacles, and line segment intersection together with geographical information system techniques for 3D obstacles. Experimental results and the subsequent analysis indicate that the COD-NETPLAN algorithm is both efficient and effective.

关键词： dbscan algorithm clustering algorithm network planning spatial clustering algorithm and obstacles

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Cross-entropy-based directional importance sampling with von Mises-Fisher mixture model for reliability analysis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期220卷 108306-108306页

作者： Zhang, Xiaobo Lu, Zhenzhou Cheng, Kai Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

In structural reliability analysis, the estimation of rare failure probability is a significant challenge. Directional importance sampling may address this challenge when a quasi-optimal sampling density is chosen. Directional importance sampling involves sampling on the unit hypersphere and one-dimensional reliability analysis. This paper develops a cross-entropy-based directional importance sampling (CE-DIS) for reliability analysis. The von Mises-Fisher mixture (vMFM) model is selected as sampling density to generate direction vectors on the unit hypersphere. The updating rules based on the cross-entropy method are derived for the vMFM model to find a quasi-optimal sampling density. In addition, the dbscan algorithm is used to choose the number of distributions in the vMFM model in case of no prior information of failure domain, and the intermediate failure event is introduced in case of not enough samples to update the distribution parameters. Several benchmark numerical examples and engineering applications are used to test the performance of the proposed CE-DIS method. The results demonstrate that the CE-DIS method significantly improves the computational efficiency compared to original directional sampling and other CE-based importance sampling methods. In addition, the CE-DIS method can be applied to different types of reliability problems, e.g., multiple design points, multiple failure modes and rare failure probability.

关键词： Directional importance sampling Cross-entropy von Mises-Fisher mixture dbscan algorithm Multiple design points

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Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge

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SENSORS 2023年第20期23卷 8578-8578页

作者： Pilat-Rozek, Magdalena Dziadosz, Marcin Majerek, Dariusz Jaromin-Glen, Katarzyna Szelag, Bartosz Guz, Lukasz Piotrowicz, Adam Lagod, Grzegorz Lublin Univ Technol Fac Math & Informat Technol PL-20618 Lublin Poland Polish Acad Sci Inst Agrophys PL-20290 Lublin Poland Warsaw Univ Life Sci Inst Environm Engn SGGW PL-02797 Warsaw Poland Lublin Univ Technol Fac Environm Engn PL-20618 Lublin Poland

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

关键词： electronic nose multidimensional data analysis principal component analysis dbscan algorithm extra trees wastewater classification performance evaluation activated sludge

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CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision

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BMC BIOINFORMATICS 2017年第Sup16期18卷 571-571页

作者： Herath, Damayanthi Tang, Sen-Lin Tandon, Kshitij Ackland, David Halgamuge, Saman Kumara Univ Melbourne Dept Mech Engn Melbourne Vic 3010 Australia Univ Peradeniya Dept Comp Engn Peradeniya 20400 Sri Lanka Acad Sinica Biodivers Res Ctr Taipei 11529 Taiwan Natl Tsing Hua Univ Inst Bioinformat & Struct Biol Hsinchu 300 Taiwan Acad Sinica Taiwan Int Grad Program Inst Informat Sci Bioinformat Program Taipei 115 Taiwan Univ Melbourne Dept Biomed Engn Melbourne Vic 3010 Australia Australian Natl Univ Res Sch Engn Coll Engn & Comp Sci Canberra ACT 2601 Australia

Background: In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning;however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm dbscan is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains. Results: Binning methods based on both compositional features and coverages of contigs had higher performances than the method which is based only on compositional features of contigs. CoMet yielded higher or comparable precision in comparison to the existing binning methods on benchmark datasets of varying complexities. MyCC (coverage) had the highest ranking score in F1-score. However, the performances of CoMet were higher than MyCC (coverage) on the dataset containing multiple strains. Furthermore, CoMet recovered contigs of more species and was 18-39% higher in precision than the compared existing methods in discriminating species from the sample of multiple strains. CoMet resulted in

