• Proceedings of the KSPS conference
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• 2006.05a
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• pp.41-44
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• 2006

The purpose of state tying is to reduce the number of models and to use relatively reliable output probability distributions. There are two approaches: one is top down clustering and the other is bottom up clustering. For seen data, the performance of bottom up approach is better than that of top down approach. In this paper, we propose a new clustering technique that can enhance the undertrained triphone clustering performance. The basic idea is to tie unreliable triphones before clustering. An unreliable triphone is the one that appears in the training data too infrequently to train the model accurately. We propose to use monophone distance to preprocess these unreliable triphones. It has been shown in a pilot experiment that the proposed method reduces the error rate significantly.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.495-503
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• 2010

The clustering is an important mechanism thai provides information for mobile nodes efficiently and improves the processing capacity for routing and the allocation of bandwidth. This paper proposes a clustering scheme based on the fuzzy relevance degree to solve problems such as node distribution found in the dynamic property due to mobility and flat structure and to enhance the routing performance. The proposed scheme uses the fuzzy relevance degree, ${\alpha}$, to select the cluster head for clustering in FSV (Fuzzy State Viewing) structure. The fuzzy relevance ${\alpha}$ plays the role in CH selection that processes the clustering in FSV. The proposed clustering scheme is used to solve problems found in existing 1-hop and 2-hop clustering schemes. NS-2 simulator is used to verify the performance of the proposed scheme by simulation. In the simulation the proposed scheme is compared with schemes such as Lowest-ID, MOBIC, and SCA. The simulation result showed that the proposed scheme has better performance than the other existing compared schemes.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.744-748
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• 2008

In this paper, we propose a novel technique that generate a new song with given example songs. Our system use k-th order Markov chain of which each state represents notes in a measure. Because we have to consider very high-dimensional space if we use notes in a measure as a state of Markov chain directly, we exploit a hierarchical clustering technique for given example songs to use each cluster as a state. Each given examples can be represented as sequences of cluster ID, and we use them for training data of the Markov chain. The resulting Markov chain effectively gives new song similar to given examples.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.505-513
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• 2006

One of the major reasons for recent blackout, like August 14, 2003 blackout in the US and Canada has been insufficient voltage/reactive power support. For the stable reactive power management, a new approach for the voltage monitoring and control structure is required in the market environment. This paper proposes the effective localized-voltage control scheme using the information from pilot buses at each zone. In this paper, the steady state voltage monitoring and control (SSVMC) is adopted and illustrated for the voltage control scheme during steady state because it is thought as the systemic algorithm to explain voltage profile phenomenon before and after contingencies. And the concept of electrical distance is applied to simultaneously achieve both clustering the voltage control zone, and selecting the pilot bus as the representative node at each control zone. Applying SSVMC based on the structure with clustering and pilot bus enables system operators to monitor and understand the system condition much more easily, to monitor and control the voltage in real-time more manageably, and to respond quickly to a disturbance. The proposed voltage control scheme has been tested on the IEEE 14-bus system with the numerical analysis to examine the system reliability and structure efficiency.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.219-222
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• 2011

There are number of methods that track the movement of an object or the change of state, such as Kalman filter, particle filter, dynamic clustering, and so on. Amongst these method, dynamic clustering method is an useful way to track cluster across multiple data frames and analyze their trend. In this paper we suggest the similarity-based dynamic clustering method, and verifies it's performance by simulation. Proposed dynamic clustering method is how to determine the same clusters for each continuative frame. The same clusters have similar characteristics across adjacent frames. The change pattern of cluster's characteristics in each time frame is throughly studied. Clusters in each time frames are matched against each others to see their similarity. Mamdani fuzzy model is used to determine similarity based matching algorithm. The proposed algorithm is applied to radar reflectivity data over time domain. We were able to observe time dependent characteristic of the clusters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3512-3528
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• 2016

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2568-2584
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• 2015

Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.

• ETRI Journal
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• v.34 no.4
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• pp.617-620
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• 2012

The denoising mapping with the closest-neighbor clustering (CNC) method in denoise-and-forward two-way relay channels is studied. Specifically, the symmetry of the constellations in source terminals A and B is utilized to reduce the complexity of the CNC method. The specific case considered first to illustrate how the constellation symmetry works in the CNC method is the quadrature phase-shift keying constellation in A and B and the single-antenna deployment in all terminals. This case study shows that an enormous complexity reduction can be achieved. Next, the result is extended to multiple-antenna scenarios and square quadrature amplitude modulations.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.18-23
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• 1993

In this paper, we attempt to estimate the state of a finite state system. In such system, we can observe time series data which has some significant behaviors corresponding to its system states. The behavior is characterized by feature parameters extracted from time series. Our thought is that the system output time series data is expressed as a sequence of behavior patterns which are represented by clusters in feature parameters space. An algorithm jointing fuzzy clustering to fuzzy finite state transition model is suggested.