scholarly journals Self-Organised direction aware data partitioning algorithm

2018 ◽  
Vol 423 ◽  
pp. 80-95 ◽  
Author(s):  
Xiaowei Gu ◽  
Plamen Angelov ◽  
Dmitry Kangin ◽  
Jose Principe
Keyword(s):  
Data Partitioning ◽  
Partitioning Algorithm

scholarly journals A fuzzy noise-rejection data partitioning algorithm

2005 ◽  
Vol 38 (1) ◽  
pp. 1-17 ◽  
Author(s):  
William W. Melek ◽  
Andrew A. Goldenberg ◽  
M.R. Emami
Keyword(s):  
Data Partitioning ◽  
Noise Rejection ◽  
Partitioning Algorithm ◽  
Fuzzy Noise

A Power Network Partitioning Method Based on Local Similarity Measure

2012 ◽  
Vol 588-589 ◽  
pp. 364-367
Author(s):  
Tao Wang ◽  
Heng Zhou ◽  
Pan Zou
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Reactive Power ◽  
Graph Model ◽  
Weighted Graph ◽  
Local Similarity ◽  
Network Partitioning ◽  
Power Network ◽  
Clustering Method ◽  
Partitioning Algorithm

A power network partitioning model based on the weighed local similarity measure is presented in this paper considering the regional decoupling characteristics of reactive power. A weighted graph model of reactive power network is established and a new measurement of local similarity based on weighed graph is defined. To utilize our measurement of similarity to partition reactive power network, a partitioning algorithm based on generalized ward hierarchical clustering method is proposed. The algorithm can ensure balance of the reactive power inside partition. Applying the proposed algorithm to IEEE 39-bus system, the results show that the proposed algorithm is feasible and effective.

An Autonomous Model to Classify Lathe's Cutting Tools Based on TSFRESH, Self-Organised Direction Aware Data Partitioning Algorithm and Machine Learning Techniques

2020 ◽  
Author(s):  
Thiago E. Fernandes ◽  
Guilherme P. C. de Miranda ◽  
Alexandre F. Dutra ◽  
Matheus A. M. Ferreira ◽  
Matheus P. Antunes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cutting Tools ◽  
Data Partitioning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Machining Processes ◽  
Data Set ◽  
Learning Techniques ◽  
Machining System ◽  
Partitioning Algorithm

The machining processes are of major importance to industries, due to the factthat these processes take part in the manufacturing of a substantial portion of mechanicalcomponents. Hence, during these processes, operational interruptions and accidents induced by fault occurrence are likely to cause economic losses. Concerning these consequences, real-time monitoring can result in productivity and safety increase along with cost reduction. This paper aims to discuss an autonomous model based on self-organised direction aware data partitioning algorithm and machine learning techniques, including features extraction and selection based on hypothesis tests, to solve the adressed problem. The model proposed in this work was evaluatedusing a data set acquired in a real machining system at the Manufacturing Processes Laboratory of Federal University of Juiz de Fora (UFJF).

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