scholarly journals FuzzyShell: A Large-Scale Expert System Shell Using Fuzzy Logic for Uncertainty Reasoning

1998 ◽  
Author(s):  
Juiyao Pan ◽  
Guilherme N. DeSouza ◽  
Avinash C. Kak
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Large Scale ◽  
Uncertainty Reasoning ◽  
Expert System Shell

A Design Expert System for Auto-Routing of Ship Pipes

1999 ◽  
Vol 15 (01) ◽  
pp. 1-9 ◽  
Author(s):  
Sang-Seob Kang ◽  
Sehyun Myung ◽  
Soon-Hung Han
Keyword(s):  
Expert System ◽  
Geometric Modeling ◽  
Large Scale ◽  
Main Tool ◽  
Piping System ◽  
Routing Problems ◽  
Routing Problem ◽  
Human Errors ◽  
Cad System ◽  
Expert System Shell

Finding the optimum route of ship pipes is a complicated and time-consuming process. Experience of designers is the main tool in this process. To reduce design man-hours and human errors an expert system shell and a geometric modeling kernel are integrated to automate the design process. Existing algorithms for routing problems have been analyzed -most of them are to solve 2-D circuit routing problems. Design of the ship piping system, especially within the engine room, is a complicated, large-scale 3-D routing problem. Methods of expert systems have been implemented to find the routes of ship pipes on the main deck of a bulk carrier. A framework of the intelligent CAD system for pipe auto-routing is suggested. The CADDS 5 of Computer vision is used as the overall CAD environment, the Nexpert Object of Neuron Data is used as the expert system shell, and the CADDS 5 ISSM is used to build user interface through which geometric models" of pipes are created and modified.

ESTIMATING GAS AND ROCK OUTBURST RISK ON THE BASIS OF KNOWLEDGE AND EXPERIENCE – THE EXPERT SYSTEM BASED ON FUZZY LOGIC

2014 ◽  
Vol 59 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Norbert Skoczylas
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Mining Industry ◽  
Automatic Assessment ◽  
Science Field ◽  
Second Stage ◽  
Two Stages ◽  
Outburst Hazard

Abstract The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic.

A pedagogic expert system shell in Prolog

1988 ◽  
Vol 23 (6) ◽  
pp. 35-38
Author(s):  
Victor Schneider
Keyword(s):  
Expert System ◽  
Expert System Shell

scholarly journals Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Rafael D. Tordecilla ◽  
Pedro J. Copado-Méndez ◽  
Javier Panadero ◽  
Carlos L. Quintero-Araujo ◽  
Jairo R. Montoya-Torres ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Local Search ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Fuzzy Pattern ◽  
Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

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

2021 ◽  
Vol 16 ◽  
pp. 155892502198897
Author(s):  
Joy Sarkar ◽  
Md Abdullah Al Faruque ◽  
Moni Sankar Mondal
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Linear Density ◽  
Unit Length ◽  
Coefficient Of Determination ◽  
Sewing Thread ◽  
External Forces ◽  
Fuzzy Logic Expert System ◽  
The Relationship ◽  
Sufficient Strength

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.

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