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.

Vague Pattern Recognition Algorithm and its Irrigation System

2012 ◽  
Vol 433-440 ◽  
pp. 5951-5956
Author(s):  
Fu Jin Zhang ◽  
Yu Chun Ma ◽  
Hong Xu Wang ◽  
Qing Zhang
Keyword(s):  
Pattern Recognition ◽  
System Design ◽  
Similarity Measure ◽  
Irrigation System ◽  
Recognition Algorithm ◽  
Pattern Recognition Algorithm ◽  
Vague Sets ◽  
Recognition Algorithms ◽  
Definition Of ◽  
Irrigation System Design

Definition of conversion from a single value data to the Vague value data is given; two conversion formulas from a single value data to the Vague value data are given; a similarity measure formula between Vague sets are given; Vague pattern recognition algorithm is given. The algorithm is applied to irrigation system design, application examples show that theVague pattern recognition algorithms and formulas are all useful.

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 An Implicit Memory-Based Method for Supervised Pattern Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yu Ma ◽  
Shafei Wang ◽  
Junan Yang ◽  
Yanfei Bao ◽  
Jian Yang
Keyword(s):  
Pattern Recognition ◽  
Implicit Memory ◽  
Recognition Algorithm ◽  
Training Set ◽  
Recognition Method ◽  
Pattern Recognition Algorithm ◽  
Physiological Processes ◽  
Psychological Evidence ◽  
Artificial Neural Network Ann ◽  
Open Question

How the human brain does recognition is still an open question. No physical or biological experiment can fully reveal this process. Psychological evidence is more about describing phenomena and laws than explaining the physiological processes behind them. The need for interpretability is well recognized. This paper proposes a new method for supervised pattern recognition based on the working pattern of implicit memory. The artificial neural network (ANN) is trained to simulate implicit memory. When an input vector is not in the training set, the ANN can treat the input as a “do not care” term. The ANN may output any value when the input is a “do not care” term since the training process needs to use as few neurons as possible. The trained ANN can be expressed as a function to design a pattern recognition algorithm. Using the Mixed National Institute of Standards and Technology database, the experiments show the efficiency of the pattern recognition method.

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.

Sign in / Sign up

Export Citation Format

Share Document