Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis

2016 ◽  
Vol 3 (3) ◽  
pp. 60-71 ◽  
Author(s):  
Caner Erden ◽  
Numan Çelebi
Keyword(s):  
Big Data ◽  
Rough Sets ◽  
Traffic Accidents ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Decision Makers ◽  
Rough Sets Theory ◽  
Relational Analysis ◽  
Accident Causation ◽  
Sets Theory

The aim of this study is to show that the decision rules generated from Rough Sets Theory can be used for a new relational analysis. Rough Sets Theory generally works with small datasets more than big data. If we can deal with the decision rules and its complexities, it is still possible to analyze big data with Rough Set Theory. That is why in this study the authors offer a statistical method to overdue problems which belongs to big data. According statistical methods, a lots of decision rules generated from rough sets theory become useful information. Using a real case data on the traffic accident which were taken place in USA in 2013, this paper finds the relationships between accident causation factors which may be referred to decision makers in the field of traffic.

Application of MMAS and Rough Sets in Compound-Fault Diagnosis of Bearing

2012 ◽  
Vol 433-440 ◽  
pp. 6319-6324 ◽  
Author(s):  
Hai Ying Kang ◽  
Ren Fa Shen ◽  
Yan Jie Qi ◽  
Wen Yan ◽  
Hai Qi Zheng
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Ant System ◽  
Fisher Criterion ◽  
Rough Sets Theory ◽  
Np Hard Problem ◽  
Sets Theory

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field. Rough set theory is a relatively new soft computing tool to deal with vagueness and uncertainty. Condition attribute reduce algorithm is the key point of rough set research. However, it has been proved that finding the best reduction is the NP-hard problem. For the purpose of getting the reduction of systems effectively, an improved algorithm is put forward. The worst Fisher criterion was adopted as heuristic information to improve the searching efficiency and Max-Min Ant System was selected. Simplify the fault diagnosis decision table, then clear and concise decision rules can be obtained by rough sets theory. This method raises the accuracy and efficiency of fault diagnosis of bearing greatly.

Compound-Fault Diagnosis of Bearing Based on Order Tracking Wavelet Packet and Rough Sets

2011 ◽  
Vol 130-134 ◽  
pp. 1681-1685 ◽  
Author(s):  
Guang Tian ◽  
Hao Tian ◽  
Guang Sheng Liu ◽  
Jin Hui Zhao ◽  
Li Ping Luo
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Wavelet Packet ◽  
Decision Rules ◽  
Vibration Signals ◽  
Rough Sets Theory ◽  
Equal Angle ◽  
Order Tracking ◽  
Start Up ◽  
Sets Theory

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field, then a new method of compound-fault diagnosis was presented. The vibration signals at start-up in the gearbox are non-stationary signals, and traditional ways of diagnosis have low precision. Order tracking and wavelet packet and rough sets theory are introduced in the compound-fault diagnosis of bearing. First, the vibration signals at start-up were resampled using computer order tracking arithmetic and equal angle distributed vibration signals were obtained, and wavelet packet has been used for equal angle distributed vibration signals decomposition and reconstruction. Then, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound-fault example proves that the proposed method has high validity and more amplitude appliance foreground.

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

Application of Rough Sets Theory in Graduate Enrollment

2014 ◽  
Vol 687-691 ◽  
pp. 1604-1607
Author(s):  
Juan Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Postgraduate Training ◽  
Rough Set Theory ◽  
Postgraduate Education ◽  
Rough Sets Theory ◽  
Graduate Enrollment ◽  
Enrollment Data ◽  
Sets Theory

Enrollment is the first step of the work of postgraduate education, and also a very crucial step. Therefore, in order to successfully carry out the postgraduate training work, be sure to do the enrollment work. In this paper, rough set theory is applied to the enrollment data of one college of a university. Then follow the general steps of data mining to research and analyze the enrollment data. Finally, draw some useful conclusions. These conclusions have a certain significance for graduate enrollment.

