• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.332-340
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• 2022

Recently, with the development of deep learning and artificial neural network technologies, research on the application of neural network has been actively conducted in the field of video coding. In particular, deep learning-based intra prediction is being studied as a way to overcome the performance limitations of the existing intra prediction techniques. This paper presents a method of context-adaptive neural network-based intra prediction model training and its coding performance analysis. In other words, in this paper, we implement and train a known intra prediction model based on convolutional neural network (CNN) that predicts a current block using contextual information from reference blocks. Then, we integrate the trained model into HM16.19 as an additional intra prediction mode and evaluate the coding performance of the trained model. Experimental results show that the trained model gives 0.28% BD-rate bit saving over HEVC in All Intra (AI) coding mode. In addition, the coding performance change of training considering block partition is also presented.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Journal of Distribution Research
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• v.17 no.3
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• pp.93-113
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• 2012

Early conflict research in channel and organization area have focused on the definition of conflict construct, its cause, consequence and identified conflict resolution management. Recent studies about conflict, however, have explored new assumption of complexity, a multidimensional conflict construct, contextual conflict management strategies, positive and negative conflict/consequence, and the conflict resolution strategy. Although many literatures exists on channel conflict resolution, little research has been done about relationship learning and performance from conflict resolution perspective. This study explores how channel members can achieve a relationship learning, as a conflict resolution mechanism, which enhance co-created value in marketing channel relationship. Therefore we propose that conflict resolution strategies(collaborating behavior and avoiding behavior) influence channel performance(effectiveness and efficiency) through relationship learning processes(learning via information exchange, joint interpretation and coordination, relationship-specific knowledge memory), in view of buyer-seller relationship. The research model is shown at

• Journal of Digital Convergence
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• v.17 no.3
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• pp.227-234
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• 2019

The purpose of this study is to present a block type smart classroom model for comprehensive supply of educational contents, classroom environment and ICT technology in African countries where educational infrastructure is weak. It will provide a contextual solution that integrates learning management, power management, and classroom environment management systems, and will be a convergence model that can optimize economic and non-economic conditions for different African countries. It can be expected to enhance utilization as it is a differentiated model from existing classrooms with a single container, as well as independent research and development centered on services, content, and solutions. Through this integrated research process, we can overcome the spatial and functional limitations appearing in single container classrooms and build a flexible space for advanced e-learning technology. The depth and scope of the follow-up study can be carried by investigating the performance and models that are in line with the educational and infrastructure conditions of the various regions.

• Korean Journal of Human Ecology
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• v.22 no.4
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• pp.619-635
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• 2013

The purpose of the study was to qualitatively analyze how entrepreneurs would succeed in business from the consumer perspective of entrepreneurial. In particularly, this study understood what has made the entrepreneurs do their own business, learning contextual and mediating conditions. In order to achieve the research goals, the study conducted in-depth interviews to a total of 11 entrepreneurs and based on data from the interviews, carried out a grounded theory analysis. According to the results, the successful entrepreneurship process paradigm model from the consumer perspective of entrepreneurial was observed with some central phenomenon, 'change of course', via casual circumstances as 'self-realization' and 'material value realization.' For the contextual conditions, there found 'anxiety for survival', 'organic motive', 'confidence in a market' and 'relational role element' as well. The interaction strategies consisted of 'internal capability improvement', 'internal and external activity directivity growth' and 'marketability judgement ability.' The mediating conditions were observed to be 'strategic cognition improvement' and 'growing of desire to succeed'. The analysis results reported that there were two different aspects as 'increase of stable dailiness' and 'productivity enlargement'. In terms of the core category, it was 'securing of stable dailiness and competitiveness in the market as well by developing characteristics and abilities of an individual for the life value realization.' Those results confirmed that once pleasure and satisfaction in daily life increase, the confidence of the entrepreneurs improves too, which would encourage them to continue the business.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.57-66
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• 2022

In this paper, we propose a legal document search method that uses the Sentence-BERT model. The general public who wants to use the legal search service has difficulty searching for relevant precedents due to a lack of understanding of legal terms and structures. In addition, the existing keyword and text mining-based legal search methods have their limits in yielding quality search results for two reasons: they lack information on the context of the judgment, and they fail to discern homonyms and polysemies. As a result, the accuracy of the legal document search results is often unsatisfactory or skeptical. To this end, This paper aims to improve the efficacy of the general public's legal search in the Supreme Court precedent and Legal Aid Counseling case database. The Sentence-BERT model embeds contextual information on precedents and counseling data, which better preserves the integrity of relevant meaning in phrases or sentences. Our initial research has shown that the Sentence-BERT search method yields higher accuracy than the Doc2Vec or TF-IDF search methods.

• Journal of Science Education
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• v.43 no.2
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• pp.239-257
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• 2019

This study attempts to understand the context of shared learning in the trading zone formed by teachers from different backgrounds and the process in which this shared learning varies in the educational context, focusing on the case of 'Woodwork Science Education Study Group.' To do this, data was collected through in-depth interviews with eight teachers who participated in the 'Woodworking Science Education Research Group' and analyzed their responses based on grounded theory. As a result, the causal conditions of the teachers' research group were 'various contexts of entering the trading zone' and the central phenomenon was 'encounter with learning in the trading zone.' Contextual conditions affecting this phenomenon were 'woodwork as a boundary object and individual transfiguration experience,' and action/interaction strategy was 'various efforts and influences in the field.' The intervention condition was 'practical effort and experience in educational field.' Final result in this model is 'the new practice of learning shared in the trading zone.' In selective coating, it was found that the practice of the teacher's research group appears as four types of' 'Extracurricular creative experience type,' 'career education type,' 'curricula education type,' and 'school management type.' The results of this study suggest that the shared learning and antonymous practice among teachers in the teachers' research group as trading zone do not only meet their learning needs but also lead to various teaching practices in the individual teachers' context of education and improve the diversity and quality of education.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.69-76
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• 2018

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a new deep neural network model for referring expression comprehension. The proposed model finds out the region of the referred object in the given image by making use of the rich information about the referred object itself, the context object, and the relationship with the context object mentioned in the referring expression. In the proposed model, the object matching score and the relationship matching score are combined to compute the fitness score of each candidate region according to the structure of the referring expression sentence. Therefore, the proposed model consists of four different sub-networks: Language Representation Network(LRN), Object Matching Network (OMN), Relationship Matching Network(RMN), and Weighted Composition Network(WCN). We demonstrate that our model achieves state-of-the-art results for comprehension on three referring expression datasets.

• Smart Media Journal
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• v.12 no.5
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• pp.28-35
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• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.