• Journal of Global Scholars of Marketing Science
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• v.10
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• pp.139-164
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• 2002

Market orientation emphasizes the capability of a firm to learn customers, competitors, and inter-functional coordination and to use this market intelligence of creating superior value in the marketplace. In recent years, academic and practitioner interest has focused on market orientation and factors that engender this orientation in organizations. Although the merits of maintaining organizational learning have been extensively discussed in the literature, little studies examine the empirical link between market orientation and organizational learning which has a strong relation with it. The objective of this study is to assess the relationship between organizational commitment, market orientation, and organizational learning and presents more close a relational structure. The relationships between organizational commitment(OC), market orientation (MO), and organizational learning(OL) were analysed by structural equation modelling. a structure of OC-MO-OL is supported by our research and past literatures.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.278-287
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• 2019

Spatial information extraction is to retrieve static and dynamic aspects in natural language text by explicitly marking spatial elements and their relational words. This paper proposes a deep learning approach for spatial information extraction for Korean language using a two-step bidirectional LSTM-CRF ensemble model. The integrated model of spatial element extraction and spatial relation attribute extraction is proposed too. An experiment with the Korean SpaceBank demonstrates the better efficiency of the proposed deep learning model than that of the previous CRF model, also showing that the proposed ensemble model performed better than the single model.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.752-759
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• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.403-410
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• 2015

Since 1970, MRI has greatly been developing in terms of strength of magnetic field, the number of receipt channels, and short time of examination. With the development of digital systems and wireless network, hospitals have also acquired, saved, and managed digital images taken by various kinds of medical imaging equipment. However, domestic universities fail to provide practice training course independently thanks to expensive practice equipment and high maintenance cost, and rely on clinical training. Therefore, this study developed a MR patient diagnosis program based on Windows PC to help out students before their working in clinical filed. The designed Relational Database of MRI Simulator is made up of seven tables according to functions and data characteristics. Regarding the designed patient information, each stepwise function was classified by the patient registration method in clinical field. In addition, on the assumption of the basic information for diagnosis, each setting and content were classified. The menu by execution step was arrayed on the left side for easy view. For patient registration, a patient's name, gender, unique ID, birth date, weight, and other types of basic information were entered, and the patient's posture and diagnosis direction were set up. In addition, the body regions for diagnosis and Pulse Sequence were listed for selection. Also, Protocol name and other additional factors were allowed to be entered. The final window was designed to check diagnosis images, patient information, and diagnosis conditions. By learning how to enter patient information and change diagnosis conditions in this program, users will be able to understand more theories and terms learned in practice and thereby to shorten their learning time in actual clinical work.

• Journal of the Korean association of regional geographers
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• v.8 no.4
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• pp.529-541
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• 2002

This paper aims to provide a context-specific interpretation on the shifting process of in-house R&D spaces in a large Korean firm in the context of rapidly changing markets and technology. Drawing on the case study of LG Electronics Company, one of the Korea's flagship companies, I examine the causes and mechanisms leading to a shift in domestic R&D spaces and the nature of learning processes between R&D teams and between R&D and other organizational units, particularly manufacturing. It appears that the current reshaping processes of domestic R&D spaces in LGE focus more on the clustering of core R&D laboratories than the geographical integration of conception and execution. However, it should not simply be viewed that such a move would be reduced to the linear model of innovation and organizational learning. Instead, it involves the firm-specific mode of regulating organizational competences. As contextual variables to induce such a firm-specific mode of organizational change, I consider the spatial form of organization, the spatial sources of knowledge and learning, and the powers of relational learning that can be made between distanciated actors and teams.

• 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.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.27-33
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• 2019

The purpose of this study was to explore the moderating effect of learning strategy levels on the relationship between academic grit and career development competence perceived by high school students. The sample for this study comprised 573 high school students, and data analysis was conducted mainly using reliability analysis, correlation analysis, K-mean cluster analysis, hierarchical regression analysis. The results of the study showed that the learning strategy levels moderated the relationship between academic grit and career development competence. This study suggest it is necessary to provide grit enhancement programs coupled with learning strategy levels in order to facilitate career development competence. The future studies need to explore the literature review for logical relationship between academic grit and career development competence, the qualitative approach for drawing on the theoretical models among the related variables, and the relational research to explore mediating or moderating effect of the individual backgrounds and related variables on the relationship between academic grit and career development competence.

• Journal of IKEEE
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• v.22 no.3
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• pp.693-699
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• 2018

CNN based object recognitions have reported splendid results. However, the recognition of mobile apps raises an interesting challenge that recognition performance of similar widgets is not consistent. In order to improve the performance, we propose a noble method utilizing relations between input widgets. The recognition process flows from the Faster R-CNN based recognition to enhancement using a relation recognizer. The relations are represented as vector translation between objects in a relation space. Experiments on 323 apps show that our method significantly enhances the Faster R-CNN only approach.

• The Mathematical Education
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• v.42 no.3
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• pp.353-368
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• 2003

Analogy, based on a similarity, is to infer the properties of the similar object from properties of an object. It can be a very useful thinking tool for learning mathematical patterns and laws, noticing on relational properties among various situations. The purpose of this study, when manipulating hint condition, figure and table conditions and the amount of original learning by using algebra word problems, is to verify the effects of analogical transfer in solving equivalent, isomorphic and similar problems according to the similarity of source problems and target ones. Five study questions were set up for the above purpose. It was 354 first grade students of S and G middle schools in Seoul that were experimented for this study. The data was processed by MANOVA analysis of statistical program, SPSS 10.0. The results of this studies would indicate that most of the students would be poor at solving isomorphic and similar problems in the performance of analogical transfer according to the similarity of source and target problems. Hints, figure and table conditions did not facilitate the analogical transfer. Merely, on the condition that amount of teaming was increased, analogical transfer of the students was facilitated. Therefore, it is necessary to have students do much more analogical problem-solving experience to improve their analogical reasoning ability through the instruction program development in the educational fields.

• School Mathematics
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• v.16 no.4
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• pp.677-690
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• 2014

The main goals of learning geometry include developing spatial ability and concepts on geometric objects based on understanding the attributes and relationships of them. While the instructions on geometric objects follow the concept development models, the ones on spatial ability are designed from the perspective of geometric transformation. However, there is a need for instructional materials to emphasizing the relationships among geometric concepts. This study hypothesizes that spatial ability stems from the intuitive understanding of geometric objects and the relational understanding on concepts, and it considers the isoperimetric problems as instructional materials to foster spatial ability.