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Spatiotemporal correlation analysis of the dynamic response of supertall buildings under ambient wind conditions

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2022年第4期31卷

作者： Yang, Bin Pan, Licheng Zhu, Haitao Sun, Siyuan Zhang, Qilin Tongji Univ Coll Civil Engn 1239 Siping Rd Shanghai Peoples R China

The structural health monitoring systems of supertall buildings are typical big data systems with abundant and high-dimensional data. Correlation analysis is a vital technique for data mining and analysis. This study aims to reveal the spatial and temporal correlation of 4 years of structural health monitoring data from the 632-m-high Shanghai Tower under normal wind and typhoon conditions. In this paper, the root mean square (RMS) value of the acceleration response is used to characterize the structural vibration intensity. The maximal information coefficient (MIC) and Pearson product-moment correlation coefficient (PPMCC) are adopted to measure the spatial correlations along Shanghai Tower under different wind conditions. The temporal correlation of structural vibration is investigated with kernel density estimation and the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The results show that the structural vibrations of Shanghai Tower in different spatial positions are correlated. The correlation of the structural vibrations of the same floor is at a high level under both normal wind and typhoons, while that of different floors rises with increasing mean wind speed. Furthermore, the MIC is more stable than the PPMCC in measuring this spatial correlation. Based on temporal correlation analysis, a circadian rhythm tendency of the structural vibration intensity of Shanghai Tower on the daily scale is identified, and it is verified that the structural response is temporally correlated. These findings can provide a structural health state estimation index based on the correlation coefficient and provide a basis for the screening of modeling data.

关键词： clustering algorithm correlation analysis structural health monitoring supertall building wind effect

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A New B-Scan Interpretation Model in Complex Environments

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Dai, Qianqi Li, Yibing Jiang, Tao Sun, Qian Harbin Engn Univ Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

In the scenario where the echo hyperbolas of different targets intersect or the opening structure of the hyperbola is destroyed, the current B-scan image interpretation model has the problems of missing detection and unable to make full use of the hyperbolic information. The hyperbola needs to be identified manually or using neural network methods. The recognition accuracy is difficult to guarantee. To solve the above problem, a new ground-penetrating radar (GPR) B-scan interpretation model based on the hyperbolic trend is proposed in this article. The model consists of four parts, including preprocessing method, hyperbolic trend clustering algorithm (HTCA), restricted algebraic distance fitting and cluster center (RADFCC) algorithm, and hierarchical filtered back-projection (HFBP). First, the GPR B-scan image is converted into a binarized image based on the ratio of the main lobe and sidelobe of the echo. Second, the points in the binarized image are classified into hyperbolic column segment clusters based on the hyperbolic trend, and further denoised according to the main lobe width of the Ricker wave. Third, by analyzing the characteristics of the GPR in the ground coupling mode, the restricted hyperbola fitting algorithm is used to fit the classified hyperbola column segment clusters to speed up the convergence of the fit and avoid the result from falling into a local optimum. Fourth, the target's location is preliminarily estimated using the parameters of the hyperbola and the cluster center is established through the location and radius of the target to obtain the accurate estimation of the target and the dielectric constant of the underground. Finally, an imaging algorithm is applied to invert the subsurface image based on the dielectric constant of the subsurface medium. The effectiveness of the proposed model has been demonstrated on both the simulated and real datasets.

关键词： clustering algorithms Fitting Market research Classification algorithms Image edge detection Pipelines Information filters Buried object detection clustering algorithm data processing ground-penetrating radar (GPR) image processing

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Multilevel Partitioning with Multiple Strategies for Complex Water Distribution Network

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2022年第12期148卷

作者： Tian, Junren Long, Zhihong Zhu, ZiPeng Xu, Gang Cheng, Weiping Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Zhejiang Peoples R China Guangzhou Water Supply Co Ltd 12 Zhongshan 1st Rd Guangzhou 510600 Peoples R China

The operation and dispatching of large-scale water supply systems is becoming more and more complex, and effective partitioning is a good response measure to this increasing complexity. This paper presents a multilevel partitioning method using freely configurable strategies suitable for multiobjective situations such as allocation of water sources, pressure regulation, and flow measurement. The method mainly relies on two strategies. Using Strategy A, water supply features are constructed based on water sources and a partition clustering is implemented that is suitable for the overall partitioning of a water supply from multiple sources. Using Strategy B, important pipes are first extracted to simplify the search path and then various constraints (pressure boundary, flow shunt, cross-river management) are used to select control points and partition by graph theory searching. This strategy is suitable for refining partitioning of the structure of a specific source. A combination of the aforementioned strategies was applied to the first-level and second-level partitioning in a case study. By flexibly configuring strategies and constraints, good partitioning is achieved and multiple management goals are accomplished satisfactorily. This research enhances understanding of the operational issues in complex water systems. The overall management of water networks can be disassembled into tasks at all levels and with their own objectives performed in stages. The solution provided in this study has the advantages of flexibility and effectiveness for a variety of complex water distribution situations, contributing insights into the real challenges of partitioning large water distribution networks.

