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International Forum on Materials Analysis and Testing Technology (IFMATT 2013)

作者： Wang, Lifu Ding, Jian Kong, Zhi Northeastern Univ Qinhuangdao Sch Control & Engn Qinhuangdao 066004 Peoples R China Northeastern Univ College Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9783037859537

A wireless sensor network (WSN) consists of spatially distributed wireless sensor nodes. The node power constrains the development of WSN. Employing techniques of clustering can reduce energy consumption of wireless sensor nodes and prolong the network lifetime. Therefore, in the study a new clustering routing algorithm is presented. The clustering algorithm uses the double-layer sensor nodes to communicate. And in order to optimize power energy consumption for WSN node energy, PSO algorithm is employed to find cluster head in each layer. Simulation results show that the algorithm not only can equal power energy of node, but also can reduce consumption in the long distance data transmission.

关键词： wireless sensor network clustering algorithm survival time particle swarm optimization

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Failure Mode and Effect Analysis using an integrated approach of clustering and MCDM under pythagorean fuzzy environment

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2021年 72卷

作者： Shahri, Majid Mardani Jahromi, Abdolhamid Eshraghniaye Houshmand, Mahmoud Sharif Univ Technol Dept Ind Engn Tehran Iran

Failure Mode and Effect Analysis (FMEA) is an effective risk analysis and failure avoidance approach in the design, process, services, and system. With all its benefits, FMEA has three limitations: failure mode risk assessment and prioritization, complex FMEA worksheets, and difficult application of FMEA tables. This paper seeks to overcome the shortcomings of FMEA using an integrated approach based on a developed Pythagorean fuzzy (PF) k-means clustering algorithm and a popular MCDM method called PF-VIKOR. In the first step, Pythagorean fuzzy numbers (PFNs) were used to collect Severity (S), Occurrence (O), and Detection (D) factors for failure modes to incorporate uncertainty and fuzziness into subjective judgments. Afterward, failure modes were clustered by developing a novel k-means clustering algorithm that accepts PFNs as input. Finally, the PF-VIKOR approach was used to analyze the ordering of cluster risks. The proposed approach was implemented in the dehydration unit of an Iranian gas refinery and the results were compared with the traditional FMEA. The findings showed the flexibility and applicability of the proposed approach in addressing real-world problems. This research provides two key contributions: (1) designing a PFN-based k-means clustering algorithm that tackles FMEA limitations and (2) using the PF-VIKOR method for prioritizing and evaluating failure mode clusters.

关键词： Failure mode and effect analysis Pythagorean fuzzy numbers clustering algorithm PF-VIKOR Gas refinery

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Multi-Operator Cell Tower Locations Prediction from Crowdsourced Data 30

Multi-Operator Cell Tower Locations Prediction from Crowdsou...

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30th International Conference on Computer Communications and Networks (ICCCN)

作者： Rahman, Mostafizur Hossain, Mohammad Arif Yuksel, Murat Univ Texas Rio Grande Valley Dept Elect & Comp Engn Edinburg TX 78539 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9780738113302

Cell tower locations are not publicly available due to business interests of wireless providers. Very often wireless providers provide exaggerated coverage maps that may mislead the public. In addition to providing a neutral check on the coverage maps, prediction of cell tower locations hosting multiple operators' access nodes could also be helpful in disaster communications and public safety in general. The localization of the disaster-affected towers can be very conducive to respond and reach to the victims. Further, victims' devices could utilize this knowledge to initiate device-to-device (D2D) or unmanned aerial vehicular (UAV) communications as alternatives to the damaged cellular infrastructure. Publicly available crowdsourced cell (base station) locations and FCC's sites can be used to predict the cell tower/site locations in the United States. In this work, we utilized a weighted k-means algorithm to predict cell tower locations from OpenCellid crowdsourced dataset and implemented a mapping algorithm to locate nearest physical towers. We map the predicted towers to two different sources of physical towers. Our comparison shows a significant accuracy in predicting tower locations regardless of sources of physical towers. The technique can be used to predict the tower locations in other countries as well.

