• 한국음향학회지
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• 제39권3호
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• pp.179-190
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• 2020

본 논문에서는 일반 빔형성, 적응 빔형성, 압축센싱을 이용하여 도래각을 추정한다. 이때, 적응 빔형성으로 Minimum Variance Distortionless Response(MVDR)과 Multiple Signal Classification(MUSIC)을 사용하였고, 압축 빔형성(compressive sensing beamforming)을 위해 무격자 압축센싱(grid-free compressive sensing)을 적용하였다. 앞서 언급한 각 기법들의 이론적 배경 및 한계점을 소개하고 모의실험 및 실제 실험을 통해 각 기법의 성능을 비교하였다. 실제 실험은 반사 신호가 존재하는 환경에서 수행하였으며, 2대의 스피커를 통해 음원을 송신하고 8대의 마이크로폰을 사용한 선배열을 통해 데이터를 수신하였다. 모의실험 및 실제 실험 결과를 통해 상관신호가 존재하지 않을 때, 적응 빔형성과 무격자 압축 빔형성이 일반 빔형성보다 고해상도의 결과를 보임을 확인하였다. 반면, 반사 신호가 존재하는 환경에서 적응 빔형성의 성능이 저하되지만, 무격자 압축 빔형성의 경우 반사 신호의 영향을 받지 않고 일반 빔형성의 분해능이 개선됨을 확인하였다.

• ETRI Journal
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• 제30권1호
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• pp.89-100
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• 2008

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4386-4404
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• 2016

Most existing salient object detection algorithms commonly employed segmentation techniques to eliminate background noise and reduce computation by treating each segment as a processing unit. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. In this paper, a novel salient object detection algorithm is proposed based on region merging. An adaptive-based merging scheme is developed to reassemble regions based on their color dissimilarities. The merging strategy can be described as that a region R is merged with its adjacent region Q if Q has the lowest dissimilarity with Q among all Q's adjacent regions. To guide the merging process, superpixels that located at the boundary of the image are treated as the seeds. However, it is possible for a boundary in the input image to be occupied by the foreground object. To avoid this case, we optimize the boundary influences by locating and eliminating erroneous boundaries before the region merging. We show that even though three simple region saliency measurements are adopted for each region, encouraging performance can be obtained. Experiments on four benchmark datasets including MSRA-B, SOD, SED and iCoSeg show the proposed method results in uniform object enhancement and achieve state-of-the-art performance by comparing with nine existing methods.

• 정보처리학회논문지B
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• 제14B권2호
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• pp.99-106
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• 2007

본 논문에서는 적응적 관심영역(AAW: Adaptive Attention Window)에 기반한 세포영상 분할 기법을 제안한다. 적응적 관심영역은 분할하기 위해, 명암지도를 이용하여 초기 관심윈도우(IAW: Initial AW)를 생성한다. 생성된 초기 관심윈도우는 쿼드-트리 분할을 이용하여 실제의 관심영역(ROI: Region of Interest)과 유사한 크기가 될 때까지 축소된다. 이렇게 생성된 적응적 관심윈도우는 세포 영상에서 배경을 제거하고 관심영역 추출의 처리 시간을 줄이기 위해서 사용된다. 마지막으로 적응적 관심영역 안에서 영역을 분할하고, 관심영역만을 분리하기 위한 영역 병합과 제거를 수행한다. 실험에서 제안된 기법은 세포영상의 관심영역을 효과적으로 분리하여 인간 시각과 유사한 향상된 영상 분할 결과를 보여준다.

• 한국정보컨버전스학회:학술대회논문집
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• 한국정보컨버전스학회 2008년도 International conference on information convergence
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• pp.113-116
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• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• 한국전문물리치료학회지
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• 제26권3호
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• pp.106-114
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• 2019

Background: It is difficult to assess whether community-dwelling frail older adults may remain pre-frail status or improve into a robust state without being directly checked by health care professionals. The health information perceived by older adults is considered to be one of best sources of potential concerns in older adult population. An online measurement system combined with item response theory (IRT) and computer adaptive testing (CAT) methods is likely to become a realistic approach to remotely monitor physical activity status of frail older adults. Objects: This article suggests an approach to provide a precise and efficient means of measuring physical activity levels of community-dwelling frail older adults. Methods: Article reviews were reviewed and summarized. Results: In comparison to the classical test theory (CTT), the IRT method is empirically aimed to focus on the psychometric properties of individual test items in lieu of the test as a whole. These properties allow creating a large item pool that can capture the broad range of physical activity levels. The CAT method administers test items by an algorithm that select items matched to the physical activity levels of the older adults. Conclusion: An online measurement system combined with these two methods would allow adequate physical activity measurement that may be useful to remotely monitor the activity level of community-dwelling frail older adults.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.371-378
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• 2000

In this study an adaptive node generation procedure in the Element-free Galerkin (EFG) method using bubble-meshing technique is proposed. Based on the error function that obtained by projected error estimation method, the initial node arrangement is defined along the background cell that is used in the numerical integration. To obtain the smooth nodal configuration, the nodal configuration are regenerated by bubble-meshing technique. This bubble meshing technique was originally developed to generate a set of well-shaped triangles and tetrahedra. Its basic idea is packing circles or spheres, called bubble, into the specified area or space naturally using some dynamic equations with attracting and repelling force. To demonstrate the performance of proposed scheme, the convergence behaviors are investigated for several problems.

• 전기학회논문지P
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• 제52권1호
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.