• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.517-524
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• 2004

The fuzzy set theory has been wide used in clustering of machine learning with data mining since fuzzy theory has been introduced in 1960s. In particular, fuzzy C-means algorithm is a popular fuzzy clustering algorithm up to date. An element is assigned to any cluster with each membership value using fuzzy C-means algorithm. This algorithm is affected from the location of initial cluster center and the proper cluster size like a general clustering algorithm as K-means algorithm. This setting up for initial clustering is subjective. So, we get improper results according to circumstances. In this paper, we propose a cluster merging using enhanced density based fuzzy C-means clustering algorithm for solving this problem. Our algorithm determines initial cluster size and center using the properties of training data. Proposed algorithm uses grid for deciding initial cluster center and size. For experiments, objective machine learning data are used for performance comparison between our algorithm and others.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.11-18
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• 2010

Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.842-848
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• 2008

This paper deal with uncertainty problem by using Type-2 fuzzy logic set for nonlinear system modeling. We design Type-2 fuzzy logic system in which the antecedent and the consequent part of rules are given as Type-2 fuzzy set and also analyze the performance of the ensuing nonlinear model with uncertainty. Here, the apexes of the antecedent membership functions of rules are decided by C-means clustering algorithm and the apexes of the consequent membership functions of rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The proposed model is demonstrated with the aid of two representative numerical examples, such as mathematical synthetic data set and Mackey-Glass time series data set and also we discuss the approximation as well as generalization abilities for the model.

• Journal of the Korea Safety Management & Science
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• v.17 no.2
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• pp.215-228
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• 2015

A creative innovation and an innovative problem-solving of industrial companies can be achieved by overcoming the challenges of technical and physical contradictions. The approaches to address conflicting and paradoxical problems, such as technical and physical contradictions have a crucial role in advancing the quality assessment for manufacturer and service provider. The term, technical contradiction, depicts the state that improvement of one ends of IFR (Ideal Final Result) leads to unfavorable condition of the other ends, and results in conflicting problem. Another type of contradictions that's discussed in this study is a physical contradiction which is due to two mutually opposing states of the means of ends, and gives paradoxical situation. By integrating the means-ends chain perspectives, the physical contradiction that is a specifically root-causes, "means", can be initially addressed to resolve the downstream problem of technical contradiction which represents a general and abstract goals, "ends". This research suggests IFR resolution processes to handle both physical contradiction of means and technical contradiction of ends by employing causal relationship with IFR, effects and causes. In summary, the study represents three major processes that resolve such contradictions are demonstrated as follows: 1) Derivation of causal and hierarchical relationship among IFR, ends and means by considering CAED (Cause-And-Effect Diagram) and LT (Logic Tree). 2) Identification of causal relationship between physical contradiction and technical contradiction by using TPCT (TRIZ Physical Contradiction Tree) and TCD (Technical Contradiction Diagram). 3) Application of integrated TRIZ principles by classifying 40 inventive principles into 4 general conditions of the separation principle of mutually opposite states in space, in time, based on conditions, and between the parts and the whole. In order to validate the proof of proposed IFR resolution processes, the analysis of the TRIZ case studies from National Quality Circle Contest in the years, 2011 to 2014 have been proposed. The suggested guidelines that are built based on TRIZ principles can uniquely enhance the process of quality innovation and assessment for quality practitioners.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1072-1079
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• 2003

This study is purported for suggesting a new clustering algorithm that enables incremental categorization of numerous documents. The suggested algorithm adopts the natures of the spherical k-means algorithm, which clusters a mass amount of high-dimensional documents, and the fuzzy ART(adaptive resonance theory) neural network, which performs clustering incrementally. In short, the suggested algorithm is a combination of the spherical k-means vector space model and concept vector and fuzzy ART vigilance parameter. The new algorithm not only supports incremental clustering and automatically sets the appropriate number of clusters, but also solves the current problems of overfitting caused by outlier and noise. Additionally, concerning the objective function value, which measures the cluster's coherence that is used to evaluate the quality of produced clusters, tests on the CLASSIC3 data set showed that the newly suggested algorithm works better than the spherical k-means by 8.04% in average.

• Journal of Korean Medical classics
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• v.20 no.3
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• pp.49-56
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• 2007

The "Somun Jogyongron(素問 調經論)" describes excess and deficiency syndromes. The study suggests that excess syndrome(實證) is caused by vigorous pathogenic fire(火邪)(the spirit(神)), pathogenic dryness(燥邪)(Gi(氣)), pathogenic wind(風邪)(blood(血)), pathogenic dampness(濕邪)(physique(形)) or pathogenic coldness(寒邪)(will(志)). When pathogenic fire is dominant within the body, Gi and blood becomes excessive and come out of the body, but the body cannot take them back, leading to the symptom in which the patient cannot stop laughing. When pathogenic dryness prevails, the lung(肺) cannot function properly. This means that the convergence(收斂) function of the clearing the lung and descending Gi(肅降) is deteriorated, and the patient shows symptoms of dyspnea and cough. Strong pathogenic wind increases the ascencling Gi in the liver(肝氣) and fuel angry emotion when the patient becomes upset. When pathogenic dampness is dominant, spleen(脾) function drops due to lumping effects, and the patient will experience abdominal distention(腹脹), which will disturb urination and defecation. When pathogenic coldness prevails, abdominal distention occurs due to condensating effects, and Yang Gj(陽氣) in the kidney(腎) is disturbed, leading to digestion disorders and eventually water-grain dysentery. Deficiency syndrome is caused by the lack of essential Gi(精氣) in the five viscera(五藏). Deficiency of sprit means the lack of Gi in the heart(心氣), so the patient becomes vulnerable to sadness. Deficiency of Gi means the lack of Gi in the lung(肺氣), so the patient may have breathing disorders. Deficiency of blood means the lack of Gi in the Liver(肝氣), so the patient can be easily scared. Deficiency of physique means the lack of Gi in the spleen(脾氣), making it difficult to use arms and legs. Deficiency of will means the lack of Gi in the kidney(腎氣), so Gowl syndrome(厥證) can ensue.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.368-374
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• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.104-107
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• 2019

Modern people have abundant food and various forms of life compared to the past, but they have come to form an unhealthy diet, such as skipping breakfast and not eating in time in a busy life. When these eating habits are maintained for a long time, it leads to digestive trouble. The most easily occurring symptoms are called reflux esophagitis and dysphagia. Among them, dysphagia requires quick and accurate diagnosis as they develop into various forms of complications or are also identified as presymptoms of gastric and laryngeal cancers. The result of the diagnosis is still passively judged by the doctor and each of results are different depending on the doctor. The result of the diagnosis here means the severity. When they identify treatment or complications following the results of the diagnosis, the wrong diagnosis may lead to excessive or insufficient treatment. In this paper, to figure out the severity of dysphagia in the diagnosis of dysphagia, we studied the development of a program using the K-means algorithm in the processing of X-ray images for identifying residual food in epiglottic vallecula and pyriform sinus in the section leading to esophagus.