• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.1
• /
• pp.131-138
• /
• 2023

This study started with questioning the fact that in the assessmentof technology, which has taken place every two years since 2010, the technology gap in the most technologically advanced countries was evaluated as 4-5 years in each evaluation. To interrogate this question, regression estimation was performed using the Gompertz model based on time series data for technology level evaluation. As a result, it would take 17 years for high-speed rail vehicle technology to reach the level of 95 % of the country with the highest technology, and 72 years to reach the level of 100 %. Recognizing the technology gap is important in establishing a technology catchup strategy. A collaborative technology catchup strategy is the best strategy for moving to an original technology development stage while competing with large global leaders without much domestic market demand. This can occur regardless of where Korea is located in the technology catchup stage.

• Journal of Animal Science and Technology
• /
• v.48 no.2
• /
• pp.151-160
• /
• 2006

This study was conducted to investigate the genetic characteristics of growth stages in Hanwoo, to provide useful information in farm management decisions. Data were taken from the nucleus herds of three farms, Namwon, Daegwalyong and Seosan, comprising 27,647 cows, 14,744 bulls, and 1,290 steers in between 1980 and 2004. According to the growth curve by year, the residuals for cows and bulls were 68.49 and 54.29, respectively, under the Gompertz model. The values were lower than in other years. Parameters, A, b and k were estimated as 423.6±5.8, 2.387±0.064 and 0.0908±0.0033 in cows and 823.3±15.3, 3.584±0.070, 0.1139±0.0032 in bulls, respectively. The fitness was higher under the Gompertz model than under the logistic model: monthly and daily estimation for cows were 379.3±7.509, 2.499±0.057, 0.114±0.0045 and 367.1±1.9003, 2.3983±0.012, 0.004±0.00003, respectively. Estimated residual mean squares were 31.85 and 998.4 in their respective models. Monthly and daily estimation of bulls were 834.6±22.00, 3.319±0.062, 0.104±0.0037 and 796.0±6.128, 3.184±0.014, 0.003±0.00003, respectively. Estimated residual mean square were 66.18 and 2106.5. Monthly and daily estimation of steers were 1049.1±144.2, 3.024±0.008, 0.067±0.0096 and 1505.1±176.6, 2.997±0.067, 0.001±0.0001, relatively. Squares, 186.0 and 1119.1. In terms of growth characteristic estimated by Gompertz model, body weight for cows and bulls were 139.53kg and 307.03kg, and the daily gains were 0.52kg and 1.04kg, respectively. Body weight for steers was 385.94kg at the inflection point. Body weight gain was 0.84kg in both models. Our results showed that cows had lower mature weight and daily weight gain, and reached the inflection point earlier than bulls or steers.

• Journal of Animal Science and Technology
• /
• v.45 no.5
• /
• pp.689-694
• /
• 2003

Weight records of Hanwoo cows from birth to 36 months of age collected in Daekwanryeong branch, National Livestock Research Institute(NLRI) were fitted to Gompertz, von Bertalanffy and Logistic functions. For the growth curve parameters fitted on individual records using Gompertz model, the mean estimates of mature weight(A), growth ratio(b) and growth rate(k) were 383.42 ${\pm}$ 97.29kg, 2.374 ${\pm}$ 0.340 and 0.0037 ${\pm}$ 0.0012, respectively, and mean estimates of body weight, age and daily gain rate at inflection were 141.05 ${\pm}$ 35.79kg, 255.63 ${\pm}$ 109.09 day and 0.500 ${\pm}$ 0.123kg, respectively. For von BertalanfTy model, the mean estimates of A, b and k were 410.47 ${\pm}$ 117.98kg, 0.575${\pm}$0.057 and 0.003 ${\pm}$ 0.001, and mean estimates of body weight, age and daily gain at inflection were 121.62 ${\pm}$ 34.94kg, 211.02 ${\pm}$ 105.53 and 0.504 ${\pm}$ O.l24kg. For Logistic model, the mean estimates of A, b and k were 347.64 ${\pm}$ 97.29kg, 6.73 ${\pm}$ 0.34 and 0.006 ${\pm}$ 0.0018, and mean estimates of body weight, age and daily gain at inflection were 173.82 ${\pm}$ 37.25kg, 324.47 ${\pm}$ 126.85 and 0.508 ${\pm}$ 0.131kg. Coefficients of variation for the A, b and k parameter estimates were 25.3%, 14.3% and 32.4%, respectively, for Gompertz model, 28.70/0, 9.9% and 33.3% for von Bertalanffy model, and 27.9°/0, 5.0% and 30.0% for Logistic model.

