Measurement Uncertainty in Decision-Making

2011 ◽  
Vol 1 (3) ◽  
pp. 40-52 ◽  
Author(s):  
Claudio De Capua ◽  
Rosario Morello ◽  
Rosario Carbone
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Measurement Uncertainty ◽  
Measurement Data ◽  
Wrong Decision ◽  
Practical Applications ◽  
Fuzzy Logic Algorithm ◽  
Methodical Approach ◽  
Concrete Problem ◽  
Comparison Measurement

In this paper, the authors examine a common issue concerning the influence of measurement uncertainty on decisions. In fact, in some practical applications, it can be necessary to put in comparison measurement data with thresholds and limits. It occurs when the conformity with fixed specifications has to be verified or if warning and alert levels have to be not exceeded. In such a circumstance, to take reliable decisions in presence of uncertainty is a concrete problem. Measurement uncertainty may reasonably be the cause of unreliable decisions. In order to manage properly the uncertainty effect, the authors have developed a decision making procedure based on a methodical approach to measurement uncertainty. In detail, a fuzzy logic algorithm estimates the probability to take a wrong decision because of the uncertainty. Such information is so used in order to optimize the decisional criteria, improving the consistency of the final computing results. Risks and costs associated to the possibility to take a mistaken decision are minimized. Consequently the algorithm singles out the most reliable decision.

Measurement Uncertainty in Decision-Making

2013 ◽  
pp. 193-207
Author(s):  
Claudio De Capua ◽  
Rosario Morello ◽  
Rosario Carbone
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Measurement Uncertainty ◽  
Measurement Data ◽  
Wrong Decision ◽  
Practical Applications ◽  
Fuzzy Logic Algorithm ◽  
Methodical Approach ◽  
Concrete Problem ◽  
Comparison Measurement

In this paper, the authors examine a common issue concerning the influence of measurement uncertainty on decisions. In fact, in some practical applications, it can be necessary to put in comparison measurement data with thresholds and limits. It occurs when the conformity with fixed specifications has to be verified or if warning and alert levels have to be not exceeded. In such a circumstance, to take reliable decisions in presence of uncertainty is a concrete problem. Measurement uncertainty may reasonably be the cause of unreliable decisions. In order to manage properly the uncertainty effect, the authors have developed a decision making procedure based on a methodical approach to measurement uncertainty. In detail, a fuzzy logic algorithm estimates the probability to take a wrong decision because of the uncertainty. Such information is so used in order to optimize the decisional criteria, improving the consistency of the final computing results. Risks and costs associated to the possibility to take a mistaken decision are minimized. Consequently the algorithm singles out the most reliable decision.

A Kalman filtering fuzzy logic algorithm for recognition of lane departure

2021 ◽  
pp. 1-8
Author(s):  
Kai Ren
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Kalman Filtering ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Fuzzy Logic Algorithm ◽  
The Road ◽  
Departure Decision ◽  
Lane Departure ◽  
Fusion Analysis

In all kinds of traffic accidents, the unconscious departure of the vehicle from the lane is one of the most important reasons leading to the occurrence of these accidents. In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.

scholarly journals Risk calculations for conformity assessment in practice

2019 ◽  
Author(s):  
Alexandre Allard ◽  
Nicolas Fischer ◽  
Ian Smith ◽  
Peter Harris ◽  
Leslie Pendrill
Keyword(s):  
Decision Making ◽  
Measurement Uncertainty ◽  
Decision Rule ◽  
Prior Information ◽  
Conformity Assessment ◽  
Global Risk ◽  
Wrong Decision ◽  
Iso 17025 ◽  
Incorrect Decision ◽  
Testing Laboratories

In 2012, the Joint Committee for Guides in Metrology (JCGM) published novel guidance on the consideration of measurement uncertainty for decision-making in conformity assessment (JCGM 106:2012). The two situations of making a wrong decision are considered: the risk of accepting a non-conforming item, denoted as the customer risk, and the risk of rejecting a conforming item, denoted as the producer risk. In 2017, the revision of ISO 17025 obliged calibration and testing laboratories to “document the decision rule employed, taking into account the level of risk (such as false accept and false reject and statistical assumptions) associated with the decision rule employed, and apply the decision rule” in the context of the decision made about the conformity of an item. However, JCGM 106:2012 can in some cases be perceived as quite difficult to apply for non-statisticians as it mainly relies on calculations involving probability distributions. In order to facilitate uptake of the methodology of JCGM 106:2012, EURAMET is funding the project EMPIR 17SIP05 “CASoft” (2018 – 2020), involving the National Measurement Institutes from France, Sweden and the UK. The objective is to make the methodology accessible to organisations involved in decision-making in conformity assessment: calibration and testing laboratories, industrialists and regulation authorities. Where the customer or producer are concerned, there are two kinds of risks arising from measurement uncertainty: specific risk which concerns the risk of an incorrect decision for a particular item and global risk which is the risk of an incorrect decision for any item chosen at random. Both kinds of risk may involve prior information, taken into account through a so-called prior probability distribution, introducing the concept of a Bayesian evaluation of the risks. If a calibration and testing laboratory performing the measurement has difficulty accessing prior information, it is likely that the industrialist in control of production processes will have some idea of the quality of the items produced. In this paper, the two problems of estimating the specific and global risks are addressed. The consideration of prior information is also discussed through a practical example as well as the use of software implementing the methodology, which will be made publically available at the end of the project.

