Fuzzy Logic-based Decision Support System Research Articles

An Industry 4.0 implementation of a condition monitoring system and IoT-enabled predictive maintenance scheme for diesel generators

In most business and residential organizations, Diesel Generators (DG) is a viable supplementary power source for ensuring an undisturbed power supply. The DG is a hybrid machine that generates electrical energy using a Diesel Engine (DE) and an Electric Generator (EG). By routinely monitoring crucial machine parameters, alternative power source efficiency can be improved. Furthermore, Condition Monitoring Systems (CMS) based on the Internet of Things (IoT) have supplanted the traditional equipment maintenance method. Predictive maintenance is also an important building block of Industry 4.0, whose entire process and performance can be fully understood by using IoT-enabled Remote Monitoring (RM) schemes. Firstly, this paper introduces a remote monitoring and data acquisition scheme to realize the concept of predictive maintenance. Secondly, this article discusses a strategy for real-time observation of DG parameters as well as a comprehensive analysis of various metrics. Thirdly, this research article includes a monitoring and analysis scheme of crucial factors in a DG, like the speed of an engine, voltage output, the current produced, power factor, coolant required, fuel consumption, and battery health. Different mathematical models are formulated by correlating experimental data and estimating the coefficients. Finally, to create suitable real-time warnings under critical circumstances, a fuzzy logic-based Decision Support System (DSS) and web-based integration elements are presented.

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.

A type-2 fuzzy rule-based model for diagnosis of COVID-19

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical examination and blood analysis results, which are the outputs of the first and second fuzzy units, are evaluated together and the result is obtained. As a result of the evaluation of the trials with 60 different scenarios by physicians, it has been revealed that the system can detect COVID-19 risk with 86.6% accuracy.

Fuzzy Control System for Predicting The Quality of Thermally Processed Raw Milk

Milk, which has a very important place in human nutrition, can be harmful in terms of human health when it is not produced, stored, processed, and necessary controls are performed under hygienic conditions. Even if raw milk contains a small number of bacteria, it is degraded in a very short time after the milking by the effects of microorganisms transmitted from the environment in various ways and constitutes the potential source of many pathogens that cause diseases in humans.Milk proteins contain essential amino acids that cannot be synthesized by the human body and must be taken from the outside. The microorganism load of milk is one of the most important indicators in determining milk quality and determining the hygienic properties of milk in the process from raw milk production to consumption. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. In this study, it was aimed to develop a fuzzy logic-based decision support system aimed at predicting the deterioration of heat-treated raw milk. As the subject of this project, it was developed to determine whether the milk produced in troops, farms and cooperatives is spoiled before the milk goes from production households to businesses.

Design and Development of a Wearable Assistive Device Integrating a Fuzzy Decision Support System for Blind and Visually Impaired People.

In this article, a new design of a wearable navigation support system for blind and visually impaired people (BVIP) is proposed. The proposed navigation system relies primarily on sensors, real-time processing boards, a fuzzy logic-based decision support system, and a user interface. It uses sensor data as inputs and provides the desired safety orientation to the BVIP. The user is informed about the decision based on a mixed voice–haptic interface. The navigation aid system contains two wearable obstacle detection systems managed by an embedded controller. The control system adopts the Robot Operating System (ROS) architecture supported by the Beagle Bone Black master board that meets the real-time constraints. The data acquisition and obstacle avoidance are carried out by several nodes managed by the ROS to finally deliver a mixed haptic–voice message for guidance of the BVIP. A fuzzy logic-based decision support system was implemented to help BVIP to choose a safe direction. The system has been applied to blindfolded persons and visually impaired persons. Both types of users found the system promising and pointed out its potential to become a good navigation aid in the future.

A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques.

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly’s behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.

Fuzzy logic–based decision support system for selection of optimum input shaping techniques in point-to-point motion systems

In this study, a novel fuzzy logic–based decision support system approach to provide assistance in the selection of suitable input shaping techniques is presented. The proposed approach selects the suitable input shaping technique for point-to-point motion type of systems such as precise positioning, crane operations, flexible robotic systems and so on. The problem solution addressed is the selection of the input shaping technique and the settings for the selection of the input shaper. Some of these design issues require extensive expertise in command shaping and system modeling studies. To overcome these problems and the necessity for such an expertise in these application areas, the proposed technique is provided as a solution. The presented study also provides a review of input shaping methods as well as their advantages and disadvantages in terms of vibration elimination performance, traveling time and robustness features. In the final section of the study, the details of the simulations, as well as experimental results, are provided to validate the achieved high performance of the proposed technique. The experimental studies are conducted on a Quanser IP02 Gantry Crane experimental setup.

