• Journal of the Korean Geographical Society
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• v.42 no.5
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• pp.769-787
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• 2007

This study aims to uncover sources of environmental learning from a student perspective using the previously unstudied case of South Korea. literature from other countries credits many sources of learning, including: media, school, personal sources, and non-governmental organizations. This analysis is based on focus group and questionnaire data collected during in-country field work. Results from South Korea are then compared with other studies carried out in the Asia-Pacific and the Western developed world. The results show that, similar to other countries including Australia, China, and India; South Korean students learn about the environment mainly through the media and schools. Television, schools, and domestic internet web pages were found to be some of the most-used sources of environmental information in South Korea, while more personal sources, such as community, family, and friends, were found to play an overall lesser instructive role. When compared internationally, South Korean students often exhibited less trust in the reliability of various sources, especially business, community, and foreign sources of information.

• Journal of Korean Elementary Science Education
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• v.33 no.4
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• pp.683-699
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• 2014

The purpose of this study was to explore the characteristics of teacher learning and changes in teachers' epistemic beliefs within a learning community of elementary science teachers. Three in-service elementary teachers who majored in elementary science education in a doctoral course of a graduate school of education participated in the study, and learning activities in the teachers' beginning learning community provided a context for the study. Data sources included field notes produced by the researcher who engaged jointly in the teacher learning community as a coach, audio-recordings of the teachers' narratives, and artifacts generated by the teachers during the process of teacher learning. Complementary analyses of these multiple sources of data revealed that epistemic beliefs of the three elementary teachers were different and that each teacher made a different plan of science instruction based on his own epistemic belief even after the learning experiences within the teacher community. It was therefore suggested that science teacher education programs should be organized in consideration of the nature of teachers as constructivist learners and their practical resources.

• Smart Structures and Systems
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• v.29 no.6
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

• The Korean Journal of Archival Studies
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• no.28
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• pp.83-121
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• 2011

Archival contents service for education is now a major service program in a foreign National Archives. Therefore We need to study their archival contents services in more depth-analysis methods for the development of our contents. My study is based on the summary of the Homepage Sites for Education of the TNA of UK and the NARA of USA. And also a depth-case study on structures of the samples, 'Coldwar' contents of the TNA and the 'McCarthy' contents of the NARA. As a results, first, the formats of archival contents for teaching-learning materials should be in consistent contents structures like a standard textbooks. Second, archival contents service for teaching-learning materials certainly have to support original images of primary resources and educational kits in order to read easily primary sources. Third, given the costs of development, it's desirable for archive to develop archival contents for teaching-learning materials in the way of cross-use by age and curriculum. Forth, when selecting primary sources for teaching-learning materials, priorities have to be given to the text-sources in the light of learning purposes for history education. Fifth, National archives must develop archival contents for teaching-learning materials in connection with standard curriculums in order to promote a nation-wide use.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.289-291
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• 2002

Host of source separation methods focus on stationary sources so higher-order statistics is necessary In this paler we consider a problem of source separation when sources are second-order nonstationary stochastic processes . We employ the natural gradient method and develop learning algorithms for both 1inear feedback and feedforward neural networks. Thus our algorithms possess equivariant property Local stabi1iffy analysis shows that separating solutions are always locally stable stationary points of the proposed algorithms, regardless of probability distributions of

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.162-165
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• 2003

Differential learning relies on the differentiated values of nodes, whereas the conventional learning depends on the values themselves of nodes. In this paper, I elucidate the differential learning in the framework maximum likelihood learning of linear generative model with latent variables obeying random walk. I apply the idea of differential learning to the problem independent component analysis(ICA), which leads to differential ICA. Algorithm derivation using the natural gradient and local stability analysis are provided. Usefulness of the algorithm is emphasized in the case of blind separation of temporally correlated sources and is demonstrated through a simple numerical example.

• Journal of the Korean association of regional geographers
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• v.8 no.3
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• pp.396-411
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• 2002

This paper aims to explore organisational change and learning involving spatial processes and outcomes. In particular, it focuses on the context specific nature of corporate learning and organisational change that can be found in the case of a large Korean firm facing radical economic change. Drawing on the case study of a large Korean firm, the Samsung Electronics Company, three main claims can be followed. First, territorial sources of learning influence the way in which the firm makes use of space/place. Second, corporate learning practices, however, are not based merely on specific localised sources or geographical proximity but on bringing together the local and the global sources by harnessing the properties of relational proximities. It reveals that firms are concerned less on specialising specific local knowledge than promoting organisational knowledge and competences by integrating a variety of knowledge distributed in and out of the boundaries of the firm. Finally, to learn and innovate in a continual basis, firms would attempt to combine codified knowledge with tacit knowledge.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.146-155
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• 2002

We present a new technique for achieving source separation when given only a single charmel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single charmel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.146-146
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• 2002

We present a new technique for achieving source separation when given only a single channel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.