关键词： Metagenomics Binning Contig coverage Contig composition dbscan algorithm

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Fault diagnosis for cell voltage inconsistency of a battery pack in electric vehicles based on real-world driving data

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 102卷

作者： Fang, Weidong Chen, Hanlin Zhou, Fumin Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Peoples R China

Cell voltage inconsistency of a battery pack is the main problem of the Electric Vehicle (EV) battery system, which will affect the performance of the battery and the safe operation of electric vehicles. In real-world vehicle operation, accurate fault diagnosis and timely prediction are the key factors for EV. In this paper, real-world driving data is collected from twenty all-electric buses for many years and divided into three driving fragments to analyze cell voltage inconsistency and summarize the voltage characteristics of the cell when an inconsistency fault occurred. A fault diagnosis method based on Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm is proposed for timely localization of the abnormal battery cell. It is found that the dbscan clustering algorithm has shown better effectiveness and accuracy as compared to K means to locate irregular battery cells. A fault prediction method based on the Least-Square Support Vector Regression (LS-SVR) is developed to predict the change of the monomer voltage. The experimental comparison show that LS-SVR has better prediction accuracy than ordinary Support Vector Regression (SVR), and it can make short-term predictions based on the voltage difference and monomer voltage value for cell consistency failures and over/under voltage faults.

关键词： Fault diagnosis Cell voltage inconsistency dbscan algorithm Fault prediction method Real-world driving data

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Automatic ship route design between two ports: A data-driven method

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APPLIED OCEAN RESEARCH 2020年第0期96卷 1页

作者： Wen, Yuanqiao Sui, Zhongyi Zhou, Chunhui Xiao, Changshi Chen, Qianqian Han, Dong Zhang, Yimeng Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China

With the forced installation of the ship's automatic identification system (AIS), a large amount of ship trajectory data in the world is generated. These data provide information on latitude, longitude, speed and course, and plenty of materials for maritime pattern extraction and vessel behavior prediction. And how to dig into these AIS data deeply to discover the ship behavior pattern is an important job. There are two key points on the automatic ship route design research: the turning area generation and the turning area linkage. In this paper, we integrate dbscan and Artificial Neural Network capable of automatic ship route design based on massive AIS data between certain ports. The main purpose of this study is to recognize the key regions by applying dbscan algorithm and then connect these regions automatically by cluster similarity measuring. Then artificial neural network has been used to learn the relationship of turning regions and generate a reasonable route with different ship dimensions. The main achievement of this study have twofold. First, a research framework for automatic generation of ship route is proposed. We can process big MS data and use them to generate ship route. Second, generation of different routes according to ships of different dimension under the research framework. The method is capable of generating ship route automatically according to different ship dimensions, which has been evaluated on two real routes around the world.

关键词： Ship route design Data-driven Ship dimensions dbscan algorithm ANN

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Spatial Cluster Analysis of Bursting Pipes in Water Supply Networks

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Procedia Engineering 2014年 70卷 1610-1618页

作者： J. Sun R. Wang X. Wang H. Yang J. Ping Graduate School at Shenzhen Tsinghua University the University Town Shenzhen 518055 China

Urban water supply network is a hidden underground asset; therefore, it is difficult to make a direct diagnosis towards the faults of pipe network by means of the detecting technology. Based on the historical data of the rupture of water supply pipe network, a spatial cluster analysis will be carried out towards the historical data of spatial fault in water supply pipeline, using density- based cluster analysis and the dbscan algorithm. K-neighborhood graph is well-used in optimizing the input parameter radius of neighborhood (Eps) and density (MinPts) and classifying the historical data of the rupture of pipeline. According to the pipe network failure point coordinates (X,Y), adopting density-based cluster analysis, a danger level classification will be researched without knowing the reasons of the rupture of pipe network, which lays the foundation for finding out pipeline rupture in local areas in the future. Meanwhile, a noise analysis will be processed in terms of the historical data of pipeline rupture in water supply network, avoiding the ineffective data caused by recording errors and regular analysis. This research focuses on the reasons that result in the rupture of water supply network, which plays a significant role in the identification of high-density and high-danger zones in water supply networks.

