scholarly journals Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

2020 ◽  
Author(s):  
John Biersdorf ◽  
Ha Bui ◽  
Tatsuya Sakurahara ◽  
Seyed Reihani ◽  
Chris LaFleur ◽  
...  
Keyword(s):  
Importance Ranking ◽  
Model Realism

Cross-layer Approach to Assess FMEA on Critical Systems and Evaluate High-Level Model Realism

2021 ◽  
Author(s):  
Julie Roux ◽  
Katell Morin-Allory ◽  
Vincent Beroulle ◽  
Regis Leveugle ◽  
Lilian Bossuet ◽  
...  
Keyword(s):  
Cross Layer ◽  
Critical Systems ◽  
Level Model ◽  
Model Realism ◽  
High Level

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

scholarly journals Identifying important nodes from content-associated heterogeneous graph by LeaderRank

2021 ◽  
Vol 2113 (1) ◽  
pp. 012082
Author(s):  
Yulong Dai ◽  
Qiyou Shen ◽  
Xiangqian Xu ◽  
Jun Yang
Keyword(s):  
Real World ◽  
Experimental Analysis ◽  
Differential Information ◽  
World Systems ◽  
Ranking Problem ◽  
Data Set ◽  
Importance Ranking ◽  
Basic Concepts ◽  
Important Nodes ◽  
Graph Nodes

Abstract Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

Combining wavelength importance ranking to the random forest classifier to analyze multiclass spectral data

2021 ◽  
pp. 110998
Author(s):  
Juliana de Abreu Fontes ◽  
Michel José Anzanello ◽  
João Batista Gonçalves de Brito ◽  
Guilherme Brandelli Bucco ◽  
Flavio Sanson Fogliatto ◽  
...  
Keyword(s):  
Random Forest ◽  
Spectral Data ◽  
Random Forest Classifier ◽  
Importance Ranking

Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia

2020 ◽  
pp. 1436-1458
Author(s):  
Yuncheng Jiang ◽  
Mingxuan Yang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Formal Concept Analysis ◽  
Web Search ◽  
Rough Set Theory ◽  
Concept Analysis ◽  
Semantic Search ◽  
Formal Concept ◽  
Importance Ranking ◽  
Keyword Searches

This article describes how the traditional web search is essentially based on a combination of textual keyword searches with an importance ranking of the documents depending on the link structure of the web. However, one of the dimensions that has not been captured to its full extent is that of semantics. Currently, combining search and semantics gives birth to the idea of the semantic search. The purpose of this article is to present some new methods to semantic search to solve some shortcomings of existing approaches. Concretely, the authors propose two novel methods to semantic search by combining formal concept analysis, rough set theory, and similarity reasoning. In particular, the authors use Wikipedia to compute the similarity of concepts (i.e., keywords). The experimental results show that the authors' proposals perform better than some of the most representative similarity search methods and sustain the intuitions with respect to human judgements.

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