• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.1014-1034
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• 2015

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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• pp.131-133
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• 2000

In this paper, we proposed a new method for localizing the independent sources generating the observed EEG based on independent component analysis (ICA). The performance of the algorithm was tested through computer simulations.

• BMB Reports
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• 제43권10호
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• pp.670-676
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• 2010

In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.

• 한국컴퓨터정보학회논문지
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• 제13권5호
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• pp.77-84
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• 2008

본 논문에서는 자연영상에 존재하는 문자들을 효율적으로 검출하기 위한 새로운 접근 방법을 제안한다. 빛 또는 조명의 영향에 의해 획득된 영상 내에 존재하는 반사성분은 문자 또는 관심객체들의 경계가 모호해 지거나 관심객체와 배경이 서로 혼합되었을 경우, 문자추출 및 인식을 함에 있어서 오류를 포함시킬 수 있다. 따라서 영상 내에 존재하는 반사성분을 제거하기 위해 먼저, 영상으로부터 Red컬러 성분에 해당하는 히스토그램에서 두개의 피크 점을 검출한다. 검출된 두 개의 피크 점들 간의 분포를 사용하여 노말 또는 편광 영상에 해당하는지를 판별한다. 노말 영상의 경우 부가적인 처리를 거치지 않고 문자영역을 검출하며 편광 영상인 경우 조명성분을 제거하기 위해 호모모픽 필터링 방법을 적용하여 반사성분에 해당하는 영역을 제거한다. 그리고 문자영역을 검출하기 위해 색 병합과 세일런스 맵을 이용하여 각각의 문자 후보영역을 결정한다. 마지막으로 두 후보영역을 이용하여 최종 문자영역을 검출한다.

• 대한기계학회논문집A
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• 제31권4호
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• pp.517-524
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• 2007

This paper presents a global localization method using circular correlation of an omni-directional image. The localization of a mobile robot, especially in indoor conditions, is a key component in the development of useful service robots. Though stereo vision is widely used for localization, its performance is limited due to computational complexity and its narrow view angle. To compensate for these shortcomings, we utilize a single omni-directional camera which can capture instantaneous $360^{\circ}$ panoramic images around a robot. Nodes around a robot are extracted by the correlation coefficients of CHL (Circular Horizontal Line) between the landmark and the current captured image. After finding possible near nodes, the robot moves to the nearest node based on the correlation values and the positions of these nodes. To accelerate computation, correlation values are calculated based on Fast Fourier Transforms. Experimental results and performance in a real home environment have shown the feasibility of the method.

• 대한의용생체공학회:의공학회지
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• 제31권6호
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.161-167
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• 2012

It is known that big brown bats can distinguish echo of a prey at various angles. In this paper, we suggest a new object localization strategy using ultrasonic echolocation. We calculate the relative energy ratio between a high frequency component of ultrasound signal and a low frequency component of ultrasound signal for a target object. We found the measure depends on bearing angle of the object in space. We also tested energy ratio of echoed FM ultrasound signals depending on frequency, based on cross-correlation. It can determine the relative angular position of objects even though the reflected signals are congested form each object.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• 전자공학회논문지CI
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• 제41권6호
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• pp.47-53
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• 2004

본 논문에서는 외향 기반 접근법을 기반으로 한 로봇의 위치 추정 알고리즘을 제안한다. 우선, 제안한 알고리즘은 주성분 분석(PCA: Principal Component Analysis)을 이용하여 취득한 영상들을 eigenspace로 투영시킴으로써 영상을 압축한다. 추출된 주성분은 eigenspace에서의 연속 외향 함수(continuous appearance function)로 나타낼 수 있다. 신경 회로망은 로봇의 위치추정을 위해 새로운 영상이 주어지면 이것을 eigenspace로 투영 시킨 후 연속 외향 함수를 통해 로봇의 현재 위치를 추정한다. 최종적으로는, 영상안의 데이터에 칼만 필터를 적용함으로써 로봇의 정확한 위치를 추정할 수 있다. 제안한 알고리즘을 실제 이동 로봇에 탑재하여 적용시킨 결과 로봇의 위치를 정확히 추정할 수 있음을 확인 할 수 있었다.

• 한국멀티미디어학회논문지
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• 제6권6호
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• pp.1000-1007
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• 2003

이 논문은 입술의 모양을 효과적으로 추출하는 방법을 제안하였다. 입술은 Point Distribution Model에 근거하여 점들의 집합으로 표현하였다. Isodata군집 알고리듬을 이용하여 전체 학습 영상을 입술 형태별로 군집화 하고 주성분 분석법을 사용하여 각 군집에 대한 입술의 형태 모델을 구하였다 추출 결과가 입력 영상의 실제 입술 위치를 올바르게 찾았는지 판정하기 위하여 입술 경계선 주변의 화소값들을 이용한 입술의 경계선 모델을 구하고 이를 반영하는 평가함수를 구성하였다. 형태 차이를 반영하여 얻은 입술 형태 모델을 사용하여 입술을 추출하기 때문에 전체 학습 영상의 평균 모양과 많은 차이를 보이는 입술을 정확하게 추출할 수 있었다. 여러 영상을 대상으로 실험하여 약 92%의 성공률을 얻었다.