• Proceedings of the Korean Society of Developmental Biology Conference
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• 2003.10a
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• pp.133-133
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• 2003

Recently, sperm has been used as a vector to carry exogenous genes for the production of transgenic animals. However, the success in cattle is low, due to deficiencies in oocyte activation and sperm decondensation caused by high disulphide bond (S=S) content in mature sperm. This study was carried out to develop an effective method for producing transgenic animals with round spermatids (RS). Two methods of embryo production - electric fusion (EC) or intracyto-plasmic injection (IC) and three activation treatments were compared. RS were isolated from bull testes by Percoll density gradients (20, 35, 40, 45 and 90%). Fusion between ooplast and RS was performed with a single DC electric pulse (1.0 KV/cm, 45 sec) in 0.28 M mannitol solution supplemented with 100 M CaCl2 and 100 M MgCl$_2$. (중략)

• Annual Conference of KIPS
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• 2011.04a
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• pp.374-375
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• 2011

보행자 탐지를 위해 많은 알고리즘들이 제안되었고 그 중 HOG 알고리즘은 가장 좋은 성능을 보이는 알고리즘으로 알려져 있다. 하지만 HOG(Histogram of Oriented Gradients) 알고리즘은 연산량이 많아 계산 속도가 느려 실시간 시스템에 적용하기는 힘들다. 본 논문은 HOG 알고리즘으로 얻어진 특징 벡터를 이용해 보행자를 인식하는 방법의 속도 개선에 대하여 연구하였다. 기존 HOG 알고리즘에서 계산량이 많은 곳이 어느 부분인지 분석하고, 그 중 기울기와 방향을 계산하는 부분의 근사화를 통해 계산 속도를 높이는 방법을 제안한다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.383-386
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• 2021

본 논문에서는 주행 중 운전자의 운전작업 중 전방 주의집중 여부를 모니터링하는 연구 방안들을 조사하고 최신 연구 동향을 분석하였으며, 자율주행자동차에서 운전자의 주의집중이 필요한 상황들에 대해 사전에 안내하는 방안을 제시하고자 한다. 연구 동향을 조사한 결과 대부분의 방법은 시각 자료 기반과 생체신호 기반으로 진행하고 있다. 연구분석 결과를 바탕으로 두 가지 방법 중 본 연구에서는 시각 자료 기반 연구 방법에 초점을 맞추어, 자동차에 설치된 카메라를 통해 수집된 영상에서 운전자의 운전작업 주의 여부를 식별하는 방법들에 대해서 분석을 진행하였다. 주행 영상에서 HoG(histogram of oriented gradients) 특징과 딥러닝 학습을 통해 운전자의 주의집중 여부를 모니터링하는 방법이 효과적임을 제시한다. 본 연구조사를 통해 분석된 운전자 모니터링 방안들을 자율주행 자동차에 적용하기 위한 운전자 주의 태만 경고시스템에 적용이 가능함을 제시한다.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1996.04a
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• pp.119-130
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• 1996

We review the factors which regulate the depth and composition of the human airway surface liquid (ASL). These include secretion from airway submucosal glands, ion and fluid transport across the surface epithelium, goblet cell discharge, surface tension and transepithelial gradients in osmotic and hydrostatic pressure. We describe recent experiments in which we have used low temperature scanning electron microscopy of rapidly frozen specimens to detect changes in depth of ASL in response to submucosal gland stimulation. We also present preliminary data in which X-ray microanalysis of frozen specimens has been used to determine the elemental composition of ASL.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.794-802
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• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.393-393
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• 2022

