A New Method of K-Means Clustering Algorithm with Events Based on Variable Time Granularity

2016 ◽  
Author(s):  
Mengxing Huang ◽  
Hongjing Lin
Keyword(s):  
Clustering Algorithm ◽  
New Method ◽  
Variable Time

scholarly journals Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5697
Author(s):  
Chang Sun ◽  
Shihong Yue ◽  
Qi Li ◽  
Huaxiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Two Phase Flow ◽  
Low Cost ◽  
Fast Response ◽  
New Method ◽  
Phase Flow ◽  
Reference Distribution ◽  
Two Phase ◽  
Long Time ◽  
Solid Liquid

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

scholarly journals Clustering Mixed Data by Fast Search and Find of Density Peaks

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shihua Liu ◽  
Bingzhong Zhou ◽  
Decai Huang ◽  
Liangzhong Shen
Keyword(s):  
Clustering Algorithm ◽  
New Method ◽  
Mixed Data ◽  
Fast Search ◽  
Density Peaks ◽  
Categorical Attributes

Aiming at the mixed data composed of numerical and categorical attributes, a new unified dissimilarity metric is proposed, and based on that a new clustering algorithm is also proposed. The experiment result shows that this new method of clustering mixed data by fast search and find of density peaks is feasible and effective on the UCI datasets.

A Multi-Index Variable Time Step Method for the Dynamic Simulation of Multibody Systems

1996 ◽  
Author(s):  
Jesús Cardenal ◽  
Javier Cuadrado ◽  
Eduardo Bayo
Keyword(s):  
Multibody Systems ◽  
New Method ◽  
Step Method ◽  
Time Step ◽  
Multi Index ◽  
Time Step Size ◽  
Variable Time ◽  
Simulation Process ◽  
Variable Time Step ◽  
Time Step Method

Abstract This paper presents a new multi-index variable time step method for the integration of the equations of motion of constrained multibody systems in descriptor form. The basis of the method is the augmented Lagrangian formulation with projections in index-3 and index-1. The new method takes advantage of the better performance of the index-3 formulation for large time steps and of the stability of the index-1 for low time steps, and automatically switches from one method to the other depending on the required accuracy and values of the time step. The modification of time steps is accomplished through the use of a global system invariant such as the kinetic energy stored in the penalty system. This energy provides a good measure of the global error introduced by the numerical integration during the simulation process, and permits a simple and reliable strategy for varying the time step. Overall, the new method is quite efficient and robust: it is suitable for stiff and non-stiff systems, it is robust for all time step sizes, it works for singular configurations, redundant constraints and topology changes. Also, the constraints in positions, velocities and accelerations are satisfied to machine precision during the simulation process. The method is always more accurate as the time step size decreases.

Automatic Optimization Algorithm of Clusters Number Based on Maximum Distance

2014 ◽  
Vol 951 ◽  
pp. 231-234
Author(s):  
Hong Bo Zhou ◽  
Jun Tao Gao
Keyword(s):  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
New Method ◽  
Maximum Distance ◽  
Validity Index ◽  
Clustering Validity Index ◽  
Automatic Optimization ◽  
Clustering Validity

K-means clustering algorithm clusters datasets according to the certain clustering number k．However k cannot be confirmed beforehand.A new clustering validity index was designed from the standpoint of sample geometry.Based on the index a new method for determining the optimal clustering number in K-means clustering algorithm was proposed．

scholarly journals A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

2021 ◽  
Vol 13 (15) ◽  
pp. 2894
Author(s):  
Xiang Wu ◽  
Fengyan Wang ◽  
Mingchang Wang ◽  
Xuqing Zhang ◽  
Qing Wang ◽  
...  
Keyword(s):  
Rock Mass ◽  
Clustering Algorithm ◽  
Point Clouds ◽  
New Method ◽  
Rock Masses ◽  
Average Deviation ◽  
Density Peaks ◽  
3D Point Clouds ◽  
Discontinuity Sets ◽  
Dip Angle

Light detection and ranging (LiDAR) can quickly and accurately obtain 3D point clouds on the surface of rock masses, and on the basis of this, discontinuity information can be extracted automatically. This paper proposes a new method to automatically extract discontinuity information from 3D point clouds on the surface of rock masses. This method first applies the improved K-means algorithm based on the clustering algorithm by fast search and find of density peaks (DPCA) and the silhouette coefficient in the cluster validity index to identify the discontinuity sets of rock masses, and then uses the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to segment the discontinuity sets and to extract each discontinuity from a discontinuity set. Finally, the random sampling consistency (RANSAC) method is used to fit the discontinuities and to calculate their parameters. The 3D point clouds of the typical rock slope in the Rockbench repository is used to extract the discontinuity orientations using the new method, and these are compared with the results obtained from the classical approach and the previous automatic methods. The results show that, compared to the results obtained by Riquelme et al. in 2014, the average deviation of the dip direction and dip angle is reduced by 26% and 8%, respectively; compared to the results obtained by Chen et al. in 2016, the average deviation of the dip direction and dip angle is reduced by 39% and 40%, respectively. The method is also applied to an artificial quarry slope, and the average deviation of the dip direction and dip angle is 5.3° and 4.8°, respectively, as compared to the manual method. Furthermore, the related parameters are analyzed. The study shows that the new method is reliable, has a higher precision when identifying rock mass discontinuities, and can be applied to practical engineering.

scholarly journals A New Method to Enhance the Clustering Algorithm

2013 ◽  
pp. 120-122
Author(s):  
Abolfazl Zanghaei ◽  
Mohammadjavad Abolhassani ◽  
Alireza Ahmadian ◽  
Mohammad Reza Ay ◽  
Houshang Saberi
Keyword(s):  
Clustering Algorithm ◽  
New Method

scholarly journals A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm

2016 ◽  
pp. btw793 ◽  
Author(s):  
Joël Lafond-Lapalme ◽  
Marc-Olivier Duceppe ◽  
Shengrui Wang ◽  
Peter Moffett ◽  
Benjamin Mimee
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
De Novo ◽  
New Method ◽  
Hierarchical Clustering Algorithm
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