• Journal of Animal Science and Technology
• /
• v.45 no.5
• /
• pp.711-724
• /
• 2003

Some growth curve models were used to fit individual growth of 1,083 Hanwoo cows born from 1970 to 2001 in Daekwanryeong branch, National Livestock Research Institute(NLRI). The effects of year-season of birth and age of dam were analyzed. In analysis of variance for growth curve parameters, the effects of birth year-season were significant for mature weight(A), growth ratio(b) and maturing rate(k)(P〈.01). The effects of age of dam were significant for growth ratio(b) but not significant for mature weight(A) and maturing rate(k). The linear term of the covariate of age at the final weights was significant for the A(P〈.01) and k(P〈.01) of Gompertz model, von Bertalanffy model and Logistic model. For the growth curve parameters fitted on individual data using Gompertz model, von Bertalanffy model and Logistic model, resulting the linear contrasts(fall-spring), Least square means of A in three nonlinear models were higher cows born at fall and A of Logistic model was significant(P〈.05) between the seasons. According to the results of the least square means of growth curve parameters by age of dam, least square means of mature weight(A) in Gompertz model was largest in 6 year and smallest estimating for 3 and 8 years of age of dam. The growth ratio(b) was largest in 2 year of age of dam and smallest estimating in 8 year. The A and k were not different by age of dam(p〉.05), On the other hand, the b was different by age of dam(p〈.01). The estimate of A in von Bertalanffy model was largest in 6 year and smallest in 8 and 9 years of age of dam. The b was largest in 2 year and tend to decline as age of dam increased. The A and k were not different by age of dam(p〉.05), On the other hand, the b was highly significant by age of dam(p〈.01).

• Journal of Digital Convergence
• /
• v.18 no.2
• /
• pp.205-211
• /
• 2020

The method of predicting the future may be predicted by technical characteristics or technical performance. Therefore, technology prediction is used in the field of strategic research that can produce economic and social benefits. In this study, we predicted the future market through the study of how to predict the future with these technical characteristics. The future prediction method was studied through the prediction of the time when the market occupied according to the demand of special product. For forecasting market demand, we proposed the future forecasting model through comparison of representative quantitative analysis methods such as CAGR model, BASS model, Logistic model and Gompertz Growth Curve. This study combines Rogers' theory of innovation diffusion to predict when products will spread to the market. As a result of the research, we developed a methodology to predict when a particular product will mature in the future market through the spread of various factors for the special product to occupy the market. However, there are limitations in reducing errors in expert judgment to predict the market.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.52 no.6
• /
• pp.19-26
• /
• 2010

Batch experiments were conducted to investigate growth and nutrient removal performance of microalgae Chlorella vulgaris by using piggery wastewater in different concentration of pollutants and the common growth models (logistic, Gompertz and Richards) were applied to compare microalgal growth parameters. Removal of nitrogen (N) and phosphorus (P) by Chlorella vulgaris showed correlation with biomass increase, implying nutrient uptake coupled with microalgae growth. The higher the levels of suspended solids (SS), COD and ammonia nitrogen were in the wastewater, the worse growth of Chlorella vulgaris was observed, showing the occurrence of growth inhibition in higher concentration of those pollutants. The growth parameters were estimated by non-linear regression of three growth curves for comparative analyses. Determination of growth parameters were more accurate with population as a variable than the logarithm of population in terms of R square. Richards model represented better fit comparing with logistic and Gompertz model. However, Richards model showed some complexity and sensitivity in calculation. In the cases tested, both logistic and Gompertz equation were proper to describe the growth of microalgae on piggery wastewater as well as easy to application.

• Journal of Information Technology Applications and Management
• /
• v.21 no.4_spc
• /
• pp.403-414
• /
• 2014

Forecasting technology in economic activity is a quite intricate procedure so researchers should grasp the point of the data to use. Diffusion models have been widely used for forecasting market demand and measuring the degree of technology diffusion. However, there is a question that a model, explaining a certain market with goodness of fit, always shows good performance with markets of different conditions. The primary aim of this paper is to explore diffusion models which are frequently used by researchers, and to help readers better understanding on those models. In this study, Logistic, Gompertz and Bass models are used for forecasting Global Wireline Subscribers and the performance of models is measured by Mean Absolute Percentage Error. Logistic model shows better MAPE than the other two. A possible extension of this study may verify which model reflects characteristics of industry better.

• The Korean Journal of Applied Statistics
• /
• v.36 no.6
• /
• pp.619-629
• /
• 2023

Fertilizers are crucial for increasing crop yield, but using too much of them without taking into account the nutrients that the crops need can increase costs for farm management and have a negative impact on the environment. Through smart agriculture, fertilizers can be applied as needed at the right time to reflect the growth characteristics of crops, reducing the burden of fertilizer losses and providing economical nutrient management. In this study, we use the total dry weight of field-cultivated red pepper and green onion grown in various growing environments to fit a nonlinear model-based crop growth model using different growth curves (logistic, Gompertz, Richards, and double logistic curve), and we propose a fertilizer distributed algorithm based on crop growth rate.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.1-23
• /
• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of Korean Society of Forest Science
• /
• v.90 no.2
• /
• pp.210-216
• /
• 2001

Pinus densiflora S. et Z. has widely been distributed, and is one of the important main foret resources in Korea. Diameter and height growth patterns were estimated using non-linear algebraic difference equation, which requires two-measurement times $T_1$ and $T_2$. To maximize data use, all possible measurement interval data were derived using Lag and Put statements in the SAS. In results, of the algebraic difference equations applied, the Schumacher and the Gompertz polymorphic equations for diameter and height, respectively showed the higher precision of the fitting. In order to allow more precise estimation of growth than those of the basic Schumacher and the Gompertz, further refinement that combine biological realism as input into the equation would be necessary.