• Journal of IKEEE
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• v.15 no.3
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• pp.191-197
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• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.9
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• pp.1-9
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• 2011

In this paper, we propose an efficient method to create clusters of geographically distributed base stations which cooperatively transmit signals in cellular mobile communication systems. The proposed method utilizes a sequential selection approach to choose candidate base stations which can provide maximum weighted sum-rate gain when they participate in the cooperative transmission with the existing cluster. In particular, the proposed method limits the maximum number of base stations in a cluster by considering the system operational and implementation complexities. Moreover, the combinations of clusters dynamically change along with variations of channel environments. Through computer simulations, performance of the proposed method is verified by comparing with the non-cooperative transmission method and the static clustering method. Numerical result shows that the proposed sequential selection based clustering method is especially advantageous for the performance improvement of lower percentile users in terms of average throughput, and thus the proposed method can effectively improve the fairness among users.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.1-10
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• 2009

Energy efficient operations are essential to increase the life time of wireless sensor network. A cluster-based protocol is the most common approach to preserve energy during a data aggregation. This paper deals with an energy awareness and autonomous clustering method based on time delay. This method consists of three stages. In the first phase, Candidate Cluster Headers(CCHs) are selected based on a time delay which reflects the remaining energy of a node, with considering coverage efficiency of a cluster. Then, time delay is again applied to declare Cluster Headers(CHs) out of the CCHs. In the last phase, the issue on an orphan node which is not included into a cluster is resolved. The simulation results show that the proposed method increases the life time of the network around triple times longer than LEACH(Low Energy Adaptive Cluster Hierarchy). Moreover, the cluster header frequency is less diverse, and the energy on cluster heads is less spent.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.31-35
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• 1997

In this paper, we propose a variable vocabulary word recognizer that is able to recognize new words not exist in training data. For the variable vocabulary word recognizer, we must have an on-line lexicon generator to transform new candidate words to the corresponding pronunciation sequences of phones without any large lexicon table. And, we also must make outputs. In order to model the phones and allophones reliably, we define Korean allophones by triphone clustering based on phonetic knowledge of preceding and succeeding phones of each phone. Using the clustering method, we generated 1,548 allophones with POW (Phonetically Optimized Words) 3,848 word DB. We evaluated the proposed word recognizer with POW 3,848 DB, PBW (Phonetically Balanced Words) 445 DB, and 244 word DB in hotel reservation task. Experimental results showed word recognition accuracy of 79.6% for the POW DB corresponding to vocabulary-dependent case, 79.4% in case of 445 word lexicon and 88.9% in case of 100 word lexicon for the PBW DB, and 71.4% for the hotel reservation DB corresponding to vocabulary-independent case.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.755-760
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• 2005

To transmit a video bit stream over low bandwith, such as mobile, channels, encoding algorithms for high bit rate like H.263+ are used. In transmitting video bit-streams, packet losses cause severe degradation in image quality. This paper proposes a new algorithm for the recovery of missing or erroneous motion vectors when H.263+ bit-stream is transmitted. Considering that the missing or erroneous motion vectors are closely related with those of neighboring blocks, this paper proposes a temporal-spatial error concealment algorithm. The proposed approach is that missing or erroneous Motion Vectors(MVs) are recovered by clustering the movements of neighboring blocks by their homogeneity. MVs of neighboring blocks we clustered according to ALA(Average Linkage Algorithm) clustering and a representative value for each cluster is determined to obtain the candidate MV set. By computing the distortion of the candidates, a MV with the minimum distortion is selected. Experimental results show that the proposed algorithm exhibits better performance in subjective and objective evaluation than existing methods.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.719-728
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• 2005

2-dimensional electrophoresis(2DE) is a separation technique to identify proteins contained in a sample. However, the image is very sensitive to its experimental conditions as well as the quality of scanning. In order to adjust the possible variation of spots in a particular image, a user should manually annotate landmark spots on each gel image to analyze the spots of different images together. However, this operation is an error-prone and tedious job. This thesis develops an automated method of extracting the landmark spots of an image based on landmark profile. The landmark profile is created by clustering the previously identified landmarks of sample images of the same type. The profile contains the various properties of clusters identified for each landmark. When the landmarks of a new image need to be fount all the candidate spots of each landmark are first identified by examining the properties of its clusters. Subsequently, all the landmark spots of the new image are collectively found by the well-known optimization algorithm $A^*$. The performance of this method is illustrated by various experiments on real 2DE images of mouse's brain-tissues.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.7
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• pp.1293-1310
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• 2011

A simple modification of well known Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is proposed to exploit cooperative diversity. Instead of selecting a single cluster-head, we propose M cluster-heads in each cluster to obtain a diversity of order M. The cluster-heads gather data from all the sensor nodes within the cluster using same technique as LEACH. Cluster-heads transmit gathered data cooperatively towards the destination or higher order cluster-head. We propose a code combining based cooperative diversity protocol which is similar to coded cooperation that maximizes the performance of the proposed cooperative LEACH protocol. The implementation of the proposed cooperative strategy is analyzed. We develop the upper bounds on bit error rate (BER) and frame error rate (FER) for our proposal. Space time block codes (STBC) are also a suitable candidate for our proposal. In this paper, we argue that the STBC performs worse than the code combining cooperation.

• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.87-95
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• 2002

This study proposes method to classify components in repository and retrieve them introducing the idea of domain orientation for successful reuse of components. About components of existing systems design pattern was applied to, us suggest component classification method to compare structural similarity between each component in relevant domain and criterion pattern. Component reusability and portability between platforms can be increased through classifying reusable components by function and giving their structures with diagram. Efficiency of component reuse can be raised because the most appropriate component to query and similar candidate components and provided in priority by use of E-SARM algorithm.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.501-512
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• 2014

Stylized facts on asset return are fat-tail, asymmetry, volatility clustering and structure changes. This paper simultaneously captures these characteristics by introducing a multi-regime models: Finite mixture distribution and regime switching GARCH model. Analyzing the daily KOSPI return from $4^{th}$ January 2000 to $30^{th}$ June 2014, we find that a two-component mixture of t distribution is a good candidate to describe the shape of the KOSPI return from unconditional and conditional perspectives. Empirical results suggest that the equality assumption on the shape parameter of t distribution yields better discrimination of heterogeneity component in return data. We report the strong regime-dependent characteristics in volatility dynamics with high persistence and asymmetry by employing a regime switching GJR-GARCH model with t innovation model. Compared to two sub-samples, Pre-Crisis (January 2003 ~ December 2007) and Post-Crisis (January 2010 ~ June 2014), we find that the degree of persistence in the Pre-Crisis is higher than in the Post-Crisis along with a strong asymmetry in the low-volatility (high-volatility) regime during the Pre-Crisis (Post-Crisis).