An analytical approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics

Versatility of fuzzy logic in chronic diseases: A review

Life-cycle cost analysis (LCCA) is an important tool in transportation asset management. Transportation agencies have used both deterministic and probabilistic LCCA approaches. Probabilistic approaches allow decision makers to evaluate the risk of an investment by using uncertain input variables, assumptions, or estimates. The incorporation of fuzzy logic–based models into the risk analysis process is explored to enhance further the traditional probabilistic LCCA method. It proposes a fuzzy logic approach for determining the timing of pavement maintenance, rehabilitation, and reconstruction (MR&R) treatments in a probabilistic LCCA model for selecting pavement MR&R strategies. Instead of using predefined service life for initial construction and future rehabilitations, the proposed approach uses performance curves and fuzzy logic triggering models to determine the most effective timing of MR&R activities. The new approach is compared with the deterministic and traditional probabilistic approaches in a simple case study. The case study demonstrates that the fuzzy logic–based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the uncertainty inherent to the process.

This paper presents a fuzzy logic model that integrates daily site reporting of activity progress and delays, with a schedule updating and forecasting system for construction project monitoring and control. The model developed assists in the analysis of the effects of delays on a project's completion date and consists of several components: An as-built database integrated with project scheduling; a list of potential causes for delays; a procedure to categorize delays; a method of estimating delay durations utilizing fuzzy logic; a procedure that updates the schedule; and, a procedure that evaluates the effects and likely consequences of delays on activity progress. This model is of relevance to researchers since it makes a contribution in project scheduling by developing a complete approach for handling the uncertainty inherent in schedule updating and activity delay analysis. It also advances the application of fuzzy logic in construction. It is of relevance to construction industry practitioners since it provides them with a useful technique for incorporating as-built data into the schedule, assessing the impact of delays on the schedule, and updating the schedule to reflect the consequences of delays and corrective actions taken. The use of fuzzy logic in the model allows linguistic and subjective assessments to be made, and thereby suits the actual practices commonly used in industry.

In this paper, we use a PLA model for performance and behaviour analysis of fuzzy system. This model makes connection between fuzzy logic and formalism. A case study contains an illustration how the proposed model can be fruitfully exploited to model traffic control systems based on fuzzy logic. Piece-Linear Aggregate model for traffic signal control system has been transformed into timed automaton for verification of safety, liveness, bounded- liveness and deadlock- freeness properties based on model checking. The system performance analysis was performed using Arena software package. A comparative analysis of traffic light controllers with fixed time and fuzzy logic algorithms is given. DOI: http://dx.doi.org/10.5755/j01.eee.19.1.3261

Mountainous watershed faces significant challenges, such as water scarcity during the summer and land degradation in the monsoon season. In these watersheds, water harvesting and conservation are essential for promoting ecological harmony and achieving sustainable development goals. Moreover, the absence of a canal system and the depletion of tube wells exacerbate the situation in hot summers. To address these issues, sub-surface dams offer an effective solution by conserving underground water with minimal evaporation loss, low maintenance costs, easy construction, reduced contamination risks, and no threat to human life. This paper compared multi-criteria decision-making approaches (analytical hierarchy process and fuzzy logic) for proposing sites for constructing the sub-surface dam through surface parameters in the Morni watershed, India. For the selection of parameters, maturity assessment has been performed. The parameters including slope, fault, geology, and land use/land cover were considered as constraints used for site identification, and then comparison was made based on conditional parameters such as climate (annual rainfall), geology (distance from fault, geomorphology), hydrogeology (slope, stream order), and socio-economic (distance from road, distance from agricultural land, distance from village) parameters. Three decision-making techniques were employed: Boolean Algorithm, Analytical hierarchy process, and Fuzzy logic. In the initial step, 1430 sites were identified as suitable for construction using Boolean approach. The overlayed analytical hierarchy process and Boolean Algorithm approach found 68 sites, whereas the overlayed Fuzzy and Boolean algorithm approach found 28 sites appropriate for the construction of sub-surface dam in the research area. The best sites proposed based on their presence on stream order greater than or equal to 3, which becomes 15 sites by the analytical hierarchy process and 12 sites in the Fuzzy approach. The field and online Google map survey were used to validate the proposed sites based on distance from the existing sub-surface dams. From comparing the distance, the Fuzzy approach was found to be more appropriate than the analytical hierarchy process approach for the selection of sub-surface dam construction sites in mountainous watersheds.

Fuzzy logic (similarity analysis) approach for sensory evaluation of chhana podo

This work is about the autonomous detection of a road traffic incident by exploiting road surveillance camera videos. Timely and autonomous detection of an incident is paramount for the reduction of traffic congestion so that countermeasures can be taken at the earliest. This paper presents a novel Fuzzy Logic based analysis framework and a video based traffic data extraction scheme to decide upon the right traffic conditions. The existing road traffic analysis approaches as reported in literature do not extract data from the road camera videos; rather they use already available data to validate their schemes. However, in the proposed approach a complete scheme is proposed which takes a raw road camera video and autonomously extracts the relevant data for the subsequent Fuzzy Logic based traffic analysis. To show the efficacy of the proposed scheme, unprocessed surveillance videos of both urban and motorway scenarios are used. The results indicate that the traffic flow and their statistics are adequately determined through the proper selection of membership functions and rule formulation. Owing to the use of fuzzy logic, our proposed framework is seen to be robust enough to reject the noisy data coming from surveillance videos.

