• Korean journal of food and cookery science
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• v.19 no.3
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• pp.300-307
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• 2003

This research investigated the consumption of various rice-based processed foods of 279 housewives in Daegu. 70% of the housewives that responded to the question graduated from a high school or college, was 30∼40 years old and full-time housewives. The main places for purchasing the rice-based processed foods were large size discount store and supermarkets, as other industrial products and the family′s preference was the most critical factors in choosing the products. The fact that the main reason of purchasing the rice breads and cookies, instead of wheat, was "They may be good for health” indicated many housewives have a positive perception of rice-based foods. Among the rice-based processed foods, the using frequency of rice cake (dduk) was the highest, with rice cookies and rice drinks being the next most frequent. However, the frequencies of cooked rice (bob) and rice flour were very low. An analysis of the correlation for the using frequency of 15 rice-based processed foods showed that the use of rice cookies and breads, instead of wheat, was highly correlated to another 13 foods. The critical reasons why they do not consume cooked rice and rice flour were uncertainty of the purity of the rice and the addition of preservatives, and that with rice bread and noodles there was no information available about the products, and hey have a poor taste. The most common reasons of using cooked rice were no time to cook and simple curiosity about the products. However, the consumers were suspicious of containers, which were a potential cause of environmental hormones, and the high price of the products. Packed rice flour was mainly used as an ingredient to give the viscosity to a product. The advantages of using rice flour were that it was available to control the amount of buying and the convenience to buy. However, it was pointed out that the taste of products containing packed rice flour were poorer than that of rice flour ground at a mill.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.439-444
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• 2016

The definition of quality continues to evolve. In recent years, there has been growing interest in how to satisfy customers' potential needs with an emphasis on customer-oriented quality. Two-dimensional quality proposed by Kano provides a useful framework for discovering quality attributes critical to customer satisfaction and it is widely employed for product and service development. In Kano model, quality attributes are classified into attractive, one-dimensional, must-be, indifferent, and reverse ones. Finding attractive elements among them is important for achieving customer satisfaction effectively. However, Kano's classification method has limitations in dealing with customers' ambiguous and complex ideas. The customer response itself includes uncertainty and incompleteness. To overcome this problem, fuzzy methods are incorporated with Kano's classification in this paper. According to numerical comparisons, it is shown that the fuzzy Kano method is useful for accommodating various response of customer and is helpful to identify potential needs.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.81-90
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• 2018

"The Framework Act on Low-Carbon Green Growth" specifies the requirements for the development and verification of emission factors for establishing reliable national greenhouse gas statistics. The scope of the regulations covers the development and validation of energy, industrial processes, solvents and other product use, agriculture, land use, land use change and emission and absorption coefficients of the forestry and waste sector as defined in the 1996 IPCC Guideline and GPG 2000, The minerals sector to be covered in this study belongs to industrial processes. As a representative method for quantifying and evaluating GHG emission factors, there are emission grade quality grading and DARS (Data Rating Rating System) in the 'Procedures for Preparing Emission Factor Documents (1997)' reported by US-EPA. However, the above two methods are not specific and comprehensive, and lack the details for accurate emission factor verification. Therefore, there is a need for a method for verifying and quantifying certified greenhouse gas emission factors that reflects characteristics of each industry sector in Korea and accord with IPCC G/L and GHG target management. In this study, we conducted a weighted study on quantitative and evaluation lists of emission factor using questionnaires to develop a more accurate methodology for quantifying national greenhouse gas emission factors in the mineral sector. Quantification and evaluation of emission factor are classified into essential verification and quality evaluation. The essential verifications are : administrative compatibility, method of determining emission factors, emission characteristics, sampling methods and analysis methods, representativeness of data. The quality evaluations consisted of the quality control of the data, the accuracy of the measurement and analysis, the level of uncertainty, not directly affect the emission factor, but consisted of factors that determine data quality.

