• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.38-43
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• 2014

본 논문은, 현존하는 영어 FrameNet 데이터를 기반으로 하여, FrameNet에 대한 전문 지식이 없는 번역가들을 통해 수행할 수 있는 한국어 FrameNet의 수동 구축 개발 과정을 제시한다. 우리 연구팀은 실제로, NLTK가 제공하는 영어 FrameNet 버전 1.5의 Full Text를 이루고 있는 5,945개의 문장들 중에서, Frame 데이터를 가진 4,025개의 문장들을 추출해내어, 번역가들에 의해 한국어로 수동번역 함으로써, 한국어 FrameNet 구축 개발을 향한 의미 있는 초석을 마련하였으며, 제시한 방법의 실효성을 입증하는 연구결과들을 웹에 공개하기도 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.473-476
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• 2012

국제 무역에 있어 한 중 일 삼국은 동아시아에서 차지하고 있는 비중이 클 뿐만 아니라, 전 세계 컨테이너 물동량의 30% 이상을 차지하며 세계 물류 흐름에서 큰 역할을 차지하고 있다. 하지만 각국의 물류 정보시스템이 각각 다르고, 관리 체제나 연계 방안이 마련되어 있지 않아 정보 연계 시 많은 문제점이 발생하고 있다. 이를 해결하기 위해 한 중 일 삼국은 정보를 공유하고 관리할 수 있도록 동북아 물류정보 서비스 네트워크(이하 NEAL-NET) 구축 사업을 진행하고 있다. 본 논문에서는 NEAL-NET 프로젝트의 진행 사항과 의의를 살펴보고, 추후 국제 물류정보 공유 협력을 위한 표준화 방안에 대해 연구하였다.

• Korean Journal of Radiology
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• v.20 no.8
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.81-83
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• 2005

The Plant Signaling Network Research Center (SigNet) is a government-funded (by Korea's Ministry of Science and Technology (MOST)/ Korea Science and Engineering Foundation (KOSEF)) research center established at the School of Life Sciences and Biotechnology of Korea University in 2003. The SigNet conducts plant biological studies, especially in the field of developmental and defense biology. The research purpose of SigNet is dissection and analysis of plant development and defense signaling network through multiscientific approaches. Knowledge acquired from SigNet research scientists will provide new integrated view of understanding and potential application of plant development and defense mechanism. The other important mission of the SigNet is nurturing Center of Excellence for future outstanding research scientists of Korea. The SigNet will continue to expend every effort to achieve the goals for the future. Through passionate research endeavor of each laboratory and partnerships within inside and outside laboratories, we will continue to develop world-leading plant research group and to educate new generations of innovative researchers. As the SigNet looks toward the future, the SigNet will try to achieve its mission of research, education and service to the community. And the defense response research of our lab will be presented at later part.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.4
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• pp.185-192
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• 2019

In this study, we, through case studies, formulated a method to implement net-zero energy daycare center at the current insulation and technology level, and calculated its energy expense. The reference model was a medium sized daycare center whose number of children was 99. We analyzed the energy consumption status for the reference model and developed TRNSYS simulation analytical model to realize net-zero energy . We assumed the reference model to be "All Electric Building" where all energy including cooking is supplied by electricity. The result is summarized as follows: First, the annual electricity consumption of daycare center was 53,291kWh. Plug load occupied the largest share of 48% followed by lighting, 10%, cooling, 9%, cooking, 9%, heating, 8%, hot water, 5% and ventilation, 2%. Second, the photovoltaic installation capacity to realize net-zero energy was 40.32kWp and its annual generation was 53,402kWh. Third, the annual energy expense(electricity bill) by realizing net-zero energy was 2,620,390won.

