• Journal of Digital Convergence
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• v.14 no.6
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• pp.499-511
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• 2016

The aim of this study was to find out effect of the orthographic representation on speech sound segmentation performance. Children's performances of the orthographic representation task and the speech sound segmentation task had positive correlation in words of phoneme-grapheme correspondence and negative correlation in words of phoneme-grapheme non-correspondence. In the case of words of phoneme-grapheme correspondence, there was no difference in performance ability between orthographic representation high level group and low level group, while in the case of words of phoneme-grapheme non-correspondence, the low level group's performance was significantly better than the high level group's. The most frequent errors of both groups were orthographic conversion errors and such errors were significantly more noticeable in the high level group. This study suggests that from the time of learning orthographic knowledge, children utilize orthographic knowledge for the performance of phonological awareness tasks.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• MALSORI
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• no.60
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• pp.29-45
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• 2006

We investigated whether the first syllable plays an important role in lexical access in Korean visual word recognition. To do so, one lexical decision task (LDT) and two form primed LDT experiments examined the nature of the syllabic neighborhood effect. In Experiment 1, the syllabic neighborhood density and the syllabic neighborhood frequency was manipulated. The results showed that lexical decision latencies were only influenced by the syllabic neighborhood frequency. The purpose of experiment 2 was to confirm the results of experiment 1 with form-primed LDT task. The lexical decision latency was slower in form-related condition compared to form-unrelated condition. The effect of syllabic neighborhood density was significant only in form-related condition. This means that the first syllable plays an important role in the sub-lexical process. In Experiment 3, we conducted another form-primed LDT task manipulating the number of syllabic neighbors in words with higher frequency neighborhood. The interaction of syllabic neighborhood density and form relation was significant. This result confirmed that the words with higher frequency neighborhood are more inhibited by neighbors sharing the first syllable than words with no higher frequency neighborhood in the lexical level. These findings suggest that the first syllable is the unit of neighborhood and the unit of representation in sub-lexical representation is syllable in Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.38-40
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• 2021

Medical domain has a massive number of data records without the response value. Self-supervised learning is a suitable method for medical data since it learns pretext-task and supervision, which the model can understand the semantic representation of data without response values. However, since self-supervised learning performance depends on the expression learned by the pretext-task, it is necessary to define an appropriate Pretext-task with data feature consideration. In this paper, to actively exploit the unlabeled medical data into artificial intelligence research, experimentally find pretext-tasks that suitable for the medical data and analyze the result. We use the x-ray image dataset which is effectively utilizable for the medical domain.

• Korean Journal of Child Studies
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• v.28 no.4
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• pp.187-196
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• 2007

This study evaluated attachment representation of school aged children, its relation to classification of family drawings, and their association with children's perceptions about families. The attachment representation of 43 children 6-9 years of age was evaluated by the Manchester Attachment Story Task (Green, Stanley, & Goldwyn, 2003) children's family drawings were classified by Fury's Family Drawing Scales (1996). Results showed that 12 children (28%) were avoidant, 23 (54%) secure, 4 (9%) resistant, and 4 (9%) were disorganized. Classification of childhood attachment representation showed a high concordance rate (86%) with family drawings. Securely Attached children showed positive perceptions such as family pride/happiness and vitality/creativity while Insecure children showed negative perceptions like emotional distance, tension and bizarreness.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.501-506
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• 1988

This paper describes an experimental system for automatic robot programming, The SNU-ARPS (Seoul National University Automatic Robot Programming System). The SNU-ARPS generates executable robot programs for pick and place operation and some simple mechanical assembly tasks by menudriven dialog. It is intended to enable the user to concentrate on the overall operation sequence instead of the knowledge regarding the details of robot languages. To convert task specifications into manipulator motions, the SNU-ARPS uses an internal representation of the world. This representation initially consists of geometric database from CAD system and is updated at each operation step to reflect the state changes of the world.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• English Language & Literature Teaching
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• v.11 no.4
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• pp.365-389
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• 2005

The purpose of this study is to present the task model that is the most effective in English language methodology based on the investigation of task-based performance in Korean EFL classroom practice. The subjects were 538 high school students and 126 high school teachers, each of whom had common experiences using the materials of task-based activities for more than one year. To analyze the data, the program SPSS WIN 11.0 including frequency distribution and chi-square analysis was used. The results of the questionnaire analysis showed that both teachers and students had a comparatively high level of satisfaction in task rationale, but that they had some mixed responses in the fields of input data, settings, and activity types. To conclude, a few suggestions are made to provide some meaningful considerations for the EFL teachers and material developers: a) task goals and rationale that encourage the learner's positive motivation; b) authenticity of input data based on the real-world context; c) collaborative learning environment that enhances communicative interaction; d) proportional representation of the creative problem-solving activities related to discussions and decision-making processes; e) systematic introduction of integrated language skills. It also suggests that the multi-lateral task model, which has some positive assets compared to previous task models, be newly introduced and applied to the second language learning classrooms.

• The Journal of Art Theory & Practice
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• no.7
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• pp.189-240
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• 2009

This essay aims to develop a critical approach of interpretation in examining the panoptical condition of representation that is said to permeate the tradition of modern realism in novels and paintings. In defining this approach, I am interested in the problem or inability of panoptical representation to tell a coherent story of solitude(solitary confinement, isolation, self-absorption, etc.) in a range of texts from prison documents to paintings and novels, and also what might occasion such an inability including social, material, or stylistic contradictions and conflicting epistemological angles. This task potentially anticipates a trajectory of readings and investigations that cuts through the history of panoptical representation, which is outside the scope of this essay. In this writing, I will engage in a series of debates with what I consider as major theories and views of panoptical representation offered by Jeremy Bentham, John Bender, and Michael Fried. Based on this, I will formulate a conceptual or methodological frame of discourse that would envisage an anti-panoptical approach of interpretation. As an attempt to validate this formulation, I will offer a reading of Henry Mayhew's Criminal Prisons of London and Scenes of Prison Life(1862), a case of panoptical representation that produces a peculiar sense of ambivalence while accounting for sites of penal solitude.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.95-104
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• 2010

As the computer and the communication technology are an unity, the collaborative learning based on constructivism is emphasized more than learning by forming external representation. Especially, online has characteristics not only to facilitate collaborative activities but to make students collaborators. In online collaborative learning, learning task is an integrated element in course design and an important portion deciding learning design, learning environment and learning process. Thus this study explored collaborative learning model according to the learning task type.