• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.970-984
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• 2014

This study aims to inquire into experience of geography teachers participating in a teacher learning community based on the grounded theory methodology. Participation observation was conducted on one of geography teacher learning communities. The total of 11 research participants were selected to conduct in-depth interviews. The data collected were analyzed by the coding method proposed by Strauss and Corbin(1990, 1998). In open coding, 125 concepts, 43 sub-categories and 17 categories were drawn and in axial coding by paradigm model, phenomenon, conditions, action/interaction and consequences turned out. In selective coding, the participants were classified into four types and the condition/consequence matrix was developed. As a result of the analysis, first, participation, obstacles and continuous participation factors of geography teachers in the teacher learning community could be understood from multi-dimensional aspects. Second, principles of the collaborative teacher learning and the factors promoting collaborative teacher learning were established. Third, the professional development of geography teachers through teacher learning community could be understood.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.418-424
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• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2035-2046
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• 2021

Glacier movement speed is the most basic measurement for glacial dynamics research and is a very important indicator in predicting sea level rise due to climate change. In this study, the two-dimensional velocity measurements of Campbell Glacier located in Terra Nova Bay in East Antarctica were observed through the SAR offset tracking technique. For this purpose, domestic KOMPSAT-5 SAR satellite images taken on July 9, 2021 and August 6, 2021 were acquired. The Multi-kernel SAR offset tracking proposed through previous studies is a technique to obtain the optimal result that satisfies both resolution and precision. However, since offset tracking is repeatedly performed according to the size of the kernel, intensive computational power and time are required. Therefore, in this study, we strategically proposed a coarse-to-fine offset tracking approach. Through coarse-to-fine SAR offset tracking, it is possible to obtain a result with improved observation precision (especially, about 4 times in azimuth direction) while maintaining resolution compared to general offset tracking results. Using this proposed technique, a two-dimensional velocity measurements of Campbell Glacier were generated. As a result of analyzing the two-dimensional movement velocity image, it was observed that the grounding line of Campbell Glacier exists at approximately latitude -74.56N. The flow velocity of Campbell Glacier Tongue analyzed in this study (185-237 m/yr) increased compared to that of 1988-1989 (140-240 m/yr). And compared to the flow velocity (181-268 m/yr) in 2010-2012, the movement speed near the ground line was similar, but it was confirmed that the movement speed at the end of the Campbell Glacier Tongue decreased. However, there is a possibility that this is an error that occurs because the study result of this study is an annual rate of glacier movement that occurred for 28 days. For accurate comparison, it will be necessary to expand the data in time series and accurately calculate the annual rate. Through this study, the two-dimensional velocity measurements of the glacier were observed for the first time using the KOMPSAT-5 satellite image, a domestic X-band SAR satellite. It was confirmed that the coarse-to-fine SAR offset tracking approach of the KOMPSAT-5 SAR image is very useful for observing the two-dimensional velocity of glacier movements.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.384-398
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• 2015

In this paper, the high-resolution Weather Research and Forecasting/Noah-MultiParameterization (WRF/Noah-MP) modeling system is configured for the Cheongmicheon Farmland site in Korea (CFK), and its performance in land and atmospheric simulation is evaluated using the observed data at CFK during the 2014 special observation period (21 August-10 September). In order to explore the usefulness of turning on Noah-MP dynamic vegetation in midterm simulations of surface and atmospheric variables, two numerical experiments are conducted without dynamic vegetation and with dynamic vegetation (referred to as CTL and DVG experiments, respectively). The main results are as following. 1) CTL showed a tendency of overestimating daytime net shortwave radiation, thereby surface heat fluxes and Bowen ratio. The CTL experiment showed reasonable magnitudes and timing of air temperature at 2 m and 10 m; especially the small error in simulating minimum air temperature showed high potential for predicting frost and leaf wetness duration. The CTL experiment overestimated 10-m wind and precipitation, but the beginning and ending time of precipitation were well captured. 2) When the dynamic vegetation was turned on, the WRF/Noah-MP system showed more realistic values of leaf area index (LAI), net shortwave radiation, surface heat fluxes, Bowen ratio, air temperature, wind and precipitation. The DVG experiment, where LAI is a prognostic variable, produced larger LAI than CTL, and the larger LAI showed better agreement with the observed. The simulated Bowen ratio got closer to the observed ratio, indicating reasonable surface energy partition. The DVG experiment showed patterns similar to CTL, with differences for maximum air temperature. Both experiments showed faster rising of 10-m air temperature during the morning growth hours, presumably due to the rapid growth of daytime mixed layers in the Yonsei University (YSU) boundary layer scheme. The DVG experiment decreased errors in simulating 10-m wind and precipitation. 3) As horizontal resolution increases, the models did not show practical improvement in simulation performance for surface fluxes, air temperature, wind and precipitation, and required three-dimensional observation for more agricultural land spots as well as consistency in model topography and land cover data.