关键词： clustering algorithm District metered area (DMA) Graph theory Large-scale networks Multilevel partitioning Water distribution

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Histogram Methods for Unsupervised clustering 20

Histogram Methods for Unsupervised Clustering

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ACM Southeast Conference (ACMSE)

作者： Benzel, Steven Stanescu, Ana Univ West Georgia Carrollton GA 30118 USA

ISBN: (纸本)9781450371056

A new algorithm is presented to find clusters in a dataset of points in R-n with no prior knowledge of possible clustering. The algorithm detects clusters in a top down fashion by testing modality of density functions generated from the dataset and splitting the set accordingly. Results on synthetic and text datasets demonstrate that the method is comparable to other established unsupervised learning algorithms, which do in fact require the number of clusters ab initio. The method proves to be particularly suitable for certain distributions and offers a valid alternative in situations where most of the well-known algorithms do not produce consistent results.

关键词： clustering algorithm Unsupervised Learning Density Functions Modality Testing

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Big Data and IoT for real-time miscarriage prediction A clustering comparative study

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Procedia Computer Science 2021年 191卷 200-206页

作者： Hiba Asri LIMA Laboratory Computer Science Departement Ibn Zohr University Agadir Morocco

Sensors and mobile phones are becoming very useful to gather a huge amount of data rapidly and to understand the behaviour of the human. Today, the real challenge is how to benefit from the use of Big Data performant tools and machine learning algorithms to get the desired value. The present paper presents a comparative study of clustering algorithms (Kmeans, Bisecting Kmeans and Gaussian mixture) for a real time miscarriage prediction. Wearable healthcare sensors ((heart rate sensor, temperature sensor and activity sensor) and mobile phone are used for gathering real time data about the pregnant women. Sensors are managed using IoT technologies such as Raspberry Pi to collect and process data in real time. Prediction’s results are sent to the doctor through a mobile phone created and the pregnant woman receives recommendations based on her behaviour. Our study compares the performance and the efficiency of the predictive models created by the three algorithms, including time to build the model, clusters distribution and centres definition. We evaluate models using the Internal clustering validation silhouette method. The dataset generated and analyzed during the current study is available on GitHub platform via the following link: https://***/hibaasri/Miscarriage-Prediction . Databricks platform and Spark are used to analyze data and build models.

关键词： Big Data clustering algorithm Machine Learning Miscarriage Prediction

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Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2022年 108卷

作者： Zhang, Ruojing Chen, Yuehong Zhang, Xiaoxiang Ma, Qiang Ren, Liliang Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct Beijing 100038 Peoples R China

Regionalization of flash floods aims to partition a geographical space into homogeneous regions in which flash floods have similar generation mechanism. In this paper, we present a flash flood regionalization approach using machine learning algorithms to generate the flash flood regionalization map. First, the random forest algorithm is used to identify thirteen key factors of flash floods from a series of rainfall and topographic factors that have great potential to drive the occurrence of flash floods. Second, the two-stage hybrid self-organizing-map-based clustering algorithm is built to delineate the homogeneous regions of flash floods according to the identified thirteen key factors. Third, the best clustering result is selected by the clustering validity indices and it is regarded as the initial regionalization map. Last, the post-processing is implemented to obtain the final regionalization map of flash floods. A case study in the Jiangxi province of China was conducted to generate the flash flood regionalization map with eighteen homogeneous regions. The regionalization map clearly divides the historical flash flood events with different densities into different regions and it is visually satisfied. The quantitative evaluation method further confirmed that the flash flood regionalization map can provide the determinant power of 77.31% for the spatial distribution of historical flash flood events. Hence, it is a valuable option to perform the flash flood regionalization of watersheds and it is beneficial for local government in the Jiangxi province of China in future flash flood mitigation and prevention.

关键词： Regionalization Flash floods clustering algorithm clustering validity indices Jiangxi province China

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Novel distance measures for intuitionistic fuzzy sets based on various triangle centers of isosceles triangular fuzzy numbers and their applications

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 191卷

作者： Garg, Harish Rani, Dimple Deemed Univ Sch Math Thapar Inst Engn & Technol Patiala 147004 Punjab India

The paper aims at introducing novel distance measures for the intuitionistic fuzzy set (IFS) to discuss the decision-making problems. The current work exploits four different notions of centers, namely centroid, orthocenter, circumcenter and incenter of the triangle. First, we mold knowledge embedded in IFSs into isosceles TFN (triangular fuzzy number). Hence, based on these TFNs, we design four-novel distance/similarity measures for IFSs using the structures of the four aforementioned centers and inspect their properties. To avoid the loss of information during the conversion of IFSs into isosceles TFNs, we included the degree of hesitation (t) between the pairs of the membership function in the process. The compensations and authentication of the proposed measures are established with diverse counter-intuitive patterns and decision-making obstacles. Further, a clustering algorithm is also given to match the objects based on confidence levels. The performed analysis shows that the proposed measures give distinguishable and compatible results as contrasted to existing ones.