Crowd sensing aware disaster framework design with IoT technologies

When a disaster occurs, a huge amount of inconsistent victim or damage information data is received by many different sources. Disaster management systems achieve the completion of a significantly vital task, which is to reduce the number of victims or amount of damage caused by a disaster, with real-time information monitoring infrastructure. A fundamental role of these systems that could help rescue teams is to make a quick and accurate decision about the region that will be affected by the disaster and the possible effects of the tragedy. Employing IoT solutions in these systems provides the possibility of rapidly and precisely orienting rescue teams to be dispatched to the disaster area and also quickly receive specific information about the effects of the disaster. To achieve this purpose, we present a post-disaster framework using the IoT communication technologies for disaster management based on the proposed crowd sensing clustering algorithm in this paper. The proposed framework provides information about the damage status of buildings with crowd density data along with efficient real-time data collection, data aggregation, and the process of monitoring dissemination stages. This framework realizes clustering of resident density by using the cellular networks and Wi-Fi connections and calculating the damage status of buildings through the designed and specifically implemented IoT unit data. Furthermore, it employs a fuzzy logic-based decision support system to manage the resources. The proposed framework, on real base stations and access points dataset, has shown significant results for identifying crowd densities with the highlighting status of buildings in the disaster area.

Decision Support System Assisted E-Recruiting System

Individuals play a significant role to enhance and accelerate the performance of the organizations and hiring right employees, is indeed a challenging task. It is because many people show interest in the single job opening. HR in large organizations, recruits and explores such people by following proper recruitment process. This recruitment process has been proven to be lengthy, tidy and costly because of a substantial number of applicants. Recruitment of suitable individuals becomes more difficult for smaller organizations as they do not have HR department. Automation can improve the process though. By doing this, the process becomes shorter, easier, and cost effective. In that respect several automated recruiting systems have been proposed in the literature. The primary limitation of the existing recruitment systems is that it simply picks out those candidates who meet the (100%) skills set and turns down the rest of them yet if they are meeting the partial criteria. It intends that as these programs have been projected to pick only those who are gathering all the prerequisites. In fact, the recruitment system can select the candidates that are even meeting the near about or close requirements. It is because if a candidate does not meet (100%), then the candidate with the lesser requirement should be kept under consideration. There can be chances where a person meeting 9 out of 10 requirements could have proved to be the best choice, but because of the system we lose such candidates. The current research proposes a Fuzzy logic-based decision support system (DSS), to overcome the above-mentioned limitation. Abiding by the Fuzzy logic, the proposed system can view the partial skills of the applicants. The system has been validated by implementing the prototype. The outcomes depict a significant improvement to overcome the limitation of the existing recruitment systems.

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

The proliferation of mobile devices and the heterogeneous environment of wireless communications have increased the need for additional spectrum for data transmission. It is not possible to altogether allocate a new band to all networks, which is why fully efficient use of the already available spectrum is the demand of the day. Cognitive radio (CR) technology is a promising solution for efficient spectrum utilization, where CR devices, or secondary users (SUs), can opportunistically exploit white spaces available in the licensed channels. SUs have to immediately vacate the licensed channel and switch to another available channel when they detect the arrival of the incumbent primary user. However, performance for the SU severely degrades if successive channel switching happens. Moreover, taking the channel-switching decisions based on crisp logic is not a suitable approach in the brain-empowered CR networks (CRNs) where sensing information is not only imprecise and inaccurate but also involves a major uncertainty factor. In this paper, we propose a fuzzy logic-based decision support system (FLB-DSS) that jointly deals with channel selection and channel switching to enhance the overall throughput of CRNs. The proposed scheme reduces the SU channel switching rate and makes channel selection more adaptable. The performance of the proposed scheme is evaluated using a Matlab simulator, and a comprehensive comparison study with a baseline scheme is presented. The simulation results are promising in terms of the throughput and the number of handoffs and making our proposed FLB-DSS a good candidate mechanism for SUs while making judicious decisions in the CR environment.

