• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.95-104
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• 1993

IPA(interpattern association) model is a method of feature extraction using a neural network. Even in the case that the reference patterns are simuklar to one another, this model can recover the reference patterns effectively. However, when the pattern whose feature pixels are lost is used as input, this model can not guarantee perfect recovery of the reference pattern. It is proposed a improved interpattern association(IPA) model for the feature extraction using neural network. The improved IPA model that combines the first interconnection weight matrix of the IPA model with the second additional weight matrix is proposed here to overcome the recovery problem of the original IPA model. The results of computer simulation and optical experiment are advanced.

• Journal of the Korean Home Economics Association
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• v.29 no.2
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• pp.241-248
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• 1991

The aim of this study is to propose the criteria for a evaluation model of the lifetime household work and organize the model. The results were as follows: Three criteria for a evaluation model of the life-time household work were proposed. $\circled1$ An appropriate framework of the evaluation model should be based on the transition of the family life. $\circled2$ The model should include major variables influencing the value of household work of homemaker. $\circled3$ The model should be flexible enough to accommodate various evaluation methods, and should reflect idiosyncracies of different evaluation methods. In view of the criteria stated above, the present study is based on the family life cycle framework. And the age of the last offspring exhibits a consistent major influence in the evaluation based on the household work hours, while the education level of homemakers shows a consistent major influence in the evaluation based on household worker. So as to reflect these two major variables in the family life cycle framework.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2004.04a
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• pp.107-114
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• 2004

In this work the Model algorithmic control method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first model is then extracted from the neural model. Results of simulations for MAC control of grade change operations are compared with plant operation data response. From the comparison, we can see that the proposed MAC method exhibits faster response for the grade change of paper and achieves stable steady-state.

• Journal of The Korean Association For Science Education
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• v.25 no.6
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• pp.635-641
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• 2005

This paper suggests a research & education (R&E) model for the gifted in science education. The model has been developed under three assumptions. The first is that using the sequences of a gifted educational program designed to facilitate the process will assist in gifted students' construction of scientific knowledge and comprehension of laboratory practice through concrete experimental experience. The second is that gifted students will be able to apply this learning to further study using and extending scientific knowledge and experience. The third is that challenging tasks and feedback at the requisite stage of development will improve instructional effectiveness. The R&E Model has five phases: engaging, exploring, planning, performing and elaborating; furthermore, it suggests roles for the mentee and mentor. The R&E model has two functions for gifted education. The first is providing guidance for gifted curriculum developers as they design a mentor program, and the second is helping a mentor improve instructional effectiveness through use of strategies. This model has potentials to educate the gifted students in the Science Education Institute for the Gifted.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.539-548
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• 1997

A physically based model for rainfall runoff simulation in agricultural watershed equipped with tile drains is presented. This model is developed from the TOPMODEL which is based on the detailed topographic information provided by Digital Elevation Model (DEM). Nine possible flow generation scenarios in the tile drained basin are suggested and used in the development of the model. The model can identify the portions of the hydrograph resulting from tile flow, subsurface flow and surface flow. The performance of the model is assessed through a calibration and validation process. The results of the analysis show that the model describes the physical system well and provides a better insight into the hillslope hydrology of agricultural watersheds with tile drainage.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• Journal of The Korean Association For Science Education
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• v.27 no.8
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• pp.724-742
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• 2007

The purpose of this study was to develop a scientific inquiry model that makes scientific inquiry accessible to science teachers as well as students. To develop a scientific inquiry model, we investigated the research process demonstrated by ten scientists who were working at academic research institutions or industrial research institutions. We collected data through scientists' journal articles, lab meetings and seminars, and observation of their inquiry process. After we analyzed the scientists' inquiry strategies and processes of inquiry, we finally developed the Scientist's Methodology of Investigation Process model named SMIP. The SMIP model consists of four domains, 15 stages, and link questions, such as "if, why", and "how". The SMIP model stressed that inquiry process is a selective process rather than a linear or a circular process. Overall, these findings can have implication science educators in their attempt to design instruction to improve the scientific inquiry process.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.359-365
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• 2013

Estimate breeding value can be used as single trait animal model was developed directly using the Fortran language program. The program is based on data computed by using the indirect method repeatedly. The program develops a common algorithm and imprves efficiency. Algorithm efficiency was compared between the two programs. Estimated using the solution is easy to farm and brand the service, pedigree data base was associated with the development of an improved system. The existing program that uses the single trait animal model and the comparative analysis of efficiency is weak because the estimation of the solution and the conventional algorithm programmed through regular formulation involve many repetition; therefore, the newly developed algorithm was conducted to improve speed by reducing the repetition. Single trait animal model was used to analyze Gauss-Seidel iteration method, and the aforesaid two algorithms were compared thorough the mixed model equation which is used the most commonly in estimating the current breeding value by applying the procedures such as the preparation of information necessary for modelling, removal of duplicative data, verifying the parent information of based population in the pedigree data, and assigning sequential numbers, etc. The existing conventional algorithm is the method for reading and recording the data by utilizing the successive repetitive sentences, while new algorithm is the method for directly generating the left hand side for estimation based on effect. Two programs were developed to ensure the accurate evaluation. BLUPF90 and MTDFREML were compared using the estimated solution. In relation to the pearson and spearman correlation, the estimated breeding value correlation coefficients were highest among all traits over 99.5%. Depending on the breeding value of the high correlation in Model I and Model II, accurate evaluation can be found. The number of iteration to convergence was 2,568 in Model I and 1,038 in Model II. The speed of solving was 256.008 seconds in Model I and 235.729 seconds in Model II. Model II had a speed of approximately 10% more than Model I. Therefore, it is considered to be much more effective to analyze large data through the improved algorithm than the existing method. If the corresponding program is systemized and utilized for the consulting of farm and industrial services, it would make contribution to the early selection of individual, shorten the generation, and cultivation of superior groups, and help develop the Hanwoo industry further through the improvement of breeding value based enhancement, ultimately paving the way for the country to evolve into an advanced livestock country.

• Water for future
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• v.36 no.5
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• pp.48-58
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• 2003

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.