• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.34-39
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• 2023

Growth modeling in plant factories can not only control stable production and yield, but also control environmental conditions by considering the relationship between environmental factors and plant growth rate. In this study, using the expolinear function, we modeled perilla [Perilla frutescens (L.) Britt.] cultivated in a plant factory. Perilla growth was investigated 12 times until flower bud differentiation occurred after planting under light intensity, photoperiod, and the ratio of mixed light conditions of 130 μmol·m^-2·s^-1, 12/12 h, red:green:blue (7:1:2), respectively. Additionally, modeling was performed to predict dry and fresh weights using the expolinear function. Fresh and dry weights were strongly positively correlated (r = 0.996). Except for dry weight, fresh weight showed a high positive correlation with leaf area, followed by plant height, number of leaves, number of nodes, leaf length, and leaf width. When the number of days after transplanting, leaf area, and plant height were used as independent variables for growth prediction, leaf area was found to be an appropriate independent variable for growth prediction. However, additional destructive or non-destructive methods for predicting growth should be considered. In this study, we created a growth model formula to predict perilla growth in plant factories.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.953-954
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• 2012

• The Korean Journal of Franchise Management
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• v.12 no.4
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• pp.13-23
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• 2021

Purpose: This study aimed to derive clues for the franchise industry to overcome difficulties such as various legal regulations and social responsibility demands and to continuously develop by analyzing the research trends related to franchises published in Korea. Research design, data and methodology: As a result of searching for 'franchise' in ScienceON, abstracts were collected from papers published in domestic academic journals from 1994 to June 2021. Keywords were extracted from the abstracts of 1,110 valid papers, and after preprocessing, keyword analysis, TF-IDF analysis, and topic modeling using LDA algorithm, along with trend analysis of the top 20 words in TF-IDF by year group was carried out using the R-package. Results: As a result of keyword analysis, it was found that businesses and brands were the subjects of research related to franchises, and interest in service and satisfaction was considerable, and food and coffee were prominently studied as industries. As a result of TF-IDF calculation, it was found that brand, satisfaction, franchisor, and coffee were ranked at the top. As a result of LDA-based topic modeling, a total of 12 topics including "growth strategy" were derived and visualized with LDAvis. On the other hand, the areas of Topic 1 (growth strategy) and Topic 9 (organizational culture), Topic 4 (consumption experience) and Topic 6 (contribution and loyalty), Topic 7 (brand image) and Topic 10 (commercial area) overlap significantly. Finally, the trend analysis results for the top 20 keywords with high TF-IDF showed that 10 keywords such as quality, brand, food, and trust would be more utilized overall. Conclusions: Through the results of this study, the direction of interest in the franchise industry was confirmed, and it was found that it was necessary to find a clue for continuous growth through research in more diverse fields. And it was also considered an important finding to suggest a technique that can supplement the problems of topic trend analysis. Therefore, the results of this study show that researchers will gain significant insights from the perspectives related to the selection of research topics, and practitioners from the perspectives related to future franchise changes.

• Proceedings of the Membrane Society of Korea Conference
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• 2001.05a
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• pp.120-123
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• 2001

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.329-330
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• 2014

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.124-124
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• 1993

• 한국생태학회:학술대회논문집
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• 1998.05a
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• pp.77-77
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• 1998

• Proceedings of the KWS Conference
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• 1997.10a
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• pp.25-28
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• 1997

• Korean Journal of Health Education and Promotion
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• v.32 no.5
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• pp.43-51
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• 2015

Objectives: Socioeconomic factors are one of the significant factors explaining drinking problems in our society. From the poverty and inequality perspective, not only absolute poverty but perceived level of poverty or inequality has a direct effect on one's health and health behaviors. The purpose of the study is to explore the growth trajectories of problem drinking in Korea in relation to poverty and perceived income. Methods: Data from 13,414 adults were analyzed using 4 years of data (2010 to 2014) from the Korea Welfare Panel. Main variables included poverty status, perceived income inequality, and problem drinking. A latent growth modeling was employed for the analysis. Results: The non-poverty group had higher initial level of problem drinking; however, the poverty group showed higher rate of increase in problem drinking rate. The perceived income inequality had no significant influence on the initial level, but over time, those with higher level of perceived income inequality showed higher rate of increase in problem drinking. Conclusions: Findings showed that poverty and inequality affect changes in problem drinking. Efforts to prevent and decrease problems related to alcohol should not only focus on changing individuals' behavior but also on decreasing the inequality gap.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.