• Journal of Information Technology Applications and Management
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• v.15 no.4
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• pp.159-188
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• 2008

This study suggests strategies which can enable to creation of new opportunities of competitive advantages while operating a long lasting and consistent business with major trading partners, based on interorganizational information systems (IOISs) specially established and installed for interorganizational transactions. Nowadays, IOISs based mechanism having been widely expanded as a conventional business infrastructure for the interorganizational transactions and/or exchanges, it is customary difficult to obtain any strongly sound advantage over the competitors who have adopted even the simplest deployment of the IOIS mechanisms. In this connection, this study intends to investigate the interorganizational collaborative activities conducted by under the auspicious of IOISs, focused on the prospect of the exploitation of IOISs rather than the implementation of the IOISs. In this study, we, firstly, suggest the concept of Electronic Collaboration which can be defined by the collaborative activities conducted by IOISs, compared to the ones conducted on off-line. In addition, we suggest the Electronic Collaboration as a multi-dimensional concept, constituted by three sub-constructs, the Electronic Information Sharing (EIS), the Electronic Joint Activity (EJA), and the construction of the Electronic Relational Knowledge Store (ERKS). Secondly, we empirically verify the effects of relational and environmental determinants on the Electronic Collaboration. In this study, the relational determinants relate to the variables created in interorganizational relationship like Trust, Influence, Relational Specific Asset-asset invested for the transaction-, and Continuity of the relationship. On the other hand, the environmental determinants relate to the variables surrounding the relationship which are difficult to control. We consider Product Complexity, Technological Uncertainty, and Market Variability as the domain of the environmental determinants. To test our hypotheses, we conducted both paper-based survey and online-based survey. After refining the data with missing responses, a total of 150 data was used for analysis. The results were as follows : Firstly, it is statistically significant that the Electronic Collaboration is composed of EIS, EJA, and ERKS. In particular, the results imply that the firms are able to accumulate relational knowledge base as well as to exchange information or knowledge, and to conduct joint activities through effort to further expand the Electronic Collaboration. Secondly, we have verified the individual effects of the relational and the environmental determinants on the Electronic Collaboration. Product Complexity has been revealed as the most influential variable affecting the Electronic Collaboration. Next, Interorganizational Trust and Technological Uncertainty, in that order, have been seen to have significant effects on the Electronic Collaboration. In other words, when products or services seem to be difficult to standardize, and the core technologies seem to rapidly change, the need for the Electronic Collaboration increase. In addition, the observation dictates that the interorganizational trust turns out to be a critical variable in building a relationship and in seeking further collaboration. The results, further, illustrate that the environmental determinants are relatively more effective than the relational determinants, which is not consistent with a few prior researches relational determinants emphasized. It is because this study doesn't consider the size of the firm. A few researchers have given an emphasis on the relational determinants like trust and influence, especially from the perspective of small firms in interorganizational relationship. However, in our study, where all the sizes of the firms are contained, electronic collaboration is considerably affected by the environmental determinants.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1545-1553
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• 2018

Recent eruptive activity at Kīlauea Volcano started on at the end of April in 2018 showed rapid ground deflation between May and June in 2018. On summit area Halema'uma'u lava lake continued to drop at high speed and Kīlauea's summit continued to deflate. GPS receivers and electronic tiltmeters detected the surface deformation greater than 2 meters. We explored the time-series surface deformation at Kīlauea Volcano, focusing on the early stage of eruptive activity, using multi-temporal COSMO-SkyMed SAR imagery. The observed maximum deformation in line-of-sight (LOS) direction was about -1.5 meter, and it indicates approximately -1.9 meter in subsiding direction by applying incidence angle. The results showed that summit began to deflate just after the event started and most of deformation occurred between early May and the end of June. Moreover, we confirmed that summit's deflation rarely happened since July 2018, which means volcanic activity entered a stable stage. The best-fit magma source model based on time-series surface deformation demonstrated that magma chambers were lying at depths between 2-3 km, and it showed a deepening trend in time. Along with the change of source depth, the center of each magma model moved toward the southwest according to the time. These results have a potential risk of including bias coming from single track observation. Therefore, to complement the initial results, we need to generate precise magma source model based on three-dimensional measurements in further research.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.