关键词： IFS Similarity distance measures Centroid Orthocenter Circumcenter Incenter clustering algorithm TFNs

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Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 1049343页

作者： Zhuang, ChangXi Chen, Chao Zhejiang Ocean Univ Maritime Sch Zhoushan Peoples R China

The autonomous generation of routes is an important part of ship intelligence and it can be realized by deep learning of the big data of automatic identification system (AIS) ship trajectories. In this study, to make the routes generated by long short-term memory (LSTM) artificial neural network more accurate and efficient, a ship route autonomous generation scheme is proposed based on AIS ship trajectory big data and improved multi-task LSTM artificial neural network. By introducing an unsupervised trajectory separation mechanism into LSTM, a fast and accurate separation of trajectories with similar paths is realized. In the process of route generation, first of all, a clustering algorithm is used to cluster the trajectories in massive AIS data according to the density of trajectory points, so as to eliminate the trajectories in the routes that do not belong to the target area. Furthermore, the routes are classified according to the type of ships, and then the classified trajectories are processed and used as datasets. Based on these datasets, an improved LSTM algorithm is used to generate ship routes autonomously. The results show the improved LSTM works better than LSTM when the generated route trajectories are short.

关键词： AIS ship trajectory big data ship intelligence route autonomous generation clustering algorithm LSTM

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A Case Study for clustering Characteristics Evaluation and clustering Dimension Selection by Using UCI News Data 20

A Case Study for Clustering Characteristics Evaluation and C...

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4th International Conference on algorithms, Computing and Systems (ICACS) / 2nd African Electronics, Computer and Communication Conference (AECCC)

作者： Huang Chih-Chien Hsu Chung-Chian Pon YuShun Liao WenWei Natl Yunlin Univ Sci & Technol Dept Informat Management 123 Univ Rd Sect 3 Touliu Yunlin Taiwan Univ Colorado Boulder Cooperat Inst Res Environm Sci 325 Broadway Boulder CO 80305 USA

ISBN: (纸本)9781450377324

The purpose of this study is to research and explore the clustering algorithm, and provide methods for clustering characteristics evaluation and clustering dimension selection, in order to help the user to understand the impact and meaning of clustering parameters and data dimensions on clustering, thereby strengthening the use of clustering algorithm. In previous studies, many scholars have proposed various types of clustering algorithms. Most of these algorithms need to set the clustering parameters, and the selection of clustering parameters will affect the results after clustering. Therefore, the user must fully understand the meaning of clustering parameters for clustering and select appropriate clustering parameters for clustering, then the clustering algorithm can be effectively used to help solve decision-making problems. Based on the above factors, this study focus on doing further analysis and description on the meaning of the clustering data distribution & the meaning of parameters to the clusters, and the relationship among the clusters, find out the important clustering feature and propose a new clustering evaluation formula, and expect to assist the decision-maker to find appropriate clustering parameters effectively.

关键词： K-Means DBSCAN Hierarchical clustering Dendrogram clustering algorithm

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Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time

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International Journal of Information Technology (Singapore) 2020年第4期12卷 1377-1384页

作者： Hassan, Syed Imtiyaz Samad, Afreen Ahmad, Omair Alam, Afshar Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard (Deemed to be University) New Delhi India

clustering is an unsupervised data mining technique where exploration is done with little knowledge of data classes. Its aim is to recognize the hidden information from the data for effective decision-making. Though many clustering algorithms has already been implemented till date, still it is an active topic of research for data mining. Researcher’s attempts to explore, compare, evaluate, and improve the different clustering algorithms available, for specialized situation and context. The purpose of all these efforts are to refine and propose improved version of algorithm after statistical evaluation by different metrices. The present research is an attempt to analysis empirically, the partitioning based clustering algorithms and hierarchical based clustering algorithm;by conducting extensive experiments. Both algorithms effectiveness has been measured through external and internal validity indices and Pearson’s correlation distance function using anatomized experiments. The parameters of evaluation that have been taken into consideration;for Internal Indices: Silhouette Index, Davies-Bouldin Validity Index and Calinski-Harabasz index;for external indices: Jaccard index, Rand Index, Entropy and Normalized Mutual Information. The other parameters of evaluation are accuracy and time of execution. Based on the experiments it may be concluded that K-means algorithm produces more promising result than hierarchical algorithm except in accuracy. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： clustering algorithm Data mining Data science Hierarchical algorithm K-means Machine learning Pearson’s correlation distance Validation indices

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