• Proceedings of the Korea Information Processing Society Conference
• /
• 2005.11a
• /
• pp.189-192
• /
• 2005

비디오 분할의 목적은 같은 내용들을 가지는 프레임들의 순서를 표현하는 각 샷의 비디오 순서 분할을 위한 것이다. 그리고 색인에 대한 각 샷으로부터 키 프레임을 선택한다. 존재하는 비디오 분할 방법들은 2가지 그룹들로 분류될 수 있다. 먼저 경계값이 할당되어야만 하는 샷 전환 검출(SCD) 접근과 클러스터 수의 사전 지식이 요구되는 클러스터 접근이다. 본 논문에서는 컬러-$x^2$명도 히스토그램 기반 FCM(fuzzy c-means) 클러스터링 알고리즘을 사용하는 비디오 분할 방법을 제안하였다. 이 알고리즘은 앞에서 기술한 2가지 접근의 혼합이다. 그리고 이것은 두 가지 접근들의 결점을 극복하도록 설계 되었다. 실험 결과들은 컬러-$x^2$명도 히스토그램 기반 FCM 클러스링 알고리즘이 강건하고 비디오 시퀀스들의 다양한 형태들에 응용할 수 있다고 제안한다.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1644_1645
• /
• 2009

본 논문은 비정상 시계열 예측을 위한 다중모델 퍼지 시스템과, 제안된 시스템의 최적화를 위한 유전 알고리즘의 응용을 다룬다. 일반적으로, 퍼지 예측시스템의 성능은 비선형 데이터가 가지고 있는 다양한 패턴이나 법칙성, 경향 등을 잘 분석하고 시스템에 반영함으로써 개선될 수 있다. 따라서, 본 논문은 원형 시계열의 특성을 보다 잘 반영할 수 있는 그들의 차분데이터를 시스템에 적용하며, 생성 가능한 차분 데이터들 중 원형 시계열의 특징에 가까운 일부를 추출하여 다중모델 퍼지 예측 시스템을 구현함으로써 다양한 원형시계열의 패턴이나 법칙성 등이 고려될 수 있도록 하였다. 다중 모델 퍼지 시스템의 각각의 예측기에는 구조가 간단한 k-means 클러스터링 기법을 적용하여 구현의 용이성을 꽤하였으며, 성능평가를 통해 선택된 최종 예측기는 RCGKA(real-coded genetic k-means clustering algorithms)를 통해 더욱 최적화된 규칙기반을 가지게 함으로써 예측성능이 개선될 수 있도록 하였다. 본 논문에 사용된 최적화 기법인 RCGKA에는 또한 성능이 우수한 다양한 유전연산자를 도입하여 더욱 예측기 성능이 강화될 수 있도록 하였으며, 시뮬레이션을 통해 제안된 예측시스템의 효용성을 증명하였다.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.1
• /
• pp.99-106
• /
• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.1924-1925
• /
• 2011

본 논문에서는 비선형 기동을 하는 기동표적의 추정된 잡음을 분석하여 표적의 기동패턴을 분석하는 알고리즘을 제시하고자 한다. 기동표적의 추정위치와 측정치에서 발생하는 잡음을 가속도와 순수 잡음으로 분리하고 분리된 성분을 분석하여 표적의 기동 패턴을 인식하고 동시에 추적을 실시하는 알고리즘을 구성한다. 잡음의 분리는 퍼지 클러스터링(FCM : Fuzzy C-means Clustering) 기법을 이용하여 적절한 추정값을 이용한다. 추정된 표적의 속도와 가속도, 잡음을 재 구성하여 기동표적의 기동패턴을 분석하고, 동시에 추적을 실시한다. 위의 과정을 통해 가속도를 분리한 후 비선형성을 지닌 기동표적의 기동패턴을 선형화 하여 칼만필터를 이용 잡음을 분리하고 가속도를 다시 보상하여 추적 알로리즘을 구성한다. 그리고 제안된 알고리즘의 수행 가능성을 보여 주기 위하여 몇 가지 예를 제시하였다.