• Knowledge Management Research
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• v.21 no.4
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• pp.157-173
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• 2020

In accommodation sharing economy, customers take a risk of uncertainty about product quality, which is an important factor affecting users' satisfaction. This risk can be lowered by the information disclosed by the facility provider. Self-presentation of the hosts can make a positive effect on listing performance by eliminating psychological distance through emotional interaction with users. This paper analyzed the self-presentation text provided by Airbnb hosts and found key aspects in the text. In order to extract the aspects from the text, host descriptions were separated into sentences and applied the Attention-Based Aspect Extraction method, an unsupervised neural attention model. Then, we investigated the relationship between aspects in the host description and the listing performance via linear regression models. In order to compare their impact between the three facility types(Entire home/apt, Private rooms, and Shared rooms), the interaction effects between the facility types and the aspect summaries were included in the model. We found that specific aspects had positive effects on the performance for each facility type, and provided implication on the marketing strategy to maximize the performance of the shared economy.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.309-315
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• 2021

The purpose of the article is to analyze the functioning of economic systems in the context of the development of their potential in a circular economy. It is determined that the functioning of economic systems to ensure their sustainability should meet modern challenges and provide for the formation of competitive institutional architecture, the introduction of structural and regulatory innovations, the transition to an innovative model of development. The specific principles of functioning of economic systems include openness, nonlinearity, multivectority, dynamism, emergence, uncertainty about the development of economic processes. It is substantiated that the linear nature of development and equilibrium are not dominant in the functioning of economic systems, and increasing the level of economic efficiency should go hand in hand with minimizing the activities of enterprises, which necessitates the use of circular economy. The main prerequisites for the transition to a circular economy are analyzed. It is determined that the basic concept of the circular economy involves the development of a system of production and consumption, which is based on processing, reuse, repair, product sharing, change of consumption patterns and new business models and systems. The main elements of the circular economy include: a closed cycle, the use of renewable energy sources, systems thinking. The correlation of the principles of sustainable development and the peculiarities of the application of the circular economy is analyzed. It is determined that the circular economy contrasts with the traditional linear economic model, which is based on the model of "take-do-consume-throw away". The advantages and disadvantages due to the use of the principles of circular economy are given. Based on the study, steps are identified to accelerate the transition from a linear economy to a circular economy.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Journal of East Asia Management
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• v.4 no.1
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• pp.53-77
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• 2023

Modern company faces an uncertain future and a competitive environment and are seeking new technologies and creative products to ensure the corporate growth and survival in the market through continuous innovation. "University Industry Cooperation(UIC)" is a point of contact for overcoming the crisis faced by companies and universities in this era and a cooperation platform for mutual prosperity. As a subsidiary of a university, "Technology Holding Company(THC)" is attracting attention as a new window for UIC in Korea. The role of THC is to establish and foster the business opportunities of their subsidiaries and to return investment profits to the university ecosystem again. But recently, the life cycle of technology is getting shorter, and the development cost is steadily increasing. In particular, with the increase of hybrid projects based on convergence and combination, the risk of conducting research(R&D) and new product development(NPD) projects is gradually increasing. A PMO refers to a project management organization that can contribute to improving the success rate of projects with increasing uncertainty by supporting project visibility and appropriate decision-making. The purpose of this study is to raise a research question on whether THC's corporate performance can be improved when "Project Management System(PMO Service)" is introduced into the subsidiary incubation system of THC. This study proposes several research methods to identify the relationship between the introduction of PMO and the corporate performance of THC.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.158-158
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• 2023

Characterizing the performance of precipitation (hereafter PRE) products in estimating the uncertainties in daily PRE in the era of global warming is of great value to the ecosystem's sustainability and human survival. This study intercompares the performance of different PRE products (gauge-based, satellite and reanalysis) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over diverse climate zones in Africa and identifies regions where they depict minimal uncertainties in order to build optimal maps as a guide for different climate users. This is achieved by utilizing various techniques, including the triple collection (TC) approach, to assess the capabilities and limitations of different PRE products over nine climatic zones over the continent. For daily scale analysis, the uncertainties in light PRE (0.1 5mm/day) are prevalent over most regions in Africa during the study duration (2001-2016). Estimating the occurrence of extreme PRE events based on daily PRE 90th percentile suggests that extreme PRE is mainly detected over central Africa (CAF) region and some coastal regions of west Africa (WAF) where the majority of uncorrected satellite products show good agreement. The detection of PRE days and non-PRE days based on categorical statistics suggests that a perfect POD/FAR score is unattainable irrespective of the product type. Daily PRE uncertainties determined based on quantitative metrics show that consistent, satisfactory performance is demonstrated by the IMERG products (uncorrected), ARCv2, CHIRPSv2, 3B42v7.0 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0, and REGEN_ALL (gauge) during the study period. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage for different stakeholders.