关键词： Cell Sites Disaster Management clustering algorithm Mapping algorithm

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Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

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Multimodal Transportation 2025年第1期4卷

作者： Farahnakian, Farshad Nevalainen, Paavo Farahnakian, Fahimeh Vähämäki, Tanja Heikkonen, Jukka Department of Computing University of Turku Turku 20500 Finland

Ship movement prediction models are crucial for improving safety and situational awareness in complex maritime shipping networks. Current prediction models that utilize Automatic Identification System (AIS) data to forecast ship movements typically rely on a fixed look-back window size. This approach does not effectively consider the necessary amount of data required to train the models properly. This paper presents a framework that dynamically determines the optimal look-back window size for AIS data, tailored to user-defined prediction intervals. Initially, a DBSCAN clustering method, along with various pre-processing techniques, has been employed to efficiently eliminate non-essential data points and address noise in the raw AIS data. Following this, Temporal Convolutional Networks (TCNs) have been trained using the dynamic characteristics of ship movements based on one month of AIS data (April 2023) collected from the Baltic Sea, evaluating various look-back window sizes to identify the optimal size required for predictions. Subsequently, the framework has been tested using an additional AIS dataset in two scenarios: 1-hour and 5-hour predictions. The experimental results indicate that the proposed framework can effectively select the necessary AIS samples for forecasting a ship's future movements. This framework has the potential to optimize prediction services by identifying the ideal look-back window size, thereby providing maritime agents with high-quality and accurate predictions to enhance their decision-making processes. © 2025 The Author(s)

关键词： AIS data clustering algorithm Deep learning Intelligent maritime transportation Ship trajectory prediction

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CH-Bin: A convex hull based approach for binning metagenomic contigs

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年 100卷 107734页

作者： Chandrasiri, Sunera Perera, Thumula Dilhara, Anjala Perera, Indika Mallawaarachchi, Vijini Univ Moratuwa Dept Comp Sci & Engn Moratuwa 10400 Sri Lanka Australian Natl Univ Sch Comp Canberra ACT 2600 Australia Flinders Univ S Australia Flinders Accelerator Microbiome Explorat Bedford Pk SA 5042 Australia

Metagenomics has enabled culture-independent analysis of micro-organisms present in environmental samples. Metagenomics binning, which involves the grouping of contigs into bins that represent different taxonomic groups, is an important step of a typical metagenomic workflow followed after assembly. The majority of the metagenomic binning tools represent the composition and coverage information of contigs as feature vectors consisting of a large number of dimensions. However, these tools use traditional Euclidean distance or Manhattan distance metrics which become unreliable in the high dimensional space. We propose CH-Bin, a binning approach that leverages the benefits of using convex hull distance for binning contigs represented by high dimensional feature vectors. We demonstrate using experimental evidence on simulated and real datasets that the use of high dimensional feature vectors to represent contigs can preserve additional information, and result in improved binning results. We further demonstrate that the convex hull distance based binning approach can be effectively utilized in binning such high dimensional data. To the best of our knowledge, this is the first time that composition information from oligonucleotides of multiple sizes has been used in representing the composition information of contigs and a convex hull distance based binning algorithm has been used to bin metagenomic contigs. The source code of CH-Bin is available at https://***/kdsuneraavinash/CH-Bin.

关键词： Convex hull Convex hull distance Metagenomic binning Multiple k values High dimensional data clustering clustering algorithm

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Rapid Identification and Spectral Moment Estimation of Non-Gaussian Weather Radar Signal

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Dong, Xichao Hu, Jiaqi Hu, Cheng Chen, Zhiyang Li, Yinghe Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Chongqing Key Lab Novel Civilian Radar Chongqing 401120 Peoples R China Beijing Inst Technol Minist Educ Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China Beijing Inst Technol Adv Technol Res Inst Jinan 250300 Peoples R China Aerosp Newsky Technol Co Ltd Wuxi 214000 Jiangsu Peoples R China

