• 융복합기술연구소 논문집
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• 제11권1호
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• 설비공학논문집
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• 제2권4호
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• pp.295-302
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• 1990

The purpose of this study is augmentation of heat transfer without additional power in two-dimensional impinging air jet. The technique of heat transfer augmentation used in this experiment is to place rod bundles in front of the flat heated surface. The effects of rod diameter, nozzle-to-target plate distance and the nozzle exit velocity on heat transfer have been investigated. The main conclusions obtained from this experiment are as follows. High heat transfer augmentation is achieved by means of flow acceleration and thinning of boundary layer by placing rod bundles in front of the flat plate. Average heat transfer coefficient becomes maximum in the case of H/B=10,D=4mm. For H/B=2,D=4mm, maximum heat transfer augmentation has been determined to be about 1.5 times larger than that of the flat plate. Heat transfer augmentation by placing the rod bundles at 12m/s is to be about 2 times more than increasing nozzle exit velocity from 12m/s to 18m/s.

• 한국컴퓨터정보학회논문지
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• 제28권9호
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• pp.47-54
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• 2023

영아는 비언어적 의사 소통 방식인 울음이라는 수단을 사용한다[1]. 하지만 영아의 울음소리를 파악하는 것에는 어려움이 따른다. 영아의 울음소리를 해석하기 위해 많은 연구가 진행되었다[2,3]. 이에 본 논문에서는 다양한 음성 데이터 증강을 통한 3D 특징 벡터를 이용한 영아의 울음소리 분류를 제안한다. 연구에서는 총 5개의 클래스 복통, 하품, 불편함, 배고픔, 피곤함(belly pain, burping, discomfort, hungry, tired)로 분류된 데이터 세트를 사용한다. 데이터들은 5가지 기법(Pitch, Tempo, Shift, Mixup-noise, CutMix)을 사용하여 증강한다. 증강 기법 중에서 Tempo, Shift, CutMix 기법을 적용하였을 때 성능의 향상을 보여주었다. 최종적으로 우수한 데이터 증강 기법들을 동시 적용한 결과 단일 특징 벡터와 오리지널 데이터를 사용한 모델보다 17.75%의 성능 향상을 도출하였다.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.9-17
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• 2024

It is widely recognized that for 3D human pose estimation (HPE), dataset acquisition is expensive and the effectiveness of augmentation techniques of conventional visual recognition tasks is limited. We address these difficulties by presenting a simple but effective method that augments input images in terms of viewpoints when training a 3D human pose estimation (HPE) model. Our intuition is that meaningful variants of the input images for HPE could be obtained by viewing a human instance in the images from an arbitrary viewpoint different from that in the original images. The core idea is to synthesize new images that have self-occlusion and thus are difficult to predict at different viewpoints even with the same pose of the original example. We incorporate this idea into the training procedure of the 3D HPE model as an augmentation stage of the input samples. We show that a strategy for augmenting the synthesized example should be carefully designed in terms of the frequency of performing the augmentation and the selection of viewpoints for synthesizing the samples. To this end, we propose a new metric to measure the prediction difficulty of input images for 3D HPE in terms of the distance between corresponding keypoints on both sides of a human body. Extensive exploration of the space of augmentation probability choices and example selection according to the proposed distance metric leads to a performance gain of up to 6.2% on Human3.6M, the well-known pose estimation dataset.

• Journal of Korean Neurosurgical Society
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• 제47권3호
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• pp.180-184
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• 2010

Objective : To prevent temporal depression after the pterional craniotomy, this study was designed to examine the safety and aesthetic efficacy of the brushite calcium phosphate cement (CPC) in the repair and augmentation of bone defects following the pterional craniotomy. Methods : The brushite CPC was used for the repair of surgically induced cranial defects, with or without augmentation, in 17 cases of pterional approach between March, 2005 and December, 2006. The average follow-up month was 20 with range of 12-36 months. In the first 5 cases, bone defects were repaired with only brushite CPC following the contour of the original bone. In the next 12 cases, bone defects were augmented with the brushite CPC rather than original bone contour. For a stability monitoring of the implanted brushite CPC, post-implantation evaluations including serial X-ray, repeated physical examination for aesthetic efficacy, and three-dimensional computed tomography (3D-CT) were taken 1 year after the implantation. Results : The brushite CPC paste provided precise and easy contouring in restoration of the bony defect site. No adverse effects such as infection or inflammation were noticed during the follow-up periods from all patients. 3D-CT was taken 1 year subsequent to implantation showed good preservation of the brushite CPC restoration material. In the cases of the augmentation group, aesthetic outcomes were superior compared to the simple repair group. Conclusion : The results of this clinical study indicate that the brushite CPC is a biocompatible alloplastic material, which is useful for prevention of temporal depression after pterional craniotomy. Additional study is required to determine the long-term stability and effectiveness of the brushite calcium phosphate cement for the replacement of bone.

