Development of Hypergraph Based Improved Random Forest Algorithm for Partial Discharge Pattern Classification

Govindarajan, Suganya; Ardila-Rey, Jorge Alfredo; Kannan, K.; Jayalalitha, S.; Sannidhi, Nikhith; Balasubramanian, M.

doi:10.1109/access.2020.3047125

Cited by 11 publications

(9 citation statements)

References 34 publications

(33 reference statements)

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“…With the development and progress of the times, there are more and more aspects of human exploration, and digital has become a tool to support human exploration. Indispensable means that due to the development of science and technology, human beings are exploring the physical universe more and more extensively, which makes the range of numbers more and more extensive, and the complexity of data is rapidly increasing [16]. In this case, people can no longer find hidden laws through simple logical reasoning.…”

Section: Data Mining Overview and Research Statusmentioning

confidence: 99%

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

Zhu

2022

Journal of Robotics

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With the rapid advancement of big data, it is becoming a great problem for people to nd objective information in the database.e relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. Association rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, con dence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often di cult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. e theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge de nition method is introduced according to the de nition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will o er a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new de nition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. is paper uses two datasets of di erent sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. e results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy.

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Section: Data Mining Overview and Research Statusmentioning

confidence: 99%

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

Zhu

2022

Journal of Robotics

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“…In addition, in recent years, with the further deepening of the "refinement" requirements of counselors, the transactional tasks in the daily work of counselors have increased, and it takes a lot of time and energy to complete the writing, standardization, sorting, summarization, and summary of various daily work materials. When uploading work, the time available for knowledge reserve and professional ability improvement is correspondingly reduced [26].…”

Section: Restricted Professional Abilitymentioning

confidence: 99%

The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

Sun

et al. 2022

Wireless Communications and Mobile Computing

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The good development of industry-specific universities (hereinafter referred to as characteristic universities) promotes the optimal allocation of national university resources and moreover promotes the development of China’s education, while counselors, as important teaching and research staff, have a very close connection with the development level of the school, directly reflecting the quality and level of education of the school and determining the height of its educational development. Talent is the first productive forces, counselors as teaching talents, and is essential to the construction of industry specialty universities. The evaluation index system of teaching ability of counselors can effectively guide counselors toward the path of specialization, scientifically realize career planning, and maximize the achievement of the general goal of the characteristic school they are in. This article aims to assess the teaching capacity of counselors and targeted training capabilities. The counselor’s ability is reflected in the teaching of teaching, completing teaching objectives and cultivates student’s hobbies. In this paper, through investigating the cultivation methods of different industries, the cultivation of counselor’s ability is to assess consensus and establish multilevel teachers’ teaching capacity cultivation system. The results show that the system can effectively improve the teaching capacity of young teachers and increase 20% of student’s satisfaction; however, it also shows that the professional competence of counselors still needs to be improved.

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“…Consequently, there can be Helly (Figure 6a), Non-Helly (Figure 6b) and Isolated hyperedges (Figure 6c) can be observed once the represented in terms of hyper-edges constituting a Hypergraph. A machine learning algorithm learns Helly and Non-Helly and isolated hyper-edges from the incidence Matrices of the Hypergraph [18].…”

Section: Hypergraph Based Hyper Featuresmentioning

confidence: 99%

“…In the first stage, hyper-edges of the HG are obtained by establishing topological and geometrical connections between the features. The hyper-edges and vertices correspond, respectively, to the data set samples and features [18]. In the second stage, Helly property of the HG is applied to the intersecting hyper-edges.…”

Section: Hypergraph Preliminariesmentioning

confidence: 99%

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Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

et al. 2021

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Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.

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Development of Hypergraph Based Improved Random Forest Algorithm for Partial Discharge Pattern Classification

Cited by 11 publications

References 34 publications

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

The Teaching Capacity of Industry Characteristic Counselors Based on Data Fusion and Random Forest Algorithm

Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

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