Doppler weather radar observations of strong convective weather phenomena, e.g., tornadoes and supercells, showed that their Doppler spectra could deviate from the Gaussian shape. Classical method for identification of non-Gaussian spectral signal (NGSS) is based on spectral width and high-order spectral moments (HOSM). The DFT processing is needed to obtain the power spectrum density to calculate HOSMs, increasing the computation complexity. Besides, power, mean velocity, and spectral width are often obtained using autocorrelation method, i.e., pulse-pair-processing (PPP) method which is generally considered to be the most efficient estimator, where a Gaussian spectrum is assumed. Thus, a bias in the mean velocity and spectral width will occur if the Doppler spectrum deviates from the Gaussian shape. In this article, a generalized PPP (GPPP) method, which calculates HOSMs using autocorrelation function directly, is proposed. It outputs two additional identification parameter products, including skewness and kurtosis, which can directly identify NGSS and contribute to select the appropriate spectral moment estimation algorithm for Gaussian-or-not spectral signal. Compared with the classical method, the NGSS identification based on the GPPP method has a lower computation complexity since the DFT processing is avoided and has a better performance especially for low signal-to-noise ratio conditions. Besides, the clustering algorithms including expectation maximization and K-means algorithms are investigated for the adaptive spectral moment estimation of NGSS for the first time. The numerical simulation experiments and verifications based on real weather radar data of an actual supercell are implemented to confirm the feasibility and superiority of proposed method.

关键词： Radar Meteorology Meteorological radar Estimation Doppler radar Doppler effect Shape clustering algorithm non-Gaussian spectral signal (NGSS) spectral moment estimation weather radar

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A Single-Image Noise Estimation algorithm Based on Pixel-Level Low-Rank Low-Texture Patch and Principal Component Analysis

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SENSORS 2022年第22期22卷 8899页

作者： Li, Yong Liu, Chenguang You, Xiaoyu Liu, Jian Harbin Inst Technol Res Ctr Adv Microscopy & Instrumentat Harbin 150001 Peoples R China Harbin Inst Technol Res Ctr Basic Space Sci Harbin 150001 Peoples R China

Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis for white Gaussian noise. First, an adaptive clustering algorithm, based on a dichotomy merge, adaptive pixel-level low-rank matrix construction method and a gradient covariance low-texture subblock selection method, is proposed to construct a pixel-level low-rank, low-texture subblock matrix. The adaptive clustering algorithm can improve the low-rank property of the constructed matrix and reduce the content of the image information in the eigenvalues of the matrix. Then, an eigenvalue selection method is proposed to eliminate matrix eigenvalues representing the image to avoid an inaccurate estimation of the noise level caused by using the minimum eigenvalue. The experimental results show that, compared with existing state-of-the-art methods, our proposed algorithm has, in most cases, the highest accuracy and robustness of noise level estimation for various scenarios with different noise levels, especially when the noise is high.

关键词： image processing image denoising noise level white Gaussian noise clustering algorithm

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Free-Form Surface Partitioning and Simulation Verification Based on Surface Curvature

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MICROMACHINES 2022年第12期13卷

作者： Liu, Hongwei Zhang, Enzhong Sun, Ruiyang Gao, Wenhui Fu, Zheng Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

To address the problem of low overall machining efficiency of free-form surfaces and difficulty in ensuring machining quality, this paper proposes a MATLAB-based free-form surface division method. The surface division is divided into two stages: Partition area identification and area boundary determination. In the first stage, the free-form surface is roughly divided into convex, concave, and saddle regions according to the curvature of the surface, and then the regions are subdivided based on the fuzzy c-means clustering algorithm. In the second stage, according to the clustering results, the Voronoi diagram algorithm is used to finally determine the boundary of the surface patch. We used NURBS to describe free-form surfaces and edit a set of MATLAB programs to realize the division of surfaces. The proposed method can easily and quickly divide the surface area, and the simulation results show that the proposed method can shorten machining time by 36% compared with the traditional machining method. It is proved that the method is practical and can effectively improve the machining efficiency and quality of complex surfaces.

