• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.100-105
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• 2013

In this study, we analyze the balance of quadruped walking robots. For this purpose, a simplified polygonal model of a quadruped walking configuration is considered. A boundary-range-based balance margin is used for determining the system stability of the polygonal walking configuration considered herein. The balance margin enables the estimation of the walking configuration's balance for effective walking. The usefulness of the balance margin is demonstrated through exemplary simulations. Furthermore, balance compensation by means of foot stepping is addressed.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.313-315
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• 2000

클러스터링에 있어서 K-means와 FCM(Fuzzy C-means)와 같은 기존의 알고리즘들은 지역적 최소 해에 수렴될 문제와 사전에 클러스터 개수를 결정해야 하는 문제점을 가지고 있다. 본 논문에서는 병렬 탐색을 통해 최적 해를 찾는 진화 알고리즘을 사용하여 지역적 최소 해에 수렴되는 문제점을 개선하였으며, 클러스터의 특성을 표준편차 벡터를 계산하여 중심으로부터 포함된 데이터가 얼마나 분포되어 있는지 알 수 있는 분산도와 임의의 데이터와 모든 중심들간의 거리의 비율로서 얻어지는 소속정도를 고려하여 클러스터간의 간격을 알 수 있는 분리도를 정의함으로써 자동으로 클러스터 개수를 결정할 수 있게 하였다. 실험데이터와 가우시안 분포에 의해 생성된 다차원 실험데이터를 사용하여 제안한 알고리즘이 이러한 문제점들을 해결하고 있음을 보인다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.9
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• pp.55-65
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• 2004

ATM was recommended by the ITU and ATM Forum as a means of transportation for B-ISDN. At this time, due to the comprehensive mature of ATM protocol, ATM has been adapted as the backbone system for carrying Internet traffi $c^{[1,2,3,4]}$. But major conceptsregarding the ATN protocol will be used on future technology. This paper presents preventive congestion control mechanisms for detecting HTR(Hard-To Reach) code in ATM systems, in particular for an improved HTR call registration method using network signaling information will discussed. In high speed circuit switching system environments, a fast HTR control mechanism is necessary. We present research results for improving HTR call registration and control methods using network signaling information and fuzzy control mechanisms. We concluded that it showed fast congestion avoidance mechanisms with a fewer system load maximized the efficiency of network resources by restricting ineffective machine attempts.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.34-36
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• 2006

In this paper, we will treat the diagnosis problem to accurately determine fault types. The judgement of fault types is accomplished by observing the cluster newly formed with faults and clustering the input current waveforms to intrinsically show the conditions with the dignet that is a clustering algorithm. The types of input current waveforms are, however, constrained during normal operation, though it considers the load character. In case of faults. new clusters are generated outside the clusters. which appear during normal operation, because the input current waveforms of the induction motor are generated by the type which is not observed in case of faults. The diagnosis about the types of faults is essential to building a fault tree about the induction motor, and it removes the causes of the faults using a fuzzy logic. We, first, constitute a fault tree, which connects with the parts and the entire system of the induction motor, and investigate fault modes which can be generated from the fault tree and the relationship of the cause and the effect of each part (of the motor). Also, we distinguish the faults of each part by means of inducing the said of fuzzy relation equations encapsulating the relationship of the fault modes and each part.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.6
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• pp.516-526
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• 2016

This paper introduces a hovering-type autonomous underwater vehicle (AUV) developed for research and its fundamental motion performance results obtained by simulation and field test. The AUV can control its motion in four degrees of freedom (DOF) by means of its horizontal and vertical thrusters, and it is designed to provide a test-bed that facilitates ease of operation and experimentation. Prior to the field tests, six DOF equations of motion are developed, and a simulation program is constructed using MATLAB and Simulink to verify the essential motion performance of the designed vehicle. Furthermore, a proportional-integral-derivative (PID) controller and fuzzy PID controller are designed, and their performances are verified through a simulation. Field tests are performed to verify the motion performance of the AUV; way-point tracking is executed by the PID and fuzzy PID controllers. The results confirmed appropriate control performance under current disturbances.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.77-90
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• 2012

As the influence of service industry into domestic economy is increasing, it also has been influenced more powerfully at the domestic GDP(Gross Domestic Product) relative importance and employment relative importance continuously. By means of these reason, the Korean government announced the synthetic countermeasure for strengthening the competitive power of service industry in December, 2006. After then, it announced phase I of 'service industry advancing plan' which is concrete execution plan in April, 2008. Also, it is announced to announce phase II plans in september, 2008 and phase III plan in December, 2008 additionally. Service quality is the most powerful competitive means at service marketing and a key point for improving service productivity. The improvement of service quality has considerable influence on survival and profitability of service industry. Therefore, the study of objective service quality measurement in service industry is very important issue. The purpose of this study is to suggest a measurement model of service quality using fuzzy theory and AHP which measuring service quality objectively in subjective environment. It is expected that this study will help the improvement of competition power of service industry as well as establishment of effective decision-making.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1203-1208
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• 2016

For young children who are not spontaneous or not accurate in verbal communication of their emotions and experiences, drawing is a good means of expressing their status in mind and thus drawing analysis with chromatics is a traditional tool for art therapy. Recently, children enjoy digital drawing via painting tools thus there is a growing needs to develop an automatic digital drawing analysis tool based on chromatics and art therapy theory. In this paper, we propose such an analyzing tool based on dominant color analysis. Technically, we use ART2 clustering and fuzzy logic to understand the fuzziness of subjects' status of mind expressed in their digital drawings. The frequency of color usage is fuzzified with respect to the membership functions. After applying fuzzy logic to this fuzzified central vector, we determine the dominant color and supporting colors from the digital drawings and children's status of mind is then analyzed according to the color-personality relationships based on Alschuler and Hattwick's historical researches.