Kernel-based Fuzzy C-means Clustering Algorithm Research Articles

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

With the transformation of the national energy and power sector, the steady advancement of intelligent power grid construction and the continuous improvement of the Ubiquitous Power Internet of Things technology framework, it has further requirements for realizing state comprehensive awareness and efficient processing of information data, and has been widely used in power equipment. The infrared image recognition technology, which has been widely used in thermal fault diagnosis of power equipment, also requires deeper research. For traditional Intuitionistic Fuzzy C-means (IFCM) algorithm for image segmentation is sensitive to the clustering center lead to low final clustering precision and detail, the time complexity and the high shortage. The paper puts forward a kind of applicable to power equipment of the infrared image segmentation based on space distribution information of Intuitionistic Fuzzy clustering algorithm. Non-target objects with high intensity and uneven image intensity in infrared image have strong interference to image segmentation. The proposed algorithm can effectively suppress the interference. Firstly, the gaussian model is introduced into the global spatial distribution information of power equipment to improve the IFCM. Secondly, the spatial operator optimization membership function of local spatial information is used to solve the problem of edge blurring and uneven image intensity. Through experiments on the data set containing 300 infrared images of power equipment, the relative regional error rate is about 10%, which is less affected by the change of fuzzy factor m. The effectiveness and applicability of this algorithm are verified, which is obviously better than other comparison algorithms.

Improving the Network Lifetime in Wireless Sensor Network for Internet of Thing Applications

Mobile Wireless sensor networks have acquired a great interest recently due to their capability to provide good solutions and low-priced in multiple fields. Internet of Things (IoT) connects different technologies such as sensing, communication, networking, and cloud computing. It can be used in monitoring, health care and smart cities. The most suitable infrastructure for IoT application is wireless sensor networks. One of the main defiance of WSNs is the power limitation of the sensor node. Clustering model is an actual way to eliminate the inspired power during the transmission of the sensed data to a central point called a Base Station (BS). In this paper, efficient clustering protocols are offered to prolong network lifetime. A kernel-based fuzzy C-means clustering algorithm (KFCM) is adopted to cluster sensor nodes, while a cluster head (CH) is selected for each cluster based on a fuzzy logic system. Results depicts that the new work performs better than the existing algorithms (as Low Energy Adaptive Cluster Hierarchy-Mobile (LEACH-M) and Low Energy Adaptive Cluster Hierarchy-Mobile Enhancement (LEACH-ME)) in terms of network lifetime, energy consumption, packet transmission and stability period.