scholarly journals Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 133
Author(s):  
Zhan-ao Xue ◽  
Min Zhang ◽  
Yong-xiang Li ◽  
Li-ping Zhao ◽  
Bing-xin Sun
Keyword(s):  
Decision Making ◽  
Information System ◽  
Rough Sets ◽  
Decision Rules ◽  
Rough Sets Theory ◽  
Quantification Method ◽  
Variable Precision ◽  
Double Quantification ◽  
Sets Theory ◽  
Variable Precision Rough Sets

Since the rough sets theory based on the double quantification method was proposed, it has attracted wide attention in decision-making. This paper studies the decision-making approach in Incomplete Ordered Information System (IOIS). Firstly, to better extract the effective information in IOIS, combined with the advantages of set-pair dominance relation and generalized multi-granulation, the generalized multi-granulation set-pair dominance variable precision rough sets (GM-SPD-VPRS) and the generalized multi-granulation set-pair dominance graded rough sets (GM-SPD-GRS) are proposed. Moreover, we discuss their related properties. Secondly, considering the GM-SPD-VPRS and the GM-SPD-GRS describe information from relative view and absolute view, respectively, we further combine the two rough sets to obtain six double-quantitative generalized multi-granulation set-pair dominance rough sets (GM-SPD-RS) models. Among them, the first two models fuse the approximation operators of two rough sets, and investigate the extreme cases of optimistic and pessimistic. The last four models combine the two rough sets by the logical disjunction operator and the logical conjunction operator. Then, we discuss relevant properties and derive the corresponding decision rules. According to the decision rules, an associated algorithm is constructed for one of the models to calculate the rough regions. Finally, we validate the effectiveness of these models with a medical example. The results indicate that the model is effective for dealing with practical problems.

scholarly journals PARAMETRIC ANALYSIS OF A GRINDING PROCESS USING THE ROUGH SETS THEORY

2020 ◽  
Vol 18 (1) ◽  
pp. 091
Author(s):  
Subham Agarwal ◽  
Shruti Sudhakar Dandge ◽  
Shankar Chakraborty
Keyword(s):  
Rough Sets ◽  
Decision Rules ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Rough Sets Theory ◽  
Related Data ◽  
Grinding Parameters ◽  
Sets Theory

With continuous automation of the manufacturing industries and the development of advanced data acquisition systems, a huge volume of manufacturing-related data is now available which can be effectively mined to extract valuable knowledge and unfold the hidden patterns. In this paper, a data mining tool, in the form of the rough sets theory, is applied to a grinding process to investigate the effects of its various input parameters on the responses. Rotational speed of the grinding wheel, depth of cut and type of the cutting fluid are grinding parameters, and average surface roughness, amplitude of vibration and grinding ratio are the responses. The best parametric settings of the grinding parameters are also derived to control the quality characteristics of the ground components. The developed decision rules are quite easy to understand and can truly predict the response values at varying combinations of the considered grinding parameters.

ROUGH SETS AND GENETIC ALGORITHMS IN LEARNING CELLULAR NEURAL NETWORKS CLONING TEMPLATE FOR DECISION MAKING SYSTEM

2004 ◽  
Vol 14 (01) ◽  
pp. 57-68 ◽  
Author(s):  
ELSAYED RADWAN ◽  
EIICHIRO TAZAKI
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Rough Sets ◽  
Decision Rules ◽  
Cellular Neural Networks ◽  
Minimal Set ◽  
Rough Sets Theory ◽  
Decision Making System ◽  
Sets Theory

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

Application of Rough Sets Theory in Graduate Enrollment

2015 ◽  
Vol 713-715 ◽  
pp. 1640-1643 ◽  
Author(s):  
Juan Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Postgraduate Training ◽  
Rough Set Theory ◽  
Postgraduate Education ◽  
Rough Sets Theory ◽  
Graduate Enrollment ◽  
Enrollment Data ◽  
Sets Theory

Enrollment is the first step of the work of postgraduate education, and also a very crucial step. Therefore, in order to successfully carry out the postgraduate training work, be sure to do the enrollment work. In this paper, rough set theory is applied to the enrollment data of one college of a university. Then follow the general steps of data mining to research and analyze the enrollment data. Finally, draw some useful conclusions. These conclusions have a certain significance for graduate enrollment.

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