A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C‐Means Clustering

Wu, Na; Wang, Ke; Wan, Liangtian; Liu, Ning

doi:10.1155/2021/6658785

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…where d ik is the distance between sample x i and the kth clustering center [18,19]. (5) Determine whether the objective function calculated by formula (15) is greater than the set threshold ε and the number of iterations N. If the number of iterations N has not reached the maximum and is greater than the threshold, return (4); otherwise, turn to (6);…”

Section: Fault Determination Processmentioning

confidence: 99%

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Zhang

Tang

Zhu

et al. 2022

International Transactions on Electrical Energy Systems

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In order to locate the single-phase-to-ground fault in distribution network, a research method of single-phase-to-ground fault location and recovery technology in distribution network is proposed. According to the characteristics of single-phase-to-ground fault in distribution network, the fault characteristics of zero-sequence current and three-phase current transient records are extracted by generalized S transform of correlation coefficient, and multi-source redundant information is formed. Fuzzy C-means clustering analysis method is used to fuse multiple fault features online location strategy. The simulation results show that the fuzzy clustering based on FCM changes continuously with the accumulation of samples, and the sample center changes with the enrichment of the samples. When the arc suppression coil is grounded with different initial phase angle transition resistance and intermittent fault, the state of each measuring point can be correctly identified, and the accurate energization rate is 97%. The test results show that the system can achieve accurate positioning for all sections.

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Section: Fault Determination Processmentioning

confidence: 99%

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Zhang

Tang

Zhu

et al. 2022

International Transactions on Electrical Energy Systems

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“…In order to better improve the clustering effect of the FCM algorithm, Wang et al 22 proposed a validity function to evaluate the clustering results according to the relative structure information of the data, and this method can accurately obtain the optimal number of clusters. Wu et al 23 added a sample feature called local data densit y to optimize the FCM algorithm, and one-dimensional data was extracted by distribution density characteristics of the eigenvalues. Wang 22 and Wu 23 gave us much inspiration for dealing with the relative positional relationship of data, and the shortcomings of FCM in finding the optimal solution cannot also be ignored.…”

Section: Related Workmentioning

confidence: 99%

“…Wu et al 23 added a sample feature called local data densit y to optimize the FCM algorithm, and one-dimensional data was extracted by distribution density characteristics of the eigenvalues. Wang 22 and Wu 23 gave us much inspiration for dealing with the relative positional relationship of data, and the shortcomings of FCM in finding the optimal solution cannot also be ignored. Therefore, we added a population-based stochastic optimization technique to the process of finding the centroid and a relative structure measurement method to the anomaly detection process.…”

Section: Related Workmentioning

confidence: 99%

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

Cui

Zhang

Chen

et al. 2022

Sci Rep

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User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

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“…According to the data structure distribution in Figure 2, the statistical feature quantity and high order cumulant of the sample data flow collected by sparse scattered points and multisensor array elements are extracted. In the process of resource allocation, the self-sparse structure is constantly adjusted according to the accumulated historical data, and the fuzzy clustering processing of the sample data information flow collected by sparse scattered points and multisensor array elements is realized by combining the learning algorithm of unbalanced data distribution and sample feature fusion [10].…”

Section: Analysis Of Data Structure Of Sensor Array Samplingmentioning

confidence: 99%

Sample Density Clustering Method Considering Unbalanced Data Distribution

Wang

2022

Mobile Information Systems

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The data distribution of the multidimensional array sensor is unbalanced in data sample collection. To improve the clustering ability of data samples, a data density clustering method of sparse scattered points and multisensor array sensor samples based on the analysis of unbalanced data distribution characteristics is proposed. The sparse scattered multisensor array network’s sample data collection structure is created using the Voronoi polygon topology. By analyzing the unbalanced parameters between data classes and reconstructing the characteristic space of data sample sequence, the time series of sample data collected by sparse scattered multisensor array is reorganized, and the statistical characteristic quantity and high-order cumulant of sample data collected by sparsely scattered multisensor array are extracted. Combined with the learning algorithm of unbalanced data distribution sample feature fusion, the fuzzy clustering of sample data information flow collected by sparse scattered multisensor array elements is realized. According to the feature clustering and convergence analysis, the sparse scattered feature detection method is adopted to realize the data density clustering and data structure optimization configuration of sparse scattered multisensor array elements. The test results show that the method in this paper has good convergence, strong spectrum expansion ability, and low error rate of data clustering when collecting samples with sparse scattered points and multisensor arrays.

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A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C‐Means Clustering

Cited by 4 publications

References 22 publications

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

Sample Density Clustering Method Considering Unbalanced Data Distribution

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