• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.85-104
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• 2023

The purpose of this study is to analyze the examination questions used in the librarian teacher recruitment exam, including the domains, content, and evaluation factors, and to propose improvements for the recruitment exam. To achieve this, examination questions for librarian teacher recruitment exams since 2002, provided by the Korea Institute for Curriculum and Evaluation, were collected and analyzed the frequency of appearances by section. The analysis revealed that, 106 questions (21.95%) on school library administration, 63 questions (13.04%) on classification and information retrieval 59 questions (12.22%) on library computerization, 58 questions (12.01%) on reading education, 56 questions (11.59%) cataloging and information service, and 18 questions (3.73%) on information media were examined. Next, analyzed the frequency of appearances in the last 10 years (2014-2023) by dividing the examination areas into specialty of librarian and school library practice, and found that there were a total of 149 questions (66.22%) related to specialty of librarian and 76 questions (33.78%) related to school library practice. Based on these findings, recommendations have been made for update assessment areas and factors, expanding the field of information media, and suggested the need for a stable and continuous teacher recruitment policy.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.400-425
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• 2018

This study presents the research results and current status of the DECOVALEX-2019 project Task B. Task B named 'Fault slip modelling' is aiming at developing a numerical method to simulate the coupled hydro-mechanical behavior of fault, including slip or reactivation, induced by water injection. The first research step of Task B is a benchmark simulation which is designed for the modelling teams to familiarize themselves with the problem and to set up their own codes to reproduce the hydro-mechanical coupling between the fault hydraulic transmissivity and the mechanically-induced displacement. We reproduced the coupled hydro-mechanical process of fault slip using TOUGH-FLAC simulator. The fluid flow along a fault was modelled with solid elements and governed by Darcy's law with the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by creating interface elements between two separating rock blocks in FLAC3D. A methodology to formulate the hydro-mechanical coupling relations of two different hydraulic aperture models and link the solid element of TOUGH2 and the interface element of FLAC3D was suggested. In addition, we developed a coupling module to update the changes in geometric features (mesh) and hydrological properties of fault caused by water injection at every calculation step for TOUGH-FLAC simulator. Then, the transient responses of the fault, including elastic deformation, reactivation, progressive evolutions of pathway, pressure distribution and water injection rate, to stepwise pressurization were examined during the simulations. The results of the simulations suggest that the developed model can provide a reasonable prediction of the hydro-mechanical behavior related to fault reactivation. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOLVAEX-2019 Task B and validated using the field data from fault activation experiments in a further study.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.139-151
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• 2018

The National Base Map construction consists of the regular correction work of dividing the whole country into two regions and carrying out the modification Plotting by aerial photographs every two years as well as the real time updating work of correcting the major change feature within two weeks by the field survey and the As-Built Drawing. In the case of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) used for real time updating work of the National base map, the coordinate transformation error is included in the positional error when applied to the National Base Map based on the World Geodetic Reference System as the coordinate system based on the Regional Geodetic Reference System. In addition, National Base Map is registered based on the outline(eaves line) of the building in the Digital Topographic Map, and the Cadastral and Architecture are registered based on the building center line. Therefore, the Building Object management standard is inconsistent. In order to investigate the improvement method, the network RTK survey was conducted directly on a location of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) and the problems were analyzed by comparing with the plane plotting position reference in National Base Map. In the case of the general structure with the difference on the Building center line and the eaves line, beside the location information was different also the difference in the ratio of the building object was different between Building center line and the eave. In conclusion, it is necessary to provide the Base data of the double layer of the Building center line and the outline of the building(eaves line) in order to utilize the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS). In addition, it is necessary to study an organic map update process that can acquire the up-to-dateness and the accuracy at the same time.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.79-91
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• 2011

