• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.11-20
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• 2021

As unmanned underwater systems have recently emerged, a high-speed underwater channel modeling technique, which is one of the most important techniques in the system, has received a lot of attention. In this paper, we proposed a high-speed sound propagation model and verified the applicability through quantitative performance analyses. We used a high-order finite difference method (FDM) for wave propagation modeling in the water, and a domain decomposition method was adopted using multiple general-purpose graphics processing units (GPUs) to increase the calculation efficiency. We compared the results of the model we proposed with the analytic solution in the half-infinite media and results of the Virtual Timeseries Experiment (VirTEX) model, which is based on the ray method. Finally, we analyzed the performance of the model quantitatively using numerical examples. Through quantitative analyses of the improvement in computational performance, we confirmed that the computational speed increases linearly as the number of GPUs increases. The computation times are increased by 2 times and 8 times, respectively, when the domain size of computation and the maximum frequency are doubled. We expect that the proposed high-speed underwater channel modeling technique is able to contribute to the enhancement of national defense as an underwater communication channel model and analysis tool to develop the underwater communication technique for the unmanned underwater system.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.423-436
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• 2021

In the future, artificial intelligence (AI) technology is expected to become a general-purpose technology (GPT), and it is predicted that AI competency will become an essential competency. Several nations around the world are fostering experts in the field of AI to achieve technological proficiency while working to develop the necessary infrastructure and educational environment. In this study, we investigated the status of software education at the liberal arts level at 31 universities in Seoul, along with precedents from domestic and foreign AI education research. Based on this, we concluded that an AI literacy education model is needed to link software education at the liberal arts level with professional AI education. And we classified 20 AI-related lectures released in the KOCW according to the AI literacy competencies required; based on the results of this classification, we propose a model for AI literacy education in the liberal arts for undergraduate students. The proposed AI literacy education model may be considered as AI·SW convergence to experience AI along with literacy in the humanities, deviating from the existing theoretical and computer-science-based approach. We expect that our proposed AI literacy education model can contribute to the proliferation of AI.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.353-366
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• 2022

The study sets out to examine the relationship between the effects of government support, network capability, and knowledge sharing on the effects of innovative behavior on innovation performance. As digital-based networks and knowledge sharing activities are rapidly becoming common in the era of the spread of Covid-19, 357 questionnaires were analyzed for incumbent employees and the research hypotheses are verified using SPSS 24 and smart PLS 3 to prove the relationships between variables. As a result of the study, it is found that innovation performance increased when government support, network capability, and knowledge sharing increased. Innovative behavior is found to significantly mediate the relationship between network capability, knowledge sharing, and innovation performance, and government support was found to have a significant effect on network capabiltiy. Therefore, in order to induce innovative actions that are important in creating innovative results, various programs to enhance the network capabilities of entrepreneurs and promote knowledge sharing should be required as government support policies. This will serve as a driving force for entrepreneurs to voluntarily develop and implement ideas more flexibly and freely, thereby enhancing competitiveness through innovative growth of companies and industries. In other words, this study verified the effectiveness of government support in the network capability to help create innovative results by inducing innovative behavior along with knowledge sharing activities, and comprehensively demonstrated the relationship between factors in the overall structure.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.16-23
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• 2022

This study examined the trend of international competitiveness over the past 10 years (2011-2020), focusing on comparative analysis with China and the United States, targeting seven major export items of Korean construction machinery based on 6 units of HS code. To this end, the export similarity index and trade competitiveness index were calculated and analyzed using UN Comtrade and Korea International Trade Association trade statistics. As a result of the analysis, competition between Korea and China has intensified over the past decade, and competition with the United States has remained at a certain level. Korean forklifts (8427.20) are exporting to the world with strong competitiveness in the global market. Excavators (8429.52) and loaders (8429.51), which have the largest export share of Korean construction machinery, have a weight advantage, but they are exporting due to price inferiority. The rest of the items were found to be inferior in price and weight, and were not competitive in the global market. These analysis results suggest the following implications. First, it is necessary to strengthen efforts to expand exports of universal construction machinery items, which are expected to increase in demand in the future, by boosting the economy and expanding infrastructure investment in accordance with eco-friendly policies. Second, excavators, which have been shown to have a quality advantage and a price competitive advantage, need to further strengthen export marketing activities not only in China and the United States but also in emerging developing countries.

