• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.14-18
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• 2002

The perceptron algorithm, which is one of a class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. However, it may not give desirable results when pattern sets are nonlinerly separable. A fuzzy version was developed to male up for the weaknesses in the crisp perceptron algorithm. This was achieved by assigning memberships to the pattern sets. However, still another drawback exists in that the pattern memberships do not consider class typicality of the patterns. Therefore, we propose a possibilistic approach to the crisp perceptron algorithm. This algorithm combines the linearly separable property of the crisp version and the convergence property of the fuzzy version. Several examples are given to show the validity of the method.

• Journal of IKEEE
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• v.24 no.1
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• pp.127-131
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• 2020

Fuzzy C-Means(FCM) algorithm is frequently used as a representative image segmentation method using clustering. FCM divides the image space into cluster regions with similar pixel values, which requires a lot of segmentation time. In particular, the processing speed problem for analyzing various patterns of the current users of the web is more important. To solve this speed problem, this paper proposes an improved FCM (Improved FCM : IFCM) algorithm for segmenting the image into the Otsu threshold and FCM. In the proposed method, the threshold that maximizes the variance between classes of Otsu is determined, applied to the FCM, and the image is segmented. Experiments show that IFCM improves performance by shortening image segmentation time compared to conventional FCM.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.257-262
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• 2019

Recently, with a great deal of attention and utilization to the UAV like a drone, many application cases using UAV in various fields have been proliferated rapidly. These UAV, however, has many risks like balance deviation and drone crash due to the external environmental factors. The attitude control algorithm for UAV is the most important portion in order to maintain the safe management of UAV, and the best solution is PID control algorithm which is generously used and almost perfect attitude control technology nowadays. In this paper, we propose the smart attitude control algorithm using fuzzy logic in order to provide safe and continuous attitude control against external environmental factors, and compare the performance through simulation study between PID and our algorithm.

• Journal of IKEEE
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• v.23 no.2
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• pp.419-424
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• 2019

The FCM algorithm finds the optimal solution through iterative optimization technique. In particular, there is a difference in execution time depending on the initial center of clustering, the location of noise, the location and number of crowded densities. However, this method gradually updates the center point, and the center of the initial cluster is shifted to one side. In this paper, we propose a TI-FCM(Triangular Inequality-Fuzzy C-Means) clustering algorithm that determines the cluster center density by maximizing the distance between clusters using triangular inequality. The proposed method is an effective method to converge to real clusters compared to FCM even in large data sets. Experiments show that execution time is reduced compared to existing FCM.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.151-158
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• 2021

The main cause of ulcer is pressure, which starts to develop when the critical body pressure (32mmHg) is exceeded, and when the critical time elapses, ulcer occurs. In this study, the keyboard mechanism of the medical bed with 4 bar links was adopted, and each key can be controlled vertically. A key has one servo drive and one sensor controller which hasseveral body pressure sensors. The sensor controllers and the servo drives are connected to the main controller by two CAN (Car Are Network) in series, respectively. By reading the maximum body pressure value of each keyboard sensor, and by calculating the error value based on the critical body pressure, the fuzzy controller moves each key so that the total error becomes zero. If the fuzzy controller fails, then it prevents ulcer by lifting and lowering the keys of the bed alternatively within a short time. Thus, the controller operates in two-stage. The validity and effectiveness of the proposed approach have been verified through experiments.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.353-364
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• 2022

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABC) and the fuzzy C-means (FCM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABC) algorithm by tuning the FCM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABC algorithm. The results from the IABC algorithm are used as initial parameters in FCM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABC-FCM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABC-FCM algorithm could be applicable in classifying the rock joint sets.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.322-328
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• 2023

The number of senior citizens with large bowel obstruction is steadily growing in Korea. Plain radiography was used to examine the severity and treatment of this phenomenon. To avoid examiner subjectivity in radiography readings, we propose an automatic segmentation method to identify fluid-filled areas indicative of large bowel obstruction. Our proposed method applies the Hough transform to locate suspicious areas successfully and applies the possibilistic fuzzy c-means unsupervised learning algorithm to form the target area in a noisy environment. In an experiment with 104 real-world large-bowel obstruction radiographs, the proposed method successfully identified all suspicious areas in 73 of 104 input images and partially identified the target area in another 21 images. Additionally, the proposed method shows a true-positive rate of over 91% and false-positive rate of less than 3% for pixel-level area formation. These performance evaluation statistics are significantly better than those of the possibilistic c-means and fuzzy c-means-based strategies; thus, this hybrid strategy of automatic segmentation of large bowel suspicious areas is successful and might be feasible for real-world use.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1865-1869
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• 2004

A new model for the propagation prediction for mobile communication network inside building is presented in this paper. The model is based on the determination of the dominant paths between the transmitter and the receiver. The field strength is predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved.

• International journal of advanced smart convergence
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• v.3 no.2
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• pp.10-13
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• 2014

In oriental medicine, the pulse beats are important signals that may let us know the conditions of one's health and disease. In other words, doctors of oriental medicine can simply analyze pulse waves anywhere and anytime to treat patients without using high-priced medical appliances. However, they are largely subjective in interpreting the pulse rates and hence their reliability is far from being perfect. The current paper aims to solve this problem by using fuzzy inference rules in judging patients' health status and to develop a software kit of intelligent electronic needles.