• Journal of Magnetics
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• 제21권2호
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• pp.286-292
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• 2016

This study evaluates the change of computer tomography (CT) number in the case of the metal artifact reduction (MAR) algorithm, using the phantom. The images were obtained from dual CT using a gammex 467 tissue characterization phantom, which is similar to human tissues. The test method was performed by dividing pre and post MAR algorithm and measured CT values of nonmagnetic materials within the phantom. In addition, the changes of CT values for each material were compared and analyzed after measuring CT values up to 140 keV, using the spectral HU curve followed by CT scan. As a result, in the cases of N rod (trabecular bone) and E rod (trabecular bone), the CT numbers decreased as keV increasing but were constant above 90 keV. In the cases of I rod (dense bone) and K rod (dense bone), the CT numbers also decreased as keV increased but were uniform above 90 keV. The CT numbers from 40 keV to 140 keV were consistent in the cases of J rod (liver), D rod (liver), L rod (muscle), and F rod (muscle). For A rod (adipose), G rod (adipose), B rod (breast) and O rod (breast), the CT numbers increased as keV increased but were constant after 90 keV. The CT numbers from 40 keV to 140 keV were consistent in the cases of C rod (lung (exhale)), P rod (lung (exhale)), M rod (lung (inhale)) and H rod (lung (exhale)). Conclusively, because dual CT exhibits no changes in image quality and is able to analyze nonmagnetic materials by measuring the CT values of various materials, it will be used in the future as a useful tool for the diagnosis of lesions.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.239-254
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• 2006

Location-based advertising or application has been one of the drivers of third-generation mobile operators' marketing efforts in the past few years. As a result, many studies on location-based marketing or advertising have been proposed for recent several years. However, these approaches have two common shortcomings. First. most of them just suggested the theoretical architectures, which were too abstract to apply it to the real-world cases. Second, many of these approaches only consider service provider (seller) rather than customers (buyers). Thus, the prior approaches fit to the automated sales or advertising rather than the implementation of CRM. To mitigate these limitations, this study presents a novel advertisement recommendation model for mobile users. We call our model MAR-CF (Mobile Advertisement Recommender using Collaborative Filtering). Our proposed model is based on traditional CF algorithm, but we adopt the multi-dimensional personalization model to conventional CF for enabling location-based advertising for mobile users. Thus, MAR-CF is designed to make recommendation results for mobile users by considering location, time, and needs type. To validate the usefulness of our recommendation model. we collect the real-world data for mobile advertisements, and perform an empirical validation. Experimental results show that MAR-CF generates more accurate prediction results than other comparative models.

• Imaging Science in Dentistry
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• 제54권2호
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• pp.191-199
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• 2024

Purpose: The aim of this study was to evaluate image artifacts in the vicinity of dental implants in cone-beam computed tomography (CBCT) scans obtained with different spatial orientations, tube current levels, and metal artifact reduction algorithm (MAR) conditions. Materials and Methods: One dental implant and 2 tubes filled with a radiopaque solution were placed in the posterior region of a mandible using a surgical guide to ensure parallel alignment. CBCT scans were acquired with the mandible in 2 spatial orientations in relation to the X-ray projection plane (standard and modified) at 3 tube current levels: 5, 8, and 11 mA. CBCT scans were repeated without the implant and were reconstructed with and without MAR. The mean voxel and noise values of each tube were obtained and compared using multi-way analysis of variance and the Tukey test(α=0.05). Results: Mean voxel values were significantly higher and noise values were significantly lower in the modified orientation than in the standard orientation (P<0.05). MAR activation and tube current levels did not show significant differences in most cases of the modified spatial orientation and in the absence of the dental implant (P>0.05). Conclusion: Modifying the spatial orientation of the head increased brightness and reduced spatial orientation noise in adjacent regions of a dental implant, with no influence from the tube current level and MAR.

