• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.729-733
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• 2003

The presence of local features within clusters incurred by multi-modal nature of data prohibits many conventional clustering techniques from working properly. Especially, the clustering of datasets with non-Gaussian distributions within a cluster can be problematic when the technique with implicit assumption of Gaussian distribution is used. Current study proposes a simple tandem clustering method composed of k-means type algorithm and hierarchical method to solve such problems. The multi-modal dataset is first divided into many small pre-clusters by k-means or fuzzy k-means algorithm. The pre-clusters found from the first step are to be clustered again using agglomerative hierarchical clustering method with Kullback- Leibler divergence as the measure of dissimilarity. This method is not only effective at extracting the multi-modal clusters but also fast and easy in terms of computation complexity and relatively robust at the presence of outliers. The performance of the proposed method was evaluated on three generated datasets and six sets of publicly known real world data.

• Journal of IKEEE
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• v.22 no.2
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• pp.440-445
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• 2018

The work presented in this paper deals with a navigation problem for a multiple mobile robots in unknown dynamic environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots' bodies must be used to detect information about the surroundings. In order to guide the robots along collision-free paths to reach their goal positions, a navigation method based on a combination of primary strategies has been developed. Most of these strategies are achieved by means of fuzzy logic controllers, and are uniformly applied in every robot. In order to improve the performance of the proposed fuzzy logic, the genetic algorithms were used to evolve the membership functions and rules set of the fuzzy controller. The simulation experiments verified that the proposed method effectively addresses the navigation problem.

• Journal of IKEEE
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• v.11 no.3
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• pp.100-107
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• 2007

One of the fundamental problems in wireless sensor networks is the efficient deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation and a experiment using two rectangular and one L shape area. We found the minimum number of sensor nodes for the complete coverage of modeled area, and discovered the optimum location of each nodes. The real deploy experiment using sensor nodes shows the 94.6%, 92.2% and 95.7% error free communication rate respectively.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2818-2820
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• 2002

In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.4
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• pp.403-411
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• 2001

A criterion for evaluation of burners has changed recently, and the environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the $NO_x$ and CO regulation. Consequently. 'good burner' means one whose thermal efficiency is high under the constraint of $NO_x$ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of $NO_x$ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro- Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro- Fuzzy learning algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of $NO_x$ and CO of the combustion gas was successfully inferred.

• Advances in Computational Design
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• v.5 no.3
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• pp.233-260
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• 2020

The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.87-92
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• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.146-153
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• 2010

For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.