• Korean Chemical Engineering Research
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• v.43 no.4
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• pp.458-466
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• 2005

Full-scale utilizations of batch separation process often require knowledge about thermodynamics and correlation techniques of physical properties of complex mixture consisting of a great number of many unknown components. Various empirical correlations have been proposed to predict the physical properties mostly about the pseudocomponent of petroleum. In this study, one parameter correlations are developed for the calculations of the critical physical properties and ideal heat capacity of the pseudo-component of coal tar fractions. Developed model can provide a tool for the design and operations for the batch distillation of coal tar mixture.

• Journal of Community Nutrition
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• v.2 no.1
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• pp.52-61
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• 2000

Dietary guidelines are a distillation of dietary advice from health professionals to the general public. They are based upon current scientific knowledge about the relationships between diet and disease, nutrients available in the food supply of a country, and the profile of morbidity and mortality in that country. With two different sets of dietary guidelines used for more than an decade in Korea. the necessity of revising dietary guidelines has been raised continuously from academia and research. Funded by a grant from the Health Technology Planing and Evaluation Board. Dietary guidelines for each age group were drafted as a research project and the one for the Korean elderly is as follows: Dietary Guidelines for the korean elderly(draft) - Have a variety of easily digestible foods on time; at least 3 meals a day and some snakes. - Be physically active to maintain appetite and/or ideal body weight. ; maintain a balance between activity and what you eat. -Increase consumption of bean-and dairy-and dairy-products. - Consume enough amounts of fresh dark-green and yellow vegetables and fresh fruits. - Consume adequate amounts of assorted kind of animal foods including fish, meat and poultry. - If you drink alcoholic beverages, limit your intake and, drink enough water and other averages; alcohol may interact with your medication and affect your appetite. Aforementioned draft and related contents are expected to be utilized as a neat base in formulating(or revising) dietary guidelines for Korean by the Government in near future.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.45-55
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• 2022

Recently, large-scale language models (LPLM) have been shown state-of-the-art performances in various tasks of natural language processing including intent classification. However, fine-tuning LPLM requires much computational cost for training and inference which is not appropriate for dialog system. In this paper, we propose compressed intent classification model for multi-agent in low-resource like CPU. Our method consists of two stages. First, we trained sentence encoder from LPLM then compressed it through knowledge distillation. Second, we trained agent-specific adapter for intent classification. The results of three intent classification datasets show that our method achieved 98% of the accuracy of LPLM with only 21% size of it.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

• Journal of Physiology & Pathology in Korean Medicine
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• v.38 no.2
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• pp.49-58
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• 2024

The rapid advancement of generative large language models has revolutionized various real-life domains, emphasizing the importance of exploring their applications in healthcare. This study aims to examine how generative large language models are implemented in the medical domain, with the specific objective of searching for the possibility and potential of integration between generative large language models and East Asian medicine. Through a comprehensive current state analysis, we identified limitations in the deployment of generative large language models within East Asian medicine and proposed directions for future research. Our findings highlight the essential need for accumulating and generating structured data to improve the capabilities of generative large language models in East Asian medicine. Additionally, we tackle the issue of hallucination and the necessity for a robust model evaluation framework. Despite these challenges, the application of generative large language models in East Asian medicine has demonstrated promising results. Techniques such as model augmentation, multimodal structures, and knowledge distillation have the potential to significantly enhance accuracy, efficiency, and accessibility. In conclusion, we expect generative large language models to play a pivotal role in facilitating precise diagnostics, personalized treatment in clinical fields, and fostering innovation in education and research within East Asian medicine.