• Asian-Australasian Journal of Animal Sciences
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• v.29 no.7
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• pp.909-924
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• 2016

The gut is the largest organ that helps with the immune function. Gut health, especially in young pigs has a significant benefit to health and performance. In an attempt to maintain and enhance intestinal health in pigs and improve productivity in the absence of in-feed antibiotics, researchers have evaluated a wide range of feed additives. Some of these additives such as zinc oxide, copper sulphate, egg yolk antibodies, mannan-oligosaccharides and spray dried porcine plasma and their effectiveness are discussed in this review. One approach to evaluate the effectiveness of these additives in vivo is to use an appropriate disease challenge model. Over the years, researchers have used a number of challenge models which include the use of specific strains of enterotoxigenic Escherichia coli, bacteria lipopolysaccharide challenge, oral challenge with Salmonella enteric serotype Typhimurium, sanitation challenge, and Lawsonia intercellularis challenge. These challenge models together with the criteria used to evaluate the responses of the animals to them are also discussed in this review.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.9 no.2
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• pp.19-31
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• 1999

Most breakthrough tests are conducted at higher concentration levels compared to those in the field of air-purifying respirator applications. For example, typical challenge concentrations for breakthrough tests agains tcarbon tetrachloride are ranged between 250-1000 ppm although applicable concentrations range for air-purifying cartridge is 5-50 ppm for carbon tetrachloride. However, no guarantee has been made that isotherms derived from the experiment at high challenge concentrations could estimate adsorption capacity at the lower concentration range where workers wear usually air-purifying respirators. Three models of adsorption isotherms (Freundlich, Langmuir and Dubinin/Radushkevich(D/R) isotherms) that have been commonly applied for respirator cartridge testing were evaluated. Adsorption capacity at each challenge concentration was calculated from the Reaction Kinetic equation fitted for the breakthrough data. These data were used for derivation of three isotherms. In general, the D/R isotherm has given the best agreement between estimated adsorption capacities and experimentally measured. If the challenge concentration of 100 ppm is included for derivation of models, Freundlich and D/R models could succes sfully produced good estimations for adsorption capacities at 50 ppm level. Estimated adsorption capacities by both models ranged in 94 - 109 % of the experimentally measured. However, Langmuir model gives underes timation in all cases.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

• The Journal of Korean Association of Computer Education
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• v.20 no.5
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• pp.1-14
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• 2017

The informatics in the 2015 revised national curriculum has established the identity as the core subject for the intelligent information society. However, while the nature, objective, scopes and contents, and achievement standards of the curriculum are systematic, there is a lack of research on effective teaching and learning, and assessment methods. This study analyzed the applicability of Bebras Challenge, which began in Lithuania in 2004 and has been attracting attention as a new informatics education model with 1.3 million students as participants in 2015 around the world. Furthermore this research presented related issues for the indigenization of Bebras Challenge. This study will be a basis for the research of teaching and learning, and assessment models as well as the spread of Bebras Challenge.

• Korean Management Science Review
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• v.20 no.2
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• pp.197-204
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• 2003

Comparison challenge approach is proposed as a form of challenger-activated. just-in-time Internet advertising. To develop a framework for a comparison challenge, we propose a theory of comparison value. A comparison is regarded valuable if a comparison opportunity is available and if the comparison is relevant and informative, has an appropriate level of detail, and is advantageous and trustworthy. Based on this theory, the CompareMe and CompareThem strategies are devised, and comparable objects are classified in terms of price and performance dominance as well as the scope of proximity. The idea is demonstrated with a comparison of PCs from five leading manufacturers. To assist in the planning of the comparison challenge, a mathematical programming model was formulated to maximize the value of comparison under the constraints of the comparison opportunity and budget. The model is applied to eight scenarios in terms of the range of comparing objects. The models under various scenarios are tested and contrasted with the real-world example of PCs. We found the ad effect of comparison challenge to be substantially better than banners (4.75 times) and similarity-based comparisons (2.77 times), providing customers with better performance and reduced prices.

• International Journal of Contents
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• v.15 no.4
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• pp.8-15
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• 2019

This paper presents a solution of the 'Quick, Draw! Doodle Recognition Challenge' hosted by Google. Doodles are drawings comprised of concrete representational meaning or abstract lines creatively expressed by individuals. In this challenge, a doodle is presented as a sequence of sketches. From the view of at the sketch level, to learn the pattern of strokes representing a doodle, we propose a sequential model stacked with multiple convolution layers and Long Short-Term Memory (LSTM) cells following the attention mechanism [15]. From the view at the image level, we use multiple models pre-trained on ImageNet to recognize the doodle. Finally, an ensemble and a post-processing method using the minimum cost flow algorithm are introduced to combine multiple models in achieving better results. In this challenge, our solutions garnered 11th place among 1,316 teams. Our performance was 0.95037 MAP@3, only 0.4% lower than the winner. It demonstrates that our method is very competitive. The source code for this competition is published at: https://github.com/ngxbac/Kaggle-QuickDraw.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.26-32
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• 2007

In sequential speaker classification, the lack of prior information about the speakers poses a challenge for model initialization. To address the challenge, a predetermined generic model set, called Sample Speaker Models, was previously proposed. This approach can be useful for accurate speaker modeling without requiring initial speaker data. However, an optimal method for sampling the models from a generic model pool is still required. To solve this problem, the Speaker Quantization method, motivated by vector quantization, is proposed. Experimental results showed that the new approach outperformed the random sampling approach with 25% relative improvement in error rate on switchboard telephone conversations.

• ETRI Journal
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• v.46 no.1
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1388-1392
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• 2008

Water resources management depends on dealing inherent uncertainties stemming from climatic and hydrological inputs and models. Dealing with these uncertainties remains a challenge. Streamflow forecasts basically contain uncertainties arising from model structure and initial conditions. Recent enhancements in climate forecasting skill and hydrological modeling provide an breakthrough for delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The approach here proposes integration and coupling of global climate models (GCM), multiple regional climate models, and numerous hydrological models to improve streamflow forecasting and characterize system uncertainty through generation of ensemble forecasts.

• Journal of Hospice and Palliative Care
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• v.14 no.2
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• pp.71-80
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• 2011

The growth of the aging population in Korea will challenge health and social services. As Korean society changes, the U.S. models of end-of-life care and geriatric care for frail older adults may have increasing relevance for the Korean healthcare system. This article reviews three U.S. models of care for frail older adults: hospice and palliative care, the Program for All-Inclusive Care for the Elderly (PACE), and the transitional care model. We describe the strengths and limitations of each model and discuss ways in which these models could be adapted for the Korean healthcare system.