• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.219-228
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• 2007

In this paper, we consider a decision-theoretic oriented, objective Bayesian inference for the ratio of two normal variances. Specifically we derive the Bayesian reference criterion as well as the intrinsic estimator and the credible region which correspond to the intrinsic discrepancy loss and the reference prior. We illustrate our results using real data analysis and simulation study.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.11-21
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• 2007

In this paper, we consider a decision-theoretic oriented, objective Bayesian inference for the difference between two normal means with unknown com-mon variance. We derive the Bayesian reference criterion as well as the intrinsic estimator and the credible region which correspond to the intrinsic discrepancy loss and the reference prior. We illustrate our results using real data analysis as well as simulation study.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1365-1374
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• 2006

In this paper, we consider a decision-theoretic oriented, objective Bayesian inference for the difference between two normal means with known variances. We derive the Bayesian reference criterion as well as the intrinsic estimator and the credible region which correspond to the intrinsic discrepancy loss and the reference prior. We show the similarity between derived two-sample results and the results for the one-sample case in Bernardo(1999).

• Corrosion Science and Technology
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• 제18권6호
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• pp.258-266
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• 2019

The objective of this study was to evaluate corrosion resistance of additive manufactured 316L stainless steel and alloy 625 powders widely used in corrosion resistance alloys of marine industry in comparison with cast alloys. Directed Energy Deposition (DED) method was used in this work for sample production. DED parameter adjustment was also studied for optimum manufacturing and for minimizing the influence of defects on corrosion property. Additive manufactured alloys showed lower corrosion resistance in seawater compared to cast alloys. The reason for the degradation of anti-corrosion property was speculated to be due to loss of microstructural integrity intrinsic to the additive manufacturing process. Application of heat treatment with various conditions after DED was attempted. The effect of heat treatments was analyzed with a microstructure study. It was found that 316L and alloy 625 produced by the DED process could recover their expected corrosion resistance when heat treated at 1200 ℃.

• Journal of Communications and Networks
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• 제8권2호
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• pp.241-252
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• 2006

Traditional service-level agreements (SLAs), defined by average delay or packet loss, often camouflage the instantaneous performance perceived by end-users. We define a set of metrics for service availability to quantify the performance of Internet protocol (IP) backbone networks and capture the impact of routing dynamics on packet forwarding. Given a network topology and its link weights, we propose a novel technique to compute the associated service availability by taking into account transient routing dynamics and operational conditions, such as border gateway protocol (BGP) table size and traffic distributions. Even though there are numerous models for characterizing topologies, none of them provide insights on the expected performance perceived by end customers. Our simulations show that the amount of service disruption experienced by similar networks (i.e., with similar intrinsic properties such as average out-degree or network diameter) could be significantly different, making it imperative to use new metrics for characterizing networks. In the second part of the paper, we derive goodness factors based on service availability viewed from three perspectives: Ingress node (from one node to many destinations), link (traffic traversing a link), and network-wide (across all source-destination pairs). We show how goodness factors can be used in various applications and describe our numerical results.

• Applied Microscopy
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• 제46권3호
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• pp.134-139
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• 2016

Detailed structural and molecular imaging of intact organs has incurred academic interest because the associated technique is expected to provide innovative information for biological investigation and pathological diagnosis. The conventional methods for volume imaging include reconstruction of images obtained from serially sectioned tissues. This approach requires intense manual work which involves inevitable uncertainty and much time to assemble the whole image of a target organ. Recently, effective tissue clearing techniques including CLARITY and ACT-PRESTO have been reported that enables visualization of molecularly labeled structures within intact organs in three dimensions. The central principle of the methods is transformation of intact tissue into an optically transpicuous and macromolecule permeable state without loss of intrinsic structural integrity. The rapidly evolving protocols enable morphological analysis and molecular labeling of normal and pathological characteristics in large assembled biological systems with single-cell resolution. The deep tissue volume imaging will provide fundamental information about mutual interaction among adjacent structures such as connectivity of neural circuits; meso-connectome and clinically significant structural alterations according to pathologic mechanisms or treatment procedures.

• Journal of Plant Biotechnology
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• 제38권2호
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• pp.117-124
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• 2011

Considering that the world population is expected to total 9 billion by 2050, it will clearly be necessary to sustain and even accelerate the rate of improvement in crop productivity. In the 21st century, we now face another, perhaps more devastating, environmental threat, namely climate change, which could cause irreversible damage to agricultural ecosystem and loss of production potential. Enhancing intrinsic yield, plant abiotic stress tolerance, and pest and pathogen resistance through agricultural biotechnology will be a critical part of feeding, clothing, and providing energy for the human population, and overcoming climate change. Development and commercialization of genetically engineered crops have significantly contributed to increase of crop yield and farmer's income, decrease of environmental impact associated with herbicide and insecticide, and to reduction of greenhouse gas emissions from this cropping area. Advances in plant genomics, proteomics and system biology have offered an unprecedented opportunities to identify genes, pathways and networks that control agricultural important traits. Because such advances will provide further details and complete understanding of interaction of plant systems and environmental variables, biotechnology is likely to be the most prominent part of the next generation of successful agricultural industry. In this article, we review the prospects for modification of agricultural target traits by genetic engineering, including enhancement of photosynthesis, abiotic stress tolerance, and pest and pathogen resistance associated with such opportunities and challenges under climate change.

