• Speech Sciences
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• v.12 no.4
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• pp.7-16
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• 2005

We investigate the effect of membership concentration on the performance of the speaker-independent recognition system by FVQ/HMM. For the membership function, we adopt the result obtained from the objective function approach by Bezdek. Membership concentration is done by varying the exponent in the membership function. The number of selected clusters is constrained to two for the sake of cheap computational cost. Experimental results showed that the recognition rate has its maximum value when the membership function was taken to be inversely proportional to the distance of the input vector from the cluster centroid. When the membership concentration was two weak or too strong, the performance was found to be relatively poor as expected. Except these extreme cases, the membership concentration was not shown to affect the recognition rate significantly. This is in accordance with the general observation that the fuzzy system is not much sensitive. to the detailed shape of the membership function as long as it is overlapped over multiple classes.

• Journal of Biomedical Engineering Research
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• v.29 no.1
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• pp.17-24
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• 2008

The aim of this paper is to report our preliminary results of investigating the spatial focalization of Zen-meditation EEG (electroencephalograph) in alpha band (8-13 Hz). For comparison, the study involved two groups of subjects, practitioners (experimental group) and non-practitioners (control group). To extract EEG alpha rhythm, wavelet analysis was applied to multi-channel EEG signals. Normalized alpha-power vectors were then constructed from spatial distribution of alpha powers, that were classified by Fuzzy C-means based algorithm to explore various brain spatial characteristics during meditation (or, at rest). Optimal number of clusters was determined by correlation coefficients of the membership-value vectors of each cluster center. Our results show that, in the experimental group, the incidence of frontal alpha activity varied in accordance with the meditation stage. The results demonstrated three different spatiotemporal modules consisting with three distinctive meditation stages normally recognized by meditation practitioners. The frontal alpha activity in two groups decreased in different ways. Particularly, monotonic decline was observed in the control group, and the experimental group showed increasing results. The phenomenon might imply various mechanisms employed by meditation and relaxation in modulating parietal alpha.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.577-586
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• 2013

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.27-31
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• 2013

In this paper, we propose a new quantization method using fuzzy C-means clustering(FCM) for lumbar ultrasound image recognition. Unlike usual histogram based quantization, our method first classifies regions into 10 clusters and sorts them by the central value of each cluster. Those clusters are represented with different colors. This method is efficient to handle lumbar ultrasound image since in this part of human body, the brightness values are distributed to doubly skewed histogram in general thus the usual histogram based quantization is not strong to extract different areas. Experiment conducted with 15 real lumbar images verified the efficacy of proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.59-65
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• 2005

This paper proposes a novel TSK fuzzy modeling algorithm. Various approaches to fuzzy modeling when noise or outliers exist in the data have been presented but they are approaches to degrade effects of outliers or large noise by using loss function in the cost function mainly. The proposed algorithm is the modified version of noise clustering algorithm, and it adopts the method that does not use loss function, but method to cluster noise in a class. Noise clustering is a prototype-based clustering algorithm and it has no capability to regress. It conducts clustering of data first, and then conducts fuzzy regression. There are many algorithms to obtain parameters of premise and consequent part simultaneously, but they need to adapt the parameters obtained for more accurate approximation. In this paper, fuzzy regression is conducted with clustering by modifying noise clustering algorithm. We propose the algorithm that parameters of the premise part and the consequent part are obtained simultaneously, and the parameters obtained are not needed to adapt. We verify the proposed algorithm through simple examples and evaluate the test results compared with existing algorithms. The proposed algorithm shows robust performance against noise and it is easy to implement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.183-188
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• 2013

A variety of cluster analysis techniques prerequisite to cluster objects having similar characteristics in data mining. But the clustering of those algorithms have lots of difficulties in dealing with categorical data within the databases. The imprecise handling of uncertainty within categorical data in the clustering process stems from the only algebraic logic of rough set, resulting in the degradation of stability and effectiveness. This paper proposes a information-theoretic rough entropy(RE) by taking into account the dependency of attributes and proposes a technique called min-mean-mean roughness(MMMR) for selecting clustering attribute. We analyze and compare the performance of the proposed technique with K-means, fuzzy techniques and other standard deviation roughness methods based on ZOO dataset. The results verify the better performance of the proposed approach.