• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.51-68
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• 1994

Various growth models were each fitted onto the data sets in an attempt to determine which growth models achieved the best forecasts for differing types of growth data. Of six such models studied, some models do significantly better than others in predicting future levels of growth. It is recommened that Weibull and the Gompertz growth curve be considered along with Pearl model by those industries presently considering the implementation of substitution analysis in their life analysis. In the early stage of growth, linear estimation should suffice to give reasonable forecasts. In the latter stage, however, as more data become availavle, nonlinear estimation should be used.

• Journal of Bio-Environment Control
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• v.2 no.1
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• pp.27-36
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• 1993

Carbon dioxide enrichment for greenhouse crops has generally been a standard commercial practice for many years. Vegetable crops such as tomato, cucumber, and lettuce respond positively to the $CO_2$ enrichment. But improper $CO_2$ enrichment leads to physiological damage and economical loss. This study was carried out to develop a $CO_2$ concentration control algorithm considering growth stage and efficiency. The measurand was $CO_2$ consumption rate and top fresh weight that represents growth stage. The weight of top fresh lettuce as a whole in the tray was measured through a non-destructive method. The demand in $CO_2$ concentration according to growth stage was investigated. The results are summarized as follows. 1. The $CO_2$ consumption rate could be measured within the error of $\pm$ 15.4mg$CO_2$/hr in the range of $CO_2$ concentration of 500-1500ppm. 2. The weight of top fresh lettuce could be measured within the error $\pm$ 4.3g in the range of 0-1400g. 3. The $CO_2$ control model developed could determine an economical $CO_2$ supply rate considering $CO_2$ consumption rate and leakage rate. 4. The $CO_2$ control algorithm based on the control model was composed of feedforward control for maintaining a stable $CO_2$ concentration level, and feedback control with $CO_2$ consumption rate and top fresh weight for adapting to the change in $CO_2$ demand by growth stage. 5. For the performance test with the developed control algorithm on lettuce the decrease in $CO_2$ supply rate was obtained without a significant decrease in top fresh weight.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.9 no.14
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• pp.45-48
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• 1986

This paper studies reliability growth model in redundancy allocation of Parallel-series system in which several series system is linked parallelly, The model is generalized by system redundancy of sub-system that have components redundancy. The stage of components in each sub-system is established differently. At the same time by assigned the different number of constraints to the sub-system, this paper deals with rather practical reliability growth model.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.5
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• pp.395-404
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• 2017

Cultivating soybeans in rice paddy field reduces labor costs and increases the yield. Soybeans, however, are highly susceptible to excessive soil water in paddy field. Controlled drainage system can adjust groundwater level (GWL) and control soil moisture content, resulting in improvement soil environments for optimum crop growth. The objective of this study was to fit the soybean growth data (canopy height and stem diameter) using Gompertz model and Logistic model at different GWL and validate those models. The soybean, Daewon cultivar, was grown on the lysimeters controlled GWL (20cm and 40cm). The soil textures were silt loam and sandy loam. The canopy height and stem diameter were measured from the 20th days after seeding until harvest. The Gompertz and Logistic models were fitted with the growth data and each growth rate and maximum growth value was estimated. At the canopy height, the $R_2$ and RMSE were 0.99 and 1.58 in Gompertz model and 0.99 and 1.33 in Logistic model, respectively. The large discrepancy was shown in full maturity stage (R8), where plants have shed substantial amount of leaves. Regardless of soil texture, the maximum growth values at 40cm GWL were greater than the value at 20cm GWL. The growth rates were larger at silt loam. At the stem diameter, the $R_2$ and RMSE were 0.96 and 0.27 in Gompertz model and 0.96 and 0.26 in Logistic model, respectively. Unlike the canopy height, the stem diameter in R8 stage didn't decrease significantly. At both GWLs, the maximum growth values and the growth rates at silt loam were all larger than the values at sandy loam. In conclusion, Gompertz model and Logistic model both well fit the canopy heights and stem diameters of soybeans. These growth models can provide invaluable information for the development of precision water management system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.268-275
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• 2017