An Analytic Approach to Fuzzy Logic Robot Control Synthesis

The robotic inverse kinematic problem, described in the following paragraph, appears in the field of robotics. Its solution can be gained numerically in general and analytically in such situations. The analytical approach is fast and puts in evidence the entire set of solutions, but cannot be applied in general. The numerical approach not always converges, not always puts in evidence all the solutions, but can be applied in general. Owing to these reasons, we will expose a new numerical algorithm based on fuzzy logic, that converges in all the tested trials. The results are very encouraging: the algorithm is fast and reliable.

In order to improve the safety, the reliability, and the efficiency of wind farm installations, thus avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is fundamental. Therefore, the main contribution of this paper consists of the development of a tolerant control scheme applied by means of a direct and viable approach. In particular, a data-driven strategy based on fuzzy logic is exploited for deriving the fault tolerant control scheme. Fuzzy theory is exploited since it is able to approximate easily unknown nonlinear models and manage uncertain data. Moreover, these fuzzy prototypes are directly identified from the wind farm measurements and lead to the straightforward design of the fault tolerant control scheme. In general, an analytic approach, where the system nonlinearity is explicitly considered, would require more complex control design methodologies. This aspect of the work, followed by the simpler solution relying on fuzzy rules, represents the key point when on-line implementations are considered for a viable application of the proposed methodology. A realistic wind farm simulator is used to validate the achieved performances of the suggested methodology.

This paper presents two mathematical models based on graph theory, and a fuzzy logic approach for the steady state analysis of three phase self excited induction generator with and without series compensation. The first model is developed using nodal admittance method and the second model is developed using loop impedance method. Both models results in simple matrix form which are convenient for computer solutions. Since these models are more generalized the steady state performance of the three phase self excited induction generator can be easily obtained with and without series compensation along with fuzzy logic algorithm. These models are so flexible, wherein an element like core loss component can be included or eliminated if required and also the leakage reactance of the stator (Xls) and rotor (Xlr) can be handled separately, by avoiding the assumptions Xls=Xlr without any modifications in the models. Next to carry out the steady state analysis, a fuzzy logic approach is used first time to find any combinations of unknown variables using above models. The analysis presented is validated by experimental results.

We have developed a new algorithm for the characterization of microcalcification clusters. Fuzzy logic is well suited to represent and to manipulate data and knowledge at different levels of the algorithm. Our algorithm is built in 3 steps: Detection and segmentation of the individual microcalcifications, measurements on the segmented microcalcifications (shape, contrast, relative localization), use of these measurements as inputs of a learning system which concludes if the current case is malignant or not. We first describe some aspects of the fuzzy segmentation we have implemented. Then we explain how we build fuzzy measurements from the segmented objects and how these measurements are manipulated into the fuzzy decision tree we are using. Finally, we present the preliminary results we obtained with our test database.

Websites are important in every organisation and tremendous effort is made to design websites that not only look and feel good, but are usable and of high quality. Nevertheless, one critical task is how to evaluate these websites to ensure that users are satisfied with its quality and usability. Although a variety of methods and approaches have been proposed, there is currently an increase in research efforts to model website quality and usability evaluation from the point of view of decision-makers which existing methods do not handle. Thus, this has led to the application of multi-criteria decision-making (MCDM) approaches in the evaluation of websites to handle complexity in decision-making. This paper, therefore, provides a review of the various MCDM methods that have been used in the usability and quality evaluation of websites. The search strategy which was adopted identified a total of 63 published articles in peer-reviewed journals and international conferences between 2005 and 2017. From the research questions formulated for the study, the papers were classified into various MCDM approaches, website genre, number and list of criteria used over the years and the localization of websites based on country. Some of the findings showed that the Analytical Hierarchy Process approach integrated with fuzzy logic has been the most common method over the years. In addition, e-commerce websites make up the most common website genre. Besides, currently most active websites are from Turkey and five is the average number of criteria for the evaluation of website quality and usability.

The uncertainty of data and weighting of variables are prima facie problems to model the green supply chain (GrSC) performance. The paper modelled GrSC performance of plastic film by fuzzy logic analysis-fuzzy analytic network process approach to overcome these hurdles. Fuzzy logic analysis is used to derive fuzzy score of variables using MATLAB. Further, fuzzy analytic network process is used to calculate global weights of variables. Thus, green score of supply chain performance for plastic film is computed as 28.83. Green supply chain performance analysis of plastic films is done for present status and computed as 48.62. The solutions developed by author's viz. adoption of best practices for blown film extrusion process and 'design for environment' tool for segregating MSW at source are analysed. It is found that, if the tools adopted simultaneously, the GrSC performance score resulted in 27.38. This is well below green score making the plastic film industry environment friendly. Approach is suitable for modelling small scale systems GrSC performance.

Assessment of transport sustainability using a hybrid approach: A comparison of four metropolitan cities of India