• 한국방사선학회논문지
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• 제12권4호
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• pp.497-503
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• 2018

안면부 CT검사 시 치아교정용 충전물과 주변 해부학적 구조와의 밀도차이에 의해 발생한 화질 저하 정도와 화질개선 방향을 실험을 통해 정량 및 정성적 분석방법을 통해 알아보고자 하였다. 실험은 64-MDCT(Discovery 750 HD, GE HEALTH CARE, Milwaukee, USA)를 사용하여 치아 충전물로 교정한 치아를 스캔하였으며 관전압 변화, 실리콘 적용, MAR 알고리즘 적용 유무에 따라 비교하였다. 그 결과 관전압 변화 시 140 kVp에서 10.36 % CT value가 감소하였으며, Silicon 물질 적용 시 약 5.81 %가 감소하여 감소율이 가장 적었다. 정성적 평가결과 MAR 알고리즘 적용 시 관찰자 10명 중 Equivalent가 7명, Acceptable로 3명이 평가하여 MAR 알고리즘 적용 시 상대적으로 가장 화질 개선 효과가 있다고 평가되었다. 따라서 현재 임상에서 사용하고 있는 검사 파라미터와 더불어 고밀도 인공물을 감소시킬 수 있는 다양한 알고리즘을 적용하여 스캔 한다면 방사선 피폭선량에 대한 불필요한 부담을 줄일 수 있을 뿐만 아니라 고밀도 인공물을 감소시켜 영상 데이터의 소실을 줄여 보다 많은 영상정보를 제공 할 수 있을 것으로 사료된다.

• 경영과학
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• 제26권1호
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• pp.65-76
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• 2009

The undirected Steiner tree problem in graphs is known to be NP-hard. The objective of this problem is to find a shortest tree containing a subset of nodes, called terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step. graph reduction rules eliminate useless nodes and edges which do not contribute to make an optimal solution. In the second step, a max-min ant colony optimization combined with Prim's algorithm is developed to solve the reduced problem. The proposed algorithm is tested in the sets of standard test problems. The results show that the algorithm efficiently presents very correct solutions to the benchmark problems.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.245-254
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• 2021

We proposed the research on an analysis and prediction model that allows the identification of outliers or abnormality in the data followed by effective and rapid imputation of missing values was conducted. This model is expected to analyze efficiently the problems in the data based on the calibrated raw data. As a result, a system that can adequately utilize the data was constructed by using the introduced KNN + MLE algorithm. With this algorithm, the problems in some of the existing KNN-based missing data imputation algorithms such as ignoring the missing values in some data sections or discarding normal observations were effectively addressed. A comparative evaluation was performed between the existing imputation approaches such as K-means, KNN, MEI, and MI as well as the data missing mechanisms including MCAR, MAR, and NI to check the effectiveness/efficiency of the proposed algorithm, and its superiority in all aspects was confirmed.

• Journal of Astronomy and Space Sciences
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• 제34권4호
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• pp.315-330
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• 2017

Halo coronal mass ejections (CMEs) originating from solar activities give rise to geomagnetic storms when they reach the Earth. Variations in the geomagnetic field during a geomagnetic storm can damage satellites, communication systems, electrical power grids, and power systems, and induce currents. Therefore, automated techniques for detecting and analyzing halo CMEs have been eliciting increasing attention for the monitoring and prediction of the space weather environment. In this study, we developed an algorithm to sense and detect halo CMEs using large angle and spectrometric coronagraph (LASCO) C3 coronagraph images from the solar and heliospheric observatory (SOHO) satellite. In addition, we developed an image processing technique to derive the morphological and dynamical characteristics of halo CMEs, namely, the source location, width, actual CME speed, and arrival time at a 21.5 solar radius. The proposed halo CME automatic analysis model was validated using a model of the past three halo CME events. As a result, a solar event that occurred at 03:38 UT on Mar. 23, 2014 was predicted to arrive at Earth at 23:00 UT on Mar. 25, whereas the actual arrival time was at 04:30 UT on Mar. 26, which is a difference of 5 hr and 30 min. In addition, a solar event that occurred at 12:55 UT on Apr. 18, 2014 was estimated to arrive at Earth at 16:00 UT on Apr. 20, which is 4 hr ahead of the actual arrival time of 20:00 UT on the same day. However, the estimation error was reduced significantly compared to the ENLIL model. As a further study, the model will be applied to many more events for validation and testing, and after such tests are completed, on-line service will be provided at the Korean Space Weather Center to detect halo CMEs and derive the model parameters.