상수도관망의 압력은 물공급 서비스의 질을 나타내는 중요한 인자이다. 압력이 낮으면 물 사용성이 크게 저하되며, 이러한 이유로 보통의 수도사업자는 압력을 중요한 모니터링 변수로 고려하고 있다. 압력을 기반으로 여러 가지 변수를 계산할 수 있지만, 그 중에서도 압력경사(Pressure Head Gradient)는 수요량, 관경, 관의 파괴 등 독립변수 또는 독립요인의 변화에 따른 종속변수로서의 압력 민감도를 나타낸다. 압력경사의 계산은 매우 간단하면서도 활용도가 높다. 따라서, 다양한 종류의 압력경사를 계산하고, 상수도관망 설계, 운영, 관리 목적에 어떻게 사용할 것인가를 연구할 필요가 있다. 본 연구에서는 먼저 관의 상태, 관경, 절점수요량을 독립변수로 하고 이를 변화시켜 그 결과로의 압력종속변수를 계산, 압력경사를 결정하였다. 이를 기반으로, 히트맵을 구축하여 결과를 비교하였다. 그 후 각각의 압력경사에 대한 모듈을 구축하였으며, 개별 압력경사 모듈의 특징을 고려하여 활용방법을 수립하였다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.15-17
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• 2023

ViT(Vision Transformer)는 트랜스포머 구조에 이미지를 패치들로 나눠 한꺼번에 인풋으로 입력하는 모델이다. CNN 기반 모델보다 더 적은 훈련 계산량으로 다양한 이미지 인식 작업에서 SOTA(State-of-the-art) 성능을 보이면서 다양한 비전 작업에 ViT 를 적용하는 연구가 활발히 진행되고 있다. 하지만, ViT 모델도 AI 모델 훈련시에 생성된 그래디언트(Gradients)를 이용해 원래 사용된 훈련 데이터를 복원할 수 있는 모델 역전 공격(Model Inversion Attacks)에 안전하지 않음이 증명되고 있다. CNN 기반의 모델 역전 공격 및 방어 기법들은 많이 연구되어 왔지만, ViT 에 대한 관련 연구들은 이제 시작 단계이고, CNN 기반의 모델과 다른 특성이 있기에 공격 및 방어 기법도 새롭게 연구될 필요가 있다. 따라서, 본 연구는 ViT 모델에 특화된 모델 역전 공격 및 방어 기법들의 특징을 서술한다.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.803-816
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• 2023

Explainable artificial intelligence is a method that explains how a complex model (e.g., a deep neural network) yields its output from a given input. Recently, graph-type data have been widely used in various fields, and diverse graph neural networks (GNNs) have been developed for graph-type data. However, methods to explain the behavior of GNNs have not been studied much, and only a limited understanding of GNNs is currently available. Therefore, in this paper, we propose an explanation method for node classification using graph convolutional networks (GCNs), which is a representative type of GNN. The proposed method finds out which features of each node have the greatest influence on the classification of that node using GCN. The proposed method identifies influential features by backtracking the layers of the GCN from the output layer to the input layer using the gradients. The experimental results on both synthetic and real datasets demonstrate that the proposed explanation method accurately identifies the features of each node that have the greatest influence on its classification.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• The Korean Journal of Ecology
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• v.25 no.3 s.107
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• pp.171-177
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• 2002

Three species of Chenopodiaceae, i.e. Suaeda japonica, Salicomia herbacea, Beta vulgaris var. cicla, were investigated to compare the physiological characteristics through ionic balances and osmoregulations under different environmental salt gradients. Plants were harvested in two weeks from treatments with salt gradients(0, 50, 100, 200 and 400 mM NaCl) and mineral nutrition gradients(1/1, l/5, 1/10 dilutions of Hoagland solution). Plants were analyzed for growth responses, ionic balances, osmolalities, conductivities, glycinebetaine and proline contents quantitatively. Three plants of Chenopodiaceae accumulated salts into tissues unlike some salt sensitive species, and showed unique adaptation patterns to overcome saline environments, i.e. strong growth stimulation for Salicomia herbacea, growth negative tolerance for Suaeda japonica, and growth positive tolerance for Beta vulgaris var cicla. The absorption of inorganic $Ca^{2+}$ ions was inhibited remarkably due to the excess uptake of $Na^+$ with increasing salinity. The $K^+$ content in plants was significantly reduced with increasing salinity. Total nitrogen content was reduced as mineral nutritions and salinity increases. Conductivity and osmolality increased with increasing salinity regardless of mineral nutritions. The ranges of glycinebetaine and proline contents were $0.2{\sim}2.5{\mu}M/g$ plant water and $0.1{\sim}0.6{\mu}M/g$ plant water, respectively.