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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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International Journal of Fuzzy Logic and Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 15, Issue 4 - Dec 2015
Volume 15, Issue 3 - Sep 2015
Volume 15, Issue 2 - Jun 2015
Volume 15, Issue 1 - Mar 2015
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Concepts and Design Aspects of Granular Models of Type-1 and Type-2
Pedrycz, Witold ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 87~95
DOI : 10.5391/IJFIS.2015.15.2.87
In this study, we pursue a new direction for system modeling by introducing the concept of granular models, which produce results in the form of information granules (such as intervals, fuzzy sets, and rough sets). We present a rationale and several key motivating arguments behind the use of granular models and discuss their underlying design processes. The development of the granular model includes optimal allocation of information granularity through optimizing the criteria of coverage and specificity. The emergence and construction of granular models of type-2 and type-n (in general) is discussed. It is shown that achieving a suitable coverage-specificity tradeoff (compromise) is essential for developing granular models.
Genetic Outlier Detection for a Robust Support Vector Machine
Lee, Heesung ; Kim, Euntai ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 96~101
DOI : 10.5391/IJFIS.2015.15.2.96
Support vector machine (SVM) has a strong theoretical foundation and also achieved excellent empirical success. It has been widely used in a variety of pattern recognition applications. Unfortunately, SVM also has the drawback that it is sensitive to outliers and its performance is degraded by their presence. In this paper, a new outlier detection method based on genetic algorithm (GA) is proposed for a robust SVM. The proposed method parallels the GA-based feature selection method and removes the outliers that would be considered as support vectors by the previous soft margin SVM. The proposed algorithm is applied to various data sets in the UCI repository to demonstrate its performance.
Hybrid Filter Based on Neural Networks for Removing Quantum Noise in Low-Dose Medical X-ray CT Images
Park, Keunho ; Lee, Hee-Shin ; Lee, Joonwhoan ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 102~110
DOI : 10.5391/IJFIS.2015.15.2.102
The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.
Protein Named Entity Identification Based on Probabilistic Features Derived from GENIA Corpus and Medical Text on the Web
Sumathipala, Sagara ; Yamada, Koichi ; Unehara, Muneyuki ; Suzuki, Izumi ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 111~120
DOI : 10.5391/IJFIS.2015.15.2.111
Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.
The Pattern Recognition System Using the Fractal Dimension of Chaos Theory
Shon, Young-Woo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 121~125
DOI : 10.5391/IJFIS.2015.15.2.121
In this paper, we propose a method that extracts features from character patterns using the fractal dimension of chaos theory. The input character pattern image is converted into time-series data. Then, using the modified Henon system suggested in this paper, it determines the last features of the character pattern image after calculating the box-counting dimension, natural measure, information bit, and information (fractal) dimension. Finally, character pattern recognition is performed by statistically finding each information bit that shows the minimum difference compared with a normalized character pattern database.
Design of Fuzzy Logic Control System for Segway Type Mobile Robots
Kwak, Sangfeel ; Choi, Byung-Jae ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 126~131
DOI : 10.5391/IJFIS.2015.15.2.126
Studies on the control of inverted pendulum type systems have been widely reported. This is because this type of system is a typical complex nonlinear system and may be a good model to verify the performance of a proposed control system. In this paper, we propose the design of two fuzzy logic control systems for the control of a Segway mobile robot which is an inverted pendulum type system. We first introduce a dynamic model of the Segway mobile robot and then analyze the system. We then propose the design of the fuzzy logic control system, which shows good performance for the control of any nonlinear system. In this paper, we here design two fuzzy logic control systems for the position and balance control of the Segway mobile robot. We demonstrate their usefulness through simulation examples. We also note the possibility of simplifying the design process and reducing the computational complexity. This possibility is the result of the skew symmetric property of the fuzzy rule tables of the system.
On Common Fixed Point for Single and Set-Valued Maps Satisfying OWC Property in IFMS using Implicit Relation
Park, Jong Seo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 132~136
DOI : 10.5391/IJFIS.2015.15.2.132
In this paper, we introduce the notion of single and set-valued maps satisfying OWC property in IFMS using implicit relation. Also, we obtain common fixed point theorems for single and set-valued maps satisfying OWC properties in IFMS using implicit relation.
Categorical Aspects of Intuitionistic Fuzzy Topological Spaces
Kim, Jin Tae ; Lee, Seok Jong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 15, issue 2, 2015, Pages 137~144
DOI : 10.5391/IJFIS.2015.15.2.137
In this paper, we obtain two types of adjoint functors between the category of intuitionistic fuzzy topological spaces in Mondal and Samanta’s sense, and the category of intuitionistic fuzzy topological spaces in Ŝostak’s sense. Also, we reveal that the category of Chang’s fuzzy topological spaces is a bireflective full subcategory of the category of intuitionistic fuzzy topological spaces in Mondal and Samanta’s sense.