• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.136-139
• /
• 2001

This paper develops a sensor based navigation method that utilizes fuzzy logic and the Dempster-Shafer evidence theory for mobile robot in uncertain environment. The proposed navigator consists of two behaviors: obstacle avoidance and goal seeking. To navigate reliably in the environment, we make a map building process before the robot finds a goal position and create a robust fuzzy controller. In this paper, the map is constructed on a two-dimensional occupancy grid. The sensor readings are fused into the map using D-S inference rule. Whenever the robot moves, it catches new information about the environment and replaces the old map with new one. With that process the robot can go wandering and finding the goal position. The usefulness of the proposed method is verified by a series of simulations. This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• Spatial Information Research
• /
• v.7 no.2
• /
• pp.223-236
• /
• 1999

Recently, the need of wast incineratory has been recognized. However, the waste incinerator is considered the typical example of NYMB syndrome as a locally unwanted facilities. Therefore, the site selection of waste incineratory should be determined very carefully with consideration of various location factors. The purpose of this study is to provide a new decision-making process model for site selection that provides a rational and a systematic way. The fuzzy set theory and AHP theory, which have merits to overcome uncertainly and complexity of spatial data, are applied to select candidate sites for the waste incineratory. The method is able to produce a more flexible and objective solution for selecting suitability sites in comparison to rigid boolean logic. The result of this study shows that geographic information systems have clear implication for informing the spatial decision making process.

• Korean Journal of Health Education and Promotion
• /
• v.30 no.5
• /
• pp.189-199
• /
• 2013

Objectives: This paper aims to explain principles of gist-based health message design and discuss their implications for health promotion and public health education. Methods: After reviewing Reyna and Brainerd's Fuzzy Trace Theory(FTT), the authors explicate how to transform FTT into a practical guidance of health message design. Our explication is based upon FTT's reasoning that human intuition, rather than analysis, takes a primary role in message recall and comprehension, followed by judgment and decision making. We expect gist-based message design to be appropriate to serve such intuition. Results: Four principles of gist-based message design are offered: (1) provision of qualitative, as well as quantitative, information of gist, (2) inclusion of visual images corresponding to gist, (3) use of effective message formats to emphasize the gist (4) inclusion of relevant reasons and contextutal information. Conclusions: Gist-based message design has theoretical and practical implications for health promotion, specifically in the field of public health education, the press and governmental communication toward the public, and provider-patient communication in medical settings.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.3121-3143
• /
• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.423-426
• /
• 2005

In this paper, we proposed a PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, $\tau_{I}$, $\tau_{D}$. by the Ziegler-Nichols formula that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and fnd the new proportion gain(Kp) and the integral time ($\tau_{I}$) from fuzzy tuner by the error and error rate of plant oueut as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant property of this algorithm is shown by simulation

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.707-711
• /
• 2007

Microarray gene expression data is believed to show the functions of living organism through the gene expression values. We have studied a method to get the informative genes from the microarray gene expression data. There are several ways for this. In recent researches to get more sophisticated and detailed results, it has used the intelligence information theory like fuzzy theory. Some methods are to add fudge factors to the significance test for more refined results. In this paper, we suggest a method to get informative genes from microarray gene expression data. We combined the difference of means between two groups and the fuzzy membership degree which reflects the variance of the gene expression data. We have called our significance test the Fuzzy Information method for Gene Expression data(FIGER). The FIGER calculates FIGER variation ratio and FIGER membership degree to show how strongly each object belongs to the each group and then it results in the significance degree of each gene. The FIGER is focused on the variation and distribution of the data set to adjust the significance level. Out simulation shows that the FIGER-test is an effective and useful significance test.

• Journal of IKEEE
• /
• v.4 no.2 s.7
• /
• pp.173-180
• /
• 2000

In this paper, we interpreted and evaluated the relation between the sensation of equilibrium and biomedical signal automatically by applying the fuzzy theory. We induced the vertigo by using the caloric test, and presented the correlation between vertigo and biomedical signal by using the quantification method. We objectively analyzed the organic relation of the biomedical signal by fuzzy rule design using the table-lookup scheme and obtained good result in recognizing the level of the sensation of equilibrium.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.327-332
• /
• 1996

Rough Set theory suggested by Pawlak has a property that it can describe the degree of relation between condition and decision attributes of data which don't have linguistic information. In this paper, by using this ability of rough set theory, we define a occupancy degree which is a measure can represent a degree of relational quantity between condition and decision attributes of data table. We also propose a method that can find an optimal fuzzy rule table and membership functions of input and output variables from data without linguistic information and examine the validity of the method by modeling data generated by fuzzy rule.

• Management & Information Systems Review
• /
• v.14
• /
• pp.79-99
• /
• 2004

Cyber education system service is in the field of software service which is highlighted after the latter half of 1990'. But the progress of this service is impeded by the lack of back office which contributes to the evaluation of learning achievement and the management of learning progress. This article points out the problem of current back office which is the most important in the cyber education system, and focuses on the new intelligent learning achievement evaluation module. First, we define the cause and effect between the learning stages using by fuzzy implication which is the important part of fuzzy theory. Next, we suggest the model which generates the results of the learning achievement evaluation. This model, suggested by this article, may contribute to the development of the cyber education system by improving the current on-line education service.

• Journal of Information Processing Systems
• /
• v.11 no.3
• /
• pp.421-437
• /
• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.