• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.237-240
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• 2004

In this study, the digital image processing with image enhancement based on homomorphic filtering was performed using geophysical imaging data such as gravity, magnetic data and sub-scenes of satellite images such as LANDSAT, IKONOS, and KOMPSAT. Windows application program for executing homomorphic filtering was designed and newly implemented. In general, homomorphic filtering is technique that is based on Fourier transform, which enhances the contrast of image by removing the low frequencies and amplifying the high frequencies in frequency domain. We can enhance the image selectively using homomorphic filtering as compared with the existing method, which enhance the image totally. Through several experiment using remotely sensed imagery and geophysical image with this program, it is concluded that homomorphic filtering is more effective to reveal distinct characteristics for some complicated and multi-associated features on image data.

• 한국멀티미디어학회논문지
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• 제18권6호
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• pp.691-700
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• 2015

Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• 대한토목학회논문집
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• 제33권2호
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• pp.691-702
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• 2013

본 연구에서는 요일별 교통량 변동 패턴 기반 평활화법을 활용하여 정량적 이상치 판정 알고리즘을 개발하였다. 또한 개발된 알고리즘을 활용하여 2010년 일반국도 상시조사 지점 중 14개 지점의 교통량 자료에 대한 이상치 필터링을 수행하여 알고리즘의 적합성 여부를 평가하였다. 그 결과 정상일 필터링율은 98.2%, 이상일 중 오필터링율은 8.0%로 평가되었다. 따라서 본 연구에서 개발된 알고리즘은 수집된 교통량 자료의 1차적인 이상치 필터링에 충분히 적용 가능할 것이다.

• 한국컴퓨터정보학회논문지
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• 제12권1호
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• pp.73-82
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• 2007

본 논문에서는 연속질의에 대한 집계연산을 수행할 때, 센서 네트워크의 수명을 길게 하기 위해 각 센서 및 클러스터 헤드에서의 데이터 전송량을 줄이기 위한 방법을 제안한다. 센서의 에너지 소모를 줄이는 가장 중요한 요소는 전승되는 메시지 수를 줄이는 것이다. 본 논문에서 제안하는 방법은 기본적으로 클러스터링, 네트워크 내 집계 및 계층적 필터링을 결합한 것이다. 계층적 필터링이란 센서 네트워크를 두 계층으로 나누어 필터링하는 것이다. 1계층 필터링은 클러스터 멤버에서 클러스터 헤드로 데이터를 전송시 필터링을 수행하고, 2계층 필터링은 클러스터 헤드에서 기지국으로 데이터를 전송시 필터링을 수행한다. 이 방법은 기존의 데이터 필터링 방법보다 더 효율적이고 효과적인 방법이다. 다양한 실험을 통해서, 제안한 방법이 다른 방법들보다 더 많은 메시지를 줄이고. 네트워크의 생존기간이 더 증가하였음을 보여준다.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.77-82
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• 2013

최근 주목을 받고 있는 Particle Filtering은 실제 객체 추적에서 발생하는 비선형, 비 가우시안 분포를 가지는 상태 벡터의 사후확률을 추정하기 위한 Monte Carlo 시뮬레이션에 기반을 둔 추적 방법론이다. 우리는 본 논문에서 Particle Filtering을 이용한 객체 추적성능을 향상시킬 수 있는 두 가지 방법론을 제안한다. 첫 번째는 확률이 가장 낮은 샘플을 이전 프레임의 추정된 상태 벡터로 대치하는 피드백 방법론이고, 두 번째는 객체 확률 분포를 추정된 객체 후보영역에 역투영하여 신뢰구간을 구함으로써 추적 박스의 정확도를 향상시키는 방법이다. 또한, 실험을 통해 구한 추적 샘플의 진화 방정식을 제시하였다. 우리는 다양한 상황이 설정된 실험 데이터 셋을 구성하여 실험을 실시하여 제안한 방법론의 우수성을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4183-4204
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• 2018

The existing en-route filtering schemes only consider some simple false data injection attacks, which results in lower safety performance. In this paper, we propose an efficient geographical information-based en-route filtering scheme (EGEFS), in which each forwarding node verifies not only the message authentication codes (MACs), but also the report identifier and the legitimacy and authenticity of locations carried in a data report. Thus, EGEFS can defend against not only the simple false data injection attacks and the replay attack, but also the collusion attack with forged locations proposed in this paper. In addition, we propose a new method for electing the center-of-stimulus (CoS) node, which can ensure that only one detecting node will be elected as the CoS node to generate one data report for an event. The simulation results show that, compared to the existing en-route filtering schemes, EGEFS has higher safety performance, because it can resist more types of false data injection attacks, and it also has higher filtering efficiency and lower energy expenditure.

• International journal of advanced smart convergence
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• 제1권1호
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• pp.48-51
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• 2012

The conventional method for filtering force plate data, low-pass filtering, does not always give accurate results when applied to force data from a custom-made, instrumented treadmill. Therefore, this study compares low-pass filtered data to the same data passed through a wavelet filter. We collected data with the treadmill running. However these include motor noise with ground reaction force at two force plates. We found that he proposed wavelet method eliminated motor noise to result in more accurate force plate data than the conventional low-pass filter, particularly at high speed motor operation. In this study we suggested the convolution wavelet (CNW) which was compared to that of a low-pass filter. The CNW showed better performance as compared to band-pass filtering particularly for low signal-to-noise ratios, and a lower computational load.

• 로봇학회논문지
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• 제3권3호
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• pp.176-185
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• 2008

In this paper, new filtering method for sensor registration is provided to estimate and correct error of registration parameters in multiple sensor environments. Sensor registration is based on filtering method to estimate registration parameters in multiple sensor environments. Accuracy of sensor registration can increase performance of data fusion method selected. Due to various error sources, the sensor registration has registration errors recognized as multiple objects even though multiple sensors are tracking one object. In order to estimate the error parameter, new nonlinear information filtering method is developed using minimum mean square error estimation. Instead of linearization of nonlinear function like an extended Kalman filter, information estimation through unscented prediction is used. The proposed method enables to reduce estimation error without a computation of the Jacobian matrix in case that measurement dimension is large. A computer simulation is carried out to evaluate the proposed filtering method with an extended Kalman filter.

• Journal of Computational Design and Engineering
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• 제2권2호
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• pp.113-118
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• 2015

An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.

• International Journal of Advanced Culture Technology
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• 제1권2호
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• pp.1-6
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• 2013

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user's preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.