• Journal of Information Processing Systems
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• v.19 no.3
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• pp.355-369
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• 2023

Since the widespread adoption of deep-learning and related distributed representation, there have been substantial advancements in part-of-speech (POS) tagging for many languages. When training word representations, morphology and shape are typically ignored, as these representations rely primarily on collecting syntactic and semantic aspects of words. However, for tasks like POS tagging, notably in morphologically rich and resource-limited language environments, the intra-word information is essential. In this study, we introduce a deep neural network (DNN) for POS tagging that learns character-level word representations and combines them with general word representations. Using the proposed approach and omitting hand-crafted features, we achieve 90.47%, 80.16%, and 79.32% accuracy on our own dataset for three morphologically rich languages: Uyghur, Uzbek, and Kyrgyz. The experimental results reveal that the presented character-based strategy greatly improves POS tagging performance for several morphologically rich languages (MRL) where character information is significant. Furthermore, when compared to the previously reported state-of-the-art POS tagging results for Turkish on the METU Turkish Treebank dataset, the proposed approach improved on the prior work slightly. As a result, the experimental results indicate that character-based representations outperform word-level representations for MRL performance. Our technique is also robust towards the-out-of-vocabulary issues and performs better on manually edited text.

• Korea Trade Review
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• v.47 no.2
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• pp.47-67
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• 2022

This study is to investigate the relationships between R&D collaboration and competitive advantage in Korean international venture firms from the perspective of resource-based theory, organizational learning and network theory. The R&D collaboration is divided into vertical cooperation within the value chain and horizontal cooperation beyond value chain. The first key finding from empirical analysis is that both vertical and horizontal R&D collaborations have significantly positive impacts on technology based competitive advantages. The vertical R&D collaboration has more impacts on competitive advantages than horizontal R&D collaboration. This suggests that R&D collaboration with purchasers and suppliers plays a greater role for venture organizations' competitiveness. Second, the potential knowledge based absorption capacity and mutual goodwill trust also work significantly to reinforce the positive influences of R&D collaboration to the competitive advantage. This implies that mutual trust between partners participating in collaboration and absorption capacity within venture organizations would strengthen the effectiveness of R&D cooperation. This study provides the practical implications that the performance and effectiveness of R&D collaboration may rely on the nature of cooperation partners and internal organization capability.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.1
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• pp.241-262
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• 2006

The University libraries need to develop the balanced scorecard concerning general management in order to measure the total performance of the organization that measure not only quantity measurement based on resources and quality measurement based on information services but also effectiveness of resource utilization, efficiency ability providing the services and potential capability of the members adopting the diverse changes flexibility as organic organization under the rapidly changing circumstances nowadays. This study developed the BSC model into the four perspectives such as customer, resource, internal process, learning and growing modifying BSC model developed by Kaplan and Norton to fit university libraries as nonprofit organization and developed the strategic map and performance measurement indicators on the strategy of each perspective. Ultimately, this study tried to provide an integrated strategic management indicators providing comprehensive picture of university libraries from strategic plan to performance applying the BSC linking strategic plan.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.23-37
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• 2024

Digital twin is an M&S (Modeling and Simulation) technology designed to solve or optimize problems in the real world by replicating physical objects in the real world as virtual objects in the digital world and predicting phenomena that may occur in the future through simulation. Digital twins have been elaborately designed and utilized based on data collected to achieve specific purposes in large-scale environments such as cities and industrial facilities. In order to apply this digital twin technology to real life and expand it into user-customized service technology, practical but sensitive issues such as personal information protection and personalization of simulations must be resolved. To solve this problem, this paper proposes a federated learning-based accelerated client training method (FACTS) for personalized digital twins. The basic approach is to use a cluster-driven federated learning training procedure to protect personal information while simultaneously selecting a training model similar to the user and training it adaptively. As a result of experiments under various statistically heterogeneous conditions, FACTS was found to be superior to the existing FL method in terms of training speed and resource efficiency.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.39-60
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• 2014

In domestic university libraries, the difference between the knowledge resource collection activities on campus is depending on the size of the university, and their collection is concentrated on some types of digital resources. In recent years, the main universities in developed countries has developed actively in social openness and share activities of their knowledge resources, through the OA-based institutional repository, for the purpose of image improvement and competitiveness as a knowledge production base. This study examined ways to improve the relevant regulations in order to effectively collect and systematically manage the knowledge resources from graduate school, research institutes, center for teaching and learning, e-learning center, museum, press, a variety of campus organizations, so as to enhance the role of the library as the right manager of knowledge resources on campus. To this end, this study, considering the improvement of relevant regulations, investigates the operating situation of the library regulations of 176 universities and suggests necessary improvement methods in order to facilitate the digital legal deposit and expand its scope.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.2
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• pp.111-127
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• 2010

This study aims for developing an instructional design strategy to improve collaborative teaching between teacher librarians and subject teachers in library-assisted instruction. Collaborative teaching is an important management activity enhancing teaching role of teacher librarians in their school community. But in the actual condition the level of the library-assisted instruction as its practical strategy is cooperation and subject teachers are leading teaching and learning except for selecting resources. Because library-assisted instruction is resource-based learning, the procedural knowledge of information literacy curricula and topics of subject specific curricula should be designed as a whole. Also, there must be possibilities of reducing trial and error and expanding successes. From these sides, the collaborative design strategy for library-assisted instruction can be planned like 'statement of learning situation-co designing-co teaching-co evaluating'.

• Hwankyungkyoyuk
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• v.11 no.2
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• pp.224-237
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• 1998

The purpose of this study that was based on the theory review about the practical use necessity of school landscape was to survey on the awareness of elementary school teachers about the applications of school landscape for environmental education. This study was carried out through the review of literature, the questionnaire to 132 elementary school teachers in Seongnam City. The findings derived from this study were as follows : First, School landscape is an important field as environmental education resource for the improvement of school environmental education. Especially, for the improvement of environmental sensitivity through field-experience learning, the practical applications of elementary school landscape is very important and urgent in aspects of environmental education theory, accessibility, and convenience. Second, Most of responded teachers give an affirmative answer about the field-experience learning for school environmental education and the necessity of practical use of school landscape for field-experience learning. Several the improvement to use elementary school landscape for environmental education being suggested by teachers are the increase of financial support, the magnification of school area, and the school landscape planning and design in consideration of environmental education by landscape architect expert. Third, Above half of teachers don't agree to use the roof garden for environmental education because of the safety of students and the school building construction. Fourth, Teachers are more satisfied with the status of school landscape maintenance than the practical usability of school landscape in aspect of environmental education and the facilities in school landscape. Teachers think that the most important functions and roles of school landscape is psychological factor.