关键词： dbscan algorithm Spatial cluster analysis Pipe burst Water supply network Data mining

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Application of Data Mining in Library-Based Personalized Learning

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2017年第12期12卷 127-133页

作者： Luo, Lin Chongqing Radio & TV Univ Chongqing Peoples R China

this paper expounds to mine up data with the dbscan algorithm in order to help teachers and students find which books they expect in the sea of library. In the first place, the model that dbscan algorithm applies in library data miner is proposed, followed by the dbscan algorithm improved on demands. In the end, an experiment is cited herein to validate this algorithm. The results show that the book price and the inventory level in the library produce a less impact on the resultant aggregation than the classification of books and the frequency of book borrowings. Library procurers should therefore purchase and subscribe data based on the results from cluster analysis thereby to improve hierarchies and structure distribution of library resources, forging on the library resources to be more scientific and reasonable, while it is also conducive to arousing readers' borrowing interest.

关键词： Digital Library Clustering algorithm dbscan algorithm

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Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information

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SENSORS 2018年第3期18卷 878-878页

作者： Wang, Chundong Zhu, Likun Gong, Liangyi Zhao, Zhentang Yang, Lei Liu, Zheli Cheng, Xiaochun Tianjin Univ Technol Key Lab Comp Vis & Syst Minist Educ Tianjin 300384 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Minist Educ Tianjin 300384 Peoples R China Nankai Univ Coll Comp & Control Engn Tianjin 300350 Peoples R China Middlesex Univ Dept Comp Sci London NW4 4BT England

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.

关键词： channel state information Sybil attack indoor AoA technology dbscan algorithm

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Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 118卷

作者： Aljumaily, Harith Laefer, Debra F. Cuadra, Dolores Velasco, Manuel Carlos III Univ Madrid Dept Comp Sci & Engn Ave Univ 30 Madrid 28911 Spain NYU Ctr Urban Sci & Progress Tandon Sch Engn 370 Jay St Brooklyn NY 11201 USA NYU Tandon Sch Engn Dept Civil & Urban Engn 370 Jay St Brooklyn NY 11201 USA King Juan Carlos Univ Dept Comp Sci & Stat Madrid Spain

The opportunities now afforded by increasingly available, dense, aerial urban LiDAR point clouds (greater than100 pts/m2) are arguably stymied by their sheer size, which precludes the effective use of many tools designed for point cloud data mining and classification. This paper introduces the point cloud voxel classification (PCVC) method, an automated, two-step solution for classifying terabytes of data without overwhelming the computational infrastructure. First, the point cloud is voxelized to reduce the number of points needed to be processed sequentially. Next, descriptive voxel attributes are assigned to aid in further classification. These attributes describe the point distribution within each voxel and the voxel's geo-location. These include 5 pointdescriptors (density, standard deviation, clustered points, fitted plane, and plane's angle) and 2 voxel position attributes (elevation and neighbors). A random forest algorithm is then used for final classification of the object within each voxel using four categories: ground, roof, wall, and vegetation. The proposed approach was evaluated using a 297,126,417 point dataset from a 1 km2 area in Dublin, Ireland and 50% denser dataset of New York City of 13,912,692 points (150 m2). PCVC's main advantage is scalability achieved through a 99 % reduction in the number of points that needed to be sequentially categorized. Additionally, PCVC demonstrated strong classification results (precision of 0.92, recall of 0.91, and F1-score of 0.92) compared to previous work on the same data set (precision of 0.82-0.91, recall 0.86-0.89, and F1-score of 0.85-0.90).

关键词： Object Classification MapReduce Big Data LiDAR dbscan algorithm RANSAC algorithm

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