• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.27-33
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• 2008

The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.743-752
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• 2007

Proteins combine with other materials to achieve their function and have similar function if their active sites are similar. Thus we can infer the function of protein by identifying the binding area of proteins. This paper suggests the novel method to select binding area of protein using MCL (Markov Cluster) algorithm. We construct the distance matrix from surface residues distance on protein. Then this distance matrix is transformed to connectivity matrix for applying MCL process. We adopted Catalytic Site Atlas (CSA) data to evaluate the proposed method. In the experimental result using CSA data (94 selected single chain proteins), our algorithm detects the 91 (97%) binding area near by active site of each protein. We introduced a new geometrical features and this mainly contributes to reduce the time to analyze the protein by selecting the residues near by active site.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.288-295
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• 2011

The paper proposes a localization method of an underwater robot using Monte Carlo Localization(MCL) approach. Localization is one of the fundamental basics for autonomous navigation of an underwater robot. The proposed method resolves the problem of accumulation of position error which is fatal to dead reckoning method. It deals with uncertainty of the robot motion and uncertainty of sensor data in probabilistic approach. Especially, it can model the nonlinear motion transition and non Gaussian probabilistic sensor characteristics. In the paper, motion model is described using Euler angles to utilize the MCL algorithm for position estimation of an underwater robot. Motion model and sensor model are implemented and the performance of the proposed method is verified through simulation.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.53-54
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• 2016

빅데이터란 대용량 데이터 활용 및 분석을 통해 가치 있는 정보를 추출하고, 이를 바탕으로 대응 방안 도출 또는 변화를 예측하는 기술을 의미한다. 그리고 빅데이터 분석에 활용되는 데이터인 페이스북과 같은 소셜 데이터, 유전자 발현과 같은 바이오 데이터, 항공망과 같은 지리정보 데이터들은 대용량 네트워크로 구성되어 있다. 네트워크 클러스터링은 서로 유사한 특성을 갖는 네트워크 내의 데이터들을 동일한 클러스터로 묶는 기법으로 네트워크 데이터를 분석하고 그 특성을 파악하는데 폭넓게 사용된다. 최근 빅데이터가 다양한 분야에서 활용되면서 방대한 양의 네트워크 데이터가 생성되고 있고, 이에 따라서 대용량 네트워크 데이터를 효율적으로 처리하는 클러스터링 기법의 중요성이 증가하고 있다. MCL(Markov Clustering) 알고리즘은 플로우 기반 무감독(unsupervised) 클러스터링 알고리즘으로 확장성이 우수해 다양한 분야에서 활용되고 있다. 하지만, MCL은 대용량 네트워크에 대해서는 많은 클러스터링 연산을 요구하며 너무 많은 클러스터를 생성하는 문제를 갖는다. 본 논문에서는 네트워크 압축을 기반으로 한 클러스터링 알고리즘을 제안함으로써 MCL보다 클러스터링 속도와 정확도를 향상시켰다. 또한, 희소행렬을 효율적으로 저장하는 CSC(Compressed Sparse Column) 자료구조와 MapReduce 기법을 제안한 클러스터링 알고리즘에 적용함으로써 대용량 네트워크에 대한 클러스터링 속도를 향상시켰다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.111-118
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• 2010

The proposed algorithm to be explained in this paper is the localization technique using directional antenna. Here, it is assumed that anchor node has the ability to transfer the azimuth of each sector using GPS modules, sector antenna, and the digital compass. In the conventional sensor network, the majority of localization algorithms were capable of estimating the position information of the sensor node by knowing at least 3 position values of anchor nodes. However, this paper has proposed localization algorithm that estimates the position of nodes to continuously move with sensor nodes and traveling nodes. The proposed localization mechanisms have been simulated in the Matlab. The simulation results show that our scheme performed better than other mechanisms (e.g. MCL, DV-distance).