• Journal of Positioning, Navigation, and Timing
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• 제10권3호
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• pp.221-228
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• 2021

The Korea Augmentation Satellite System (KASS) is a Korean Satellite Based Augmentation System (SBAS) that has been under development since 2014 with the goal of providing Approach Procedure with Vertical guidance (APV)-I Safety of Life (SoL) services. KASS Control Station (KCS) is a subsystem that controls and monitors KASS systems. It also serves to store data generated by KASS. KCS has now completed detailed design and implementation and verification is in progress. This paper presents verification procedures and verification items for KCS verification activities and presents management measures for defects occurring during the verification phase.

• 인터넷정보학회논문지
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• 제24권4호
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• pp.85-92
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• 2023

X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI)과 같은 의료데이터에서 딥러닝을 활용해 질병 유무 판별 태스크와 같은 문제를 해결하려는 시도가 활발하다. 대부분의 데이터 기반 딥러닝 문제들은 높은 정확도 달성과 정답과 비교하는 성능평가의 활용을 위해 지도학습기법을 사용해야 한다. 지도학습에는 다량의 이미지와 레이블 세트가 필요하지만, 학습에 충분한 양의 의료 이미지 데이터를 얻기는 어렵다. 다양한 데이터 증강 기법을 통해 적은 양의 의료이미지와 레이블 세트로 지도학습 기반 모델의 과소적합 문제를 극복할 수 있다. 본 연구는 딥러닝 기반 갈비뼈 골절 세그멘테이션 모델의 성능 향상과 효과적인 좌우 반전, 회전, 스케일링 등의 데이터 증강 기법을 탐색한다. 좌우 반전과 30° 회전, 60° 회전으로 증강한 데이터셋은 모델 성능 향상에 기여하지만, 90° 회전 및 ⨯0.5 스케일링은 모델 성능을 저하한다. 이는 데이터셋 및 태스크에 따라 적절한 데이터 증강 기법의 사용이 필요함을 나타낸다.

• 로봇학회논문지
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• 제18권3호
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• pp.346-351
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• 2023

With the release of numerous open driving datasets, the demand for domain adaptation in perception tasks has increased, particularly when transferring knowledge from rich datasets to novel domains. However, it is difficult to solve the change 1) in the sensor domain caused by heterogeneous LiDAR sensors and 2) in the environmental domain caused by different environmental factors. We overcome domain differences in the semi-supervised setting with 3-stage model parameter training. First, we pre-train the model with the source dataset with object scaling based on statistics of the object size. Then we fine-tine the partially frozen model weights with copy-and-paste augmentation. The 3D points in the box labels are copied from one scene and pasted to the other scenes. Finally, we use the knowledge distillation method to update the student network with a moving average from the teacher network along with a self-training method with pseudo labels. Test-Time Augmentation with varying z values is employed to predict the final results. Our method achieved 3rd place in ECCV 2022 workshop on the 3D Perception for Autonomous Driving challenge.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.369-371
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• 2011

증강현실 기술을 이용하여 데이터 가시화을 통하여 사용자들은 새로운 경험을 할 수 있기 때문에 교육용 어플리케이션에 적합한 미디어라고 할 수 있다. 본 논문에서는 화학실험에서의 위험성을 배제하고 몰입형 실험을 하기 위하여 증강현실을 이용한 실험 시스템이다. 가상 화학 실험 시스템은 사용자의 손을 활용하여 가상의 객체와 상호작용을 이룰 수 있는 인터페이스를 통해, 기존 증강현실의 몰입감 저하의 문제점인 마커를 사용하지 않고, 3D 객체를 컨트롤 하는 기법을 사용했다. 시스템에 대한 몰입감을 극대화하기 위해서 협업이 가능한 가상 화학 실험 시스템을 제안했다.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.49-54
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• 2024

데이터 불균형은 분류 문제에서 흔히 마주치는 문제로, 데이터셋 내의 클래스간 샘플 수의 현저한 차이에서 기인한다. 이러한 데이터 불균형은 일반적으로 분류 모델에서 과적합, 과소적합, 성능 지표의 오해 등의 문제를 야기한다. 이를 해결하기 위한 방법으로는 Resampling, Augmentation, 규제 기법, 손실 함수 조정 등이 있다. 본 논문에서는 손실 함수 조정에 대해 다루며 특히, 불균형 문제를 가진 Multi-Class 블랙박스 동영상 데이터에서 여러 구성의 손실 함수(Cross Entropy, Balanced Cross Entropy, 두 가지 Focal Loss 설정: 𝛼 = 1 및 𝛼 = Balanced, Asymmetric Loss)의 성능을 I3D, R3D_18 모델을 활용하여 비교하였다.