• Journal of the Korea Society of Computer and Information
• /
• v.3 no.2
• /
• pp.131-138
• /
• 1998

In usual statistical data analysis, we describe statistical data by exact values. However, in modem complex and large-scale systems, it is difficult to treat the systems using only exact data. In this paper, we define these data as fuzzy data(ie. Linguistic variable applied to make the member-ship function.) and Propose a new method to get an analysis of fuzzy survey data based on the maximum entropy Principle. Also, we propose a new method of discrimination by measuring distance between a distribution of the stable state and estimated distribution of the present state using the Kullback - Leibler information. Furthermore, we investigate the validity of our method by computer simulations under realistic situations.

• Journal of Convergence Society for SMB
• /
• v.3 no.1
• /
• pp.23-30
• /
• 2013

In this paper, fuzzy entropy and similarity measure to measure the uncertainty and similarity of data as real value were introduced. Design of fuzzy entropy and similarity measure were illustrated and proved. Obtained measures were applied to the calculating process and discussed. Extension of data quantification results such as decision making and fuzzy game theory were also discussed.

• The KIPS Transactions:PartB
• /
• v.12B no.5 s.101
• /
• pp.587-594
• /
• 2005

This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on Inか entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.

• Journal of Information Processing Systems
• /
• v.15 no.4
• /
• pp.986-1016
• /
• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.08a
• /
• pp.189-192
• /
• 2000

A fuzzy controller for the evaluation of the quantization parameters in the H.263 coder has been introduced. We adopted a Mamdani fuzzy controller with centroid defuzzification. The inputs are variance, entropy, current motion vector and previous motion vector. This results is obtained a effective rate control technique using fuzzy Quantization.

• Journal of Information Processing Systems
• /
• v.17 no.5
• /
• pp.918-932
• /
• 2021

Evaluation of the collaboration of the upstream and downstream enterprises in the manufacturing supply chain is important to improve their synergistic effect. From the supply chain perspective, this study establishes the evaluation model of the manufacturing enterprise collaboration on the basis of fuzzy entropy according to synergistic theory. Downstream enterprises carry out coordinated capital, business, and information flows as subsystems and research enterprises as composite systems. From the three subsystems, the collaboration evaluation index is selected as the order parameter. The compound fuzzy matter-element matrix is established by using its improved algorithm. Subordinate membership and standard deviation fuzzy matter-element matrixes are constructed. Index weight is determined using the entropy weight method. The closeness of each matter element is then calculated. Through a representative of the home appliance industry, namely, Gree Electric Appliances Inc. of Zhuhai, empirical analysis of data in 2011-2017 from the company and its upstream and downstream enterprise collaboration shows a good trend, but the coordinated development has not reached stability. Gree Electric Appliances Inc. of Zhuhai need to strengthen the synergy with upstream and downstream enterprises in terms of cash, business, and information flows to enhance competitiveness. Experimental results show that this method can provide precise suggestions for enterprises, improve the degree of collaboration, and accelerate the development and upgrading of the manufacturing industry.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.4
• /
• pp.295-300
• /
• 2007

The similarity analysis for fuzzy set pair or crisp set pair are carried out. The similarity measure that is based on distance measure is derived and proved. The proposed similarity measure is considered with the help of analysis for uncertainty or certainty part of the membership functions. The usefulness of proposed similarity is verified through the computation of similarity between fuzzy set and crisp set or fuzzy set and fuzzy set. Our results are also compared with those of previous similarity measure which is based on fuzzy number.

• Journal of Information Processing Systems
• /
• v.20 no.3
• /
• pp.285-295
• /
• 2024

Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders' vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.25 no.6
• /
• pp.36-41
• /
• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. we show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.45
• /
• pp.11-23
• /
• 1998

This paper presents a more efficient evaluation of alternatives by use of multi-criteria decision making methodlogy under fuzzy intersectional dependence relations. The performance evaluation of most systems such as weapons, enterprise systems etc. are multiple criteria decision making problems. The descriptions and judgements on these systems are usually linguistic and fuzzy. The traditional methods of Analytic Hierarchy Process(AHP) are mainly used in crisp(non-fuzzy) decision applications with a very unbalanced scale of judgements and rank reversal. To overcome these problems, we will propose a new, general decision making method for evaluation models using fuzzy AHP(FAHP) under fuzzy intersectional dependence relations. The T.M.S alternatives A, B and C will be evaluted by the Fuzzy Analytic Hierachy Process (FAHP) based on entropy weight in this study. We will use symmetric triangular fuzzy numbers to indicate the relative strength of the elements in the hierachy and degree of intersection between criteria. These problems are evaluated by five criteria : tactical criteria, technology criteria, maintenance criteria, economy criteria, advacement criteria.