• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.6069-6091
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• 2017

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.

• 한국콘텐츠학회논문지
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• 제10권1호
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• pp.19-26
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• 2010

본 논문은 사각형 특징 기반 분류기를 제안하여 실시간으로 얼굴 영역을 검출하며, 계산의 효율성과 검출 성능을 동시에 만족시키는 강인한 검출 알고리즘을 구현하고자 한다. 제안한 알고리즘은 특징 생성, 분류기 학습, 실시간 얼굴 영역 검출의 세 단계로 구성된다. 특징 생성은 제안된 5개의 사각형 특징으로 특징 집합을 구성하며, SAT(Summed-Area Tables)를 이용하여 특징 값을 효율적으로 계산한다. 분류기 학습은 AdaBoost 알고리즘을 이용하여, 분류기를 계층적으로 생성한다. 또한 중요한 얼굴 패턴은 다음 레벨에 반복적으로 적용함으로써 우수한 검출 성능을 가진다. 실시간 얼굴 영역 검출은 생성된 사각형 특징 기반 분류기를 통해, 빠르고 효율적으로 얼굴 영역을 찾아낸다. 또한 얼굴 영역을 검출한 영역을 인식의 입력 영상으로 사용하여 PCA와 KNN 알고리즘을 이용하여 기존의 매칭 방법인 Point to point 방법이 아닌 Class to Class 방식을 이용하여 인식률을 향상시켰다.

• 천문학회보
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• 제39권1호
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• pp.39.1-39.1
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• 2014

We present the results of our analysis of the RR Lyrae (RRL) variable stars detected in two transition-type dwarf galaxies (dTrans), ESO294-G010 and ESO410-G005 in the Sculptor group, which is known to be one of the closest neighboring galaxy groups to our Local Group. Using deep archival images from the Advanced Camera for Surveys (ACS) onboard the Hubble Space Telescope (HST), we have identified a sample of RR Lyrae candidates in both dTrans galaxies [219 RRab (RR0) and 13 RRc (RR1) variables in ESO294-G010; 225 RRab and 44 RRc stars in ESO410-G005]. The metallicities of the individual RRab stars are calculated via the period-amplitude-[Fe/H] relation derived by Alcock et al. This yields mean metallicities of <[Fe/H]>_{ESO294} = -1.77 +/- 0.03 and <[Fe/H]>_{ESO410} = -1.64+/- 0.03. The RRL metallicity distribution functions (MDFs) are investigated further via simple chemical evolution models; these reveal the relics of the early chemical enrichment processes for these two dTrans galaxies. In the case of both galaxies, the shapes of the RRL MDFs are well-described by pre-enrichment models. This suggests two possible channels for the early chemical evolution for these Sculptor group dTrans galaxies: 1) The ancient stellar populations of our target dwarf galaxies might have formed from the star forming gas which was already enriched through "prompt initial enrichment" or an "initial nucleosynthetic spike" from the very first massive stars, or 2) this pre-enrichment state might have been achieved by the end products from more evolved systems of their nearest neighbor, NGC 55.

• 한국정보통신학회논문지
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• 제18권7호
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• pp.1518-1524
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• 2014

본 논문은 주변탐색(Surrounder Search: SuSe)이라는 새로운 공간질의 방법을 제안한다. 이 기법은 현재 사용자의 위치를 중심으로 주변에서 가까운 관심영역의 공간객체를 탐색하는 것이다. 사용자 중심의 주변탐색은 증강현실과 같이 사용자가 관심 있어 하는 공간객체 중 가까운 것을 찾기 때문에 기존의 공간질의와 구별된다. 기존 기법은 질의점과 객체 사이의 최단거리(MINDIST)를 기준으로 주변을 탐색하지만 제안 기법에서는 객체들 사이에 숨어있지만 관심의 대상인 숨은 객체를 식별하기 위해서 각도(Angle)를 함께 고려하여 탐색한다. 제안 기법의 특징은 기존기법이 거리만을 사용하여 가까운 객체를 탐색한 것과 달리 거리는 멀지만 숨은 객체까지도 찾아냄으로써 사용자의 선호도를 더 세밀하게 반영한다. 실험결과에서 제안기법인 SuSe는 최근접 이웃 탐색기법인 NN(Nearest Neighbor)과 비교하여 보다 정밀한 공간객체 탐색이 가능하며 향상된 탐색성능을 타나낸다.

