A Decision-Making Method for Fire Detection Data Fusion Based on Rough Set Approach

2010 ◽  
Author(s):  
Naiwei Cheng
Keyword(s):  
Decision Making ◽  
Data Fusion ◽  
Rough Set ◽  
Fire Detection

Multisensor Data Fusion Based on Rough Set and ART2 Network

2012 ◽  
Vol 241-244 ◽  
pp. 912-915
Author(s):  
Xiao Hui Chen ◽  
Hui Liu
Keyword(s):  
Decision Making ◽  
Data Fusion ◽  
Rough Set ◽  
Sample Space ◽  
Sensor Data ◽  
Knowledge Reduction ◽  
Multisensor Data Fusion ◽  
Distribution Rules ◽  
Sensor Clustering ◽  
Multisensor Data

A difficulty of multisensor data fusion lies in the switching of the state of sensor clusters. That is, which direction should the sensor data been fused into at a given moment? In this paper, firstly, the rough set was used for access of knowledge. The typical clustering distributions of 54 sensors within one day were regarded as sample space for the decision-making table of the "data - fusion distribution". Next, based on the method of knowledge reduction using rough set, the redundant properties and samples of data in one month were removed. Then, the ART2 network was applied for clustering and analyzing, and the distribution rules of multisensor data fusion are formed. The experiment results show that the model is efficient in classification and rapid in sensor clustering distribution decide.

scholarly journals Fire Risk Assessment in Healthcare Settings: Application of FMEA Combined with Multi‐Criteria Decision Making Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fateme Omidvari ◽  
Mehdi Jahangiri ◽  
Reza Mehryar ◽  
Moslem Alimohammadlou ◽  
Mojtaba Kamalinia
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Fire Risk ◽  
Fire Detection ◽  
Healthcare Setting ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Healthcare Settings ◽  
Fire Risk Assessment ◽  
Risk Parameters

Fire is one of the most dangerous phenomena causing major casualties and financial losses in hospitals and healthcare settings. In order to prevent and control the fire sources, first risk assessment should be conducted. Failure Mode and Effect Analysis (FMEA) is one of the techniques widely used for risk assessment. However, Risk Priority Number (RPN) in this technique does not take into account the weight of the risk parameters. In addition, indirect relationships between risk parameters and expert opinions are not considered in decision making in this method. The aim is to conduct fire risk assessment of healthcare setting using the application of FMEA combined with Multi‐Criteria Decision Making (MCDM) methods. First, a review of previous studies on fire risk assessment was conducted and existing rules were identified. Then, the factors influencing fire risk were classified according to FMEA criteria. In the next step, weights of fire risk criteria and subcriteria were determined using Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) and different wards of the hospital were ranked using Interval-Valued Intuitionistic Fuzzy Combinative Distance-based Assessment (IVIFCODAS) method. Finally, a case study was performed in one of the hospitals of Shiraz University of Medical Sciences. In this study, fire alarm system (0.4995), electrical equipment and installations (0.277), and flammable materials (0.1065) had the highest weight, respectively. The hospital powerhouse also had the highest fire risk, due to the lack of fire extinguishers, alarms and fire detection, facilities located in the basement floor, boilers and explosive sensitivity, insufficient access, and housekeeping. The use of MCDM methods in combination with the FMEA method assesses the risk of fire in hospitals and health centers with great accuracy.

Research on the Consumer Demand of Decision Making in the E-Commence System Building Process

2011 ◽  
Vol 71-78 ◽  
pp. 2895-2898
Author(s):  
Jian Min Xie ◽  
Qin Qin
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Consumer Satisfaction ◽  
Rough Set Theory ◽  
Consumer Demand ◽  
Successful Implementation ◽  
Business Decision ◽  
Building Process ◽  
Demand Reduction ◽  
The Impact

In the process of developing e-commerce system, enterprises are able to accurately understand and grasp the needs of the user enterprise that is the key to the successful implementation of e-commerce. Based on this, the article proposed a new method which was based on the process of developing consumer demand for e-business decision-making though a rough set theory. This new approach is built using rough set decision model to calculate the different needs of the impact on consumer satisfaction, come to an important degree of each demand and the demand reduction order. This method overcomes the traditional rough set method cumbersome bottlenecks, and helps operating; cases studies show that the proposed method is simple and effective.

A Novel Hybrid Clustering Method for Multi-Attribute Decision-Making Datasets

2012 ◽  
Vol 482-484 ◽  
pp. 103-108
Author(s):  
Kai Ping Liu ◽  
Wen Chin Chen ◽  
Ting Cheng Chang
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Uncertain Data ◽  
Clustering Method ◽  
Hybrid Scheme ◽  
Index Function ◽  
Single Attribute ◽  
Multi Attribute Decision Making

A function is proposed for descritizing and classifying the uncertain data of multi-attribute decision-making (MADM) datasets using a hybrid scheme incorporating fuzzy set theory, Rough Set (RS) theory and a modified form of the PBMF index function. The proposed MADM index function is used to extend the applicability of the single-attribute decision-making (SADM) function. The validity of the proposed MADM index function is evaluated by comparing the descritizing results obtained for a simple hypothetical function with those obtained using a SADM function and the conventional PBMF function.

A User-Centered Medical Device Design Decision Making Approach Using Hybrid Rough Cooperative Game Model

2021 ◽  
Author(s):  
Liting Jing ◽  
Junfeng Ma
Keyword(s):  
Decision Making ◽  
Medical Device ◽  
Set Theory ◽  
Cooperative Game ◽  
Rough Set ◽  
Rough Set Theory ◽  
Design Decision ◽  
Device Design ◽  
Medical Device Design ◽  
User Centered

Abstract With the advancement of new technologies and diverse customer-centered design requirements, the medical device design decision making becomes challenge. Incorporating multiple stakeholders’ requirements into the medical device design will significantly affect the market competitiveness and performance. The classic design decision making approaches mainly focused on design criteria priority determination and conceptual schemes evaluation, which lack the capacity of reflecting the interdependence of interest among stakeholders and capturing the ambiguous influence on the overall design expectations, leading to the unreliable decision making results. In order to relax these constraints in the medical device design, this paper incorporates rough set theory with cooperative game theory model to develop a novel user-centered design decision making framework. The proposed approach is composed of three components: 1) end/professional user needs identification and classification, 2) evaluation criteria correlation diagram and scheme value matrix establishment using rough set theory; and 3) fuzzy coalition utility model development to obtain optimal desirability considering users’ conflict interests. We used a blood pressure meter case study to demonstrate and validate the proposed approach. Compared with the traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, the proposed approach is more robust.

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