• KSBB Journal
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• 제30권1호
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• pp.11-20
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• 2015

Radiation causes various pathophysiological alterations in living animals, and it causes death at high doses by multiple mechanisms, including direct DNA damage and indirect oxidative stress. The search for useful radioprotectors has been an important issue in the field of radiation biology. Ideal radioprotectors should have low toxicity and an extended window of protection. As many synthetic compounds have toxic side effects, the natural products have attracted scientific attention as radioprotectors. Natural products that have been recently shown to be effective with various biological activities were found to have radioprotective effect. The aim of this review is to summary the recent research of the radioprotective effects of natural foods, especially focused on the glycobiological properties of mushrooms.

• 한국가스학회지
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• 제3권1호
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• pp.41-47
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• 1999

최근 가스 및 화학 공장에 대한 위험성 평가 기법으로서 가장 많이 적용되고 있는 것은 HAZOP이다. 이 HAZOP은 매우 논리적이고 체계화된 기법이나 많은 분석 시간을 요구한다. 그러므로 HAZOP 수행시 적절한 워크쉬트 프로그램은 분석의 속도를 증진시키고 많은 자료를 관리하는데 매우 중요한 도구이다. 따라서 본 연구에서는 실제 HAZOP 수행 경험을 바탕으로 보다 사용하기 편리하고 쉬운 프로그램을 개발하였으며 그 특징으로는 HAZOP의 일반적인 절차를 메뉴로 구성한 화면과 유연할 편집 기능, 그리고 검색을 통한 기록 정보에 대한 탐색 및 고찰을 할 수 기능이라 할 수 있다. 그리고 개발된 프로그램을 이용하여 기존에 수행되었던 HAZOP 수행 결과에 대한 경향 분석 사례를 보이고 유용성을 확인하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권2호
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• pp.213-223
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• 2004

본 논문에서는 실시간 응용을 위해 형판 정합 방법을 기반으로 하면서 동시에 외형 기반 (appearance_based) 방법에서 제시하는 학습 모델을 이용한 새로운 얼굴 검출 방법을 제안한다. 우선, 빛이나 조명의 영향에 의한 오류를 방지하기 위한 효과적인 전처리 과정으로 최소-최대 정규화(Min-max Normalization) 방법과 히스토그램 정규화 방법을 적용시킨다. 그런 뒤에 입력 영상과 형판을 PCA 변환하여 각각의 주성분(PC : Principal Component)을 생성하고 이를 LDA 변환한다. PCA 및 LDA 변환된 형판을 이용하여 입력 영상과의 거리 값을 구한 후 거리 값이 가장 작은 영역을 얼굴 영역으로 선택하고, 선택된 영역은 SVM을 이용하여 얼굴인지 아닌지를 검증하는 과정을 거친다. 또한, 본 논문에서는 실시간 얼굴 검출 방법을 위해 전체 영역이 아닌 $\pm$12 화소 크기의 탐색 윈도우를 이용하여 시스템의 속도 및 정확도를 고려하도록 하였다. 실제 환경과 같은 6개 부류의 동영상을 중심으로 실험한 결과, 본 논문에서 제안하는 방법이 기존의 PCA 변환만을 이용한 방법보다 좋은 성능을 보여줌을 알 수 있었고, 또한 SVM을 이용한 얼굴 검증 과정을 추가한 방법이 PCA 변환과 LDA 변환을 사용한 방법보다 좋은 성능을 보여줌을 알 수 있었다.

• 한국정보통신학회논문지
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• 제12권12호
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• pp.2146-2152
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• 2008

본 논문에서는 3차원 디지털 시네마 영상의 압축을 위하여 다해상도 기반 렌더링(MultiResolution-based Rendering, MRBR) 기법을 이용한 JPEG2000 압축코덱 구조에 대하여 제안하였다. 스테레오 영상에 이산 웨이블릿 변환(discrete wavelet transform, DWT)과 다해상도의 웨이블릿 영역에서 스테레오 정합(stereo matching)기법을 적용하여 변이정보를 추출하고 기준영상과 같이 전송한다. 또한 추출된 다른 시점의 영상은 비폐색영역으로 인한 화질열화가 발생하므로 이를 보상하기 위하여 비폐색영역이 포함된 원 주파수정보와 대상 시점에서 주파수정보의 차이를 같이 전송한다. 변이정보는 변이공간에서의 동적계획법(dynamic programming)을 이용하여 추출하였다. DWT의 특성상 상위 부대역은 하위 부대역과 높은 상관성을 갖는다. 따라서 coarse-to-fine 방법을 이용하여 상위 부대역에서 얻어진 변이정보를 하위 부대역에 적용하여 탐색영역을 제한함으로써 일반적인 동적계획법에 비하여 연산량을 단축시켰으며 정확도를 향상시켰다.

