• Journal of Digital Convergence
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• v.17 no.8
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• pp.159-170
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• 2019

IC industry is one of the foundation and core industries of modern information industry. Therefore, the study of this industry has important theoretical and practical significance. The main purpose of this study is to measure the degree of close correlation between the two indexes through correlation analysis of the selected indicators, so as to study the development trend and direction of IC. Based on the theory of induced innovation and the theory of comparative advantage, this paper analyzes the correlation between the profit of the IC industry and the following three indicators by using chart analysis method, covariance analysis method and correlation coefficient analysis method. These three indicators are R&D expenditure, new product development costs and annual export amount of IC. The selected data are mainly from CHINA STATISTICS YEARBOOK ON HIGH-TECHNOLOGY INDUSTRY. Through the research, it is found that the profit of China's IC industry is positively correlated with the first two indicators and negatively correlated with the annual export amount.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.497-502
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• 2019

Metal oxide nanostructures are promising materials for advanced applications, such as high sensitive gas sensors, and high capacitance lithium-ion batteries. In this study, tin oxide (SnO) nanostructures were grown on a Si wafer substrate using a two-zone horizontal furnace system for a various substrate temperatures. The raw material of tin dioxide ($SnO_2$) powder was vaporized at $1070^{\circ}C$ in an alumina crucible. High purity Ar gas, as a carrier gas, was flown with a flow rate of 1000 standard cubic centimeters per minute. The SnO nanostructures were grown on a Si substrate at $350{\sim}450^{\circ}C$ under 545 Pa for 30 minutes. The surface morphology of the as-grown SnO nanostructures on Si substrate was characterized by field-emission scanning electron microscopy (FE-SEM) and atomic force microscopy (AFM). Raman spectroscopy was used to confirm the phase of the as-grown SnO nanostructures. As the results, the as-grown tin oxide nanostructures exhibited a pure tin monoxide phase. As the substrate temperature was increased from $350^{\circ}C$ to $424^{\circ}C$, the thickness and grain size of the SnO nanostructures were increased. The SnO nanostructures grown at $450^{\circ}C$ exhibited complex polycrystalline structures, whereas the SnO nanostructures grown at $350^{\circ}C$ to $424^{\circ}C$ exhibited simple grain structures parallel to the substrate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.156-162
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• 2021

The number of smart farms has increased to save labor in agricultural production as the subsidy become available from central and local governments. The number of illegal greenhouses has also increased, which causes serious issues for the local governments. In the present study, we developed Mask-RCNN model to detect greenhouses based on satellite images. Greenhouses in the satellite images were labeled for training and validation of the model. The Mask-RC NN model had the average precision (AP) of 75.6%. The average precision values for 50% and 75% of overlapping area were 91.1% and 81.8%, respectively. This results indicated that the Mask-RC NN model would be useful to detect the greenhouses recently built without proper permission using a periodical screening procedure based on satellite images. Furthermore, the model can be connected with GIS to establish unified management system for greenhouses. It can also be applied to the statistical analysis of the number and total area of greenhouses.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.251-257
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• 2021

With the development of new media, great changes are taking place in the way people get information. The change is the use of video content that can deliver content in a more three-dimensional way than words or photos. After 2016, the number of live video streaming content providers and users has increased. In this paper the write takes the 1 personal live video streaming content as the research object. And the write takes live video streaming content on YouTube live or Douyu TV as a research example. In this paper, the writer analyzes the digital information content in the live video streaming case. And the writer expounds the necessity of these visual information and the characteristics of real-time live video streaming content. Especially since 2020, because of the influence of the COVID-19, the live video streaming industry has begun to combine with the traditional industry. It is expected that the integration of digital cutting-edge technology and live video streaming will not only provide diversity in the content, but also create more social value for the video content consumption culture. Therefore, The writer thinks it is necessary to conduct in-depth research on the social responsibility of real-time live content in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1749-1760
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• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.647-660
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• 2022

To prepare an atmospheric correction model suitable for CAS500-4 (Compact Advanced Satellite 500-4), this letter examined an atmospheric correction experiment using Sentinel-2 images having similar spectral characteristics to CAS500-4. Studies to compare the atmospheric correction results depending on different Aerosol Optical Depth (AOD) data are rarely found. We conducted a comparison of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Sen2Cor, and Second Simulation of the Satellite Signal in the Solar Spectrum - Vector (6SV) version 1.1 and 2.1, using Geo-Kompsat 2A (GK2A) Advanced Meteorological Imager (AMI) and Aerosol Robotic Network (AERONET) AOD data. In this experiment, 6SV2.1 seemed more stable than others when considering the correlation matrices and the output images for each band and Normalized Difference Vegetation Index (NDVI).

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.3
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• pp.11-20
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• 2022

The main purpose of this study is to investigate the applicability of the shear wave tomography technology as a non-destructive testing method to evaluate the debonding between the track concrete layer (TCL) and the hydraulically stabilized based course (HSB) of concrete slab tracks for the Korea high-speed railway system. A commercially available multi-channel shear wave measurement device (MIRA) is used to evaluate debonding defects in full-scaled mock-up test specimen that was designed and constructed according to the Rheda 200 system. A part of the mock-up specimen includes two artificial debonding defects with a length and a width of 400mm and thicknesses of 5mm and 10mm, respectively. The tomography images obtained by a MIRA on the surface of the concrete specimens are effective for visualizing the debonding defects in concrete. In this study, a simple image processing method is proposed to suppress the noisy signals reflected from the embedded items (reinforcing steel, precast sleeper, insert, etc.) in TCL, which significantly improves the readability of debonding defects in shear wave tomography images. Results show that debonding maps constructed in this study are effective for visualizing the spatial distribution and the depths of the debondiing defects in the railway concrete slab specimen.

• Maritime Security
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• v.4 no.1
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• pp.115-138
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• 2022

The Korean military is preparing for successful manpower reduction using advanced science and technology, in addition to carrying out the initiative of the Defense Innovation 4.0. Accordingly, studies on core technologies related to defense reform have been conducted both internally and externally in the military, and the corresponding results have also been applied. Nevertheless, compared to the development of such technologies, it is considered necessary to have more preparation for the policies related to the operation of the newly introduced equipment. As for the placement of personnel and the organization of time in service (TIS) with respect to the operation of surveillance equipment, there has been a tendency to sustain the conventional practice. Therefore, this study intends to suggest the schemes for facilitating policy improvements in the operation of manpower and security regulations in the field of information for the purpose of introducing a successful next-generation coastal guard system. To do this, the approach of this study was focused on the policies for the operation of the guard system. This is in contrast to previous studies that centered on its equipment and technologies. In addition, how to efficiently operate the guard system was also studied in view of cognitive science by deriving the most efficient time for a person to execute surveillance through the monitor based on the previous studies.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.