• 한국재난정보학회 논문집
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• 제19권3호
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• pp.532-544
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• 2023

연구목적: 신규로 사용되는 바이오연료를 기존 연료와 혼합하여 사용하는 경우 발생하는 위험성과 물성 변화를 열분석 방법(DSC, TGA)을 사용하여 화학 화재의 원인물질의 위험성을 확인하고, 평가할 방법과 그에 따른 물질의 위험성 관련 데이터를 확보함으로써 화재 원인 감식과 감정에 활용하기 위함이다. 연구방법: 본 실험에 사용된 연구 방법으로는 시차주사열량계(DSC : Differential Scanning Calorimeter)에 의하여 피크의 위치, 모양, 개수, 피크의 면적으로부터 열량 변화의 정량적인 정보를 통하여 열유속 차이(Difference in heat flux)를 측정하였고, 열중량분석(TGA : Analyzer)을 시행함으로써 특정한 온도에서 분해열 등에 의해 발생한 무게 변화를 연속적으로 측정하였다. 연구결과: 먼저 열 유속의 그래프에서 물질의 끓는점과 물질이 가지고 있는 고유 특성치 또는 분해에 필요한 에너지를 확인할 수 있다. 둘째 바이오디젤의 함량이 증가할수록 많은 피크를 확인 할 수 있었다. 셋째 비점이 낮은 물질들이 함유하고 있다는 것을 분석 결과로 확인할 수 있었다. 결론: 현재 새로운 에너지원으로 사용되고 있는 바이오디젤의 위험성을 다양한 물리·화학적 분석기법(DSC+TGA)을 통하여 사용함으로써 물질의 물적 위험성을 평가할 수 있음을 보여주었다. 아울러 본 연구의 시험방법별 차이의 비교와 실험에 대한 노하우를 축적하고 활용한다면 향후 위험물의 물성 연구와 물질 위험성 평가 연구에 있어 도움이 되리라 기대한다.

• 지구물리와물리탐사
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• 제25권4호
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• pp.227-241
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• 2022

전파형 역산은 석유가스 탐사를 위한 탄성파 자료처리 분야에서 지층의 속도 모델을 추정하는데 사용되는 역산 기법이다. 최근 탄성파 자료처리에 딥러닝 기술의 활용이 급격하게 증가하고 있는데, 전파형 역산 기술도 마찬가지로 다양한 연구가 이루어지고 있다. 초기에는 머신러닝 기술을 활용한 자료처리 기법이 전파형 역산을 위한 입력자료의 전처리 목적으로 활용되는 수준이었으나, 딥러닝 기술을 통해 전파형 역산을 직접적으로 구현하는 연구가 등장하기 시작하였다. 딥러닝 기술을 활용한 전파형 역산은 순수 데이터 기반 접근법, 물리 기반 신경망 활용법, 인코더-디코더 구조 활용법, 신경망 재매개변수화를 이용한 구현법, 물리정보 기반 신경망 기법 등으로 구분할 수 있다. 이 논문에서는 딥러닝 기반 전파형 역산 기법을 발전 과정 순서로 체계화하여 각각의 접근법에 대한 이론과 특징을 설명하였다. 전파형 역산 기술에 딥러닝 기법을 도입한 초기에는 데이터 과학의 기본 원리에 충실하게 대량의 학습자료를 준비하고 순수 데이터 기반 예측 모델을 적용하여 속도 모델을 역산하는 연구로 시작하였다. 최근 연구 동향은 탄성파 자료의 잔차나 파동방정식 자체의 물리정보를 심층 신경망에 활용하여 순수 데이터 기반 접근법의 단점을 보완해 나가는 방향으로 진행되고 있다. 이러한 발전으로 대량의 학습자료가 필요하지 않고, 전파형 역산의 태생적 한계점인 주기 놓침 현상을 완화하며 계산 시간을 획기적으로 줄일 수 있는 딥러닝 기반 전파형 역산 기술이 등장하고 있다. 딥러닝 기술의 도입으로 전파형 역산 기술은 탄성파 자료처리 분야에서 가치가 더 높아질 것으로 생각된다.