• 치위생과학회지
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• 제20권1호
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• pp.44-50
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• 2020

Background: Smartphones are a modern necessity. While they are convenient to use, smartphones also have side effects such as addiction. This study assessed the relationship between smartphone use, a part of everyday life in modern society, and oral health. Methods: An analysis was conducted using 2017 Korea Youth Risk Behavior Web-based Survey data. The propensity score estimation algorithm used logistic regression and 1:1 matching algorithm using nearest-neighbor matching. After matching, a total of 15,032 participants were classified into two groups containing 7,516 teenagers each who did and did not use smartphones, respectively. Results: Comparison of oral health behaviors according to smartphone use revealed a statistically significant difference in the frequency of tooth brushing per day, use of oral hygiene products, intake of foods harmful to oral health, and experience of oral health education (p<0.05). The factors affecting oral pain experience of adolescents were examined. Compared to male participants, female participants had an odds ratio of 1.627 for oral pain (p<0.05). According to the household income level, compared to the group with higher income, the group with lower income showed higher oral pain experience (p<0.05). Oral pain experience was 1.601 times more frequent among teenagers using smartphones (p<0.05). Conclusion: The results of this study indicated that use of smartphones by adolescents affected their oral health. These findings indicate the need for improved oral health management through the use of effective school oral health programs and individual counseling by oral health professionals, promotion of information dissemination through public media, and development of prevention strategies.

• 한국항해항만학회지
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• 제39권4호
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• pp.307-312
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• 2015

ARPA(Automatic Radar Plotting Aid)는 자동레이더 플로팅 장치로써, 레이더 물표의 상대침로와 상대방위로 구성된 운동벡터에 본선의 침로와 방위로 구성되는 운동벡터를 가감 연산(벡터연산)하여, 물표의 진침로와 진방위 및 최근접점과 근접시간을 계산하는 장치를 말한다. 본 연구의 목적은 ARPA 레이더를 구현하기 위한 물표의 획득 및 추적 기술을 개발하는 것으로, 이에 관한 여러 선행 연구를 검토하여 적용 가능한 알고리듬 및 기법을 조합하여 기초적인 ARPA 기능을 개발하였다. 주요 연구내용으로, 레이더 영상에서 물표를 획득하기 위하여, 회색조 변환, 가운시안 평활 필터 적용, 이진화 및 라벨링(Labeling)과 같은 순차적 영상 처리 방법을 고안하였고, 이전 영상에서의 물표가 다음 영상에서의 어느 물표인지를 결정하는데 근접이웃탐색알고리듬을 사용하였으며, 물표의 진침로와 진방위를 계산하는 거동해석에 칼만필터를 사용하였다. 또한 이러한 기법을 전산 구현하여 실선실험을 수행하였고, 이를 통해 개발된 ARPA의 기능이 실용상 사용가능함을 검증하였다.

• 한국축산식품학회지
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• 제41권6호
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• pp.936-949
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• 2021

This study discriminated fatty acid profile and flavor characteristics of Beijing You Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC) eggs. Fatty acid profile and flavor characteristics were analyzed to identify differences between BYC and DBYC eggs. Four classification algorithms were used to build classification models. Arachidic acid, oleic acid (OA), eicosatrienoic acid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA) and 35 volatile compounds had significant differences in fatty acids and volatile compounds by gas chromatography-mass spectrometry (GC-MS) (p<0.05). For fatty acid data, k-nearest neighbor (KNN) and support vector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS data failed in classification models. For electronic nose data, classification accuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree was all 100%. The overall results indicated that BYC and DBYC eggs could be discriminated based on electronic nose with suitable classification algorithms. This research compared the differentiation of the fatty acid profile and volatile compounds of various egg yolks. The results could be applied to evaluate egg nutrition and distinguish avian eggs.

• 디지털융복합연구
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• 제19권4호
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• pp.25-31
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• 2021

고령화 시대에 따라 고령운전자 역시 증가하고 있으며, 이들에 의한 교통사고 심각성에 대한 관심이 높아지고 있다. 이에 고령운전자에 의한 사고심각도 예측 모형의 필요성이 점차 요구됨에 따라, 본 연구에서는 기계학습 기법을 활용하여 고령운전자에 의한 차대사람 사고심각도 예측을 위한 모형 정립 및 분석을 수행하고자 한다. 이를 위해 4개의 기계학습 알고리즘 (Logistic Model, KNN, RF, SVM)을 활용, 예측 모형을 개발하고 각 결과를 비교하였다. 연구 결과에 따르면 Logistic과 SVM 모형이 상대적으로 높은 예측력을 보였으며, 정확도 측면에서는 RF가 높은 것으로 나타났다. 추가적으로 각 중요 변수들을 이용하여 교차분석을 수행한 후 그 결과를 제시하였다. 본 연구의 결과들은 고령화시대에 고령운전자에 의한 사고심각성을 예방하기 위한 안전정책 및 인프라 개발에 활용될 것으로 판단된다.

• 한국군사과학기술학회지
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• 제24권3호
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• pp.264-271
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• 2021

Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.343-364
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• 2021

In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.