Yong Xiao¹, Xin Jin¹, Jingfeng Yang², Yanhua Shen^3,*, Quansheng Guan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1293-1313, 2021, DOI:10.32604/cmes.2021.012562 - 19 February 2021

Abstract User-transformer relations are significant to electric power marketing, power supply safety, and line loss calculations. To get accurate user-transformer relations, this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization (QPSO) and Fuzzy C-Means Clustering. The main idea is: as energy meters at different transformer areas exhibit different zero-crossing shift features, we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations. The proposed method contributes in three main ways. First, based on the… More >

Yifei Wei^{1, *}, Yinxiang Qu¹, Min Zhao¹, Lianping Zhang², F. Richard Yu³

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1515-1532, 2020, DOI:10.32604/cmc.2020.09130 - 30 April 2020

Abstract Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the… More >

Ruikang Xing^1,*, Chenghai Li¹

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 767-780, 2019, DOI:10.32604/cmc.2019.04500

Abstract In clustering analysis, the key to deciding clustering quality is to determine the optimal number of clusters. At present, most clustering algorithms need to give the number of clusters in advance for clustering analysis of the samples. How to gain the correct optimal number of clusters has been an important topic of clustering validation study. By studying and analyzing the FCM algorithm in this study, an accurate and efficient algorithm used to confirm the optimal number of clusters is proposed for the defects of traditional FCM algorithm. For time and clustering accuracy problems of FCM More >

U. Kanimozhi, D. Manjula

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 701-709, 2018, DOI:10.1080/10798587.2017.1307626

Abstract We are witnessing the era of big data computing where computing the resources is becoming the main bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of data is of high dimensionality, feature selection is necessary for further improving the clustering and classification results. In this paper, we propose a new feature selection method, Incremental Filtering Feature Selection (IF2S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset of features and for effective More >