An adaptive new state recognition method based on density peak clustering and voting probabilistic neural network

2020 ◽  
Vol 97 ◽  
pp. 106835
Author(s):  
Junyou Shi ◽  
Yi Deng ◽  
Zili Wang ◽  
Xuhao Guo
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Recognition Method ◽  
Density Peak ◽  
State Recognition ◽  
Density Peak Clustering

HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm

2019 ◽  
Vol 33 (08) ◽  
pp. 1950012 ◽  
Author(s):  
Jianhua Jiang ◽  
Wei Zhou ◽  
Limin Wang ◽  
Xin Tao ◽  
Keqin Li
Keyword(s):  
Clustering Algorithm ◽  
High Dimensionality ◽  
Test Cases ◽  
Low Density ◽  
Recognition Method ◽  
Density Peak ◽  
Irregular Shapes ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Density Peak Clustering

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.

scholarly journals Damage Pattern Recognition and Crack Propagation Prediction for Crumb Rubber Concrete Based on Acoustic Emission Techniques

2021 ◽  
Vol 11 (23) ◽  
pp. 11476
Author(s):  
Jianjie Sun ◽  
Xi Chen ◽  
Zhengwu Fu ◽  
Giuseppe Lacidogna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ultimate Load ◽  
Crumb Rubber ◽  
Clustering Methods ◽  
Clustering Method ◽  
Density Peak ◽  
Crumb Rubber Concrete ◽  
Density Peak Clustering ◽  
Rubber Concrete

In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environment of the specimens was a four-point bending load. Six clustering methods including k-means, fuzzy c-means (FCM), self-organizing mapping (SOM), Gaussian mixture model (GMM), hierarchical model, and density peak clustering method were analyzed; the results illustrated that the density peak clustering has the best performance. Next, the optimal clustering algorithm was used to cluster AE signals so as to study the evolution behavior of different damage modes, and the ultimate load of crumb rubber concrete was predicted by an artificial neural network. The results indicated that the combination of AE techniques and appropriate clustering methods such as the density peak clustering method and the artificial neural network could be used as a practical tool for structural health monitoring of crumb rubber concrete.

scholarly journals Combined Classifier for Face Recognition using Legendre Moments

2012 ◽  
Vol 1 (2) ◽  
pp. 107-118 ◽  
Author(s):  
Sridhar Dasari ◽  
I.V. Murali Krishna
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Probabilistic Neural Network ◽  
Recognition Method ◽  
Linear Discriminant ◽  
Face Images ◽  
Legendre Moments

In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers.DOI: 10.18495/comengapp.12.107118

scholarly journals A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network

Algorithms ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 34 ◽  
Author(s):  
Jing Xu ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
State Recognition ◽  
Complex Equipment

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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