scholarly journals Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Zhang ◽  
Lili Pang
Keyword(s):  
Pattern Recognition ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Recognition Accuracy ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Internal Governance ◽  
Social Organizations ◽  
Definition Of ◽  
Internal Supervision

This paper proposes a multiple collaborative supervision pattern recognition method within social organizations based on data clustering algorithm to realize diversified supervision within social organizations and improve the effect of the said pattern recognition. Firstly, the characteristics and functions of social organizations are analyzed, and the definition of social organizations is given. Further, this paper studies the meaning and characteristics of social organization supervision, analyzes the failure of internal supervision of social organizations, and then determines the internal governance elements of social organizations. In addition, the basic steps of pattern recognition are given. Finally, multiple collaborative supervision patterns recognition within social organizations is realized based on data clustering algorithm. Experiments show that this method can improve the recognition accuracy of multiple collaborative supervision patterns and reduce the recognition time.

scholarly journals Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3424 ◽  
Author(s):  
Ning Liu ◽  
Bo Fan ◽  
Xianyong Xiao ◽  
Xiaomei Yang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Power Cables ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Belief Network ◽  
Incipient Faults ◽  
Sparse Autoencoder ◽  
Traditional Pattern

Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

scholarly journals Evaluation of the seismogenic (M6+) nodes for the territory of Bulgaria and adjacent areas

2021 ◽  
Vol 82 (3) ◽  
pp. 174-176
Author(s):  
Alexander Gorshkov ◽  
Olga Novikova ◽  
Sonia Dimitrova ◽  
Aleksander Soloviev ◽  
Maxim Semka ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Recognition Algorithm ◽  
Seismic Events ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Pattern Recognition Algorithm ◽  
Zonation Pattern ◽  
Earthquake Catalogues ◽  
Definition Of ◽  
Seismogenic Nodes

In this study seismogenic nodes capable to generate earthquakes with magnitudes M ≥ 6 are identified for the territory of Bulgaria and adjacent areas. Definition of nodes is based on a morphostructural zonation. Pattern recognition algorithm Cora-3 is applied to identify the seismogenic nodes, characterized by specific geological and geophysical data. The pattern recognition method is trained on information for 30 seismic events with M ≥ 6 for the period 29 BC–2020, selected from historical and instrumental Bulgarian earthquake catalogues. As a result, 56 seismogenic nodes are recognized, most of them in southwestern Bulgaria.

On Objective-Based Roughc-Regression

2015 ◽  
Vol 19 (1) ◽  
pp. 36-42
Author(s):  
Akira Sugawara ◽  
◽  
Yasunori Endo ◽  
Naohiko Kinoshita ◽  
Keyword(s):  
Pattern Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Pattern Recognition Method ◽  
Clustering Methods ◽  
Recognition Method ◽  
Numerical Examples

The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods,c-regression based on fuzzy set theory, called Fuzzyc-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Roughk-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Roughc-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

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