• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.781-786
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• 2017

This study aims to acquire a basic clustering technology of hybrid rocket motor for lunar lander, including the oxidizer flow distribution characteristic and the simultaneous ignition characteristic. The experimental setups were established to conduct a series of ground firing test of a clustered motor. The gaseous oxygen (GOX) and the HDPE (High Density PolyEthylene) were used as the oxidizer and the solid fuel, respectively. Experimental results which are the simultaneous pyrotechnic ignition characteristic, the oxidizer distribution characteristic and the pressure traces of each combustion chamber imply that the hybrid rocket clustered motor works successfully.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.258-268
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• 2012

Wireless Sensor Network(WSN) is constituted by low-cost and low-energy, So the most important issue is that the task of the sensor performs successfully by using less energy. In previous WSN, determination of the header and gathering sensor data solution by header give great affection to the performance of network. In this paper, we propose a Hybrid transmission method which considers the direction of data collections. In the proposed hybrid routing method, all of the sensors determine that transmission the data to the sink node directly or indirectly using the head node depend on the location of the head node in the cluster. The performance is compared with the LEACH(Low Energy Adaptive Clustering Hierarchy) by experimental analysis. The results show that the preposed method can reduce the communication distance and energy consumption by avoiding the detour direction of transmission of the data.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.33-40
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• 2003

Data mining plays an important role in a knowledge discovery process and various algorithms of data mining can be selected for the specific purpose. Most of traditional hierachical clustering methode are suitable for processing small data sets, so they difficulties in handling large data sets because of limited resources and insufficient efficiency. In this study we propose a hybrid neural networks clustering technique, called PPC for Pre-Post Clustering that can be applied to large data sets and find unknown patterns. PPC combinds an artificial intelligence method, SOM and a statistical method, hierarchical clustering technique, and clusters data through two processes. In pre-clustering process, PPC digests large data sets using SOM. Then in post-clustering, PPC measures Similarity values according to cohesive distances which show inner features, and adjacent distances which show external distances between clusters. At last PPC clusters large data sets using the simularity values. Experiment with UCI repository data showed that PPC had better cohensive values than the other clustering techniques.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.35-40
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• 2009

FCM 알고리즘은 입력 벡터와 각 클러스터의 유클리드 거리를 이용하여 구해진 소속도만를 비교하여 데이터를 분류하기 때문에 클러스터링 된 공간에서의 데이터들의 분포에 따라 바람직하지 못한 클러스터링 결과를 보일 수 있다. 이러한 문제점을 개선하기 위해 대칭적 성질을 이용하는 대칭성 측도에 퍼지 이론을 적용하여 군집간의 거리에 따른 변화와 군집 중심의 위치, 그리고 군집 형태에 따라 영향을 덜 받는 개선된 FCM이 제안되었다. 본 논문에서는 효과적으로 패턴을 분류하기 위해 개선된 FCM 알고리즘을 적용한 개선된 하이브리드 네트워크를 제안한다. 제안된 하이브리드 네트워크는 개선된 FCM 알고리즘을 입력층과 중간층의 학습구조 적용하고 중간층과 출력층의 학습구조는 일반화된 델타학습법을 적용한다. 제안된 방법의 인식성능을 평가하기 위해 2차원 좌표평면 상의 데이터를 기존의 Max_Min 신경망을 이용한 FCM 기반 RBF 네트워크와 FCM 기반 RBF 네트워크, HCM 기반 네트워크와 제안된 방법 간의 학습 및 인식 성능을 비교 및 분석하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.250-251
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• 2015

본 논문에서는 자기 조직화 기능을 갖는 Kohonen의 SOM(Self organization map) 신경회로망과 주어지는 데이터에 따라 초기의 클러스터 개수를 설정하여 처리하는 수정된 K-Means 알고리즘을 결합한 Hybrid Kohonen Network 를 제안한다. 또한, 실제의 항공영상에 적용하여 고전적인 K-Means 알고리즘 및 고전적인 SOM 알고리즘보다 우수함을 보인다.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.159-168
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• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.384-396
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• 2003

Hybrid query processing method is used for preventing server overload that is created by heavy user connection in Web GIS. In Hybrid query processing method, both server and client participate in spatial query processing. But, Hybrid query processing method is restricted in scalability of server and it can't be fundamentally solution for server overload. So, it is necessary for Web GIS to be brought in web clustering technique. In this thesis, we propose load-balancing method that uses proximity of query region. In this paper, we create tile groups that have relation each tile in same group is very close, and forward client request to the server that can have maximum rate of buffer reuse with considering characteristic of spatial query. With out load balancing method, buffet in server is optimized for exploring spatial index tree and increase rate of buffer reuse, so it can be reduced amount of disk access and increase system performance.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.253-258
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• 2014

In smartphone sensing which monitors various social phenomena of the individuals by using embedded sensors, managing metadata is one of the important issue to process large-scale data, improve the data quality, and share collected data. In this paper, we proposed a document clustering scheme for the large-scale metadata management architecture which is designed as a hybrid back-end consisting of a cluster head and member nodes to reduce the server-side overhead. we also verified that the proposed scheme is more efficient than the distance based clustering scheme in terms of the server-side overhead through simulation results.

• Journal of the Korea Society for Simulation
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• v.15 no.1
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• pp.69-75
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• 2006

P2P services occupy more then 50% of the internet traffics. A huge number of query packets are generated since pure P2P based models rely on message flooding for their query mechanisms. In this study, the numbers of query messages generated in the pure P2P and hybrid P2P model are analyzed. The results show that hybrid P2P models also could suffer from message flooding. To reduce the message flooding, this study proposes an interest based clustering mechanism for hybrid P2P services. By applying this clustering algorithm, it could reduce 99.998% of the message flooding. The proposed algorithm also reduces the cost of the joining operations by storing previous supernodes.