Decision-making support for transportation and logistics combining rough set fuzzy logic algorithm

2021 ◽  
pp. 1-10
Author(s):  
Tian Chen
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Rough Set ◽  
Large Scale ◽  
Operating Cost ◽  
Vehicle Scheduling ◽  
Fuzzy Logic Algorithm ◽  
Logistics Mode ◽  
The Cost ◽  
Tracking Technology

To lower the operating cost of an enterprise, improving the efficiency of logistics, or reducing the cost of circulation is one of the crucial means for the enterprise to cut the costs. In this paper, combined with the rough set fuzzy logic algorithm, in response to the features of the large-scale vehicle scheduling problem, the data collected by monitoring in real time provided by the tracking technology, in conjunction with the data mining technology, are used to support the decision making based on the reverse logistics mode and establish a vehicle scheduling optimization model with the purpose to support the decision making in transportation logistics.

scholarly journals Analysis of The Impact of Fuzzy Logic Algorithm On Handover Decision in A Cellular Network

2017 ◽  
Vol 5 (5) ◽  
pp. 46-62 ◽  
Author(s):  
Emily Teresa Nyambati ◽  
Vitalice K. Oduol
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Handover Decision ◽  
Fuzzy Logic Algorithm ◽  
Gsm Network ◽  
Work Done ◽  
The Impact

Fuzzy logic is one of the intelligent systems that can be used to develop algorithms for handover. For success in handing over, the decision-making process is crucial and thus should be highly considered. The performance of fixed parameters is not okay in the changing cellular system environments. The work done on this paper aims to analyse the impact of utilising the fuzzy logic system for handover decision making considering the Global System for Mobile communication (GSM) network. The results from the different simulations show that the need to handover varies depending on the input(s) to the Fuzzy Inference System (FIS). By increasing the number of data, thus the criteria parameters used in the algorithm, an Optimised Handover Decision (OHOD) is realised.

scholarly journals Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Hangyao Wu ◽  
Zeshui XU
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Human Perception ◽  
Future Trends ◽  
Comprehensive Overview ◽  
Practical Applications ◽  
Intelligent Decision Making ◽  
Wide Range ◽  
Basic Ideas

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making.

Fuzzy Logic Models for Non-Programmed Decision-Making in Personnel Selection Processes Based on Gamification

Informatica ◽  
2018 ◽  
Vol 29 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Javier Albadán ◽  
Paulo Gaona ◽  
Carlos Montenegro ◽  
Rubén González-Crespo ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Personnel Selection ◽  
Logic Models ◽  
Selection Processes

MEMPREDIKSI JUMLAH PRODUKSI ULOS BATAK DENGAN MENGGUNAKAN LOGIKA FUZZY DENGAN METODE TSUKAMOTO (Studi Kasus Cv. Ala Dos Roha)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Sri Handayani Sianipar ◽  
Fince Tinus Waruwu ◽  
Lince Tomoria Sianturi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Method ◽  
Production Data ◽  
Speed Up

Ulos batak toba is one of indonesia traditional fabric, precisely the traditional cloth of the batak toba. From time to time the ulos fabric was growing in terms of  type and motif. One of the companies that produces ulos batak is cv. Ala dos roha. The authors conducted this study aimed at predicting the amount of production of ulos batak to produced later. The author uses the previous request, inventory and production data using fuzzy logic tsukamoto. The final result of the calculation with this method will be more effective and efficient so as to speed up the decision making time to predict the amount of production to be produced next.Keywords: prediction, amount of  production, method of tsukamoto

Pengembangan Aplikasi Pendukung Keputusan Pemilihan Program Studi dengan Metode Fuzzy Logic (Studi Kasus: Universitas Multimedia Nusantara)

2016 ◽  
Vol 7 (1) ◽  
pp. 12-18
Author(s):  
Joko Haryanto ◽  
Seng Hansun
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Application ◽  
Prospective Students ◽  
Fuzzy Logic Method ◽  
Index Terms ◽  
Selection Of

This paper describes the development of decision support system application to assist students who want to enter college so that no one choose the majors incorrectly. This application uses fuzzy logic method because fuzzy logic is very flexible in data which are vague and can be represented as a linguistic variable. The purpose of this application is to assist students to choose available majors at University Multimedia Nusantara which are appropriate with his/her capabilities. This application accepts five kinds of input values i.e. Mathematics, Indonesian, English, Physics, and TIK. Received input will be processed by the calculation of the system for decision-making and the application will generate output that shows how great a match for each majors. With this application, prospective students can find out where the majors that match his/her capabilities. This application has ninety nine percentage of match result accuracy. Index Terms—fuzzy logic, decision support system, UMN, selection of major

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