• ETRI Journal
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• 제29권4호
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• pp.530-532
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• 2007

This paper presents a simple method to implement a complete on-line portable wireless holter including an electrocardiogram (ECG) monitoring, processing, and communication protocol. The proposed algorithm significantly reduces the hardware resources of threshold estimation for ECG compression, using the standard deviation updated with each new input signal sample. The new method achieves superior performance in terms of hardware complexity, channel occupation and memory requirements, while keeping the ECG quality at a clinically acceptable level.

• 한국정보처리학회논문지
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• 제4권7호
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• pp.1749-1758
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• 1997

전력수요의 예측은 예측기간에 따라 중장기 전력수요 예측과 단기 부하 예측으로 구분할 수 있다. 기존의 단기 부하예측은 주로 역전파 알고리즘(back propagation algorithm)다층퍼셉트론을 이용하여 예측을 하였으나 이는 학습시간이 많이 걸릴 뿐만 아니라 학습도중에 지역최소점(local minima)에 빠져 학습이 계속되지 못한다는 문제가 있다. 본 논문은 이러한 역전파 알고리즘의 문제점을 해결할 수 있는 방법으로 Radial Basis 함수(Radial Basis Function)를 이용하여 동적 단기부하 예측 모형을 제안한다. Radial Basis 함수는 하나의 은닉층(hidden layer)을 갖고 있으며, 전방향(feed-forward)학습을 한다는 특징이 있다. 본 논문에서 제안한 단기 부하 예측모형은 학습을 하기 위하여 시간대별 부하량을 클러스터링 하고, 이 클러스터의 중심값을 Radial Basis 함수의 은닉층으로 하여 학습을 한 다음 예측하고자 하는 패턴을 한 단위로 하여 시단대별로 예측하였다. 기존의 연구에서의 클러스터링 방법으로는 통계학의 K-Means 방법이나 Kohonen의 LVQ(Learning Vector Quantization)을 주로 이용하였으나 본 논문에서는 패턴의 분류에 있어서 다른 알고리즘보다 편차가 작은 Pal, et. al.의 GLVQ(Generalized LVQ) 알고리즘을 이용하였다. 본 논문에서 이용한 데이타는 1995년 3월 1일-3일, 6월 1일-3일, 7월 1일-3일, 9월 1일-3일, 11월 1일-3일의 72시간 데이타를 입력하여 월별 4일의 24시간의 예측시간으로 예측하였다. 실험결과 월별 1일과 3일까지의 학습데이타로 1시간 후의 부하량을 24시간동안 예측한 결과 1.3795%의 평균 오차율로 예측하였다.

• 한국통신학회논문지
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• 제16권6호
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• pp.498-508
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• 1991

본 논문은 비트레벨에서, 3 - 상태 마코프체인의 모델을 사용한 종단대 종단 디지털 접속부에서의 개개 링크들의 에러성능을 평가하는 성능 분석 알고리즘을 표현했다. 링크모델은 개개링크의 버스트에러를 제안했으며, 또한 그것은 여러가지 개개링크들을 연결시키는 방법과 종단대 종단 디지털 접속부에 대한 모델을 추출하는 방법을 나타냈다. 이 결과적인 종단대 종단 모델이 주어진 블럭크기에 대한 비트에러율과 블럭에러와 같은 성능 파라미터를 계산하는데 사용될 수 있다.