Prediction System for Diagnosis and Detection of Coronavirus Disease-2019(COVID-19):A Fuzzy-Soft Expert System

In early December 2019,a new virus named“2019 novel coronavirus(2019-nCoV)”appeared in Wuhan,China.The disease quickly spread worldwide,resulting in the COVID-19 pandemic.In the currentwork,we will propose a novel fuzzy softmodal(i.e.,fuzzy-soft expert system)for early detection of COVID-19.Themain construction of the fuzzy-soft expert systemconsists of five portions.The exploratory study includes sixty patients(i.e.,fortymales and twenty females)with symptoms similar to COVID-19 in(Nanjing Chest Hospital,Department of Respiratory,China).The proposed fuzzy-soft expert systemdepended on five symptoms of COVID-19(i.e.,shortness of breath,sore throat,cough,fever,and age).We will use the algorithm proposed by Kong et al.to detect these patients who may suffer from COVID-19.In this way,the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not.Finally,we present the comparison between the present system and the fuzzy expert system.

Variable universe fuzzy expert system for aluminum electrolysis

In aluminum electrolytic process, the variables affect the current efficiency and the stability of electrolysis cells. AIF3 addition and aluminum tapping volume are two important factors that affect economic benefits of aluminum electrolytic production. Fuzzy logic provides a suitable mechanism to describe the relationship between the process variables and the current efficiency. Fuzzy expert system based on Mamdani fuzzy inference process for aluminum electrolysis was adopted to adjust A1F3 addition and aluminum tapping volume. A novel variable universe approach was applied in the system to solve the problem that different electrolysis cells have different universes of variables. The system was applied to 300 kA aluminum electrolysis cells in a aluminum plant. Experimental results showed that the electrolyte temperature was kept stably between 945 and 955℃, the current efficiency reached 93.5%, and the DC power consumption was 13 000 kW.h per ton aluminum.

Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic ele- ment is the test-diagnosis fault unit. Every underlying fault cause＇s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.

Fuzzy Logic-Based System for Liver Fibrosis Disease

The diagnosis of liver fibrosis(LF)is crucial as it is a deadly and life-threatening disease.Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system,which helps to take decisions about patients’health as experts can.The historical data of a patient’s health can have vagueness,inaccurate,and can also have missing values.The fuzzy logic theory can deal with these issues in the dataset.In this paper,a multilayer fuzzy expert system is developed to diagnose LF.The model is created by using multiple layers of the fuzzy logic approach.This system aids in classifying the health of patients into different classes.The proposed method has two layers,i.e.,layer 1 and layer 2.The input variables used in layer 1 for diagnosing liver fibrosis are Appetite,Jaundice,Ascites,Age,and Fatigue.Similarly,in layer 2,the input variables are Platelet count,White blood cell count,spleen,SGPT ALT(Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase),SGOT ALT(Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase),Serum bilirubin,and Serum albumin.The output variables for this developed system are no damage,minimal damage,significant damage,severe damage,and cirrhosis.This research work also presents the examination of results based on performance parameters.The proposed system achieves a classification accuracy of 95%.Moreover,other performance parameters such as sensitivity,specificity,and precision are calculated as 97.14%,92%,and 94.44%,respectively.

Fuzzy Soft Expert Matrix Theory and Its Application in Decision-Making

A unique mathematical strategy for dealing with uncertainty is fuzzy soft set theory. In this paper, we propose fuzzy soft expert matrices and describe numerous varieties of fuzzy soft expert matrices, as well as specific operations. Finally, by applying these matrices to decision-making scenarios, we widen our methodology.

Power Consumption Prediction for an Intelligent Air-Cushion Track Vehicle： Fuzzy Expert System

This paper describes the unique structure of an intelligent air-cushion system of a hybrid electrical air-cushion track vehicle working on swamp terrain. Fuzzy expert system （FES） is used in this study to control the swamp tracked vehicle＇s intelligent air cushion system while it operates in the swamp peat. The system will be effective to control the intelligent air-cushion system with total power consumption （PC）, cushion clearance height （CCH） and cushion pressure （CP）. Ultrasonic displacement sensor, pull-in solenoid electromagnetic switch, pressure sensor, micro controller and battery pH sensor will be incorporated with the FES to investigate experimentally the PC, CCH and CP. In this study, we provide illustration how FES might play an important role in the prediction of power consumption of the vehicle＇s intelligent air-cushion system. The mean relative error of actual and predicted values from the FES model on total power consumption is found as 10.63 %, which is found to be alomst equal to the acceptable limits of 10%. The goodness of fit of the prediction values from the FES model on PC is found as 0.97.

