• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.44-55
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• 1997

In order to determine procedures for a, pp.opriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected according to data characteristics. Three different growth curve models were fitted onto data sets in an attempt to determine which growth curve models achieved the best forecasts for types of growth data. The analysis of the results gives rise to an a, pp.oach for selecting a, pp.opriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the goodness of fit test.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.167-177
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• 2010

Recent competitive and technological changes during the past decade have accelerated the need for better capital recovery methods. Competition and technology have together shortened the expected lives of property which could not have been forecasted several years ago. Since the usage of technological growth models has been prevalent in various technological forecasting environments, the various forms of growth models have become numerous. Of six such models studied, some models do significantly better than others, especially at low penetration levels in predicting future levels of growth. A set of criteria for choosing an appropriate model for technological growth models was developed. Two major characteristics of an S-shaped curve were elected which differentiate the various models; they are the skewness of the curve and underlying assumptions regarding the variance of error structure of the model.

• Journal of Korean Society for Quality Management
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• v.23 no.1
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• pp.95-105
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• 1995

The error structure of nonlinearized technological growth models, such as, the Pearl curve, the Gompertz curve and the Wei bull growth curve, has zero mean and a constant variance over time. Transformed models, however, like the linearized Fisher-Pry model. the linearized Gompertz growth curve, and the linearized Weibull growth curve have increasing variance from t = 0 to the inflection point.

• Development and Reproduction
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• v.15 no.1
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• pp.9-15
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• 2011

The population growth and reproduction of Tetrahymena pyriformis were studied under shaken (aerobic) and unshaken (anaerobic) conditions by applying the growth models, exponential and logistic growth models and the population growth of Tetrahymena was showed the logistic growth model under both, shaken and unshaken conditions and also, the more oxygenated samples had greater population size (N) and three times faster growth rate (r) than less oxygenated samples during incubation periods.

• 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.

• Nuclear Engineering and Technology
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• v.39 no.2
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• pp.129-132
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• 2007

It is generally known that software reliability growth models such as the Jelinski-Moranda model and the Goel-Okumoto's non-homogeneous Poisson process (NHPP) model cannot be applied to safety-critical software due to a lack of software failure data. In this paper, by applying two of the most widely known software reliability growth models to sample software failure data, we demonstrate the possibility of using the software reliability growth models to prove the high reliability of safety-critical software. The high sensitivity of a piece of software's reliability to software failure data, as well as a lack of sufficient software failure data, is also identified as a possible limitation when applying the software reliability growth models to safety-critical software.

• Food Science and Biotechnology
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• v.18 no.5
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• pp.1243-1252
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• 2009

User-friendly software was developed to determine the shelf-life of perishable Korean seasoned side dishes in real time based on growth models of spoilage and pathogenic microorganisms. In the program algorithm, the primary spoilage and fastest-growing pathogenic organisms are selected according to the product characteristics, and their growth is simulated based on the previously monitored or recorded temperature history. To predict the growth of spoilage organisms with confidence limits, kinetic models for aerobic bacteria or molds/yeasts from published works are used. Growth models of pathogenic bacteria were obtained from the literature or derived with regression of their growth rate data estimated from established software packages. These models are also used to check whether the risk of pathogenic bacterial growth exceeds that of food spoilage organisms. Many example simulations showed that the shelf-lives of the examined foods are predominantly limited by the growth of spoilage organism rather than by pathogenic bacterial growth.

• BMB Reports
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• v.52 no.1
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• pp.70-85
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• 2019

Reduction of insulin/insulin-like growth factor 1 (IGF1) signaling (IIS) extends the lifespan of various species. So far, several longevity mouse models have been developed containing mutations related to growth signaling deficiency by targeting growth hormone (GH), IGF1, IGF1 receptor, insulin receptor, and insulin receptor substrate. In addition, p70 ribosomal protein S6 kinase 1 (S6K1) knockout leads to lifespan extension. S6K1 encodes an important kinase in the regulation of cell growth. S6K1 is regulated by mechanistic target of rapamycin (mTOR) complex 1. The v-myc myelocytomatosis viral oncogene homolog (MYC)-deficient mice also exhibits a longevity phenotype. The gene expression profiles of these mice models have been measured to identify their longevity mechanisms. Here, we summarize our knowledge of long-lived mouse models related to growth and discuss phenotypic characteristics, including organ-specific gene expression patterns.

• Korea Trade Review
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• v.46 no.6
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• Knowledge Management Research
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• v.14 no.4
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• pp.19-46
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• 2013

This paper aims to show the learning process and the educational effects of Divide-and-Combine principles and State Models, which are included in the Butterfly Model for creative problem solving. In our State Models, there are Time State Model, Space State Model, and Whole-Parts State Model. We have taught middle school students (for 18 hours), high school students (for 24 hours), and undergraduate students (for 1 semester) about our proposed Models when they solved contradiction problems. Also, we have made the students learn our contradiction resolution algorithms by themselves based on team-based discussion. By learning and by using our Models, the students had the higher level of expertise in contradiction problems and had the growth mindset that made them have confidence in themselves and kept them challenging themselves about problems. Also, learning and solving with our Models improved the students' growth mindset as well as their problem-solving ability.