• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.4
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• pp.27-43
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• 1990

This study aimed at developing a generalized model on the estimation of the long - term run - off volume for practical purpose. During the research period of last 3 years( 1986-1988), 3 types of estimation model on the long - term run - off volume(Effective rainfall model, unit hydrograph model and barne's model for dry season) had been developed by the author. In this study, through regressional analysis between determinant factors (bi of effective rainfall model, ai of unit hydrograph model and Wi of barne's model) and catchment characteris- tics(catchment area, distance round the catchment area, massing degree coefficient, river - exte- nsion, river - slope, river - density, infiltration of Watershed) of 11 test case areas by multiple regressional method, a new methodology on the derivation of determinant factors from catchment characteristics in the watershed areas having no hydrological station was developed. Therefore, in the resulting step, estimation equations on run - off volume for practical purpose of which input facor is only rainfall were developed. In the next stage, the derived equations were applied on the Kang - and Namgye - river catchment areas for checking of their goodness. The test results were as follows ; 1. In Kang - river area, average relative estimation errors of 72 hydrographs and of continuous daily run - off volume for 245 days( 1/5/1982 - 31/12) were calculated as 6.09%, 9.58% respectively. 2. In Namgye - river area, average relative estimation errors of 65 hydrographs and of conti- nuous daily run - off volume for 2fl days(5/4/1980-31/12) were 5.68%, 10.5% respectively. In both cases, relative estimation error was averaged as 7.96%, and so, the methodology in this study might be hetter organized than Kaziyama's formula when comparing with the relative error of the latter, 24~54%. However, two case studies cannot be the base materials enough for the full generalization of the model. So, in the future studies, many test case studies of this model should he carries out in the various catchment areas for making its generalization.

• School Mathematics
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• v.13 no.4
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• pp.581-598
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• 2011

Given the growing agreement that algebra should be taught in the early stage of the curriculum, considerable studies have been conducted with regard to early algebra in the elementary school. However, there has been lack of research on how to organize mathematic lessons to develop of algebraic reasoning ability of the elementary school students. This research attempted to gain specific and practical information on effective algebraic teaching and learning in the elementary school. An exploratory qualitative case study was conducted to the fourth graders. This paper focused on the associative law of the multiplication. This paper showed what kinds of activities a teacher may organize following three steps: (a) focus on the properties of numbers and operations in specific situations, (b) discovery of the properties of numbers and operations with many examples, and (c) generalization of the properties of numbers and operations in arbitrary situations. Given the steps, this paper included an analysis on how the students developed their algebraic reasoning. This study provides implications on the important factors that lead to the development of algebraic reasoning ability for elementary students.

• School Mathematics
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• v.4 no.1
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• pp.29-47
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• 2002

The purpose of this study is to investigate various perspectives on the approaching to school algebra. School algebra plays an important role in school mathematics, but many students have been in difficulties for the loaming of school algebra. This problem has been continued for the long time. This paper supposed that this problem may be caused by the approaching stage to the school algebra. In order to investigate this problem, we first described the definition of algebra in chapterII. And in chapterIII, we divided the perspectives of approaching to the school algebra into the two parts. The one is the intrinsic nature of algebra, i.e. generalization, abstraction, and structural aspects. The other is the extrinsic representation of algebra, i.e. problem solving, modelling, functions. Each perspectives are investigated through various research studies. We hope that this works on each perspectives help a teacher prepare school algebra.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.12-14
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• 1999

오늘날 멀티미디어 및 인터넷 서비스가 눈에 띄게 증가하면서 다양한 응용분야에서의 동영상 데이터 활용을 급증하였고 이에 사용자가 원하는 동영상 데이터를 빠르고 정확하게 검색하기 위한 내용기반 검색기법이 필수적이다. 본 논문은 high-level features와 더불어 동영상의 고유 내용 속성에 속하는 low-level features를 자동 일반화(generalization)하여 다단계 관리하고 features에 대한 가중치 적용질의를 제공함으로써 기존 내용기반 검색 연구와는 뚜렷한 차별성을 갖는다. 또한 low-level features와 high-level features간의 자동변환(translation)을 가능하게 함으로써 동영상 데이터베이스의 사용자 접근 효율을 한단계 높이고 보다 의미구조화된 동영상 관리 및 내용기반 검색을 지원한다.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.251-258
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• 2003

