• 한국철학논집
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• 제29호
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• pp.375-396
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• 2010

본 연구는 사회·정서적 학습 이론(the social and emotional learning, SEL)에 근거를 두고 초등 도덕교육의 목표인 초등학생들의 "바르고 선한 인성(character)"을 어떻게 하면 보다 효과적으로 함양할 것인가? 의 물음에 답하기 위해, 사회·정서적 능력 중에서도 특히 사회적 인식(social awareness) 및 대인 관계 기술(relationship skill) 함양에 초점을 두고 탐구하였다. 본 연구가 SEL에 토대를 두고자 하는 이유는 초등학생들의 도덕성 발달이란 도덕적 지식(앎)의 발달만으로 가능하지 않고, 도덕적 지식을 행동으로 옮기는데 있어 중요한 가교의 역할(예를 들면, 자동차가 움직이기 위해 연료가 필수적이듯이 일종의 도덕적 에너지의 역할)을 하고 있는 사회·정서적 능력의 발달이 병행될 때 가능한 바, 그동안 우리나라 도덕교육에서는 이러한 사회·정서적 능력 혹은 도덕적 감정의 필요성에 대해 강조하면서도 이를 어떻게 하면 효과적으로 교육할 것인가에 대해 구체적인 실천 방안 마련이 미흡하였기 때문이다. 초등학생들의 사회 인식 및 대인 관계 능력 함양의 모색은 도덕과 수업을 통한 방안과 도덕과 수업 이외의 활동을 통한 방안을 중심으로 구안하였다.

• Journal of Information Processing Systems
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• 제14권2호
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• pp.418-434
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• 2018

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

• Smart Structures and Systems
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• 제6권2호
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• pp.147-165
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• 2010

Impact damage detection in composite structures has gained a considerable interest in many engineering areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper compares two advanced signal processing methods for impact location in composite aircraft structures. The first method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing-box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for various angles of strain wave propagation. The study demonstrates that both approaches are capable of good impact location estimates in this complex structure.

• 한국과학교육학회지
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• 제8권2호
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• pp.17-22
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• 1988

Computer based instruction in the elementary science curriculum will be played an important role through the fifth curriculum reform from the 1989 school year. This is essential for the science instruction because the strategies on the problem-solvings and inquiry approaches can be utilized for the science classroom. Computer education can be thought as the education about the computer and the education using the computer. Of them the education using the computer means the computer assisted instruction(CAI) what is called all the possible activities using the computer in the classroom. Student achievement as the result of CAI depends on the learning activities of students and the instructional techniques and strategies of teachers using the computer. However, computer based education to enhance the student achievement is pointed out the lacks of the standardized Korean alphabet code and the compatibility of qualified software. These problems will be relieved according to the coding for the Korean alphabet of SUPER PILOT program language.

• 전자공학회논문지B
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• 제32B권5호
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• pp.812-820
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• 1995

This paper proposes an approach of identifying nonlinear mappings from input/output data. The approach is based on the universal approximation by the fuzzy integration of local affine mappings. A connectionist model realizing the universal approximator is suggested by using a processing unit based on both the radial basis function and the weighted sum scheme. In addition, a learning method with self-organizing capability is proposed for the identifying of nonlinear mapping relationships with the given input/output data. To show the effectiveness of our approach, the proposed model is applied to the function approximation and the prediction of Mackey-Glass chaotic time series, and the performances are compared with other approaches.

• East Asian mathematical journal
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• 제36권4호
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• pp.455-474
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• 2020

In this study, we investigate the latus rectum, one of the geometric measures of the conics, as one of the ways in which learners harmonize the geometric and algebraic approaches to conics from a pedagogical point of view. We also introduce the conical curve of Biot as presented in 'The Discourse on the Latus Rectum in conics(2013)' by Takeshi Sugimoto and reinterpret it for visualization and use as teaching material. Therefore, we expect that the importance of mathematical concepts will be recognized in conics and students can experience geometry learning that is explored in the school field and have a positive effect in developing the power to apply even in the context of applied problems.

• The Journal of Korean Physical Therapy
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• 제22권6호
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• pp.91-98
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• 2010

Neurostimulation approaches have been developed and explored to modulate neuroplastic changes of cortical function in human brain. As one of the most primary noninvasive tools, transcranial direct current stimulation (tDCS) was extensively studied in the field of neuroscience. The alternation of cortical neurons depending on the polarity of the tDCS has been used for improving cognitive processing including working memory, learning, and language in normal individuals, as well as in patients with neurological or psychiatric diseases. In addition, tDCS has great advantages: it is a non-invasive, painless, safe, and cost-effective approach to enhance brain function in normal subjects and patients with neurological disorders. Numerous previous studies have confirmed the efficacy of tDCS. However, tDCS has not been considered for clinical applications and research in the field of physical therapy. Therefore, this review will focus on the general principles of tDCS and its related application parameters, and provide consideration of motor behavioral research and clinical applications in physical therapy.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 SMICS 2004 International Symposium on Maritime and Communication Sciences
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• pp.106-109
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

• Journal of Information Processing Systems
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• 제13권4호
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• pp.928-940
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• 2017

State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM-UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.366-370
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• 2022

The article deals with the theoretical and methodological foundations of innovative approaches in the modern education system. The issues of introducing computerized and telecommunication technologies are characterized, which allow switching to distance learning (DL), which is a promising form of the system of open education support in the modern educational process. Special attention is paid to the study of practical technologies of vocational training and the activities of a teacher and innovative areas of vocational training of students.