• Journal of the Korean Geosynthetics Society
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• v.19 no.4
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• pp.1-9
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• 2020

This study analyzes contaminant movement under transient flow in a rubble mound revetment offshore waste landfill barrier system that prevents contaminant runoff. The barrier system consists of bottom layer and side barrier. For the bottom layer system, impermeable clay layer is used. For the side barrier system, the HDPE barrier sheet (primary element) plays the main role, and the intermediate protection layer (supplementary element) is responsible for the barrier. Seepage, advection, dispersion numerical analysis was carried out using SEEP / W and CTRAN / W programs. As a result, under abnormal conditions considering the fluctuation in tidal range, the volume and direction of the flow velocity vector of the pore water change with time and the dispersion concentration of the contaminant changes. When comparing the case of 2 m tidal range and 8 m tidal range, the larger the tide value, the higher the concentration of contaminant under abnormal conditions. It was found that the rate of change of the concentration of the contaminant changed depending on the change in the tidal range, and as a result, the outflow of the pollutant was smaller than that in the steady flow state.

• Smart Media Journal
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• v.9 no.4
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• pp.9-16
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• 2020

Recently, various studies are being conducted to integrate Image Segmentation into smart factory industries and autonomous driving fields. In particular, Image Segmentation systems using deep learning algorithms have been researched and developed enough to learn from large volumes of data with higher accuracy. In order to use image segmentation in the autonomous driving sector, sufficient amount of learning is needed with large amounts of data and the streaming environment that processes drivers' data in real time is important for the accuracy of safe operation through highways and child protection zones. Therefore, we proposed a novel DFCN algorithm that enhanced existing FCN algorithms that could be applied to various road environments, demonstrated that the performance of the DFCN algorithm improved 1.3% in terms of "loss" value compared to the previous FCN algorithms. Moreover, the proposed DFCN algorithm was applied to the existing U-Net algorithm to maintain the information of frequencies in the image to produce better results, resulting in a better performance than the classical FCN algorithm in the autonomous environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.579-586
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• 2021

This study examines the application of big data and tasks of tourism based on the case of Jeju Special Self-Governing Province, which used big data for regional tourism policy. Through the use of big data, it is possible to understand rapidly changing tourism trends and trends in the tourism industry in a timely and detailed manner. and also could be used to elaborate existing tourism statistics. In addition, beyond the level of big data analysis to understand tourism phenomena, its scope has expanded to provide a platform for providing real-time customized services. This was made possible by the cooperative governance of industry, government, and academia for data building, analysis, infrastructure, and utilization. As a task, the limitation of budget dependence and institutional problems such as the infrastructure for building personal-level data for personalized services, which are the ultimate goal of smart tourism, and the Personal Information Protection Act remain. In addition, expertise and technical limitations for data analysis and data linkage remain.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.1-11
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• 2021

Thermal protection system structures such as double-panel structures are used on the skin of the fuselage and wings to prevent the transfer of high heat into the interior of an high supersonic/hypersonic aircraft. The thin-walled double-panel skin can be exposed to acoustic loads by high power engine noise and jet flow noise, which can cause sonic fatigue damage. In order to predict the fatigue life of the skin, the octave bandwidth SPL should be calculated as narrow bandwidth PSD or acoustic load history using interpolation method. In this paper, a method of converting the octave bandwidth SPL acoustic load into a narrow bandwidth PSD and reconstructed acoustic load history was investigated. The octave bandwidth SPL was converted to the narrow bandwidth PSD using various interpolation methods such as flat, log and linear scale, and the probabilistic characteristics and fatigue damage results were compared. It was found that average error of fatigue damage index by the log scale interpolation method was relatively small among three methods.

• Journal of the Korea Institute of Building Construction
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• v.21 no.1
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• pp.21-29
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• 2021

The objective of this study is to evaluate the effect of the application of double layered bubble sheet on the curing of slab and wall concrete placed at the job site in cold weather and to offer a feasibility of Concrete IoT Management System(CIMS), which is wireless sensor network developed by the authors, to manage early age quality of the concrete in terms of temperature, maturity and strength development. Test results indicated that the application of bubble sheet enhances the insulation performance, which results in an increase of the temperature by around 1~20. 6℃. It is found that CIMS can gather the temperature, maturity and strength development data from the sensors embedded from 30 m far from CIMS successfully. Predicted compressive strengths by CIMS had good agreement with measured ones within 2 MPa error level until 7 days. It is thought that the combination of the bubble sheet application for cold weather protection and CIMS for quality management tool in cold weather concreting contributes to shorten the time for the form removal by one day.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.803-813
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• 2021

