scholarly journals A Proposition for Combining Rough Sets, Fuzzy Logic and FRAM to Address Methodological Challenges in Safety Management: A Discussion Paper

Safety ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 50
Author(s):  
Hussein Slim ◽  
Sylvie Nadeau
Keyword(s):  
Fuzzy Logic ◽  
Rough Sets ◽  
Safety Management ◽  
Sociotechnical Systems ◽  
Rule Base ◽  
Generation Process ◽  
Discussion Paper ◽  
Advantages And Disadvantages ◽  
Resonance Analysis ◽  
Third Phase

In recent years, the focus in safety management has shifted from failure-based analysis towards a more systemic perspective, redefining a successful or failed performance as a complex and emergent event rather than as a conclusion of singular errors or root causes. This paradigm shift has also necessitated the introduction of innovative tools capable of capturing the complex and dynamic nature of modern sociotechnical systems. In our research, we argued at previous stages for adopting a more systemic and human-centric perspective to evaluate the context of aircraft de-icing operations. The Functional Resonance Analysis Method (FRAM) was applied in the first stage for this purpose. Consequently, fuzzy logic was combined with FRAM in the second stage to provide a quantified representation of performance variability. Fuzzy logic was used as a quantification tool suitable for computing with natural language. Several limitations were found in the data collection and rule generation process for the first prototype. In the third phase, the model was further improved by integrating rough sets as a data-mining tool to generate and reduce the size of the rule base and classify outcomes. In this paper, we reflect on the three stages of the project and discuss in a qualitative manner the challenges and limitations faced in the development and application of the models. A summary of the advantages and disadvantages of the three models as experienced in our case are presented at the end. The objective is to present an outlook for future studies to address methodological limitations in the study of complex sociotechnical systems.

scholarly journals A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM

2020 ◽  
Vol 12 (5) ◽  
pp. 1918
Author(s):  
Hussein Slim ◽  
Sylvie Nadeau
Keyword(s):  
Fuzzy Logic ◽  
Rough Sets ◽  
Assessment Model ◽  
Rule Base ◽  
Data Sets ◽  
Mathematical Framework ◽  
Real World Data ◽  
Second Stage ◽  
Fuzzy Logic Approach ◽  
Input Variables

The task to understand systemic functioning and predict the behavior of today’s sociotechnical systems is a major challenge facing researchers due to the nonlinearity, dynamicity, and uncertainty of such systems. Many variables can only be evaluated in terms of qualitative terms due to their vague nature and uncertainty. In the first stage of our project, we proposed the application of the Functional Resonance Analysis Method (FRAM), a recently emerging technique, to evaluate aircraft deicing operations from a systemic perspective. In the second stage, we proposed the integration of fuzzy logic into FRAM to construct a predictive assessment model capable of providing quantified outcomes to present more intersubjective and comprehensible results. The integration process of fuzzy logic was thorough and required significant effort due to the high number of input variables and the consequent large number of rules. In this paper, we aim to further improve the proposed prototype in the second stage by integrating rough sets as a data-mining tool to generate and reduce the size of the rule base and classify outcomes. Rough sets provide a mathematical framework suitable for deriving rules and decisions from uncertain and incomplete data. The mixed rough sets/fuzzy logic model was applied again here to the context of aircraft deicing operations, keeping the same settings as in the second stage to better compare both results. The obtained results were identical to the results of the second stage despite the significant reduction in size of the rule base. However, the presented model here is a simulated one constructed with ideal data sets accounting for all possible combinations of input variables, which resulted in maximum accuracy. The same should be further optimized and examined using real-world data to validate the results.

