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Journal Cover   International Journal of Fuzzy Computation and Modelling
  [1 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2052-353X - ISSN (Online) 2052-3548
   Published by Inderscience Publishers Homepage  [400 journals]
  • Image coding using fuzzy edge classifier and fuzzy F-transform:
    • Authors: Deepak Gambhir, Navin Rajpal
      Pages: 235 - 251
      Abstract: To achieve high compression ratio and good quality of compressed image, a new image compression scheme using fuzzy edge classifier and fuzzy F-transform is proposed. In the proposed scheme, fuzzy edge classifier decides the smooth or edge block, based on membership value of each block which is obtained from Gaussian function. Each smooth fuzzy block is encoded with block mean and edge block is processed using fuzzy F-transform. This encoding scheme is further decoded by applying inverse fuzzy F-transform to edge blocks and mean value to smooth block, to reconstruct the image. The output image of the decoding process shows some artefacts due to mean value of smooth blocks which is further improved by proposed Gaussian block image enhancement scheme. The experimental results show that the proposed scheme to compress the images not only improves the artefacts appearing in reconstructed image but also improves the compression ratio. The PSNR calculated in the dual fuzzy proposed method is superior than PSNR calculated in JPEG, fuzzy F-transform and fuzzy F-transform with single mean value of smooth blocks.
      Keywords: fuzzy transform; FTR; Gaussian enhancement; artefact reduction; edge detection; image coding; fuzzy edge classifiers; F-transform; compression ratio; image quality; image compression; image reconstruction; image processing; PSNR; peak SNR; signal to noise ratio
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3 (2015) pp. 235 - 251
      PubDate: 2015-06-16T23:20:50-05:00
      DOI: 10.1504/IJFCM.2015.069929
      Issue No: Vol. 1, No. 3 (2015)
  • Influence of mood states on information processing during decision making
           using fuzzy reasoning tool and neuro-fuzzy system based on Mamdani
    • Authors: Sachi Nandan Mohanty, Dilip Kumar Pratihar, Damodar Suar
      Pages: 252 - 268
      Abstract: This study provides a comparison between traditional fuzzy reasoning tools and a neuro-fuzzy system, both developed based on Mamdani approach in order to determine the influence of mood states on information processing during decision making. To begin, participants responded to questions on positive and negative prospects involving gains and losses on a health risk problem and explained the reasons for their decisions in writing. Three independent input variables, namely flexibility, originality and fluency were then derived from the participants' reasons for their choices. Four linguistic terms, such as low, medium, high and very high were used to represent each of the input variables. Using Mamdani's approach, both traditional fuzzy reasoning tool and a neuro-fuzzy system were designed for a three-input, one-output process. The neuro-fuzzy system was trained using a back-propagation algorithm. Compared to the traditional fuzzy reasoning tool, the neuro-fuzzy system could provide better results.
      Keywords: information processing; fuzzy logic; neuro-fuzzy systems; flexibility; fluency; originality; neural networks; mood states; decision making; fuzzy reasoning; Mamdani approach; health risks
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3 (2015) pp. 252 - 268
      PubDate: 2015-06-16T23:20:50-05:00
      DOI: 10.1504/IJFCM.2015.069930
      Issue No: Vol. 1, No. 3 (2015)
  • Interval transportation problem in urban region
    • Authors: Arpita Panda, Chandan Bikash Das
      Pages: 269 - 286
      Abstract: The unit transportation cost is not fixed in urban region but there are available certain types of vehicles in prepaid system, depending on the distance from source to destination. In this paper we represent a transportation problem with interval source and interval demand but interval of unit transportation cost is determined depending on the prepaid costs of single trip of N-number of vehicles. The limits of the transportation costs are determined by our proposed algorithm. Then an uncertain multi-level programming model is developed for such a type of transportation problem. By another proposed algorithm convert it a multi-objective crisp model. Then solve this model by fuzzy programming technique. A numerical example is presented.
