K-Means algorithm-based detection for wavelength division multiplexed OOK PD-NOMA system over turbulent optical channel

Jana, Debi Pada; Shukla, Abhishek Mani; Gupta, Sumanta

doi:10.1117/1.oe.61.3.036111

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…x = containing n object, where each object has attributes of m dimensions, based on the similarity between the objects, the n objects are ultimately clustered into specified k class clusters. Each object belongs to and belongs to only one cluster class, and the distance of this object to the center of this class cluster is minimal [18][19].…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Optimization of network security protection posture based on data clustering

Zhu

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper focuses on the challenges facing network security in the digital era and proposes a network security posture optimization method based on data clustering. Three mainstream network security models, namely, P2DR security operation and maintenance model, line defense model, and three-dimensional defense model, are analyzed, and the limitations of existing security products are pointed out. The application potential of big data technology in network security is emphasized, and a comprehensive technical process containing information extraction, posture modeling, security trend prediction, and security policy deployment is constructed. The ARMA model and reinforcement learning building model are introduced, and the improved K-means algorithm is proposed to address the shortcomings of traditional methods. Experiments are conducted using the DARPA2000 dataset, and the results show the enhanced algorithm’s significant improvement in clustering accuracy and stability, with a maximum threat value of about 160, demonstrating better stability and effectiveness than the traditional method. The posture value exceeds 500 in a specific period, highlighting the dynamic changes in network security and confirming the practicality and effectiveness of the technique. The results of this study provide new strategies and perspectives for network security protection, and have essential reference and guidance value for practical applications and future research.

show abstract

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Optimization of network security protection posture based on data clustering

Zhu

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…Using the improved K-means algorithm to find the initial clustering center, the specific implementation steps are as follows: The improved K-means method is used to analyze the purchase behavior of customers, and the optimal number k of clusters is obtained, and the stability of cluster center is obtained. This method can improve the accuracy of clustering and reduce the clustering time [19]. This method makes use of the data structure information contained in clustering analysis, thus shortening the clustering distance required by clustering analysis and improving the efficiency of clustering analysis.…”

mentioning

confidence: 99%

Construction of Insurance Consumers' Purchasing Behavior Model Based on Big Data Analysis

Ding,

Zuo

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

The social capital theory is selected as the theoretical basis, and the decision-making of household commercial insurance purchase is taken as the research object. By using the frontier theory of big data, an improved K-means clustering algorithm is proposed to segment customers first, then the results of the segmentation are predicted and analyzed. Finally, an insurance consumer purchase behavior model is proposed, and an empirical test is carried out according to the proposed model. Social capital and its three dimensions can promote the possibility in family commercial insurance, thus ultimately promoting residents' families to make purchase decisions on commercial insurance.

show abstract

Application of improved support vector machine model in fault diagnosis and prediction of power transformers

Wang

2023

Adv Control Appl

View full text Add to dashboard Cite

Power transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K‐means algorithm into the model and constructs a high‐precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

show abstract

K-Means algorithm-based detection for wavelength division multiplexed OOK PD-NOMA system over turbulent optical channel

Cited by 5 publications

References 0 publications

Optimization of network security protection posture based on data clustering

Optimization of network security protection posture based on data clustering

Construction of Insurance Consumers' Purchasing Behavior Model Based on Big Data Analysis

Application of improved support vector machine model in fault diagnosis and prediction of power transformers

Contact Info

Product

Resources

About