• Journal of the Korean Data and Information Science Society
• /
• v.18 no.1
• /
• pp.219-228
• /
• 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
• /
• v.14 no.1
• /
• pp.11-21
• /
• 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
• /
• v.17 no.4
• /
• pp.1365-1374
• /
• 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
• /
• v.18 no.6
• /
• pp.258-266
• /
• 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
• /
• v.8 no.2
• /
• pp.241-252
• /
• 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
• /
• v.46 no.3
• /
• pp.134-139
• /
• 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
• /
• v.38 no.2
• /
• pp.117-124
• /
• 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.

• Journal of the Society of Disaster Information
• /
• v.19 no.3
• /
• pp.532-544
• /
• 2023

Purpose: To identify and evaluate the risk of chemical fire causative substances by using thermal analysis methods (DSC, TGA) for the hazards and physical property changes that occur when newly used biofuels are mixed with existing fuels It is to use it for identification and evaluation of the cause of fire by securing data related to the method and the hazards of the material according to it. Method: The research method used in this experiment is the differential scanning calorimeter (DSC: Difference in heat flux) through quantitative information on the caloric change from the location, shape, number, and area of peaks. flux) was measured, and the weight change caused by decomposition heat at a specific temperature was continuously measured by performing thermogravimetric analyzer (TGA: Thermo- gravimetric Analyzer). Result: First, in the heat flux graph, the boiling point of the material and the intrinsic characteristic value of the material or the energy required for decomposition can be checked. Second, as the content of biodiesel increased, many peaks were identified. Third, it was confirmed through analysis that substances with low expected boiling points were contained. Conclusion: It was shown that the physical risk of the material can be evaluated by using the risk of biodiesel, which is currently used as a new energy source, through various physical and chemical analysis techniques (DSC + TGA).In addition, it is expected that the comparison of differences between test methods and the accumulation and utilization of know-how on experiments in this study will be helpful in future studies on physical properties of hazardous materials and risk assessment of materials.

• Geophysics and Geophysical Exploration
• /
• v.25 no.4
• /
• pp.227-241
• /
• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.