      Keywords: interval transport problem; ITP; cost varying transport problem; multi-level programming; fuzzy programming; urban regions; cities; transport costs; modelling
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3 (2015) pp. 269 - 286
      PubDate: 2015-06-16T23:20:50-05:00
      DOI: 10.1504/IJFCM.2015.069937
      Issue No: Vol. 1, No. 3 (2015)
  • Credibility hypothesis testing of fuzzy exponential distributions
    • Authors: S. Sampath, B. Ramya
      Pages: 287 - 311
      Abstract: Under fuzzy environment, formulating an appropriate membership function is a vital process. Usually membership functions involve certain constants which are in general unknown. Recently, Sampath and Ramya (2014b) suggested a procedure to test the validity of a hypothesis regarding the credibility distribution of a fuzzy variable against a rival hypothesis using a criterion called 'membership ratio criterion' and made a detailed study with reference to triangular credibility distributions. This paper is devoted to study the properties of the test resulting from the application of membership ratio criterion when the competing hypotheses are statements about fuzzy exponential distribution.
      Keywords: fuzzy variables; credibility distribution; membership ratio; credibility hypothesis; best credibility rejection region; fuzzy exponential distribution
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3 (2015) pp. 287 - 311
      PubDate: 2015-06-16T23:20:50-05:00
      DOI: 10.1504/IJFCM.2015.069954
      Issue No: Vol. 1, No. 3 (2015)
  • Designing of fuzzy corrosion controller for gas pipelines
    • Authors: J. Gopalakrishnan, Ganga Agnihotri, D.M. Deshpande
      Pages: 312 - 332
      Abstract: Corrosion rate in metallic underground gas pipeline depends on quality of pipeline coatings, soil resistance, soil pH, soil aeration etc. Mathematical modelling of pipeline corrosion process is very difficult. Fuzzy corrosion controller is developed to control corrosion in underground gas pipelines to operate in constant pipe to soil potential mode and constant voltage mode. Conventional transformer rectifier based controllers need human intervention during startup. Pipe to soil potential is the corrosion process monitoring parameter in underground laid pipelines. Error and change in error are considered as inputs for the designed fuzzy controller and deployed at field to protect gas pipeline. In the proposed controller Mamdani type implication and centre of area defuzzification method is used. Controller is designed with different fuzzy sets and obtained results are reported. Fuzzy controller performance is vetted by electrical resistance probe result.
      Keywords: corrosion control; controller design; impressed current cathodic protection; ICCP; pipe to soil potential; PSP; virtual instrumentation; fuzzy logic; electrical resistance probes; fuzzy control; underground pipelines; gas pipelines; fuzzy sets
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3 (2015) pp. 312 - 332
      PubDate: 2015-06-16T23:20:50-05:00
      DOI: 10.1504/IJFCM.2015.069955
      Issue No: Vol. 1, No. 3 (2015)
  • Fuzzy classifier with automatic rule generation for fault diagnosis of
           hydraulic brake system using statistical features
    • Authors: R. Jegadeeshwaran, V. Sugumaran
      Pages: 333 - 350
      Abstract: This study focuses on the condition monitoring of a hydraulic brake system using vibration signal through a machine learning approach. The machine learning approach has three main steps: feature extraction, feature selection and feature classification. Statistical features were used for the fault diagnosis of hydraulic brake system. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. C4.5 decision tree algorithm was used for selecting best features that will distinguish the fault conditions of the brake from given train samples. For feature classification, fuzzy logic was used as a classifier. A necessary rule set was formed automatically by using decision tree algorithm. The generated rule set is fed to fuzzy classifier. The procedure to build fuzzy classifier is also explained and the results were discussed.
      Keywords: decision tree; statistical features; feature extraction; feature selection; feature classification; fuzzy inference engine; rule generation; fuzzy classifiers; automatic rule generation; fault diagnosis; hydraulic brakes; condition monitoring; vibration signals;
      Citation: International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3 (2015) pp. 333 - 350
      PubDate: 2015-06-16T23:20:50-05:00
      DOI: 10.1504/IJFCM.2015.069958
      Issue No: Vol. 1, No. 3 (2015)
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