scholarly journals Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm

2021 ◽  
Vol 2132 (1) ◽  
pp. 012008
Author(s):  
MingYu Wang ◽  
Rui Cheng
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Power Grid ◽  
Electrical Equipment ◽  
Heterogeneous Data ◽  
Classification Method ◽  
Stable Operation ◽  
Label System ◽  
Noise Clustering ◽  
Feature Mining

Abstract With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.

Analysis and Prevention of Grid Over-Voltage Ferromagnetic Resonance

2014 ◽  
Vol 960-961 ◽  
pp. 929-934
Author(s):  
Ming Jun Lv ◽  
Xin Zhao ◽  
Xiang Dong Zhao ◽  
Jian Guo Liu ◽  
Feng Zhen Liu ◽  
...  
Keyword(s):  
Power System ◽  
Ferromagnetic Resonance ◽  
Preventive Measures ◽  
Power Grid ◽  
Electrical Equipment ◽  
Normal Operation ◽  
Stable Operation ◽  
Power Outages ◽  
The Future ◽  
Great Harm

Over-voltage in the power system can be caused by a lot of reasons , including higher frequency ferromagnetic resonance overvoltage which occurs in normal operation and causes great harm. Overvoltage events often result in damages to electrical equipment or even power outages . In this paper,ferromagnetic resonance is analyzed to study harm , causes, conditions , and phenomena and to handle resonance and develop practical preventive measures. The related analysis is important to work for the future operation of the power grid to prevent and limit the ferromagnetic resonance over voltage which provides some help to further ensure the safety and stable operation of the power grid .

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

scholarly journals An environmental dependence of the physical and structural properties in the Hydra cluster galaxies

2020 ◽  
Vol 500 (1) ◽  
pp. 1323-1339
Author(s):  
Ciria Lima-Dias ◽  
Antonela Monachesi ◽  
Sergio Torres-Flores ◽  
Arianna Cortesi ◽  
Daniel Hernández-Lang ◽  
...  
Keyword(s):  
Structural Properties ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Broad Band ◽  
Visible Region ◽  
Early Type ◽  
Cluster Galaxies ◽  
Using Data ◽  
Stellar Masses ◽  
Environmental Dependence

ABSTRACT The nearby Hydra cluster (∼50 Mpc) is an ideal laboratory to understand, in detail, the influence of the environment on the morphology and quenching of galaxies in dense environments. We study the Hydra cluster galaxies in the inner regions (1R200) of the cluster using data from the Southern Photometric Local Universe Survey, which uses 12 narrow and broad-band filters in the visible region of the spectrum. We analyse structural (Sérsic index, effective radius) and physical (colours, stellar masses, and star formation rates) properties. Based on this analysis, we find that ∼88 per cent of the Hydra cluster galaxies are quenched. Using the Dressler–Schectman test approach, we also find that the cluster shows possible substructures. Our analysis of the phase-space diagram together with density-based spatial clustering algorithm indicates that Hydra shows an additional substructure that appears to be in front of the cluster centre, which is still falling into it. Our results, thus, suggest that the Hydra cluster might not be relaxed. We analyse the median Sérsic index as a function of wavelength and find that for red [(u − r) ≥2.3] and early-type galaxies it displays a slight increase towards redder filters (13 and 18 per cent, for red and early type, respectively), whereas for blue + green [(u − r)<2.3] galaxies it remains constant. Late-type galaxies show a small decrease of the median Sérsic index towards redder filters. Also, the Sérsic index of galaxies, and thus their structural properties, do not significantly vary as a function of clustercentric distance and density within the cluster; and this is the case regardless of the filter.

scholarly journals Multi-zone prediction analysis of city-scale travel order demand

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248064
Author(s):  
Pengshun Li ◽  
Jiarui Chang ◽  
Yi Zhang ◽  
Yi Zhang
Keyword(s):  
Prediction Model ◽  
Clustering Algorithm ◽  
Intelligent Transportation System ◽  
Spatial Clustering ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Short Term ◽  
Prediction Ability ◽  
Demand Prediction ◽  
Zone Division

Taxi order demand prediction is of tremendous importance for continuous upgrading of an intelligent transportation system to realise city-scale and personalised services. An accurate short-term taxi demand prediction model in both spatial and temporal relations can assist a city pre-allocate its resources and facilitate city-scale taxi operation management in a megacity. To address problems similar to the above, in this study, we proposed a multi-zone order demand prediction model to predict short-term taxi order demand in different zones at city-scale. A two-step methodology was developed, including order zone division and multi-zone order prediction. For the zone division step, the K-means++ spatial clustering algorithm was used, and its parameter k was estimated by the between–within proportion index. For the prediction step, six methods (backpropagation neural network, support vector regression, random forest, average fusion-based method, weighted fusion-based method, and k-nearest neighbour fusion-based method) were used for comparison. To demonstrate the performance, three multi-zone weighted accuracy indictors were proposed to evaluate the order prediction ability at city-scale. These models were implemented and validated on real-world taxi order demand data from a three-month consecutive collection in Shenzhen, China. Experiment on the city-scale taxi demand data demonstrated the superior prediction performance of the multi-zone order demand prediction model with the k-nearest neighbour fusion-based method based on the proposed accuracy indicator.

Mid-Term Stability Assessment of Jiangxi Power Grid

2013 ◽  
Vol 732-733 ◽  
pp. 882-887
Author(s):  
Yong Chun Su ◽  
Hao Wei Jia
Keyword(s):  
Power System ◽  
Power Grid ◽  
Assessment Method ◽  
Control Measures ◽  
Assessment Process ◽  
Jiangxi Province ◽  
Stable Operation ◽  
Stability Assessment ◽  
Term Stability ◽  
The Stability

Mid-term stability assessment is an important work to support power system operation in a province power grid of China every year. The stability assessment method and process was introduced in this paper. As an example, the stability of Jiangxi province power system was evaluated in the following two years. Weak area and weak transmission line were found out in each power supply area. Prevention and control measures were proposed. According to problems among the assessment process and using the state monitoring data, an approach was discussed to increase the assessment result accuracy. The analysis conclusion provides the reference to the safe and stable operation of Jiangxi power system.

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