• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.125-139
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• 2021

Continuous subway line expansion over the years in Seoul metropolitan area has contributed to improved accessibility to public transport. Since public transport accessibility has a significant impact on housing decisions, quantitative analysis of correlation between housing prices and public transport accessibility is regarded as one of the most important factors for planning better housing policies. This study defines the reduction of traveling time resulted from the construction of new metro stations despite them not being the closest stations as 'Network Expansion Effect', and seeks to understand how the Network Expansion Effect impacts on housing prices. The study analyzes monthly rent data converted from upfront lump sum deposit, so called Jeonse in Korea, from 2012 to 2018, through 'A Modified Repeat Sales Model.' As a result, the effect of 'Network Expansion' on rental prices in Seoul has stronger during the period of 2017 to 2018 than the base period of 2012 to 2014, which suggests the 'Network Expansion' has a meaningful effect on rent. In addition, in comparison between the most and the least affected group of apartments by 'Network Expansion Effect', the most affected group has more price increase than the least affected group. These findings also indicate that different levels of 'Network Expansion Effect' have various influences on the value of residential real estate properties.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.149-162
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• 2020

This paper applied components of experience-based communication in terms of experience theory of Burnd H. Schmitt and context-based communication in the messenger platform environment through the scenario-based survey method, in order to study the influence of individual experiences, shared experiences, ubiquitous connectivity, and contextual usefulness on the perceived value and use intention of chatbot. Through this, the study is to provide companies in various service industries with practical approaches to further promote the use of chatbot. The implications of this study are as follows. First, as most chatbots still do not exceed the human planning level of designing them, it is necessary to consider how to design individual experience elements functionally according to the customer's intention to speak when developing the chatbot. Second, the chatbot should be designed not only from the perspective of completing specific tasks at any real time in anywhere, but also from the overall perspective of enhancing the quality of interaction, including the situation to which the customer belongs. Third, since the chatbot is likely to be anthropomorphized by users, it is important to be cautious about determining the chatbot's 'persona' and 'tone and manner' when developing the chatbot. Customer satisfaction is the most important criterion for the success of chatbot development. In other words, the quality of planning and data rather than the quality of artificial intelligence algorithms determines the utilization of chatbot. This is why companies are trying to make interactions with chatbot as close as possible to human interactions.

• Journal of Ginseng Research
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• v.46 no.5
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• pp.628-635
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• 2022

Background: Ulcerative colitis (UC) is the large intestine disease that results in chronic inflammation and ulcers in the colon. Rg3-enriched Korean Red Ginseng extract (Rg3-RGE) is known for its pharmacological activities. Persicaria tinctoria (PT) is also used in the treatment of various inflammatory diseases. The aim of this study is to investigate the attenuating effects of Rg3-RGE with PT on oxazolone (OXA)-induced UC in mice. Methods: A total of six groups of mice including control group, OXA (as model group, 1.5%) group, sulfasalazine (75 mg/kg) group, Rg3-RGE (20 mg/kg) group, PT (300 mg/kg) group, and Rg3-RGE (10 mg/kg) with PT (150 mg/kg) group. Data on the colon length, body weight, disease activity index (DAI), histological changes, nitric oxide (NO) assay, Real-time PCR of inflammatory factors, ELISA of inflammatory factors, Western blot, and flow cytometry analysis were obtained. Results: Overall, the combination treatment of Rg3-RGE and PT significantly improved the colon length and body weight and decreased the DAI in mice compared with the treatment with OXA. Additionally, the histological injury was also reduced by the combination treatment. Moreover, the NO production level and inflammatory mediators and cytokines were significantly downregulated in the Rg3-RGE with the PT group compared with the model group. Also, NLR family pyrin domain containing 3 (NLRP3) inflammasome and nuclear factor kappa B (NF-𝛋B) were suppressed in the combination treatment group compared with the OXA group. Furthermore, the number of immune cell subtypes of CD4⁺ T-helper cells, CD19⁺ B-cells, and CD4⁺ and CD25⁺ regulatory T-cells (Tregs) was improved in the Rg3-RGE with the PT group compared with the OXA group. Conclusion: Overall, the mixture of Rg3-RGE and PT is an effective therapeutic treatment for UC.

• 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.

• Smart Media Journal
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• v.11 no.3
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• pp.39-53
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• 2022

Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.714-724
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• 2021

The point cloud content is immersive content recorded by acquiring points and colors corresponding to the real environment and objects having three-dimensional location information. When a point cloud content consisting of three-dimensional points having position and color information is enlarged and rendered, the gap between the points widens and an empty hole occurs. In this paper, we propose a method for improving the quality of point cloud contents through inverse transformation-based interpolation using depth information for holes by finding holes that occur due to the gap between points when expanding the point cloud. The points on the back are rendered between the holes created by the gap between the points, acting as a hindrance to applying the interpolation method. To solve this, remove the points corresponding to the back side of the point cloud. Next, a depth map at the point in time when an empty hole is generated is extracted. Finally, inverse transform is performed to extract pixels from the original data. As a result of rendering content by the proposed method, the rendering quality improved by 1.2 dB in terms of average PSNR compared to the conventional method of increasing the size to fill the blank area.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.196-205
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• 2021

As industrial safety increases, various industrial accident prevention technologies using smart factory technology are being studied. However, small and medium enterprises (SMEs), which account for the majority of industrial accidents, are having difficulties in preventing industrial accidents by applying these smart factory technologies due to practical problems. In this study, customized monitoring and warning systems for each type of industrial accident were developed and applied to the actual field. Through this, we demonstrated industrial accident prevention technology through appropriate smart factory technology used by SMEs. A customized monitoring system using vision, current, temperature, and gas sensors was established for the four major disaster types: worker body access, short circuit and overcurrent, fire and burns due to high temperature, and emission of hazardous gas. In addition, a notification method suitable for each work environment was applied so that the monitored risk factors could be recognized quickly, and real-time data transmission and display enabled workers and managers to understand the disaster risk effectively. Through the application and demonstration of these appropriate smart factory technologies, the spread of these industrial safety technologies is to be discussed.