The photosynthetic rates of crops depend on growth environment factors, such as light intensity and temperature, and their photosynthetic efficiencies vary with growth stage. The objective of this study was to compare two different models expressing canopy photosynthetic rates of romaine lettuce (Lactuca sativa L., cv. Asia Heuk romaine) using three variables of light intensity, temperature, and growth stage. The canopy photosynthetic rates of the plants were measured 4, 7, 14, 21, and 28 days after transplanting at closed acrylic chambers ($1.0{\times}0.8{\times}0.5m$) using light-emitting diodes, in which indoor temperature and light intensity were designed to change from 19 to $28^{\circ}C$ and 50 to $500{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, respectively. At an initial $CO_2$ concentration of $2,000{\mu}mol{\cdot}mol^{-1}$, the canopy photosynthetic rate began to be calculated with $CO_2$ decrement over time. A simple multiplication model expressed by simply multiplying three single-variable models and a modified rectangular hyperbola model were compared. The modified rectangular hyperbola model additionally included photochemical efficiency, carboxylation conductance, and dark respiration which vary with temperature and growth stage. In validation, $R^2$ value was 0.849 in the simple multiplication model, while it increased to 0.861 in the modified rectangular hyperbola model. It was found that the modified rectangular hyperbola model was more suitable than the simple multiplication model in expressing the canopy photosynthetic rates affected by environmental factors (light Intensity and temperature) and growth factor (growth stage) in plant factory modules.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.89-104
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• 2021

Food delivery service combined with IT technology and HMR (Home Meal Replacement) are rapidly growing due to the COVID-19. Recently, the demand for salads along with HMR has increased among office workers in their 20s and 30s who are interested in health and beauty. Freshcode is a food startup with 6 years of experience that started selling salad products through O2O service. Freshcode applied for a patent for a service that collects orders from nearby areas and delivers them on the same day to a designated delivery address 'FCOSPOT' to save shipping costs. In March 2021, in recognition of the growth potential of the regular delivery service, Freshcode received an investment of 6 billion won in Series A. This study may have practical implications to early-stage startups and scale-up stage startups through a longitudinal case study on the growth of a single company. As for the research method, the lean startup methodology and lean canvas were used in the early stage of startup. In particular, the process of the build-measure and learn feedback-loop, which is the core of lean startup methodology, was applied to each major decision-making step. In the scale-up stage after 5 years, the business model canvas was used to schematize the growth as a food online O2O platform to verify continuous innovation. This case study has three main findings. First, the idea of 'FCOSPOT' was successfully implemented through the Lean Startup methodology. Second, Freshcode demonstrated the scalability of the differentiated business model of shared base delivery O2O. Third, a key factor of success was the digital integrated communication operation strategy that maximizes the experience for the created customers.

• Journal of Biosystems Engineering
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• v.34 no.4
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• pp.269-277
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• 2009

In this research, the vegetation growth and rice taste of the liquid fertilizer applied fields (LF) were compared with those of chemical fertilizer applied fields(CF) in order to confirm the possibility of methane fermentation digested sludge as liquid fertilizer using precision agriculture and remote sensing technology. In panicle initiation stage, the vegetation growth at LF was 60%~80% of it at CF and there were significant difference of nitrogen contents between CF and LF. The estimation model of nitrogen contents was established by GNDVI (R=0.607, RMSE=$1.04\;g/m^2$, n=36, p<0.01). In heading stage, vegetation growth at LF went close to it at CF as ratio of 80%~95%. The nitrogen content estimation model was also established (R=0.650, RMSE=$1.73\;g/m^2$, n=35, p<0.01) and there were significant difference of spatial variability between LF and CF. There were not significant difference of rice taste and it's elements, when three samples, which were more than twice of standard deviation, were excepted. The protein contents estimation model using GNDVI of before harvesting (R=0.700, RMSE=0.470%, n=29, p<0.01) were more suitable to predict the protein contents at harvesting comparing with it of heading stage(R=0.610, RMSE=0.521%, n=29, p<0.01).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.1-13
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• 2018

