• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.4
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• pp.67-77
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• 2002

This paper introduces the calibration results of the fatigue crack growth models for damage tolerance analysis of the aircraft structures. Generalized Willenborg model and Wheeler model are calibrated with experimental data tested under the load spectrum of a trainer. The retardation factors such as, shut-off ratio in Generalized Willenborg model and shaping exponent in Wheeler model, are evaluated for aluminum alloys AL2024-T3511, AL7050-T7451 and AL7075-T73511. It is shown that the retardation effect of the crack growth rate depends on the yield strength of material and the maximum stress in the load spectrum. Generalized Willenborg model and Wheeler model give satisfactory prediction of crack growth life but the calibration of the experimental parameters with test is required.

• Journal of the korean Society of Automotive Engineers
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• v.12 no.2
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• pp.43-50
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• 1990

The fatigue growth behavior of surface cracks cannot be adequately predicted solely by stress intensity factor analysis. This is caused by different plastic deformation due to variations in the stress field triaxiality along the crack tip. Therefore, a new model which accounts for the crack closure phenomenon is proposed in this paper to predict the fatigue crack growth patterns for surface cracks. Fatigue tests were performed to develop the new model for the prediction and to assess the accuracy of the analysis. The predicted crack growth behavior for PMMA and Aluminum alloy 7075-T6 materials agreed well with the experimental data.

• Journal of Korean Society of Forest Science
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• v.97 no.4
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• pp.368-373
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• 2008

The objectives of this study were to validate existing growth models of Pinus densiflora and Larix leptolepis grown in Chonbuk regions, and to examine suitability of models to different regions using spatially varied data set. In the valuating model predicted of Pinus densiflora, except to DBH growth model, basal area and height prediction models were biased to fit to different region. And in the valuating predicted height, basal area and DBH model of Larix leptolepis, they were adequate to new data set acquired from different region. Therefore, existing prediction models, except DBH model, of Pinus densiflora have the limitation of practicality that could not be suitable for application to different region. However, owing to high compatibility shown predicted DBH, basal area and height models of Larix leptolepis, they will be adequate to use as the prediction models where data are available around eastern mountain areas of Jeollabukdo.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.205-211
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• 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 Ceramic Society
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• v.31 no.10
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• pp.1141-1146
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• 1994

Cyclic fatigue experiment was carried out to predict the life time of alumina ceramics. Four kinds of model were suggested to obtain the adequate representative static stress corresponding to the cyclic stress applied to the alumina specimens. Arithmetic mean stress model gives 21.81 of the crack growth exponent, integrated stress model gives 22.15, maximum stress model gives 24.57, and equivalent static stress model gives 24.43. It is considered that the equivalent static stress model is the most reasonable and gives the best adequate crack growth exponents value.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.119-122
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• 1995

Application of the PWFRM and SRGM for software reliability Prediction offers not only the judging base of model but also themselves with good applicabilty as easy-to-use tool.

• The korean journal of orthodontics
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• v.37 no.1 s.120
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• pp.16-28
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• 2007

Objective: Evaluation of individual growth is important in orthodontics. The aim of this study was to develop a convenient software that can evaluate current growth status and predict further growth. Methods: Stature data of 2 to 20 year-old Koreans (4893 boys and 4987 girls) were extracted from a nationwide data. Age-sex-specific continuous functions describing percentile growth curves were constructed using natural cubic spline function (NCSF). Then, final stature prediction algorithm was developed and its validity was tested using longitudinal series of stature measurements on randomly selected 200 samples. Various accuracy measurements and analyses of errors between observed and predicted stature using NCSF growth curves were performed. Results: NCSF growth curves were shown to be excellent models in describing reference percentile stature growth curie over age. The prediction accuracy compared favorably with previous prediction models, even more accurate. The current prediction models gave more accurate results in girls than boys. Although the prediction accuracy was high, the error pattern of the validation data showed that in most cases, there were a lot of residuals with the same sign, suggestive of autocorrelation among them. Conclusion: More sophisticated growth prediction algorithm is warranted to enhance a more appropriate goodness of model fit for individual growth.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• Nuclear Engineering and Technology
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• v.48 no.4
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• pp.1036-1046
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• 2016

We propose a primary water stress corrosion cracking (PWSCC) initiation model of Alloy 600 that considers the stress triaxiality factor to apply to finite element analysis. We investigated the correlation between stress triaxiality effects and PWSCC growth behavior in cold-worked Alloy 600 stream generator tubes, and identified an additional stress triaxiality factor that can be added to Garud's PWSCC initiation model. By applying the proposed PWSCC initiation model considering the stress triaxiality factor, PWSCC growth simulations based on the macroscopic phenomenological damage mechanics approach were carried out on the PWSCC growth tests of various cold-worked Alloy 600 steam generator tubes and compact tension specimens. As a result, PWSCC growth behavior results from the finite element prediction are in good agreement with the experimental results.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.