Coastal erosion has been a threat to coastal communities and emerged as an urgent problem. Among the coastal communities that are under perceived threat, Cotonou located in Benin, West Africa, is considered as one of the most dangerous area due to its high vulnerability. To address this problem, in 2013, the Benin authorities established seven groynes at east of Cotonou port, and two additional intermediate groynes have recently been integrated in April 2018. However, there is no quantitative analysis of groynes so far, so it is hard to know how effective they have been. To analyze effectiveness, we used optical satellite images from different time periods, especially 2004 and 2020, and then compared changes in length, width and area of shoreline in Cotonou. The study area is divided into two sectors based on the location of Cotonou port. The difference of two areas is that Sector 2 has groynes installed while Sector 1 hasn't. As result of this study, shoreline in Sector 1 showed accretion by recovering 1.20 km² of area. In contrast, 3.67 km² of Sector 2 disappeared due to coastal erosion, although it has groynes. This may imply that groynes helped to lessen the rate of average erosion, however, still could not perfectly stop the coastal erosion in the area. Therefore, for the next step, we assume it is recommended to study how to maximize effectiveness of groynes.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.73-82
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• 2022

Marine fisheries resources face major anthropogenic threat from unregulated fishing activities; thus require precise detection for protection through marine surveillance. Korea developed an efficient land-based small fishing vessel monitoring system using real-time V-Pass data. However, those data directly do not provide information on fishing activities, thus further efforts are necessary to differentiate their activity status. In Korea, especially in Busan, longlining is practiced by many small fishing vessels to catch several types of fishes that need to be identified for proper monitoring. Therefore, in this study we have improved the existing fishing status classification method by applying Hidden Markov Model (HMM) on V-Pass data in order to further classify their fishing status into three groups, viz. non-fishing, longlining and other types of fishing. Data from 206 fishing vessels at Busan on 05 February, 2021 were used for this purpose. Two tiered HMM was applied that first differentiates non-fishing status from the fishing status, and finally classifies that fishing status into longlining and other types of fishing. Data from 193 and 13 ships were used as training and test datasets, respectively. Using this model 90.45% accuracy in classifying into fishing and non-fishing status and 88.23% overall accuracy in classifying all into three types of fishing statuses were achieved. Thus, this method is recommended for monitoring the activities of small fishing vessels equipped with V-Pass, especially for detecting longlining.

• Journal of the Daesoon Academy of Sciences
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• v.35
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• pp.393-426
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• 2020

In the past, holy sites were mainly designated on a basis of archaeological norms and endowed with a specific fixed identity according to historical, religious, and contextual interpretations. However, approaches to these sites are more flexible in recent times. These locations transcend the boundaries of space and time to enable the experience of diverse transformation and reveal multiple religious identities which are embedded in the complex interaction between power and authority. In this regard, the dynamic meanings of the religious symbology of Daesoon Jinrihoe's temple complexes, imagery, and the spatial structures enable us to grant them a new identity by re-establishing these structures as World Heritage sites. Temple complexes (dojang) correspond to the outstanding universal values identified by UNESCO in that the spiritual activities conducted at these holy sites draw the same attention as would be drawn by historical value. In this context, this study aims to explore the potential for Daesoon Jinrihoe's temple complexes to be designated UNESCO world heritage sites. To carry out this study, existing religious heritage sites such as Mount Athos Monasteries in Greece and Lumbini in Nepal are examined as case studies, and the operational plan, conservation, protection of relics, and interaction with its neighboring community and tourists are likewise closely examined in this study.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.11-17
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• 2022

Data mining is the task of accessing useful information from a large capacity of data. It can also be referred to as searching for correlations that can provide clues about the future in large data warehouses by using computer algorithms. It has been used in the tourism field for marketing, analysis, and business improvement purposes. This study aims to analyze the tourist profile in Turkey through data mining methods. The reason relies behind the selection of Turkey is the fact that Turkey welcomes millions of tourist every year which can be a role model for other touristic countries. In this study, an anonymous and large-scale data set was used under the law on the protection of personal data. The dataset was taken from a leading tourism company that is still active in Turkey. By using the k-means clustering algorithm on this data, key parameters of profiles were obtained and people were clustered into groups according to their characteristics. According to the outcomes, distinguishing characteristics are gathered under three main titles. These are the age of the tourists, the frequency of their vacations and the period between the reservation and the vacation itself. The results obtained show that the frequency of tourist vacations, the time between bookings and vacations, and age are the most important and characteristic parameters for a tourist's profile. Finally, planning future investments, events and campaign packages can make tourism companies more competitive and improve quality of service. For both businesses and tourists, it is advantageous to prepare individual events and offers for the three major groups of tourists.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.