Damage Localization of Stacker’s Track Based on EEMD-EMD and DBSCAN Cluster Algorithms

Li, Shupan; Qin, Na; Huang, Darong; Huang, Deqing; Ke, Lanyan

doi:10.1109/tim.2019.2919375

Cited by 37 publications

(7 citation statements)

References 39 publications

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“…An improved DBSCAN algorithm is implemented at visible light communication systems to improve the signal-to-noise ratio and weaken the damage of noise to the communication quality [24]. A few more studies utilizing the DBSCAN algorithm in different fields of research can be found in very recent works [25][26][27][28]. On the other hand, importantly, the recent article [29] searches a way to determine the correct values of the DBSCAN parameters, by detecting the sharp increase in distance.…”

Section: Methodsmentioning

confidence: 99%

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

HABİBOĞLU,

HERNANDEZ,

UYAVER

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to measure the shapes of the formed structures by the self-assembly processes of these amino acids. We present the results in quantitative and qualitative ways. To the best of our knowledge, the geometric shapes of the formed structures by the self-assembly processes of these amino acids are not measured quantitatively in the literature. Analytical measurements and comparisons of these aggregations might help us to identify the self-aggregations quickly at early stages in our simulations and hence provide us with more opportunity to experiment with different parameters of the molecular simulations (like temperature, mixture rates, density etc.). We first implement DBSCAN method to identify the main self-aggregation cluster and then we develop and implement two algorithms to measure the shapes of the formed structures by the self-assembly processes of these amino acids. The measurements, which are completely in line with our simulation results, are presented in quantitative and qualitative ways.

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Section: Methodsmentioning

confidence: 99%

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

HABİBOĞLU,

HERNANDEZ,

UYAVER

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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“…The classical DBSCAN algorithm defines clusters as dense regions, separated by regions of lower density (Li et al , 2020; Sabo and Scitovski, 2020b). The DBSCAN algorithm has two global parameters: the maximum radius range ( Eps ) and the minimum number of neighbors ( MinPts ).…”

Section: Related Work On Dbscan Ap and Rough Set Theorymentioning

confidence: 99%

“…For instance, algorithms such as K-means (Hartigan and Wong, 1979) and Clustering Large Applications based on Randomized Search (CLARANS) (Ng and Han, 2002) were designed based on a partitioning approach; Gaussian mixture models (Fraley and Raftery, 2002) and COBWEB (Fisher, 1987) belong to a model-based approach; Divisive Analysis (DIANA) (Kaufman and Rousseeuw, 1990) and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) (Zhang et al , 1996) were developed based on a hierarchical approach; Statistical Information Grid (STING) (Wang et al , 1997) and Clustering in Quest (CLIQUE) (Agrawal et al , 1998) were introduced as a grid-based approach; and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al , 1996) and its variant Ordering Points to Identify the Clustering Structure (OPTICS) (Ankerst et al , 1999) are examples of a density-based approaches. Due to the fact that density-based clustering returns clusters of an arbitrary shape, is robust to noise and does not require prior knowledge on the number of clusters, DBSCAN has been widely applied in numerous fields (Li et al , 2020; Sabo and Scitovski, 2020a; Gan and Tao, 2018).…”

Section: Introductionmentioning

confidence: 99%

An improved density-based approach to risk assessment on railway investment

Guo

Yongxiang

et al. 2021

DTA

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PurposeDensity-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.Design/methodology/approachFirst, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.FindingsThe IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.Originality/valueThis study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

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“…EEMD represents an extension of EMD. During EEMD decomposition, the noise added into the original signals cannot be eliminated, which may cause reconstruction errors [ 32 ]. On the basis of EEMD, CEEMDAN was proposed by Colominas et al for further elimination of the mode mixing phenomenon.…”

Section: Review Of Ceemdanmentioning

confidence: 99%

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Shang

et al. 2020

Entropy

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To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

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Damage Localization of Stacker’s Track Based on EEMD-EMD and DBSCAN Cluster Algorithms

Cited by 37 publications

References 39 publications

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

An improved density-based approach to risk assessment on railway investment

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

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