In this paper, a DS (Data Synchronization) server for mobile communication environments is constructed and the suitability and the performance of its operations are validated. The DS server provides a way to update the newest data and keep data consistency for clients (mobile devices). In addition, the DS server constructed in this paper supports various synchronization types, and detects all changes and conflicts. In case of data conflicts, the DS server resolves the conflicts according to the several policies implemented in this work. The DS server conforms to the OMA(Open Mobile Alliance) DS standard protocol for interoperability with other mobile devices and servers. In addition to the transmission-by record scheme proposed by the OMA DS standard protocol, the DS server constructed in this paper also provides the transmission-by field scheme for the enhancement transmission performance between the server and clients. In order to validate its operations, data synchronization between the DS server and the SCTS (SyncML Conformance Test Suit), the suitability validation tool provided by the OMA, is performed. The validation results show that the DS server constructed in this paper satisfies all of the test cases except the Large Object function. The Large Object function will be implemented later because the function is not needed for the personal information synchronization process which this paper aims for. Also, synchronization times of the DS server are measured while increasing the number of data and clients. The results of the performance evaluations demonstrate that the DS server is scalable, in the sense that it has not suffered from any serious bottlenecks with respect to the number of data and clients. We expect that this work will provide a framework for various studies in the future for improving mobile DS operations.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.670-691
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• 2018

This study presents the research results of the BMT(Benchmark Model Test) simulations of the DECOVALEX-2019 project Task B. Task B named 'Fault slip modelling' is aiming at developing a numerical method to predict fault reactivation and the coupled hydro-mechanical behavior of fault. BMT scenario simulations of Task B were conducted to improve each numerical model of participating group by demonstrating the feasibility of reproducing the fault behavior induced by water injection. The BMT simulations consist of seven different conditions depending on injection pressure, fault properties and the hydro-mechanical coupling relations. TOUGH-FLAC simulator was used to reproduce the coupled hydro-mechanical process of fault slip. A coupling module to update the changes in hydrological properties and geometric features of the numerical mesh in the present study. We made modifications to the numerical model developed in Task B Step 1 to consider the changes in compressibility, Permeability and geometric features with hydraulic aperture of fault due to mechanical deformation. The effects of the storativity and transmissivity of the fault on the hydro-mechanical behavior such as the pressure distribution, injection rate, displacement and stress of the fault were examined, and the results of the previous step 1 simulation were updated using the modified numerical model. The simulation results indicate that the developed model can provide a reasonable prediction of the hydro-mechanical behavior related to fault reactivation. The numerical model will be enhanced by continuing interaction and collaboration with other research teams of DECOVALEX-2019 Task B and validated using the field experiment data in a further study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.247-254
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• 2006

Online testing method is one of the numerical experiment methods using experimental information for a numerical analysis directly. The method has an advantage in that analysis can be conducted without using an idealized mechanical model, because mechanical properties are updated from element test for a numerical analysis in real time. The online testing method has mainly been used for the geotechnical seismic engineering, whose major target is sand. A testing method that may be applied to a consolidation problem has recently been developed and laboratory and field verifications have been tried. Although related research thus far has mainly used a method to update average reaction for a numerical analysis by positioning an element tests at the center of a consolidation layer, a weakness that accuracy of the analysis can be impaired as the thickness of the consolidation layer becomes more thicker has been pointed out regarding the method. To clarify the effectiveness and possible analysis scope of the online testing method in relation to the consolidation problem, we need to review the results by applying experiment conditions that may completely exclude such a factor. This research reviewed the results of the online consolidation test in terms of reproduction of the consolidation settlement and the dissipation of excess pore water pressure of a clay specimen by comparing the results of an online consolidation test and a separated-type consolidation test carried out under the same conditions. As a result, the online consolidation test reproduced the change of compressibility according effective stress of clay without a huge contradiction. In terms of the dissipation rate of excess pore water pressure, however, the online consolidation test was a little faster. In conclusion, experiment procedure needs to improve in a direction that hydraulic conductivity can be updated in real time so as to more precisely predict the dissipation of excess pore water pressure. Further research or improvement should be carried out with regard to the consolidation settlement after the end of the dissipation of excess pore water pressure.