Development of fuzzy logic-based decision support system for multi-response parameter optimization of green manufacturing process: a case study

The aim of this paper is to development of decision support system based on fuzzy logic for green manufacturing (GM) process. The fuzzy logic-based decision support system (FLDS) consists of subtractive clustering with Takagi–Sugeno–Kang fuzzy logic (TSK-FL) model which predicts and optimizes the process parameters of GM process. Here, subtractive clustering method is used for extraction of cluster centers which influences the process parameters of GM process, while TSK-FL method is used for prediction and optimization of GM process parameters. An experiment has been performed on machining of natural filler-reinforced polymer composites using abrasive water jet machining process to show the strength and working significance of proposed model. Initially, the historical database has been created using the results of the theoretical experiment of Taguchi (L27) orthogonal array. Second, normalization process has been performed on the historical data to transform original data sequence to comparable sequence data, which then provided as input to the FLDS system for optimization of the process parameters of GM process. In addition, prediction model has been developed for optimum prediction of response parameters for GM process using proposed FLDS system. Finally, the confirmatory and performance analysis has been tested to verify the experimental results. The result shows that predictions through proposed model are comparable with experimental results with accuracies more than 95% and establishes the most optimal combinations of process parameters for GM process which directly or indirectly improves the efficiency as well as performance of GM process. The research suggests that the developed model can be used as systematic approach for prediction and parameter optimization in GM applications.

Design of an intelligent decision support system for global outsourcing decisions in the apparel industry

Global outsourcing can be defined as assigning a specific business component to a global supplier. While global outsourcing enhances the capability of the enterprise, it also facilitates flexible enterprise structures. Many factors need to be considered simultaneously for global outsourcing decisions, and purchasing managers need a structured method of using these criteria in their decision-making. The scope of this study was to develop a fuzzy logic-based global outsourcing decision support system to evaluate the “make or buy” decision in the apparel industry. A fuzzy logic-based decision support system is constructed due to the similarity between fuzzy logic and human decision-making systems. The proposed approach decreases the dependency of the decision-making process on the decision-maker, establishing an effective system that can easily be applicable to most of the apparel industry.

Fuzzy Logic-Based Decision Support System for Dairy Cattle

Fuzzy Logic-Based Decision Support System for Dairy Cattle

The role of reference conditions in water quality assessment: application of a fuzzy logic-based Decision Support System (DSS) in the Danube and Elbe River Basins

The recent defi nition and submission of WFD-compliant River Basin Management Plans required large efforts of water managers and highlighted a number of data, knowledge and methodological gaps that need to be fulfi lled during the next management cycle. The complexity of the WFD implementation process calls for easy-touse, reliable and scientifi cally sound decision support tools that consider relevant aspects of the basin of concern. For this purpose, a Decision Support System (DSS) for risk-based assessment and management of river basins was developed within the EU MODELKEY project. The MODELKEY DSS allows assessing the chemical and ecological status of water bodies by applying both the One-Out All-Out principle, suggested by the WFD, and a novel Integrated Risk Assessment (IRA) methodology, developed within the project. In this paper, main results obtained by DSS application to two large European rivers, i.e. Elbe and Danube, are presented and discussed, based on a set of biological, chemical and physico-chemical indicators, which have been evaluated in relation to available reference conditions. The WFD ecological and chemical status assessment as well as analysis of intermediate results was strongly affected by the methods used to derive reference conditions, the applied indicators, and the methods used to aggregate indicators results. Minimally disturbed conditions (MDC) are the best choice as reference conditions, being characterized by generally high index values (good status) for all biological indices. Where MDC are not available, historical conditions could be a good substitute; they should be preferred to least disturbed sites (in particular when the river is generally impaired as the Danube River).

A Fuzzy Logic-Based Decision Support System on Anesthetic Depth Control for Helping Anesthetists in Surgeries

In this study, a fuzzy logic-based anesthetic depth decision support system (ADDSS) was realized for anesthetic depth control to help anesthetists in surgeries. Depth of anesthesia for a patient can change according to anesthetic agent and characteristic properties of a patient such as age, weight, etc. During the surgery, depth of anesthesia of a patient is determined by the experience of anesthetist controlling of systolic arterial pressure (SAP) and heart pulse rate (HPR) parameters. Anesthetists could have tired and lost attention by inhaling of anesthetic gas leaks in long lasted operations. For that reason, improper anesthetic depth could be applied to the patients. So anesthesia could not be safety and comfortable. To remove this unwanted situation, an ADDSS was proposed for anesthetists. By the help of this system, precise anesthetic depth could have provided. Thus, the anesthetist will spend less time to provide anesthetic and the patient will have a safer and less expensive operation. This study was performed under sevoflurane anesthetic.