Genetic Algorithms of Structural Fuzzy Reliability Index

In this paper the simple generation algorithms are improved. According to the geometric meaning of the structural reliability index, a method is proposed to deal with the variables in the standard normal space. With consideration of variable distribution, the correlation coefficient of the variables and its fuzzy reliability index, the feasibility and the reliability of the algorithms are proved with an example of structural reliability analysis and optimization.

An Intelligent Approach to Sensory Evaluation: LVQ Neural Network

Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intelligent approach utilizes predefined class information for supervised learning in order to solve the converting problem and keep the fuzziness and imprecision of the whole sensory information. The method is validated by the experiment on stimulation evaluation of cigarette sensory.

Study on the evaluation system for the cleaner production of the mine

It is important to establish the evaluation system of the cleaner production of the mining enterprise, which canprovide the technical support and guidance for the cleaner production evaluation and facilitating the promotion ofcleaner productive techniques so as to realize the integration of economic development and environmental protection.This paper, according to the characteristic of mining and Analytic Hierarchy Process (AHP), establishes the evaluationindex system, puts forward the overall quantitative evaluation method based on Fuzzy Mathematics and the experts’experience, and establishes the evaluation system of cleaner production. The main problems in cleaner production of themine are analyzed by calculation, and some countermeasures and suggestions are proposed.

Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy

Fused deposition modeling （FDM） is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empiri- cally reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thicknes, of acrylonitrile-butadiene- styrene （ABSP 400） parts fabricated using the FDM tech- nique. It was found that contraction prevailed along the directions of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi＇s parameter design. Because Taguchi＇s philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The pre- dicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.

Combining Knowledge-and Data-Driven Fuzzy Approach to Evaluate Shelf-Life of Various Seafood Products

Due to the complexity of the deterioration process of seafood products,relying on one indicator is not adequate to determine the quality of such products.Usually,shelf-life was estimated based on various indicators complicating the decision-making process.Decision Support Systems are considered as a good solution.The current study aims to establish a simple and novel fuzzy model based on a combination of knowledge-and data-driven approaches to define a fuzzy quality deterioration index(FQDI)in various seafood products(rainbow trout,threadfin bream,and white shrimp samples)during cold storage.Total volatile basic nitrogen(TVB-N)and psychrotrophic microorganisms counts(PMCs)were determined based on traditional methods.The sensory analysis was performed by a data-driven fuzzy approach.Overall,the shelf-life of the rainbow trout fillet was estimated to be 8 d,based on all the freshness parameters.However,the shelf-life of the Japanese threadfin bream fillet was 5-7 d according to the microbial and chemical parameters,respectively.This time for shrimp samples was 6-8 d using sensory score and TVB-N contents.The results of data-driven fuzzy approach showed all of the quality properties were considered as the'Important'-'Very Important'(defuzzification score>75).The TVB-N and PMCs were the most and weakest freshness quality properties(defuzzified-values:84.64 and 78.75,respectively).Based on FQDI,the shelf-life of the rainbow trout,Japanese threadfin bream,and shrimp samples were estimated to be 8,5,and 7 d,respectively.This method was able to successfully provide a comprehensive deterioration index for evaluating the seafood shelf-life.Such a total index can be considered as a comprehensive output(y variable)to predict seafood freshness by rapid and nondestructive method.

Evolved clustering analysis of 300 MW boiler furnace pressure sequence based on entropy characterization

The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.