关键词： regional division free-form surface surface curvature clustering algorithm Voronoi diagrams

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Novel methodology for developing a safety standard based on clustering of experts' assessments of safety requirements

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SAFETY SCIENCE 2021年 140卷

作者： Aly, Shady Tyrychtr, Jan Kvasnicka, Roman Vrana, Ivan Helwan Univ Fac Engn Helwan Cairo Egypt Czech Univ Life Sci Prague Fac Econ & Management Dept Informat Engn Prague Czech Republic Czech Univ Life Sci Prague Fac Econ & Management Dept Syst Engn Prague Czech Republic

The practice of developing product or system safety standards is currently conducted with high level of subjectivity, which affects the reliability of development outcomes. The decision to adopt specific safety requirements into the standard is usually based on the knowledge, intuition and experience of the technical expert involved in developing the body of the safety standard made up of those requirements or specifications. This is usually carried out without analyzing risk attributes or factors addressed by each safety requirement constituting a safety standard. This can lead to inaccuracy and lack of reliability due to either under-estimation or overestimation of risks involved. This research presents unprecedented, novel, structured and objective methodology, that aims to improve the practice of developing safety standards. In order to accomplish the above end, the proposed methodology acts to prioritize the system' safety requirements of each system's design elements based on a set of commonly identified risk factors they address. We have investigated the currently existing risk prioritization and multi-criterion decision making techniques, and found that machine learning data clustering approaches can be adequate especially in case of relatively large number of assessed decision alternatives (i.e., the assessed safety requirements). We have elaborated the merits of the proposed clustering-based methodology over existing prominent ones. The proposed methodology starts with analyzing product or system safety to identify common risk factors. A numerical scale is used to enable objective quantification of risk factors' values for each safety requirement by multiple experts. The obtained experts' numerical assessments of each safety requirement are then averaged to represent the raw data set containing risk profiles of each safety requirement assessed. This risk factors data set is then used by fuzzy c-means clustering algorithm, to organize them into groups of di

关键词： Safety standards development Safety requirements assessment and prioritization Risk assessment clustering algorithm Fuzzy c-means

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Development of decision support tool for clustering urban regional risk based on R-ArcGIS Bridge

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APPLIED SOFT COMPUTING 2021年 110卷

作者： Zhao, Ming Liu, Xiang Sun, Zengfeng Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Nutrichem Co Ltd Beijing 102206 Peoples R China China Acad Urban Planning & Design Minist Housing & Urban Rural Dev Beijing 100044 Peoples R China

Cities with dense population are susceptible and vulnerable to natural disasters and man-made accidents, making regional risk analysis thus significant for urban public safety. Although risk maps are able to present the broad characteristics of regional risk spatial distribution, quantitative identification of risk clusters in urban areas is still a challenge. In this context, we design and develop a user-friendly customized ArcGIS add-in in this study. In particular, a novel technical routing is proposed for customizing ArcGIS add-in tool based on R-ArcGIS Bridge. The design, architecture and implementation of the tool as well as its core functional modules are introduced. Moreover, R scripts are developed to implement K-means algorithm and Gap statistics validity index for clustering regional risk, respectively. Based on a case study of a typical urban district in China, we introduce the add-in's functionalities as well as its related decision-making procedures. Results successfully obtained provide evidence that the tool is able to partition regional risk clusters and estimate the optimal number of clusters in urban areas. The simple loosely-coupled architecture also suggests a promising future for embedding some novel geospatial clustering algorithms to extend its capabilities in the next step. This work offers new insights on promoting future urban regional risk management with the use of GIS and clustering algorithms, and it provides a valuable demonstration on extending and enhancing the analysis capacity of GIS by harnessing its power with the statistical analysis capability of R packages through R-ArcGIS Bridge. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Regional risk analysis clustering algorithm Add-in R-ArcGIS Bridge Urban public safety

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