• Journal of Veterinary Clinics
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• v.37 no.5
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• pp.249-254
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• 2020

Neutrophil extracellular trap (NET) formation is an immune response for the invasion of microbes. The purpose of this study is to examine the effect of zinc on NET formation of porcine peripheral blood polymorphonuclear cells (PMNs). The NET formation of PMNs was measured by fluorescence microplate reader. The production of tumor necrosis factor (TNF)-α in the culture supernatants from zinc-treated peripheral blood mononuclear cells (PBMCs) was measured by enzyme-linked immunosorbent assay (ELISA). Zinc itself did not have no effect on NET formation. However, the NET formation of PMNs was increased by culture supernatants from PBMCs treated with zinc. Also, the NET formation of PMNs was increased by recombinant porcine (rp) TNF-α. The production of TNF-α in PBMCs culture supernatants was shown to increase upon zinc treatments. These NET formations of PMNs increased by either culture supernatant from PBMCs treated with zinc or rpTNF-α were inhibited by treatment of anti-rpTNF-α polyclonal antibody (pAb). These results suggested that zinc has an immunostimulating effect on the NET formation of PMNs, which is mediated by TNF-α released from zinc-treated PBMCs. Therefore, zinc may play an important role for NET formation in the defense of porcine inflammatory diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1841-1857
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• 2023

CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object's height and width. However, because the objects have different sizes, directly regressing their height and width will make the model difficult to converge and lose the intrinsic relationship between object's width and height, thereby reducing the stability of the model and the consistency of prediction accuracy. For this problem, we proposed an algorithm based on the regression of the diagonal half-length and the center angle, which significantly compresses the solution space of the regression components and enhances the intrinsic relationship between the decoded components. First, encode the object's width and height into the diagonal half-length and the center angle, where the center angle is the angle between the diagonal and the vertical centreline. Secondly, the predicted diagonal half-length and center angle are decoded into two length components. Finally, the position of the object bounding box can be accurately obtained by combining the corresponding center point coordinates. Experiments show that, when using CenterNet as the improved baseline and resnet50 as the Backbone, the improved model achieved 81.6% and 79.7% mAP on the VOC 2007 and 2012 test sets, respectively. When using Hourglass-104 as the Backbone, the improved model achieved 43.3% mAP on the COCO 2017 test sets. Compared with CenterNet, the improved model has a faster convergence rate and significantly improved the stability and prediction accuracy.

• Journal of the Korean Society for Heat Treatment
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• v.16 no.5
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• pp.275-280
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• 2003

• Journal of Environmental Science International
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• v.12 no.8
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• pp.841-846
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• 2003

The ecosystem model was applied to estimate the regional distribution of the net production(or consumption) of phytoplankton and the net uptake(or regeneration) rate of nutrients in Masan Bay for scenario analysis to find a proper management plan. At the surface level, net production of phytoplankton is 200 mgC/㎡/day at the entrance of the bay, and 400∼1000 mgC/㎡/day at the center of the bay. The inner area of the bay showed more than 2000 mgC/㎡/day. All areas of the bottom level have a net consumption, with the center of the bottom level showing more than 600 mgC/㎡/day. For dissolved inorganic nitrogen, the results showed a net uptake rate of 100∼900 mg/㎡/day at the surface level. It showed that the net regeneration is above 50 mg/㎡/day at the bottom level. For dissolved inorganic phosphorus, the net uptake rate showed 10.0∼80.0 mg/㎡/day at the surface level, and the regeneration rate showed 0∼20.5 mg/㎡/day at the bottom level. Therefore, in order to control the water quality in Masan Bay, it is important to consider the re-supplement of nutrients regenerated in the water column.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.273-277
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• 2018

기계가 주어진 텍스트를 이해하고 추론하는 능력을 기계독해 능력이라 한다. 기계독해는 질의응답 태스크에 적용될 수 있는데 이것을 기계독해 질의응답이라 한다. 기계독해 질의응답은 주어진 질문과 문서를 이해하고 이를 기반으로 질문에 적합한 답을 출력하는 태스크이다. 본 논문에서는 구조화된 표 형식 데이터로부터 질문에 대한 답을 추론하는 TableQA 태스크를 소개하고, $S^3-NET$을 이용하여 TableQA 문제를 해결할 것을 제안한다. 실험 결과, 본 논문에서 제안한 방법이 EM 96.36%, F1 97.04%로 우수한 성능을 보였다.