A support vector machine (SVM) is supposed to provide a good generalization performance, but the actual performance of a actually implemented SVM is often far from the theoretically expected level. This is largely because the implementation is based on an approximated algorithm, due to the high complexity of time and space. To improve this limitation, we propose ensemble of SVMs by using Bagging (bootstrap aggregating) and Boosting. By a Bagging stage each individual SVM is trained independently using randomly chosen training samples via a bootstrap technique. By a Boosting stage an individual SVM is trained by choosing training samples according to their probability distribution. The probability distribution is updated by the error of independent classifiers, and the process is iterated. After the training stage, they are aggregated to make a collective decision in several ways, such ai majority voting, the LSE(least squares estimation) -based weighting, and double layer hierarchical combining. The simulation results for IRIS data classification, the hand-written digit recognition and Face detection show that the proposed SVM ensembles greatly outperforms a single SVM in terms of classification accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.

• Investigative Magnetic Resonance Imaging
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• v.25 no.1
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• pp.10-22
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• 2021

Purpose: To overcome the difficulty in building a large data set with a high-quality in medical imaging, a concept of 'blended-transfer learning' (BTL) using a combination of both source data and target data is proposed for the target task. Materials and Methods: Source and target tasks were defined as training of the source and target networks to reconstruct cardiac CINE images from undersampled data, respectively. In transfer learning (TL), the entire neural network (NN) or some parts of the NN after conducting a source task using an open data set was adopted in the target network as the initial network to improve the learning speed and the performance of the target task. Using BTL, an NN effectively learned the target data while preserving knowledge from the source data to the maximum extent possible. The ratio of the source data to the target data was reduced stepwise from 1 in the initial stage to 0 in the final stage. Results: NN that performed BTL showed an improved performance compared to those that performed TL or standalone learning (SL). Generalization of NN was also better achieved. The learning curve was evaluated using normalized mean square error (NMSE) of reconstructed images for both target data and source data. BTL reduced the learning time by 1.25 to 100 times and provided better image quality. Its NMSE was 3% to 8% lower than with SL. Conclusion: The NN that performed the proposed BTL showed the best performance in terms of learning speed and learning curve. It also showed the highest reconstructed-image quality with the lowest NMSE for the test data set. Thus, BTL is an effective way of learning for NNs in the medical-imaging domain where both quality and quantity of data are always limited.

• Journal of Korean Library and Information Science Society
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• v.13
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• pp.1-28
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• 1986

An outline of the scope and history of encyclopaedias is essentially a guide to the story of the development of scholarship, for encyclopaedias stand out as landmarks throughout the centuries, recording much of what was known at the time of publication. the early stage of encyclopaedias originated a summaries of scholarship in forms comprehensible to their readers, so that compiled their works single-handedly. The impact of Christianity brought a new phase in Western encyclopaedia making. As religion is emphasized in the encyclopaedias of those time, it pervades the whole of their contents. It was made for Christian education. The general trend in treatment in the Middle ages was arranged by subject. Most of the encyclopaedias issued before the introduction of printing into Europe having been arranged in a methodical or classical form, the alphabetically arranged encyclopaedia has a history of less than 1000 years. By influence of printing and Renaissance, a turning point came with encyclopaedia making. There were just as increasingly preferred to put practical topics first. Until those time, thought in terms of arranging their entries in alphabetical order has already familiarized. By generalization of public education in 19th-century, there were increasingly the number of purchasers so that prevailed commercial publication. It was those time that was settled the features of contemporary such as multivolume compendium of all available knowledge, complete with maps and a very detailed index, as well as numerous adjuncts such as bibliographies, illustrations, lists of abbreviations and foreign expressions, gazetteers, and so on.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.123-126
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• 1995

Characteristics of control system design using Universal Learning Network (U.L.N.) are that a system to be controlled and a controller are both constructed by U.L.N. and that the controller is best tuned through learning. U.L.N has the same generalization ability as N.N.. So the controller constructed by U.L.N. is able to control the system in a favorable way under the condition different from the condition of the control system in learning stage. But stability can not be realized sufficiently. In this paper, we propose a robust control method using U.L.N. and second order derivatives of U.L.N.. The proposed method can realize better performance and robustness than the commonly used Neural Network. Robust control considered here is defined as follows. Even though initial values of node outputs change from those in learning, the control system is able to reduce its influence to other node outputs and can control the system in a preferable way as in the case of no variation. In order to realize such robust control, a new term concerning the variation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivatives of criterion function with respect to the parameters. Finally it is shown that the controller constricted by the proposed method works in an effective way through a simulation study of a nonlinear crane system.