This study examines the relationship between the organizational general characteristics (industry, size, location, development stage, and company age) and success factors of Korean venture firms using secondary data. Among the industries with the highest sales figures in 2016 are food / fiber / (non) metals, and the smallest category was software development. The sectors with the highest net profit were computer / semiconductor / electronic components, and the smallest category was telecommunication equipment / broadcasting equipment. The industries with the largest sales growth rate are IT / broadcasting services and software development. The industries with the highest net profit margin of sales are energy / medical / precision, and the smallest is telecommunication equipment / broadcasting equipment. In terms of the number of employees, venture firms with more than 100 employees have the largest sales and net profit, with employees between 1 and 9 have the smallest. However, these results are predictable. In general, the number of employees is highly correlated with sales and net profit. Rather, the sales growth rate and the net profit margin of sales may be meaningful. In particular, with employees between 50 ~ 99, the growth rate of sales and the net profit margin of sales were high. In terms of location, Seoul / Incheon / Gyeonggi were the regions with the highest sales and Daejeon / Sejong / Chungcheong / Gangwon were the least regions. Gwangju / Jeolla / Jeju and Seoul / Incheon / Gyeonggi were almost similar in the areas with the largest net profit. However, Daejeon / Sejong / Chungcheong / Gangwon had the lowest net profit. Unusually, the areas with the highest sales growth rate and the highest net profit margin of sales were Gwangju / Jeolla / Jeju, and the smallest areas were Busan / Jeonnam / Ulsan In the relationship between the stage of development and the performance of the company, the sales of maturity and decline stages were the highest and establishing stage was the lowest. Net profit was also the highest in mature stage and the smallest in establishing stage. The sales growth rate shows a typical pattern in the order of establishing stage, early growth stage, high growth stage, maturity stage, and decline stage. In terms of business performance, sales and net profit are the highest with 21 years or more of company age, and the smallest is less than 3 years. In addition, the sales growth rate was the highest in three years or less, and the net profit margin of sales was the highest in 4 to 10 years. This study can present lots of useful implications by suggesting integrated research model and examining the success factors of Korean venture firms and presenting the application methods of secondary data in analyzing the current status of venture industry in Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.6
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• pp.59-76
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• 2019

Most technology-based entrepreneurship(TBE) go through an process of decline or disappear without overcoming the valley of death(VoD). The purpose of this study is to identify the growth dimension of TBE and to test the influence of firms activities on firms growth over time. This study identified the two-dimensional growth dimension divided by size and profit through exploratory factor analysis(EFA) of a number of growth indicators. Then, we defined the discrete state of growth firm in four states, divided by size and profit, and five states, including the closure of business. Multi-nomial logit model is used to predict the effect of TBE activities on a discrete state of growth firm(size×profit, closure of business) based on multiple independent variables. The independent variables are based on five representative firms activities: employment, marketing, R&D, financial activities, and general management activities. The growth stage of TBE over time has been categorized into three stages: early stage, middle stage, and late stage of business, taking into account the main periods during which the survival rate of startups sharply decreases. The analytical data of this study was based on the secondary data of the start-up supporting companies of government and public institutions. The subjects of analysis were TBE within 10 years. As a result of the empirical analysis, the employment and marketing activities of TBE show that early and mid-term activities had an effect on the state of firms growth. However, if there is a difference, employment activities have both positive and negative effects, while marketing activities have only a positive effect on size and profit growth. And besides, R&D activities, financial activities, and general management activities throughout the entire process of firms growth were found to be firms activities that have both positive and negative effects on firms growth. In addition, the age of the founder, the firms' industry, and the geographic location of the firms, which are general characteristics of the company, were found to have a distinctive effect on the growth status of the firms according to the growth stage.