• 한국수학교육학회지시리즈D:수학교육연구
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• 제9권1호
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• pp.25-45
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• 2005

Background Phenomenography suggests that more variation is associated with wider ways of experiencing phenomena. In the discipline of mathematics, broadening the 'lived space' of mathematics learning might enhance students' ability to solve mathematics problems Aims The aim of the present study is to: 1. enhance secondary school students' capabilities for dealing with mathematical problems; and 2. examine if students' conception of mathematics can thereby be broadened. Sample 410 Secondary 1 students from ten schools participated in the study and the reference group consisted of 275 Secondary 1 students. Methods The students were provided with non-routine problems in their normal mathematics classes for one academic year. Their attitudes toward mathematics, their conceptions of mathematics, and their problem-solving performance were measured both at the beginning and at the end of the year. Results and conclusions Hierarchical regression analyses revealed that the problem-solving performance of students receiving non-routine problems improved more than that of other students, but the effect depended on the level of use of the non-routine problems and the academic standards of the students. Thus, use of non-routine mathematical problems that appropriately fits students' ability levels can induce changes in their lived space of mathematics learning and broaden their conceptions of mathematics and of mathematics learning.

• 한국과학교육학회지
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• 제23권4호
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• pp.386-400
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• 2003

This paper presents an analysis of the Developmental Approaches in Science, Health and Technology (DASH) program, a K-6 curriculum developed by the Curriculum Research & Development Group (CRDG) at the University of Hawaii employing the curriculum analysis framework created by Posner. Using this framework the analyst found that the DASH design is based on the research on learning, teaching, and assessment now driving efforts to reform science education at the elementary level. DASH embraces the constructivist idea that learning is a personal and social process and the recapitulation model that new concepts are built out of theories previously learned. DASH provides an understandable, exciting, and memorable experience in the operations of science, health, and technology, and develops their capacity to use the skills and knowledge of science, health, and technology both in and outside school. A number of studies of DASH have examined its functionality, effectiveness of pedagogy and what students learn. The innovative nature of DASH necessitated a multidimensional assessment that included both quantitative and qualitative research techniques. Ongoing development of the DASH program in the research setting of a university laboratory school permits ever deeper connections with emerging curriculum theory and curriculum practice, and allows new linkages as ideas are tested in research classrooms.

• 대한수학교육학회지:학교수학
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• 제10권4호
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• pp.557-571
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• 2008

이 논문은 드모르간의 음수 지도 방법을 연구하는 것을 목적으로 한다. 이를 위하여 우선 드모르간이 제시한 대수발달 단계에 따라 드모르간의 음수관을 정리하고, 드모르간의 음수 지도 방법을 불가능한 뺄셈의 탐색, 불가능한 뺄셈에 대한 수정규칙 탐구, 불가능한 뺄셈에 대한 의미의 구성의 3단계로 나누어 고찰하였다. 드모르간의 음수 지도 방법의 특징은 방정식 지도와 결합되었다는 점, 불가능한 뺄셈 기호를 사용한다는 점, 역사발생적 과정을 준수하는 점진적 형식화를 추구한다는 점이다. 또한, 드모르간의 방법을 학교수학의 방법과 비교함으로써, 그 장점과 단점을 분석하였다. 드모르간은 수학적 실재를 형식과 의미를 동시에 갖는 것으로 보았던 자신의 수학관에 따라 음수를 설명하였으며, 대수의 발달 단계에 맞추어 음수를 서로 상이한 존재로 간주하였고 이에 따라 여러 단계를 거쳐 음수를 지도하도록 하고 있다. 그의 이러한 세심한 조처는 음수의 지도가 단시간에 마무리될 수 없는 성격의 것임을 분명히 인식하게 해 준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• 소성∙가공
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• 제31권4호
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• pp.173-178
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• 2022

The present study employed two different machine-learning approaches, the extreme gradient boosting (XGB) and light gradient boosting machine (LGBM), to predict a compressive deformation behavior of additively manufactured Ti-6Al-4V. Such approaches have rarely been verified in the field of metallurgy in contrast to artificial neural network and its variants. XGB and LGBM provided a good prediction for elongation to failure under an extrapolated condition of processing parameters. The predicting accuracy of these methods was better than that of response surface method. Furthermore, XGB and LGBM with optimum hyperparameters well predicted a deformation behavior of Ti-6Al-4V additively manufactured under the extrapolated condition. Although the predicting capability of two methods was comparable, LGBM was superior to XGB in light of six-fold higher rate of machine learning. It is also noted this work has verified the LGBM approach in solving the metallurgical problem for the first time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• 컴퓨터교육학회논문지
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• 제14권2호
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• pp.33-45
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• 2011

최근 등장한 스마트 러닝에 대한 관심의 확산에 따라 본 논문에서는 스마트 러닝의 개념을 도구적 접근, 환경적 접근, 그리고 이론적 접근을 통해 살펴보았다. 스마트 러닝을 수업에 적용하기 위한 원리로서는 교육내용 측면에 있어서 풍부한 학습자원의 활용, 교육방법에 있어서 상호작용을 통한 참여적 환경, 그리고 교육경험에 있어서 실제적 맥락과 경험제공을 들 수 있다. 이들 개념 및 원리에 근거하여 제시된 스마트 러닝 교수학습모형 설계는 목표설정, 자원확인, 환경선정, 수업과정 설계, 수업도구 개발, 수업적용, 그리고 평가 및 분석의 단계로 접근될 수 있다. 이 같은 스마트 러닝 현황을 기반으로 향후 개별적 수업상황에 적합한 구체적인 개발전략의 지속적 연구가 요구된다.

• 한국문헌정보학회지
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• 제44권1호
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• pp.29-51
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• 2010

지식정보사회에서는 창의적이고 자주적인 사람을 요구한다. 본 논문에서는 구성주의 교수-학습이 학습자의 자주적 학습능력을 향상시킨다고 보아 자주적 학습을 지원하기 위한 학교도서관의 구성주의적 교수-학습 매체센터로서의 운영방안을 제시하였다. 구성주의를 바탕으로 한 자주적 학습은 학교도서관이 교수-학습 매체센터로 운영될 때 가능하며 다양한 학습자료와 직접적인 상호작용이 클수록 창의적이고 자주적 능력이 향상된다고 판단된다.

• Current Optics and Photonics
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• 제7권5호
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• pp.545-556
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• 2023

This paper introduces volume-sharing multi-aperture imaging (VMAI), a potential approach proposed for volume reduction in space-borne imagers, with the aim of achieving high-resolution ground spatial imagery using deep learning methods, with reduced volume compared to conventional approaches. As an intermediate step in the VMAI payload development, we present a phase-1 design targeting a 1-meter ground sampling distance (GSD) at 500 km altitude. Although its optical imaging capability does not surpass conventional approaches, it remains attractive for specific applications on small satellite platforms, particularly surveillance missions. The design integrates one wide-field and three narrow-field cameras with volume sharing and no optical interference. Capturing independent images from the four cameras, the payload emulates a large circular aperture to address diffraction and synthesizes high-resolution images using deep learning. Computational simulations validated the VMAI approach, while addressing challenges like lower signal-to-noise (SNR) values resulting from aperture segmentation. Future work will focus on further reducing the volume and refining SNR management.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.