MODELS AND FUZZY MICROCLIMATE CONTROL SYSTEM IN THE STORAGE OF ARCHIV GE OF ARCHIVAL DOCUMEN AL DOCUMENTS

2021 ◽  
Vol 2021 (3) ◽  
pp. 54-61
Author(s):  
Avaz Marakhimov ◽  
◽  
Abdushukur Abdullaev ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Automatic Control ◽  
Management System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Rule Base ◽  
Fuzzy Inference Systems ◽  
Advantages And Disadvantages ◽  
Inference Systems

In this article, the main object of research is the creation of appropriate microclimatic conditions to ensure reliable and high-quality storage of archival documents, as well as automatic control of the optimal values of the main parameters of the external and internal environment that directly affect the quality of storage. To control the microclimate, three categories of models for automatic control of these parameters are considered separately in the archives: the “white box”, “black box” and “gray box " models. The results of the analysis of the advantages and disadvantages of the considered models are presented. The generalized structure of the microclimate management system is also given, as well as a list of controlled and changeable parameters of the microclimate management system of archives. It is proposed to use the fuzzy logic apparatus to create microclimate control systems in archival repositories, which allows synthesizing stable algorithms for its functioning in conditions of uncertainty. The specific steps that need to be performed when designing and using fuzzy inference systems and which are implemented based on the rules of fuzzy logic are listed. When designing and using fuzzy inference systems, it is necessary to observe certain stages that are implemented based on the rules of fuzzy logic. A generalized algorithm for forming a rule base with a technique for implementing the fuzzy inference procedure is presented. The tasks that need to be solved when designing a fuzzy control system are indicated. A system of automatic temperature control in archival repositories with a fuzzy logic controller is presented.

scholarly journals Resilience Engineering for Sociotechnical Safety Management

2021 ◽  
pp. 477-492
Author(s):  
Riccardo Patriarca
Keyword(s):  
Complexity Theory ◽  
Research Agenda ◽  
Safety Management ◽  
Sociotechnical Systems ◽  
Cognitive Engineering ◽  
Engineering Systems ◽  
Resilience Engineering ◽  
Organizational Contexts ◽  
Resonance Analysis

Modern societies call for a reconsideration of risk and safety, in light of the increasing complexity of human-made systems. Technological artefacts, and the respective role of humans, as well as the organizational contexts in which they operate, dramatically changed in the last decades with an even more severe transformation expected in the future. Rooted in human factors, ergonomics, cognitive engineering, systems thinking and complexity theory, the discipline of resilience engineering proposes innovative approaches for safety challenges imposed by the dynamic, uncertain, and intertwined nature of modern sociotechnical systems. Resilience engineering aims to provide support means for ensuring that systems can sustain required operations under both expected and unexpected conditions. This chapter aims to provide a summary of the scientific field of resilience engineering, as well as a description of two methods common in the field, the resilience analysis grid and the functional resonance analysis method. Following two examples, the chapter proposes a multidisciplinary research agenda for the field.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

scholarly journals Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

2005 ◽  
Vol 5 (6) ◽  
pp. 821-832 ◽  
Author(s):  
A. Zischg ◽  
S. Fuchs ◽  
M. Keiler ◽  
G. Meißl
Keyword(s):  
Risk Management ◽  
Fuzzy Logic ◽  
Expert System ◽  
Temporal Variability ◽  
Hazard Index ◽  
Rule Base ◽  
Spatial And Temporal Variability ◽  
Snow Avalanches ◽  
System Behaviour ◽  
Wet Snow

Abstract. The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

scholarly journals Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Yi Fu ◽  
Howard Li ◽  
Mary Kaye
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Rule Base ◽  
Control Laws ◽  
Robotic Vehicle ◽  
Road Following

Autonomous road following is one of the major goals in intelligent vehicle applications. The development of an autonomous road following embedded system for intelligent vehicles is the focus of this paper. A fuzzy logic controller (FLC) is designed for vision-based autonomous road following. The stability analysis of this control system is addressed. Lyapunov's direct method is utilized to formulate a class of control laws that guarantee the convergence of the steering error. Certain requirements for the control laws are presented for designers to choose a suitable rule base for the fuzzy controller in order to make the system stable. Stability of the proposed fuzzy controller is guaranteed theoretically and also demonstrated by simulation studies and experiments. Simulations using the model of the four degree of freedom nonholonomic robotic vehicle are conducted to investigate the performance of the fuzzy controller. The proposed fuzzy controller can achieve the desired steering angle and make the robotic vehicle follow the road successfully. Experiments show that the developed intelligent vehicle is able to follow a mocked road autonomously.

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