• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.1-13
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• 2018

The number of deaths in Korea is higher than the OECD average. Therefore, an e-Call system is being developed as a vehicle ICT-based emergency rescue system that automatically detects an accident in the event of a vehicle accident and transmits related information to the center. In order to overcome the limitations of social acceptability and function of e-Call system, we propose a model that allows users to be aware of the necessity of service voluntarily. We predicted the market share of e-call services according to the proposed business model and analyzed it through B/C analysis. Benefits are calculated on a penetration basis, and device purchase and communications costs are calculated for each period. B/C analysis shows that pessimistic scenarios are 0.98 in 2025 and 1.01 in 2030. In an optimistic scenario, it is 1.05 in 2025 and 1.20 in 2030, which is more economical.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.434-445
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• 2020

In this study, the minimization of harbor oscillation using permeable breakwater was applied to the actual harbor and investigated an effect of minimization by computer simulation in order to take into account the water quality problems and measures of harbor oscillation by infragravity waves at the same time. The study site is Mukho harbor located at East coast of Korea that harbor oscillation has been occurred frequently. The infragravity waves obtained by analyzing the observed field data for five years focused on the distribution between wave periods of 40 s and 70 s and wave heights in less than 0.1 m was 94% of analyzing data. The target wave periods was 68.0 s. The most effective method of minimization of harbor oscillation by infragravity waves was to install a detached permeable breakwater with transmission coefficient of 0.3 on the outside harbor and replace some area of the vertical wall in the harbor with wave energy dissipating structure to achieve a reflectivity of 0.9 or less. The amplitude reduction rate of this method shown in 27.4%. And the effect of the difference in transmission coefficient of permeable breakwater on the reduction rate of the amplitude was not significant.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.247-259
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• 2021

This study aimed to look for educational ways to prepare for the future society for education and people of talent who will lead the post-COVID-19 era. To this end, the factors necessary for the type of future talent in the post-COVID-19 era were identified by analyzing Big data. Based on the deducted factors composing the type of talent in the post-COVID-19 era, policy direction according to the emergence of the post-COVID-19 era were deducted through the interviews with the group of experts and delphi survey, and on the basis of this, this study sought for"a plan for the educational change in line with cultivation of people of talent in the post-COVID-19 era. The results of this study are as follows. First, through the big data analytics and analysis of the interviews, convergence, ICT utilization ability, creativity, self-regulated competency and leadership were found to be the factors necessary for the type of talent in the post-COVID-19 era. Second, it considered the innovation of digital education system and the support for vulnerable classes as the issue for cultivation of people of talent in the post-COVID-19 era. Third, the most important policy with regard to the educational direction for cultivation of people of talent in the post-COVID-19 era was cultivation of convergence talents. Convergence is a very important variable in the post-COVID-19 era since it creates new values by connecting things that are separated from each other. Hopefully, this study will build a basis for competency development, education and training in preparation for the post-COVID-19 era.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.113-126
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• 2021

This study was conducted to analyze the forest status and deforestation area changes of the DMZ region in North Korea based on satellite images. Using growing and non-growing season's RapidEye satellite images, land cover of the North Korean DMZ was classified into stocking land(conifer, deciduous, mixed), deforested land(unstocked mountain, cultivated mountain, bare mountain), and non-forest areas. Deforestation rates in the Yeonan-baecheon, Beopdong-Pyeonggang, Heoyang-Geumgang and Tongcheon-Goseong district were calculated as 14.24%, 16.75%, 5.98%, and 16.63% respectively. Forest fire and land use change of forest were considered as the main causes of deforestation of DMZ. Changes in deforestation area were analyzed through Google Earth images. As a results, it was shown that the area of deforestation was on a decreasing trend. This study can be used as basic data for establishing inter-Korean border region's forest cooperation strategies by providing forest spatial information on the North Korea's DMZ.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.99-111
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• 2021

The connected farm that agricultural land, agricultural machinery and farmer are connected with an IoT gateway is in the commercialization stage. That has increased productivity, efficiency and profitability by intimate information exchange among those. In order to develop the educational program of intelligent agricultural machinery and the agricultural machinery safety education performance indicator, this study analyzed patent trends of agricultural machine with unmanned technology used in agriculture and efficiency technology applied advanced technologies such as ICT, robots and artificial intelligence. We investigated and analyzed patent trends in agricultural machinery of Korea, the USA and Japan as well as the countries in Europe. The United States is an advanced country in the field of unmanned technology and efficiency technology used in agriculture. Agricultural automation technology in Korea is insufficient compared to developed countries, which means rapid technological development is needed. In the sub-fields of field automation technology, path generation and following technology and working machine control technology through environmental awareness have activated.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.41-57
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• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.428-434
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• 2022

This study was conducted to derive the site index of forest productivity of Robinia pseudoacacia (honey plant) to characterize suitable planting sites and to investigate the effect of the site environmental factors on the site index using the quantification theory I method. The data used in the analysis were growth factors (stand age, dominant height, etc.) of the 6th national forest resources survey and various site environmental factors of a forest soil map (1:5,000). The average site index value of the R. pseudoacacia stand in Korea was 14 (range, 8 to 18). The environmental factors affecting the site index were parent rock, climatic zone, soil texture, local topography, and altitude. The accuracy of the estimation model using quantification theory I was only 33%. However, the correlation between the site index and the site environmental factors was statistically significant at the 1% level. Results of quantification analysis between site index and site environmental factors revealed that metamorphic and igneous rocks received high grades as parent rocks, climate zones received higher grades than central temperate zone, clay loam and silt loam received high grades in soil texture, and hillside received a high grade in local topography. Analysis of the partial correlation between site topographical factors and forest productivity (site index) found that soil class and altitude were partially correlated to x by 0.4129 and 0.4023, respectively, indicating that these factors are the most influential variables.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.76-83
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• 2022

With the recent development of IT technology, research and interest in various biosignal measuring devices is increasing. As an aging society is in full swing, research on the elderly population using IT-related technologies is continuously developing. This study is about the development of life pattern detection and fall detection algorithm, which is one of the medical service areas for the elderly, who are rapidly developing as they enter a super-aged society. This study consisted of a system using a 3-axis accelerometer and an electrocardiogram sensor, collected data, and then analyzed the data. It was confirmed that behavioral patterns could be classified from the actual research results. In order to evaluate the usefulness of the human activity monitoring system implemented in this study, experiments were performed under various conditions, such as changes in posture and walking speed, and signal magnitude range and signal vector magnitude parameters reflecting the acceleration of gravity of the human body and the degree of human activity. was extracted. And the possibility of discrimination according to the condition of the subject was examined by these parameter values.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.