Analysis of Potential Failure Modes in an Assembly Line by Fuzzy Expert Systems

The failure modes and effects analysis （FMEA） is widely applied in manufacturing industries in various phases of the product life cycle to evaluate the system, its design and processes for failures that can occur. The FMEA team often demonstrates different opinions and these different types of opinions are very difficult to incorporate into the FMEA by the traditional risk priority number model. In this paper, for each of the Occurrence, Severity and Detectivity parameters a fuzzy set is defined and the opinion of each FMEA team members is considered. These opinions are considered simultaneously with weights that are given to each individual based on their skills and experience levels. In addition, the opinion of the costumer is considered for each of the FMEA parameters. Then, the Risk Priority Numbers （RPN） is calculated using a Multi Input Single Output （MISO） fuzzy expert system. The proposed model is applied for prioritizing the failures of Peugeot 206 Engine assembly line in IKCo （Iran Khodro Company）.

FAULT DIAGNOSIS EXPERT SYSTEM FOR ROTATING MACHINERY BASED ON A FUZZY PROBABILITY LOGIC INFERENCE MODEL

A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.

Intelligent Control of Grate-kiln-cooler Process of Iron Ore Pellets Using a Combination of Expert System Approach and Takagi-Sugeno Fuzzy Model

Grate-kiln-cooler has become a major process of producing iron ore pellets in China. Due to the diversity of the raw materials used and the multi-device multi-variable characteristics,this process still encounters with control problem. An attempt was proposed to deal with this issue. The three-device-integrated feature of the process was firstly analyzed to obtain control strategy,and then an intelligent control system using a combination of expert system approach and Takagi-Sugeno（ T-S） fuzzy model was developed. Expert system approach was used to diagnose and remedy the abnormal conditions,while T-S fuzzy model was used to stabilize the thermal state. In the construction of T-S fuzzy rules,antecedents were identified by fuzzy c-mean clustering algorithm incorporated with subtractive clustering algorithm,and consequent parameters were identified by recursive least square algorithm. The control system was applied in a Chinese pelletizing plant and the application results demonstrated its effectiveness of stabilizing the thermal states within three devices.

Pattern recognition of acoustic sea-bed profiling records (An expert system based on the fuzzy set theory)

An expert system based on the fuzzy set theory has been developed for geological interpretation of Acoustic Seabed Profiling Records(ASPR). After successively extracting each state of several main pattern characteristics shown on the ASPRs, the similarities between this pattern characteristic-state set and the standard ones corresponding to different geological categories of marine sediments are computed respectively By comparillg these values of sidrilarities, the conclusion of geological classification to the ASPR can be derived.

Novel Model for Intrusion Detection

It's very difficult tha t the traditional intrusion detection methods based on accurate match adapt to the blur and uncertainty of user information and expert knowledge, it results in failing to report the variations of attack signature. In addition security itself includes fuzziness, the judgment standard of confidentiality, integrity and availability of system resource is uncertain. In this paper fuzzy intrusion detection based on partial match is presented to detect some types of attacks availably and alleviate some of the difficulties of above approaches, the architecture of fuzzy intrusion detection system(FIDS) is introduced and its performance is analyzed.

Computer Aided Design System for Developing Musical Fountain Programs

A computer aided design system for developing musical fountain programs was developed with multiple functions such as intelligent design, 3-D animation, manual modification and synchronized motion to make the development process more efficient. The system first analyzed the music form and sentiment using many basic features of the music to select a basic fountain program. Then, this program is simulated with 3-D animation and modified manually to achieve the desired results. Finally, the program is transformed to a computer control program to control the musical fountain in time with the music. A prototype system for the musical fountain was also developed. It was tested with many styles of music and users were quite satisfied with its performance. By integrating various functions, the proposed computer aided design system for developing musical fountain programs greatly simplified the design of the musical fountain programs.

An intelligent control method for a large multi-parameter environmental simulation cabin

The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced.Due to the diffculties of control methods and the easily damaged characteristics,control systems for the large multi-parameter environmental simulation cabin are diffcult to be controlled quickly and accurately with a classical PID algorithm.Considering the dynamic state characteristics of the environmental simulation test chamber,a lumped parameter model of the control system is established to accurately control the multiple parameters of the environmental chamber and a fuzzy control algorithm combined with expert-PID decision is introduced into the temperature,pressure,and rotation speed control systems.Both simulations and experimental results have shown that compared with classical PID control,this fuzzy-expert control method can decrease overshoot as well as enhance the capacity of anti-dynamic disturbance with robustness.It can also resolve the contradiction between rapidity and small overshoot,and is suitable for application in a large multi-parameter environmental simulation cabin control system.