• Journal of Oral Medicine and Pain
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• 제46권3호
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• pp.75-83
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• 2021

Odontogenic infection in the oral and maxillofacial regions caused by bacteria (mostly of oral origin) is one of the most common diseases encountered by dentists. Localized infection can easily be treated with incision and drainage followed by antibiotics. Emergence of multidrug resistant (MDR) bacteria called "Superbacteria" has become one of the serious problems in modern society, due to its small window of opportunity for treatment and high casualty. The acronym "ESKAPE", encompassing the common and serious MDR pathogens stand for Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. Literature search was performed in Medline, PubMed and Google Scholar ranging from 2012 to 2020. ESKAPE patient's infection period was longer than that of non-ESKAPE group, and the treatment method due to antibiotic resistance was also complicated. The purpose of this study is to investigate infection caused by ESKAPE pathogens in the oral and maxillofacial regions through literature review and to inform dental surgeons of the danger of ESKAPE pathogens and to suggest viable treatment options. Many studies worldwide reported infections associated with ESKAPE pathogens, but only limited number of studies targeted infection in oral and maxillofacial regions. Further research is required with more data on ESKAPE bacteria and their infection, especially in oral and maxillofacial regions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• Journal of Animal Science and Technology
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• 제63권1호
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• pp.1-15
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• 2021

As a result of intensive breeding, litter size has considerably increased in pig production over the last three decades. This has resulted in an increase in farrowing complications. Prolonged farrowing will shorten the window for suckling colostrum and reduce the chances for high-quality colostrum intake. Studies also agree that increasing litter sizes concomitantly resulted in decreased piglet birth weight and increased within-litter birth weight variations. Birth weight, however, is one of the critical factors affecting the prognosis of colostrum intake, and piglet growth, welfare, and survival. Litters of uneven birth weight distribution will suffer and lead to increased piglet mortality before weaning. The proper management is key to handle the situation. Feeding strategies before farrowing, management routines during parturition (e.g., drying and moving piglets to the udder and cross-fostering) and feeding an energy source to piglets after birth may be beneficial management tools with large litters. Insulin-like growth factor 1 (IGF-1)-driven recovery from energy losses during lactation appears critical for supporting follicle development, the viability of oocytes and embryos, and, eventually, litter uniformity. This paper explores certain management routines for neonatal piglets that can lead to the optimization of their colostrum intake and thereby their survival in large litters. In addition, this paper reviews the evidence concerning nutritional factors, particularly lactation feeding that may reduce the loss of sow body reserves, affecting the growth of the next oocyte generation. In conclusion, decreasing birth weight and compromised immunity are subjects warranting investigation in the search for novel management tools. Furthermore, to increase litter uniformity, more focus should be placed on nutritional factors that affect IGF-1-driven follicle development before ovulation.

• Clinical and Experimental Vaccine Research
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• 제12권4호
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• pp.265-290
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• 2023

Rare but serious thrombotic incidents in relation to thrombocytopenia, termed vaccine-induced immune thrombotic thrombocytopenia (VITT), have been observed since the vaccine rollout, particularly among replication-defective adenoviral vector-based severe acute respiratory syndrome coronavirus 2 vaccine recipients. Herein, we comprehensively reviewed and summarized reported studies of VITT following the coronavirus disease 2019 (COVID-19) vaccination to determine its prevalence, clinical characteristics, as well as its management. A literature search up to October 1, 2021 using PubMed and SCOPUS identified a combined total of 720 articles. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, after screening the titles and abstracts based on the eligibility criteria, the remaining 47 full-text articles were assessed for eligibility and 29 studies were included. Findings revealed that VITT cases are strongly related to viral vector-based vaccines, which are the AstraZeneca COVID-19 vaccine (95%) and the Janssen COVID-19 vaccine (4%), with much rarer reports involving messenger RNA-based vaccines such as the Moderna COVID-19 vaccine (0.2%) and the Pfizer COVID-19 vaccine (0.2%). The most severe manifestation of VITT is cerebral venous sinus thrombosis with 317 cases (70.4%) and the earliest primary symptom in the majority of cases is headache. Intravenous immunoglobulin and non-heparin anticoagulant are the main therapeutic options for managing immune responses and thrombosis, respectively. As there is emerging knowledge on and refinement of the published guidelines regarding VITT, this review may assist the medical communities in early VITT recognition, understanding the clinical presentations, diagnostic criteria as well as its management, offering a window of opportunity to VITT patients. Further larger sample size trials could further elucidate the link and safety profile.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• 전자공학회논문지CI
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• 제37권2호
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• pp.31-43
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• 2000

본 논문은 얼굴영상에서 눈과 입 부위를 추출하기 위한 알고리즘을 제안하였다. 첫째로, 눈과 입의 에지 이진 화소 집합의 고유 값 (Eigenvalue) 과 고유 벡터 (Eigenvector) 로 부터 추출한 정보들은 눈과 입을 찾기 위한 좋은 특징이 된다. 눈과 입 부위의 긍정적 샘플과 부정적 샘플로부터 추출한 고유 특징들로 다층 신경망을 학습하여 특정 영역이 눈과 입 부위 포함하는 정도를 나타내도록 하였다. 둘째로, 시스템의 강건성 확보를 위해 서로 다른 구조의 단일 MLP를 묶어서 그 결과를 이용하는 Ensemble network 구조를 사용하였다. 두 눈과 입에 각각 별도의 Ensemble network을 사용하였고, 각 Ensemble network내 MLP들의 출력이 최대가 되는 영역의 중심 좌표들을 평균하여 최종 위치를 결정하였다. 셋째로, 특징 정보 추출 검색 영역을 즐기기 위해 얼굴 영상 에지 정보와 눈과 입의 위치 관계를 이용해 눈과 입의 대략적인 영역을 추출하였다. 제안된 시스템은 적은 수의 정면 얼굴에서 추출한 고유 특징들로 학습된 Ensemble network을 사용하여 학습에 사용되지 않은 다른 사람들의 정면얼굴 뿐만 아니라 일정한 범위 내 자세 변화에서도 좋은 일반화 성능을 얻고 있으며, 작은 범위 내에서의 얼굴 크기 변화나 좌우 20°이내의 자세 변화에 대해서도 신경망의 일반화 기능을 이용하여 강건한 결과를 얻고 있음을 확인하였다.