• Journal of Korea Multimedia Society
• /
• v.18 no.12
• /
• pp.1555-1561
• /
• 2015

This paper tries to converge computer and art by applying anamorphosis principle in drawing technique to mobile application. As comparing to current anamorphosis which shows one image at the round cup, we focus on the variability which shows several variable images at the mobile device according to the color board. The usage of the proposed algorithm is able to extended to various areas such as souvenir and public relation.

• Asian Journal of Business Environment
• /
• v.12 no.1
• /
• pp.11-16
• /
• 2022

Purpose: This study is aimed to map the provinces in Indonesia based on the education and ICT indicators using several unsupervised learning algorithms. Research design, data, and methodology: The education and ICT indicators such as student-teacher ratio, illiteracy rate, net enrolment ratio, internet access, computer ownership, are used. Several approaches to get deeper understanding on provincial strength and weakness based on these indicators are implemented. The approaches are Ensemble K-Mean and Fuzzy C Means clustering. Results: There are at least three clusters observed in Indonesia the education quality, participation, facilities and ICT Access. Cluster with high education quality and ICT access are consist of DKI Jakarta, Yogyakarta, Riau Islands, East Kalimantan and Bali. These provinces show rapid economic growth. Meanwhile the other cluster consisting of six provinces (NTT, West Kalimantan, Central Sulawesi, West Sulawesi, North Maluku, and Papua) are the cluster with lower education quality and ICT development which impact their economic growth. Conclusions: The provinces in Indonesia are clustered into three group based on the education attainment and ICT indicators. Some provinces can directly implement e-learning; however, more provinces need to improve the education quality and facilities as well as the ICT infrastructure before implementing the e-learning.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.6
• /
• pp.146-153
• /
• 2010

For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

• Smart Structures and Systems
• /
• v.19 no.2
• /
• pp.127-136
• /
• 2017

Fiber reinforced polymer (FRP) has received widespread attention in the field of civil engineering because of its superior durability and corrosion resistance. This article presents the damage mechanisms of a novelty composite called carbon nanofiber modified flax fiber polymer (CNF-modified FFRP). The ability of acoustic emission (AE) to detect damage evolution for different configurations of specimens under uniaxial tension was examined, and some useful AE characteristic parameters were obtained. Test results shows that the mechanical properties of modified composites are associated with the CNF content and the evenness of CNF dispersed in the epoxy matrix. Various damage mechanisms was established by means of scanning electron microscope images. The fuzzy c-means clustering were proposed to classify AE events into groups representing different generation mechanisms. The classifiers are constructed using the traditional AE features -- six parameters from each burst. Amplitude and peak-frequency were selected as the best cluster-definition features from these AE parameters. After comprehensive comparison, a correlation between these AE events classes and the damage mechanisms observed was proposed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.2
• /
• pp.249-253
• /
• 2004

When we decide the software quality on the basis of the software measurement, the transitive property which is a requirement for an equivalence relation is not always satisfied. Therefore, we propose a scheme for classifying the software quality that employs a tolerance relation instead of an equivalence relation. Given the experimental data set, the proposed scheme generates the tolerant classes for elements in the experiment data set, and generates the tolerant ranges for classifying the software quality by clustering the means of the tolerance classes. Through the experiment, we showed that the proposed scheme could product very useful and valid results. That is, it has no problems that we use as the criteria for classifying the software quality the tolerant ranges generated by the proposed scheme.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.99-100
• /
• 2006

One of the fundamental problems in sensor networks is the deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation using two dimensional L shape model. The actual length of the L shape model is about 100m each. We found the minimum number of 15 nodes are sufficient for the complete coverage of modeled area. We also found the optimum location of each nodes. The real deploy experiment using 15 sensor nodes shows the 95.7%. error free communication rate.