Fuzzy Expert System for Severity Measurement of COVID-19 Suspect

The pandemic of COVID-19 disease is spread over the world. The symptoms of COVID-19 disease can vary from mild to severe illness. Also, these symptoms are complex and uncertain in nature. The severity score is useful to treat the suspect that highly depends on symptoms. To handle with this problem, the current study makes use of the Fuzzy Expert System which is one in every of the foremost suitable methods in modelling systems with high uncertainty and complexity. In this study, the fuzzy-based expert system is designed to measure the severity of COVID-19 disease in suspect. Keywords: Fuzzy Logic, Expert System, COVID-19 .

Effect of sucrose catalyst in the catalytic converter on performance and emission of spark ignition engine

In this study, experimental attempts are made to reduce exhaust gas toxic emission from the spark ignition (SI) engine. For this, a sucrose catalyst is coated inside the metallic substrate. The obtained emission level was compared with the results of commercial catalysts for lean-burn operations. The engine was operated at 20%, 40%, 60%, 80% and 100% loads and the highest NOx conversion efficiency of 60.217% at 40% engine load and 70.732% of HC conversion efficiency at 100% engine loadwas achieved. Exhaust emissions from the sucrose-coated catalytic converterare observed as lower than the conventional commercial converter. Also, this paper attempts to predict the emission characteristics of both rigskept under observation using a fuzzy logic expert system (FLES). Both the input and output responses from the real-time SI engine is used to train and test the proposed FLES. The FLES proposed in this study can predict the emission characteristicsof both conventional and sucrose coated catalytic converter with an accuracy of 97%.

Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings

This paper concerns the development of an automatic tool, based on Fuzzy Logic, which is able to identify the proper solutions for the energy retrofitting of existing buildings. Regarding winter heating, opaque and glazing surfaces are considered in order to reduce building heat dispersions. Starting from energy diagnosis, it is possible to formulate retrofitting proposals and to evaluate the effectiveness of the intervention considering several aspects (energy savings, costs, intervention typology). The innovation of this work is represented by the application of a fuzzy logic expert system to obtain an indication about the proper interventions for building energy retrofitting, providing as inputs only few parameters, with a strong reduction in time and effort with respect to the software tools and methodologies currently applied by experts. The novelty of the paper is the easy handling properties of the developed tool, which requires only a few data about the buildings: not many such methods were developed in the last years. The energy requirements for winter heating before and after particular interventions were evaluated for a consistent set of buildings in order to produce the required knowledge base for the tool’s development. The identified appropriate inputs and outputs, their domains of discretization, the membership functions associated to each fuzzy set, and the linguistic rules were deduced on the basis of the knowledge determined in this was. Therefore, the system was successfully validated with reference to further buildings characterized by different design and architecture features, showing a good agreement with the intervention opportunities evaluated.

​Fuzzy Logic Expert System for Taxonomic Variation of Solonchaks

Background: The main objective of this research is to apply fuzzy logic to four Solonchaks, in order to determine their degree of remoteness or rapprochement with their central taxonomic concept. Therefore, we identify their possible seasonal taxonomic variation on the criteria established by World Reference Base (WRB). Methods: We have studied the seasonal evolution of salinity in a region of Algeria (Case of Rélizane), during two years 2012 and 2013 by applying fuzzy logic on the four soils. Result: The results reveal that the salinity increased during the dry period for all soils and it decreased during the wet period. On the taxonomic level, the application of fuzzy logic on the four soils revealed that the Solonchaks indices (Is) are always significantly higher than those of Calcisols indices (Ic). The four profiles have a similar behavior regarding the variation of Is. Indeed, when the salinity increases the soils come closer to the central taxonomic concept of the Solonchaks. Likewise, when the salinity decreases the soils move away from their central taxonomic concept. Consequently, they approach the central taxonomic concept of Calcisols. Thus, the variation of Isis closely related to the seasonal variation of salinity. Fuzzy logic, exhibited high precision concerning the membership value between soils over time. The application of fuzzy logic for other soil classifications in the world is possible.

Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

The paper presents automatic control of an aircraft in the longitudinal channel during automatic landing. There are two crucial components of the system presented in the paper: a vision system and an automatic landing system. The vision system processes pictures of dedicated on-ground signs which appear to an on-board video camera to determine a glide path. Image processing algorithms used by the system were implemented into an embedded system and tested under laboratory conditions according to the hardware-in-the-loop method. An output from the vision system was used as one of the input signals to an automatic landing system. The major components are control algorithms based on the fuzzy logic expert system. They were created to imitate pilot actions while landing the aircraft. Both systems were connected with one another for cooperation and to control an aircraft model in a simulation environment. Selected results of tests presenting control efficiency and precision are shown in the final section of the paper.

Modeling the seam strength of denim garments by using fuzzy expert system

The main purpose of this study is to predict and develop a model for forecasting the Seam Strength (SS) of denim garments with respect to the thread linear density (tex) and Stitches Per Inch (SPI) by using a Fuzzy Logic Expert System (FLES). The seam strength is an important factor for the serviceability of any garments. As seams bound the fabric pieces together in a garment, the seams must have sufficient strength to execute this property even in the unexpected severe conditions where the garments are subjected to loads or any additional internal or external forces. Sewing thread linear density and number of stitches in a unit length of the seam are the two of the most important factors that affect the seam strength of any garments. But the relationship among these two specific variables and the seam strength is complex and non-linear. As a result, a fuzzy logic based model has been developed to demonstrate the relationship among these parameters and the developed model has been validated by the experimental trial. The coefficient of determination ( R2) was found to be 0.98. The mean relative error also lies withing acceptable limit. The results have suggested a very good performance of the model in the case of the prediction of the seam strength of the denim garments.

Banking front-line personnel assessment by fuzzy logic approaches

The article is dedicated to the qualitative assessment of banking personnel and the interpretation of the results with a developed fuzzy logic expert system. The authors proposed to evaluate human resources based on the Company Loyalty, Customer Service Quality and Intra-Corporate Communication which are linguistic terms for personnel to be assessed. To interpret the results of the received staff assessment, a fuzzy expert system was developed which enables the Business Efficiency of Personnel to be estimated. The expert system was tested at the front-line office of the regional bank. Regression and correlation analysis revealed high correlations between the Business Efficiency of Personnel and the quantitative results of employees. The practical relevance of the research is conditioned by the growing need to assess credit managers during the trial period or at the introduction of new products, while quantitative indicators are absent. The methods used for research comprise survey, ranking of the factors and factor analysis.