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2001.11a
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• pp.49-52
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• 2001

본 논문에서는 몬테카를로 기법 및 최소결합손실(MCL : Minimum Coupling Loss)기법을 기반으로 하여 무선통신 서비스간 보호비를 만족시키도록 최적의 보호대역을 자동으로 설정할 수 있는 알고리즘을 개발하고 그 견과를 기술하였다. 여기서 개발된 보호대역 자동 설정 알고리즘은 통계적인 몬테카를로 기법 및 간섭원과 대상 수신기 사이에 간섭이 존재하지 않도록 요구되는 이격도 계산을 위한 최소결합손실기법을 적용하여, 주어진 간섭보호비를 만족할 수 있는 최악의 상황에 대한 보호대역을 먼저 설정한 후 보호대역값을 변화시키면서 희망 수신기에서의 간섭확률을 계산을 통해 최적의 보호대역을 설정할 수 있다. 이 기법은 어떠한 형태의 무선통신 서비스에 대해서도 동시에 두가지의 간섭원이 영향이 미칠 경우에도 적용할 수 있도록 설계되었다. 또한 본 연구에서는 TDD시스템간의 간섭 시나리오를 설정하고, 개발된 보호대역 자동 선정 알고리즘을 사용한 시뮬레이터와 ITU-R에서 개발된 SEAMCAT에 의해 얻어진 보호대역 도출 결과를 비교, 분석함으로써 개발된 알고리즘의 신뢰성을 검증하였다.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.58-63
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• 2013

Since radio interference can degrade the performance of systems or limit the system operation, an accurate assessment of radio interference with the existing systems should be conducted prior to the operation of a new system. In this paper, we present an evaluation methodology for the radio interference between radio-navigation satellite service (RNSS) systems and mobile communication service (MS) system. Radio interferences from RNSS systems into MS system using minimum coupling loss (MCL) method are simulated and vice-versa, and the frequency sharing condition between two systems are derived in a same geographical area.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.249-255
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• 2007

Under the situation of a fire, it is difficult for a rescue robot to use sensors such as vision sensor, ultrasonic sensor or laser distance sensor because of diffusion, refraction or block of light and sound by dense smoke. But, braille blocks that are installed for the visaully impaired at public places such as subway stations can be used as a map for autonomous mobile robot's localization and navigation. In this paper, we developed a laser sensor stan device which can detect braille blcoks in spite of dense smoke and integrated the device to the robot developed to carry out rescue mission in various hazardous disaster areas at KIST. We implemented MCL algorithm for robot's attitude estimation according to the scanned data and transformed a braille block map to a topological map and designed a nonlinear path tracking controller for autonomous navigation. From various simulations and experiments, we could verify that the developed laser sensor device and the proposed localization method are effective to autonomous tracking of braille blocks and the autonomous navigation robot system can be used for rescue under fire.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.425-431
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• 2012

In the case of outdoor moving of robot, it is differ to indoor moving case due to it cannot prepare map early for entire outdoor environments, there is nearly no research based on map because most outdoor robots use GPS. In this paper, we implement outdoor robot localization that using precision GPS and then GPS data applying MCL algorithm in outdoor environments of plane of 2 dimensional without incline section. We also perform a simple mission scenario by using applied robot localization in outdoor robot. We applying OpenRTM based on middleware, we can be controlled and grasped situation of the outdoor robot by remote through server by manager.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.345-353
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• 2010

A general solution in global self-position location of robot is to generate multiple hypothesis in self-position of robot, which is to look for the most positive self-position by evaluating each hypothesis based on features of observed landmark. Markov Localization(ML) or Monte Carlo Localization(MCL) to be the existing typical method is to evaluate all pairs of landmark features and generated hypotheses, it can be said to be an optimal method in sufficiently calculating resources. But calculating quantities was proportional to the number of pairs to evaluate in general, so calculating quantities was piled up in wide environments in the presence of multiple pairs if using these methods. First of all, the positive and promising pairs is located and evaluated to solve this problem in this paper, and the newly locating method to make effective use of calculating time is proposed. As the basic method, it is used both RANSAC(RANdom SAmple Consensus) algorithm and preemption scheme to be efficiency method of RANSAC algorithm. The calculating quantity on each observation of robot can be suppressed below a certain values in the proposed method, and the high location performance can be determined by an experimental on verification.