Clustering By Fast Search And Find Of Density Peaks Algorithm Research Articles

Research on the optimal layout of pressure monitoring points in water-cooling pipelines of nuclear fusion device based on fusion distance

In view of the safety and stability requirements of the cooling water system (CWS) of the nuclear fusion device, it is necessary to optimize the pressure monitoring points of the water-cooling pipelines. To optimize the layout of pipeline pressure monitoring points, a method based on clustering by fast search and find of density peaks (CFSFDP) algorithm is proposed. By combining the fusion distance of the pipeline spatial attributes and the efficient CFSFDP clustering algorithm, the layout of the pressure monitoring points in the pipeline network can be effectively optimized. Further simulations and analysis show that this method can quickly realize the optimal layout of pressure monitoring points, and the rationality and effectiveness of the fusion distance is verified. Compared with other typical clustering algorithms, the CFSFDP algorithm for cluster analysis reflects the high efficiency advantages.

An Adaptive Pulse Separation Strategy for PD Detection in Frequency-Tuned Resonant Tests

Frequency-tuned resonant (ACRF) test is one of the most widely used technologies for power equipment diagnosis. However, the intense interference generated by the variable-frequency power source hinders its further application in partial discharge (PD) detection. For this reason, an adaptive PD separation method based on improved clustering by fast search and find of density peaks (CFSFDP) is proposed in this article to separate PD signals and disturbances under the ACRF test. First, the characteristics of the PD signals and the interference signals generated from the ACRF test system are analyzed and compared. Then, an improved CFSFDP algorithm is proposed to realize the automatic cluster center selection and signal separation, and the working principle of the PD separation strategy based on the improved CFSFDP algorithm is introduced. Finally, the effectiveness of the proposed method is validated by performing the ACRF test on a defective cable. The experimental results show that this method can realize the separation and detection of PD signals in ACRF tests. The proposed method provides a feasible solution for performing PD detection using the ACRF test system without the restriction of the test system structure.

Underdetermined mixing matrix estimation based on joint density-based clustering algorithms

In underdetermined blind source separation (UBSS), the estimation of the mixing matrix is crucial because it directly affects the performance of UBSS. To improve the estimation accuracy, this paper proposes a joint clustering analysis method based on density based spatial clustering of applications with noise (DBSCAN) and clustering by fast search and find of density peaks (CFSFDP). In the reprocessing, the observed signals in the time domain are transformed into sparse signals in the frequency domain through a short time Fourier transform (STFT), and single-source-point (SSP) detection is used to enhance the linear clustering characteristic of signals. In addition, to facilitate the use of density-based clustering analysis, mirroring mapping is used to transform the linear clustering into compact clustering on the positive half unit circle (or sphere). For the estimation of the underdetermined mixing matrix (UMM), the DBSCAN algorithm is first used to search for high-density data points, and automatically find the number of clusters and the cluster centers; then, the CFSFDP algorithm is used to search the density peaks of the data clusters, so as to further modify the cluster centers. Because each cluster center corresponds to a column vector of the mixing matrix, the proposed algorithm can estimate the UMM through cluster analysis. The simulation results show that the proposed algorithm can not only improve the estimation accuracy of the UMM, but also provide a more robust estimator. In addition, the joint clustering method also makes up for the shortcomings of the CFSFDP algorithm that requires human intervention.

Density Peak Clustering Algorithm Considering Topological Features

The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.

Grouping Method of Semiconductor Bonding Equipment Based on Clustering by Fast Search and Find of Density Peaks for Dynamic Matching According to Processing Tasks

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem.

Deployment of a charging network based on a cost-benefit model and the CFSFDP algorithm

PurposeThis research aims to focus on the problem of optimizing the layout of charging stations.Design/methodology/approachThe authors estimate the total number of charging stations by the number of existing gas stations using clustering by fast search-and-find of density peaks (CFSFDP) algorithm.FindingsThis paper reveals the relationship between the distance and the population. Priority should be given to the short-distance and high-frequency travel charging needs in the city, followed by low frequency and travel, which can improve the use efficiency of the whole charging network.Originality/valueFirst, the total number of charging stations is estimated by the number of existing gas stations. This method can fully consider the growth potential of charging vehicles instead of the traditional cars. Besides, the cost-benefit method is proposed to explore the marginal coverage distance of the city’s charging network and can be used to reveal the relationship between the distance and the population.

Fast density clustering strategies based on the k-means algorithm

Clustering by fast search and find of density peaks (CFSFDP) is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this study, we investigate the application of the k-means algorithm, which is a fast clustering technique, to enhance the scalability of the CFSFDP algorithm while maintaining its clustering results as far as possible. Toward this end, we propose two strategies. First, based on concept approximation, an acceleration algorithm (CFSFDP+A) involving fewer distance calculations is proposed to obtain the same clustering results as those of the original algorithm. Second, to further expand the scalability of the original algorithm, an approximate algorithm (CFSFDP+DE) based on exemplar clustering is proposed to rapidly obtain approximate clustering results of the original algorithm. Finally, experiments are conducted to illustrate the effectiveness and scalability of the proposed algorithms on several synthetic and real data sets.