• Journal of the Korean Geosynthetics Society
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• v.20 no.4
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• pp.63-71
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• 2021

Recently, high-resolution cameras in smartphones enable measurement of minute objects such as cracks in concrete using image processing techniques. The technology to investigate the crack width using an application at an adjacent distance of the close shot range has already been implemented, but the use is limited, so it is necessary to verify the usability of the high-resolution smartphone camera to measure cracks at a longer distance. This study focuses on recognizing the size of subdivided crack widths at a thickness within 1.0 mm of crack width at a distance of 2 m. In recent Android-based smartphones, an experiment was conducted focusing on the relationship between the unit pixel size, which is a measurement component, and the shooting distance, depending on the camera resolution. As a result, it was possible to confirm the necessity of a smartphone lens for the classification and quantification of microcrack widths of 0.3 mm to 1mm. The universal telecentric lens for smartphones needed to be installed in an accurate position to minimize the effect of distortion. In addition, as a result of applying a 64 MP high-resolution smartphone camera and double magnification lens, the crack width could be calculated within 2 m in pixel units, and crack widths of 0.3, 0.5, and 1mm could be distinguished.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.245-255
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• 2022

Evaluating the qualitative the qualitative process of water resources by using various indicators, as one of the most prevalent methods for optimal managing of water bodies, is necessary for having one regular plan for protection of water quality. In this study, zoning maps were developed on a yearly basis by collecting and reviewing the process, validating, and performing statistical tests on qualitative parameters҆ data of the Iranian aquifers from 1995 to 2020 using Geographic Information System (GIS), and based on Inverse Distance Weighting (IDW), Radial Basic Function (RBF), and Global Polynomial Interpolation (GPI) methods and Kriging and Co-Kriging techniques in three types including simple, ordinary, and universal. Then, minimum uncertainty and zoning error in addition to proximity for ASE and RMSE amount, was selected as the optimum model. Afterwards, the selected model was zoned by using Scholar and Wilcox. General evaluation of groundwater situation of Iran, revealed that 59.70 and 39.86% of the resources are classified into the class of unsuitable for agricultural and drinking purposes, respectively indicating the crisis of groundwater quality in Iran. Finally, for validating the extracted results, spatial changes in water quality were evaluated using the Groundwater Quality Index (GWQI), indicating high sensitivity of aquifers to small quantitative changes in water level in addition to severe shortage of groundwater reserves in Iran.

• Journal of Korean Society of Rural Planning
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• v.28 no.2
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• pp.71-85
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• 2022

The food self-sufficiency rate of agricultural products in Korea, excluding rice, is around 20%, and the government is promoting various policies including a Multiple-Purpose Utilization of Paddy Fields project, to increase the self-sufficiency rate of major grains. The project for Multiple-Purpose Utilization of Paddy Fields is being promoted as a part of a program to create farmland infrastructure to facilitate the cultivation of crops other than rice in rice paddies, and pilot projects were started in four regions in 2020. The purpose of this study is to analyze the economic effects of the pilot project for Multiple-Purpose Utilization of Paddy Fields, and to propose policies to increase the effectiveness of the project. In order to analyze the economic effect, we estimated the change in farm income generated by switching from rice to other crops, and measured the effect of welfare change using the Equilibrium Displacement Model (EDM). As a result of the analysis, social welfare is expected to increase when the pilot project for Multiple-Purpose Utilization of Paddy Fields is implemented, and the income of the beneficiary farmers is also expected to improve compared to that of single-cropping when double-cropping is implemented. However, it was found that the economic feasibility of the project differs depending on the crops converted. Juksan-myeon, Gimje-si, which is an area where soybean production was successful, was analyzed from the viewpoint of increasing the economic feasibility of the pilot project. Their success factors were analyzed into four major factors: infrastructure, farming methods, education, and collaboration with local agricultural organizations. If such a success story can be utilized in the future project implementation process, it can contribute to the improvement of farm household income and national economic welfare.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.